rt as ereees —— eres == Se M) mit ir i Ratt Wa iN nA y i iN tk sa ii i) AGUNG ates Uhit Csesee Way! AAs Sees eee ce ee = = ual iy ie Wi HOH ine i mes MBL/WHOI A 0 0301 OO40b786 1 U.S. DEPARTMENT OF COMMERCE ENVIRONMENTAL SCIENCE SERVICES ADMINISTRATION A World Atlas of Atmospheric Radio Refractivity B. R. Bean, B. A. Cahoon, C. A. Samson, and G. D. Thayer Institute for Telecommunication Sciences and Aeronomy Institutes for Environmental Research Boulder, Colo. ESSA Monograph 1 UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON : 1966 U.S. DEPARTMENT OF COMMERCE JOHN T. CONNOR, Secretary ENVIRONMENTAL SCIENCE SERVICES ADMINISTRATION RoBeRT M. WHITE, Administrator Institutes for Environmental Research GEORGE S. BENTON, Director This work was supported in part by the U.S. Navy Weather Research Facility, Contract Number 7560: NABUSTDS 65 Ser. 300. Library of Congress Catalog Card Number: Map 66-74 For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402. Price $2.25 Preface This atlas has been prepared for the radio engineer who wants an estimate of the behavior of the radio refractive index at any point on the earth. It has many limitations, which the authors have tried to point out in the text, but should provide useful informa- tion for engineering design and predictions of tropospheric radio circuits. The work upon which this atlas is based has been in progress for several years in the Radio Meteorology Section of the Central Radio Propagation Laboratory, National Bureau of Standards, Boulder, Colo.* Many people have contributed to this research effort through the years, and it is not feasible to acknowledge them all individually. Those most directly involved with the preparation of material for this atlas include W. B. Sweezy and W. A. Williams, who were responsible for most of the computer programming; Mrs. B. J. Weddle, who assisted in processing and cataloging of the data; T. D. Stevens, who did most of the compiling, checking, and plotting of the maps, graphs, and tables; and L. P. Riggs, J. D. Horn, and Mrs. G. E. Richmond (deceased), who contributed to the atlas project in its early stages. The National Weather Records Center of the U. S. Weather Bureau in Asheville, N. C., supplied the meteorological data upon which this atlas is based, and also performed the preliminary conversions of these data to refractivity parameters. Finally, we thank the personnel of the U. S. Navy Weather Research Facility at Norfolk, Va., who foresaw a need for such an atlas and have arranged for support of the project over a period of years. *Now the Institute for Telecommunication Sciences and Aeronomy (ITSA), Environmental Science Services Administration. ili Preface ... Abstract .. Contents Tem EN troduction erecta er erate relekerate ratoketten fevetehevere eter elerevctenereleveteiovelsveloyeleleravelorereleVole Vel efencvelorel sees -y-2: QMDISCUSSIONTOL basicul) abay cryeictercteveroie terse veieteleretoleteieee eietetelcierefetellelels/cVeloyelsre/ ese e\eteiel sheer steye ereleraieloievoh-velei= SRaVWVOrldu Maps of NiCz)isreyeterersoicisis crete: eletereteteyo1cbetVetatohe a1 cher efovelenoiel lets levereVetede lolate\evelo/e1=telslevel\s cfs s\eieha) seine Shee Developmen ticwrersicteicrerskre siete ctererchelete istered eveiersiereh ole eles elsiofel sfeleistolelsfereiel eleletslelelerscsleFeinieisisreve)-ici- i= Figure 1. Three-part exponential fit to mean N-profile: Koror............. cece cece eee Figure 2. Three-part exponential fit to mean N-profile: Dakar.......... 0. cece cece eee eee St2 ee DISCUSSIONNOLN CZ) map © ONLOUTS yates eneropeneislicielcsetetetevodsteloaslerefcls7sta\iaic\letel efefetereieinvone) leis} eleustelelerielle¥- 3885 Reliability, of Contours) ot NiCe) Maps e cece 0 11e ciel oie clernials/oleriesle/cfclels erelele/ ele e101 Je ee eisle eis 1s SVAmeProblemeAreasnote ie) Miap Sipemienst trrenctetonelcUsccteretalienc ist etepevonekeleielcreiet a) shehicnerelcpevelekesolelene) sveiereuenalers Figure Figure 8. Five-year mean wet refractivity term: February..............c cece cece eee ee eeeee AmHive-vearsmeanmwet retractivaty aterm: Wiayirrneryeteretyate «tsiekejeisnes 43 Figure A-24. Wet-term scale height in km, H,,: November...............--eeeeeeeceeeeeeees 44 Figure A-25. Mean density tropopause altitude in km, 2: November.............ceeeceeecers 44 Figure A-26. Standard prediction error of the exponential fit to the mean wet-term: profiles MeDE UAT. oi ioiac cos snc eves. sie) a feue' sy eeuevetereras eieies ev ereie. et e149 ecsyoze sets le, evencpelereusbe eral 45 Figure A-27. Standard prediction error of the exponential fit to the mean wet-term: profiles: May) .sciscstecsiscsisie sie asereiarsie’e openers ate bara is o:4iie'e ats; veiere oreiaie oie sverarace, o:eueia sieveparesi8s 45 Figure A-28. Standard prediction error of the exponential fit to the mean wet-term: profiles: AUGUst: o:5.2: vianlaryiere ccleciescieitiolecic ciel cteieieiereiere crisis cicicrers eiereicic encvensreneneiele 53 Figure B-3: Monthly mean AN: February... scscc2.cc0sscoscesscsdcecrccvcesecsscesene cuce 53 Higure B-45 Monthly mean, AiWNi: Marchincac ssc cies ocle cere cicle cise ere ia)eiisree eralarereleietotejerel siete oie cians 54 igure: B=55: Monthly smean: A Nise April'ss syarccts.c crv.e tars aieva aleretslasere) oleie\ eves evetevers ise overs ava lal enevsvere tele 54 Migure: B-6-. Monthly, mean’ AW 's), Mayic loci asco eievsn eretorsysie dle steie eho: eiersievetela)snciesyel star clereyareysile he 55 Figure B=". Monthly mean AN: 2diume@as ea scci ec’ orcie cvisleie cr eieisicve ciereie e cies ec evsreie cies ersieisiareieie eine 55 Figure: B-8;, Monthly mean: AW: Jullyiicc cress s01sce cue ecco e ers cals cle slsiele sities c.ccie © ees shelesereeie ue 56 Bigure B-9.. Monthly mean AN: Augusts.).ch2 iis ccs cs ove viclsers a0 cere sive s ¢ ise ¥ wiieie se semen 56 Figure B-10. Monthly mean AN: September............. cc cece cee cece eee c eects eee eee cecs 57 Hioures 5-115 sMonthivemeansA Nis October cies scise ccc clemisteisiciee cise eieieveta sill sieeisieteicioteeieremiens 57 Figure B-12. Monthly mean AN: November..............ccececcccccccscccecvcacsressesuas 58 Figure B-13. Monthly mean AN: December........ er sete lete ale er e/statialelctelo/ovetsl lake’ sictetel sy siteieleeeaeis 58 Figure B-14. Annual mean of sea-level refractivity, No........ceeeecccceeeceeceeceseeeesaae 59 Figure B-15. Annual mean of refractivity gradient between surface and 1 km, AN............ 59 Figure B-16. Slope of regression line of AN versus Nz, Di... .cccccecceecceecceceveceeseueas 60 Figure B-17. Correlation coefficient of AN versus Ns........ceccceecccecceeeeeceeteeeecens 60 Figure B-18. Standard prediction error of the regression line of AN versus Ny.........++ .- oo {hil Figure B-19. Standard prediction error of the regression line of AN versus N, as a percent of AN 61 Figure B-20. Areas of doubtful applicability of using N, to predict AN.............0000ee ees 62 12. Appendix C. World Maps and Cumulative Distribution Charts of Gradients of Ground-Based Atmospheric Layers. ........... 0. ccc cece ccc eee e eee e eee e cece eeeeneeareee 63 Figure C-1. Percent of time gradient > 0 (N/km): February...........c.eceeceeeeeceeetees 64 Figure C-2. Percent of time gradient = 0 (N/km): May..........cccc cee cec cee teceeeeecees 64 Figure C-3. Percent of time gradient => 0 (N/km): AuguSt..........c ccc cece eee eeeeeerees 65 Figure C-4. Percent of time gradient => 0 (N/km): November.............seccecceccceccees 65 Figure C-5. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: February..... 66 Figure C-6. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: February...... 66 Figure C-7. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: May......... 67 Figure C-8. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: May.......... 67 Figure C-9. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: August....... 68 Figure C-10. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: August........ 68 Figure C-11. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: November.... 69 Figure C-12. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: November..... 69 Figure C-13. Percent of time gradient — —100 (N/km): February.............ceeeeeeee ees 70 Figure C-14. Percent of superrefractive layers thicker than 100 m: February............... 70 Figure C-15. Percent of time gradient = -100 (N/km): May.........cccccccccecccsceerces 71 Figure C-16. Percent of superrefractive layers thicker than 100 m: May.................-0- ufil vi Figure C-17. Percent of time gradient = -100 (N/km): August........ ,wdoKasbooooouHnOUO dE 72 Figure C-18. Percent of superrefractive layers thicker than 100 m: August..............-++> 72 Figure C-19. Percent of time gradient — -100 (N/km): November...............02e0ee08 73 Figure C-20. Percent of superrefractive layers thicker than 100 m: November.......... sccon a) Figure C-21. Percent of time gradient — -157 (N/km): February.................06 oRintevere 74 Figure C-22. Percent of ducting layers thicker than 100 m: February..............seeeee-++> 74 Figure C-28. Percent of time gradient = -157 (N/km): May..............2cccecceeeeerees Wd, Figure C-24. Percent of ducting layers thicker than 100 m: May...............eeeeeeeeerees 75 Figure C-25. Percent of time gradient — -157 (N/km): August............. 0. cece eee eee 76 Figure C-26. Percent of ducting layers thicker than 100 m: August.............eceeeeeeeee> 76 Figure C-27. Percent of time gradient — -157 (N/km): November...............eeeeeeeee 717 Figure C-28. Percent of ducting layers thicker than 100 m: November..........0.++see00-++> 77 Figure C-29. Percent of time trapping frequency < 8000 Mc/s: February................--+> 78 Figure C-30. Percent of time trapping frequency < 1000 Mc/s: February..............---- 78 Figure C-31. Percent of time trapping frequency < 300 Mc/s: February.............2.+0---- 79 Figure C-32. Percent of time trapping frequency < 3000 Mc/s: May..............--e-----> m9 Figure C-338. Percent of time trapping frequency < 1000 Mc/s: May.............eeeeeeeees 80 Figure C-34. Percent of time trapping frequency < 300 Mc/s: May...........eeceseccceeress 80 Figure C-35. Percent of time trapping frequency < 3000 Mc/s: August.............ee00--+> 81 Figure C-36. Percent of time trapping frequency < 1000 Mc/s: August...........seeeeeereee 81 Figure C-37. Percent of time trapping frequency < 300 Mc/s: August.........-+2022eeeeeees 82 Figure C-38. Percent of time trapping frequency < 3000 Mc/s: November...........2-200+-+> 82 Figure C-39. Percent of time trapping frequency < 1000 Mc/s: November..............+.+-+: 83 Figure C-40. Percent of time trapping frequency < 300 Mc/s: November.............+++++- 83 Figure C-41. Lapse rate of refractivity (N/km) exceeded 25 percent of time for 100-m layer: February........ SSIES OO ALA OD On BTR OO TOCA DO Ona DH mOn Oban ROO Tone 84 Figure C-42. Lapse rate of refractivity (N/km) exceeded 10 percent of time for MOOZMPlA Ver MEDEUALY ic ictoncrsie cheever cites ciateva)siovelovels e/eie/elele/clatsta\elels\e/ eroleteleyelevelofoveievsls/slststelelslieve) siete= 84 Figure C-43. Lapse rate of refractivity (N/km) exceeded 5 percent of time for NOOSMMlay era CDEUATYeretisversreeteieieveretaveterelcieverelsielsyerersrersievereratsie sicveistelstellciefelalel steleieteleiele\ereleis:e)+1s 85 Figure C-44. Lapse rate of refractivity (N/km) exceeded 2 percent of time for NO Ocme] Ayer ELC DEUATY foreseperelele erorcteteteve love soverole\slejeiorelevevoreisvelelcioleTa isis etelevs/olslelevelsieieinieie/sisveleleiele ieiele 85 Figure C-45. Lapse rate of refractivity (N/km) exceeded 25 percent of time for NO Oermylayetacw Va yicterarscoterereterereterateralsteveleneteretevele terele eteloveleietenetevolotal eller ielereie)isnelelevoleieistersieievst nie feledefeler 86 Figure C-46. Lapse rate of refractivity (N/km) exceeded 10 percent of time for NO OSmapl ayers Mia yicracrscreievarveteveteteletstetetelefevetetehere) eveyeleveleloyelerciavelelcleieilere|(cheteleleletelovetoteyelelevcisvels)slerelshels 86 Figure C-47. Lapse rate of refractivity (N/km) exceeded 5 percent of time for OO-mylayercwMla yer cpt irete rte teieielelereleveroiererotersterefetoneictersiciexchercvoleroreloreisieleleheieleforerencisis 87 Figure C-48. Lapse rate of refractivity (N/km) exceeded 2 percent of time for lO Ot; D, and W, are the mean sea-level values of the dry and wet terms (reduced from the surface values using the free-atmosphere scale heights), H, is the wet-term scale height, H, is the tropo- spheric dry-term scale height, H, is the strato- spheric dry-term scale height, and 2 is the altitude above mean sea level of the point of transition between the tropospheric and strato- spheric dry-term exponentials. The altitude, 2:, may thus be thought of as an effective density tropopause. Examples of the application of this model to actual mean refractivity profiles are given in figures 1 and 2: a very good fit (Koror) and one of the worst fits encountered (Dakar). The good fit obtained in figure 1 is especially signifi- cant since Koror represents a climatic type (equatorial station with a very high mean sur- face refractivity, 387.6) for which exponential models of N were previously thought to be un- satisfactory [Misme et al., 1960]. Dakar (fig. 2) is an example of the climatic type (character- ized by a persistent low-level temperature in- version with dry subsiding air above) where this model (or any other simple model) of N versus z is inadequate to explain the N-structure at low latitudes. It was found that the behavior of the wet term (measured on figs. 1 and 2 by Sw, the rms error over the first 8 km) was a good indicator of whether or not the data would pro- vide a good fit to the profile. However, it can be noted in figure 2 that, even though the rms wet-term error below 3 km is 14.6 N-units, the profile at Dakar above an altitude of 6 km is well represented by the three-part exponential. Maps were prepared for each of the 4 “‘sea- sonal” months of the parameters necessary to utilize the three-part exponential in estimating upper-air refractivity. These are the reduced- to-sea-level values, D, and W.; the three scale heights, Hw, H,, and H.,; and the transition alti- tude, z:. The surface values of N can be recov- ered by substituting the elevation of the surface above sea level at the desired location in (38), which amounts to inverting the process used to reduce the surface values of N to sea level. The series of maps given in appendix A can be used to estimate the mean value of N at any desired altitude for each of the seasonal months at any world location except those areas outlined in figure A-30 (which summarizes the wet-term rms error values found in figures A-26 through A-29). 3.2. Discussion of N(z) Map Contours The world maps of N(z) parameters reveal a number of interesting trends. Some of these are: (1) The D, scale height, H,: (a) is smaller than average over the arctic seas in winter because of dense stratified air; (b) remains higher than average over land areas during their warm seasons due to a steep temperature lapse rate with height. (2) The D, scale height, H,: shows a minimum in the equatorial region because of the colder temperatures found above the tropical tropopause. WORLD MAPS OF N(z) 5 (3) The wet scale height, H.: (a) is larger than average in the general area of the equatorial heat belt during all sea- sons. This indicates a steep temperature gradi- ent with a very small lapse of absolute humidity with height in the turbulently mixed deep layer of warm air. However, in some tropical areas KOROR, CAROLINE ISLANDS 7°20'N, 134° 29' £, ELEV. 29m MAY — 141 PROFILES 300 OF WET ANDO DRY EXPONENT! + AL TERMS Ng = 387.6 200 Syit0.27N N - UNITS ote Ww (EXP) Hw = 2.66 km Z—km FIGURE 1. Three-part exponential fit to mean N-profile: Koror. definite changes may occur in Hy because of seasonal shifting of small, but persistent, anti- cyclonic circulations which modify to a consid- erable vertical extent the normal zonal trans- port of water vapor in those latitudes. The seasonal differences of H. in the Coral Sea area seem to confirm the existence of such a cellular structure northeast of Australia [Hutchings, 1961]. (b) is larger than average over two types of convectively heated continental interiors: (1) high-latitude land masses where the sea-level wet term is less than 20 N-units; (2) temperate desert steppe regions where the sea-level wet term is between 20 and 60 N-units. (c) is lower than average in areas where subsidence or tradewind ducting persistently occurs below 3 km. (4) The dependence of the dry sea-level values, D., upon temperature (because Do = 77.6 P/T) is revealed in such features as the 332 high in Siberia during February and the 260 low in the Sahara Desert during May and August. (5) The wet sea-level values, W., are also 6 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY very dependent upon temperatures because of the larger water content possible at high tem- peratures. There are two exceptions: (a) Large interior deserts, where moun- tains block the normal moisture flow, tend to have low wet-term values relative to their tem- peratures. (b) In August, when the monsoonal mois- ture is trapped south of the Himalayas, India shows unusually high wet-term values. (6) The intersection height, 2, is closely re- lated to the height of the tropopause, but in areas where an isothermal layer precedes the stratospheric increase of temperature, the D, T ale [S31 DAKAR, SENEGAL =— Ns | T 14°40' N, 17°30'W, ELEV. 40m | MAY — 246 PROFILES n 4 + ; — | | Ns = 357.5 | | | T 200 LL Sy: tl4.6Nn N - UNITS | | | aw (EXP) Hy: 79 km FIGURE 2. Three-part exponential fit to mean N-profile: Dakar. curve may intersect the D, curve as much as 2 km below the tropopause heights given in fig- ures D-1 through D-4 (see appendix D). 3.3. Reliability of Contours of N(z) Maps Although radiosonde stations are the only worldwide source of upper-air meteorological data, many areas of the world had few, if any, radiosonde reporting stations before 1957. As a result of the IGY, many new stations were established, especially in the lower latitudes; however, radiosonde data are still not available for a number of large areas, such as Brazil, China, and the Indian Ocean. High latitudes also show a noticeable sparsity of upper-air data; fortunately, there is a fairly small and uniform transition in most parameters at these far-south and far-north latitudes. Even in the U.S.A., where the first radiosonde network was established in 1938, radiosonde stations are still several hundred miles apart. The maps were hand-contoured by interpola- tion between the widely-spaced plotted data points, using a technique similar to that used in the analysis of synoptic weather maps. Each map was then carefully checked by another an- alyst to make certain all data points had been properly considered. The contours were modi- fied in some areas on the basis of other informa- tion or considerations not accounted for in the machine-analyzed radiosonde data. For in- stance, supplementary surface data [Knoll, 1941; Serra, 1955; Bean and Cahoon, 1957; UNESCO, 1958; Bean et al., 1960b; Air Min- istry, 1961; Dodd, 1965] were considered in the contouring of those parameters (D. and W.) dependent upon surface observations. Also, if spurious “high” centers of the wet term (such as the isolated values found at Tananarive, Malagasy Republic) were produced at high ele- vation stations by reduction-to-sea-level pro- cedures, these values were smoothed to some extent. It was also found that the wet scale heights derived from mean N-profile data for stations at altitudes greater than 1 km tend to give more unrealistic sea-level wet terms than the average wet-term scale height of 3.0 km suggested by Hann [List, 1958]. When this “standard atmosphere’”’ scale height was sub- stituted for Hw, the maximum error of N (z) values calculated from (3) or (4) for all sta- tions above 1 km which are listed in table A-1 was 6.2 percent of the true 5-year mean value at Tananarive in August; the second largest error was 5.5 percent at Nairobi in February. Another contouring check was made of all modified contours; a third analyst reviewed the smoothing to be sure it was consistent with the original plotted data as well as with the sup- plementary information. To further check the contouring, calculated N(z) values (using (3) and (4) with values read from figs. A-1 through A-25) were com- pared with actual observed values at 32 repre- sentative stations. The results of this check (reported in detail in table 2 of sec. 7) em- phasize that, although some error undoubtedly results in N(z) values below 1 km due to con- touring, it is not a problem for N(z) values at 3 km or above. 3.4. Problem Areas of N(z) Maps The use of wet and dry scale heights in a bi- exponential radio refractive index model has proved to be a good indicator of climatic differ- ences [Bean, 1961; Misme, 1964]. The dry term, or atmospheric density component of re- fractivity, decreases with height in a uniform manner throughout the troposphere so that its scale height is an accurate indicator of the degree of density stratification, but the water- vapor component (the wet term) is not so well- behaved. Because the saturation vapor pres- sure, és, is itself an exponential function of temperature (which generally decreases linearly WORLD MAPS OF N(z) 7 with height), one of the best wet-term models is probably an exponential curve [Reitan, 1963; Dutton and Bean, 1965]. However, an exponen- tial model of the wet term must be used with discretion because humidity is extremely vari- able, both vertically and horizontally (because of its high dependence upon the temperatures within the different air masses, as well as var- ious terrain and land-water effects). To show actual physical changes in Hw, the wet scale height, it would be desirable to pre- sent contoured values based not only on a large number of stations, but also on data representa- tive of various times of day. Figures A-6, A-12, A-18, and A-24 present the seasonal values of Hy, but worldwide maps of the diurnal vari- ability of the wet scale heights are not yet avail- able. There are three specific areas of the world where the assumption of an exponential distri- bution of the wet term is largely invalid and can be used only with reservations. Two of the areas have one thing in common — a low sea- level wet term. At continental stations in high latitudes where strong temperature inversions persist during winter months, the wet term at 3 km may be as large as, or even larger than, that at the surface (because of the increase of water vapor “capacity” with temperature), and the result is a negative or a very large positive value of the wet scale height, neither of which is physically realistic. At any tropical desert station where the sea level wet term is < 30 N-units, deceivingly high wet scale heights also may result. Fortunately, because of the small contribution of the wet term in these cases, the total N-error remains small. The wet-term pro- files at nine stations with low values of W. were examined, and the largest error at any height was 3.7 percent of the true 5-year N(z) value at Niamey (a desert station) in February (fig. 3). In the arctic areas (represented by Barrow, Alaska, in this same figure) the maximum error never exceeded 1.5 percent of the total N(z) value. The third area presents a more serious prob- lem because it exists in a subtropical climate (15°-35° north and south of the equator) where the wet term contributes from 14 to 14 of the total N. The sharp decrease of humidity and increase of temperature which is found in at- mospheric layers between the surface and 3 km in the subsidence regions of semipermanent sub- tropical highs destroys the exponential distri- bution of the wet term. In fact, in regions such as this, the exponential fit may be valid only at two or three points. This can be noted in figure 4, where the mean wet term for May is graphed, and the Hy value from the least-squares fit from 0-3 km of log W versus height is indicated, for 8 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY four dissimilar stations: Dakar (low-level sub- sidence), Antofagasta (intermediate subsid- ence-trade wind ducting), Hilo (trade wind in- version), and Balboa (steep exponential gra- dient). A check of figure A-30 reveals that the first three of these stations are located in areas where, because the rms of the wet-term error exceeds 5 N-units for at least 1 month, the three- part exponential model is not recommended. | BARROW (Hy= -47 33) _| ——— NIAMEY (Hy = 4.95) 49} ——-— WET REFRACTIVITY TERM | | 0 05 Wo 1S HEIGHT ABOVE SURFACE IN KILOMETERS FIGURE 38. Five-year mean wet refractivity term: February. WET REFRACTIVITY TERM 300 200 WORLD MAPS OF N(z) ANTOFAGASTA (Hw= 1.33) = — — — BALBOA (H,=2.83) © DAKAR (Hy=1.79) = — HILO (Hy=2.16) 05 10 15 20 25 30 HEIGHT ABOVE SURFACE IN KILOMETERS FIGURE 4. Five-year mean wet refractivity term: May. 35 9 4. World Maps of AN 4.1. Twelve maps of monthly mean AN were pre- pared from the 5-year mean values obtained by interpolation of mandatory pressure level radio- sonde data for the 268 stations listed in table B-1 and located in figure B-1 in appendix B. These maps, contoured in the same manner as the N(z) maps (sec. 3.3), are figures B-2 through B-13. Previous work has shown that a good corre- lation may exist between AN and surface N on a monthly mean basis [Bean and Thayer, 1959; Bean and Cahoon, 1961b; CCIR, 1965]. In fact, in many areas of the world this seasonal corre- lation is very high, and in such areas a regres- sion line might provide better estimates of monthly mean AN than the maps in appendix B (if the mean value of surface N were available for that particular month of that year for the desired location). This regression line could also be used with the N, distribution data from table A-1 to provide estimates of the AN distri- bution. Correlations were therefore calculated for the 12 monthly mean values of AN and sur- face N for each of the stations in the sample. The equations resulting from these calculations were put into the form of deviations from the annual means: BN =b(N; —N,) +} AN+S.E., (5) where N, is the surface value of N, the single bar represents a monthly mean value, the dou- ble bar represents the annual mean, 0 is the slope of the regression line, and S.E. is the standard error of estimate. The equations were put in this form because the intercept of the equations in the ordinary form is equal to AN — bN;, which is too unwieldy for contouring on maps. Maps, which appear in appendix B, were prepared of the slope (b), the annual means (AN and N.), and the standard error of pre- diction and correlation coefficient of the regres- sion lines.° Development 4.2. Discussion of Contours and Reliability ofAN Maps The world AN maps of this atlas do not show as much detail as may be found in other publi- cations which consider only specific areas [Bean : 5 N, is an approximate sea-level value of Ns, defined by the equa- tion No = Nse®-1#, where z is the elevation above sea level in km. 10 et al., 1960b; du Castel, 1961; Rydgren, 1963; CCIR, 1963]. It was necessary in this study to omit some of the available radiosonde data in areas with relatively dense weather networks (e.g., the U.S.A. and Europe) in order to obtain a more nearly uniform worldwide coverage. Even with this coverage, the map scale size pre- cluded the contouring detail which would be necessary if localized terrain effects were to be included; for example, mountainous locations (higher than 1 km) probably have lower values of AN than those indicated in figures B-2 through B-13. Some dissimilarity in the con- tour patterns between the maps in appendix B and other AN maps may also be found because of the differences in the time period used in the samples; such disagreements emphasize the fact that 5 years of data are not adequate for reliable means in many areas. The map contours of worldwide AN indicate that: (1) Low values of AN are characteristic of: (a) large desert and steppe regions such as the Sahara, the Australian interior, the south- western U.S.A., and the Asian region southeast of the Caspian Sea all year ; (b) high plateau areas during all seasons except winter. (2) High values of AN are found in: (a) all areas where large masses of subsid- ing air prevent the normal diffusion of water vapor, creating an unusually large N-gradient between the moist surface air and the very dry air at 1 km. Specific examples are: (1) continental west coasts at latitudes 20°— 35° in the summer hemisphere and 10°- 25° in the winter. In fact, the true AV may be higher than indicated on the maps at locations such as Dakar, Senegal, where a very thick (~250 m) surface or near-surface ducting layer occurs much of the time; (2) tropical ocean areas where a trade- wind inversion leads to a persistent elevated ducting layer below 1 km. [Note: Ina few cases where the entire thickness of an elevated layer lies between 1 km and the height of the 850-mb level, the interpolation method gives a map value which may be 5 to 10 N-units too high.] (b) southeast Arabia and the Gulf of Per- sia during July and August, when orographic subsidence traps moisture from the southwest monsoon in the gulf and lowlands between the mountains. (c) Siberia and the Canadian interior in winter because of the intense surface tempera- ture inversion. (d) the Mediterranean and Black Seas during summer when convective mixing greatly increases the near-surface humidity. (e) India in the spring, when increased heating over land produces a low-pressure re- gion which leads to onshore winds and humid WORLD MAPS OF AN 11 weather conditions until the onset of the mon- soon. __In winter a combination of high and low AN values appear near the tip of southwest Africa as the subsidence from the South At- lantic High causes a large moisture gradient to appear off the coast, whereas a small humidity gradient is characteristic of the dry plateau region inland. 5. World Maps of Extreme N-Gradients c/s, t is the total thickness of the duct in km, dn/dz is the average gradient over the duct The gradient of N near the surface of the (n/km), and 7 is the radius of the earth in km. earth is of particular importance in many appli- Equation (6) is derived under the assumption cations of telecommunications; e.g., extreme of a constant gradient throughout the duct, but values of these initial gradients are responsible moderate departures from this assumption do for much of the unusual behavior of radio sys- _ not seem to affect the results greatly. The fmin tems. Superrefractive gradients (defined hereas corresponds to an absolute attenuation of the values between —100.0 N/km and -156.9 N/km) guided energy of about 3 dB/km (5 dB/mile) are responsible for greatly extended service hori- _—_ [Kerr, 1951]. zons, and may cause interference between widely The maps in appendix C were prepared from separated radio circuits operating on the same the cumulative distributions discussed above. frequency. Ground-based radio ducts (layers The distributions of gradients for the 0 to having a negative gradient larger in absolute 100-m layer were used to obtain maps of the value than 156.9 N/km) can cause prolonged positive (subrefractive) gradient exceeded for 5.1. Development spacewave fadeouts within the normal radio ho- —_ 10 and 2 percent of the observations at any lo- rizon [Bean, 1954] and allow radar to track ob- cation, and the percent of observations with 0 jects many hundreds of kilometers beyond the or positive N-gradients. Maps were also pre- normal radio horizon. On the other hand, subre- _ pared of the extreme values of negative gradi- fractive gradients (zero or positive gradients) ents observed; these are referred to as “lapse produce greatly reduced radio horizons, and rates” of N (i.e., decrease with height, a term may result in diffraction fading on normally normally used in referring to atmospheric tem- line-of-sight microwave paths. perature gradients; it is used here to avoid the In the process of obtaining the mean N-profile awkwardness of referring to a very strong nega- for each station and month, cumulative distri- tive gradient as being “less than” a given nega- butions were prepared of the gradients occur- tive value). Included in appendix C are maps ring between the surface and the 50-m and of the lapse rate of N exceeded for 25, 10, 5, 100-m levels. _Each gradient was calculated as and 2 percent of the observations for the 100-m the simple difference between the surface N Jayer. Cumulative probability distribution and the value at 50 or 100 m above the surface, charts of the gradients at 22 representative divided by the height interval. For 99 out of the world locations are also presented. 112 stations in the mean N sample, cumulative Other maps in appendix C were prepared distributions were also prepared of the gradi- from the distributions of superrefractive and ents and thicknesses of all observed ground- ducting layer gradients, thicknesses, and frmin based superrefractive or ducting layers, regard- values for ducts. These include the percent of less of the thickness of the layer (except that time that the lapse rate of N in the ground- no layer less than 20 m thick was included, be- — pased layer is equal to or larger than 100 N/km cause the gradients obtained in such cases are and equal to or larger than 157 N/km (ducting not considered to be reliable). In addition, gradient), the percent of superrefractive layers cumulative distributions were prepared of the that were more than 100 m thick, and the per- minimum trapped frequency for each of the ob- cent of ducting layers that were more than 100 served ducts in the sample. (This sample size m thick (the last two refer to the percent of averaged 208 pieces of data for each month; thick layers out of the number of observed lay- for all months the smallest sample was 30 and ers of that type). The distributions of fimin the largest, 620). The minimum trapped fre- values were used to prepare maps of the per- quency refers to the approximate lower limit of cent of all observations which showed ground- frequencies that will be propagated in a duct in based ducts having an fmin value of less than ee eee mode, and is given by [Kerr, 3900 Mc/s, 1000 Mc/s, and 300 Mc/s. ] ? > 1.2 * 10° (c/s) Poin = dn | ’ (6) 5.2. Discussion of Gradient Map Contours 3/2 == oe | 2/2 2 Oa dz yr ] (Subrefraction) where min is the minimum trapped frequency in Ground-based subrefractive layers may be 12 found in the same tropical and subtropical loca- tions as superrefractive layers, because a small change in relative humidity at high tempera- tures produces a very noticeable change in ab- solute humidity, and the N-change (either posi- tive or negative) with height is highly depend- ent upon the variation of absolute humidity. For instance, subrefractive gradients occur quite often during the afternoon at stations which experience superrefraction or ducting during the night and early morning. Other sta- tions may have nocturnal subrefraction during winter and superrefraction during the same hours in summer. However, subrefraction, un- like superrefraction, rarely occurs at surface temperatures below 10°C (the only exception would be locations greater than 1 km above sea level). The surface conditions conducive to subre- fractive gradients are of two rather opposite types: (a) temperature > 30°C; relative humidity < 40 percent; (b) temperature 10° to 30°C; relative humid- ity > 60 percent. Type (a) is usually found during the day- light hours of months when intense solar heat- ing occurs at warm, dry continental locations and forms a very nearly homogeneous surface layer (no decrease of density with height) which may be several hundred meters thick. Since a moist parcel of air is less dense than a dry parcel at the same temperature and pres- sure, the intense convection which occurs with- in such a layer of absolutely unstable air tends to concentrate the available water vapor near the top of the layer, because a moist adiabatic upper boundary is formed where the super- adiabatic lapse rate changes abruptly to a sub- adiabatic or very stable lapse rate. The result is an increase (sometimes as large as 50 percent of the surface value) with height of the wet term through the ground-based layer. This in- crease, coupled with no change in the dry term, leads to a subrefractive (or positive) gradient. This layer may retain its subrefractive na- ture throughout the early evening hours at sta- tions where conditions are favorable for the development of a temperature inversion. As the ground cools rapidly, the air very near the ground cools and becomes more dense, but the water vapor which is trapped between the two stable layers causes the positive wet-term gradi- ent from surface to the top of the original layer to remain large enough to overbalance the slightly decreasing gradient of the dry term. This evening subrefraction is an outgrowth of type (a); however, it may resemble type (b) at the surface because it can be found with a temperature as low as 20°C and a relative hu- midity as high as 60 percent. WORLD MAPS OF EXTREME N-GRADIENTS 13 Type (b) occurs most often during night and early morning hours, and is characteristic of coastal trade-wind and sea-breeze areas where differential heating of land and sea results in the advection of air which is warmer and more humid than the normal surface layer. In this type, both dry and wet terms may increase with height, creating a surface layer of subrefrac- tion which is generally more intense in gradient than type (a) but not so thick. This form of subrefraction might also be found for short periods in any location where frontal passages or other synoptic changes create the necessary conditions. Type (a) subrefraction is hard to evaluate from figures C-1 through C-4 because its per- centage occurrence at any specific location is so dependent upon the time of day represented by the radiosonde data at that location. For in- stance, because the local radiosonde observa- tion times in the southwestern U.S.A. were 0800 and 2000 for the data period used in this atlas, only the subtype (a) of evening subre- fraction is recorded. Because conditions are more suitable for inversions in February and May, these months appear to have surface- based subrefractive layers more often than Au- gust. However, a detailed check of midafter- noon observations near White Sands, N. Mex., reveals that midday subrefractive conditions are quite prevalent during much of August and Sep- tember. The same diurnal problem is found in northern Africa and the desert region south and east of the Caspian Sea, where many of the sta- tions take observations between 0300 and 0600 LST. Furthermore, even at those stations which do have midday data, the ‘“‘motorboating”’ problem (i.e., humidities too low to be mea- sured by the radiosonde — see sec. 5.5) during the warmest seasons at very dry locations prob- ably masks out a large percentage of subrefrac- tive occurrences; e.g., the occurrence of subre- fraction recorded in November and February for the interior of Australia (where afternoon observations are included) is probably too low. Figures C-1 through C-4 reveal that type (b) subrefraction can be expected 10 to 20 percent of the time in the western Mediterranean Sea and the Red Sea area, and also in the Indone- sian-Southwest Pacific Ocean region. These lo- cations seem to indicate a slight seasonal trend, with a higher probability of occurrence during winter months. Another region with a 10 to 20 percent level of subrefractive gradient occur- rence is the Ivory Coast and Ghana lowlands of Africa where onshore winds prevail all year. Occurrences of type (b) subrefraction exceed 5 percent at these locations and times of year: (1) Southeast coast of U.S.A. all months; 14 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY (2) Hawaiian Islands all months except May (3) euth Africa all months except Novem- ber ; (4) Southeast coast of South America in No- vember and February ; ) Southern California in November ; (6) North Indian Ocean in May; ) Isthmus of Panama in November. 5.3. Discussion of Gradient Map Contours (Superrefraction and Ducting) Superrefractive and ducting gradients in ground-based layers are most often associated with temperature inversions (temperature in- creasing with height within the layer), not only because a positive temperature height-gradient causes a negative N-gradient, but also because the low eddy diffusion qualities associated with a temperature inversion often lead to a steep negative gradient of humidity through the in- version. However, previous investigations [Bean, 1959] have shown that there are at least two other typical situations encountered in the formation of strong ground-based gradients: The first of these is the arctic situation, where, with surface temperatures below about —20°C, a strong temperature inversion (typical of con- tinental arctic air masses) produces a superre- fractive or ducting layer, while the vapor pres- sure may actually increase with height. More often, in this case, the wet term is negligible throughout the layer. The second case is typical of very humid tropical areas when the surface temperature is 30°C or greater. In these loca- tions (which are usually coastal) a common occurrence is a slight decrease of temperature with height, accompanied by a very strong lapse of absolute humidity. Such profiles may show only a slight decrease of relative humidity with height, but, because the saturation vapor pressure is nearly an exponential function of temperature, the resulting vapor pressure gra- dient may be very large, thus causing a steep N-gradient. Figures C-41, C-45, C-49, and C-53 show that persistent ducting (D) or superrefractive (SR) initial gradients can be found more than 25 percent of the time for at least two seasons in seven general areas of the world: (1) Dakar - Fort Lamy transitional strip in Africa (D: all seasons), (2) northern Arabian Sea including coastal areas of the Gulf of Aden and the Persian Gulf (D: all seasons), (8) India, Bay of Bengal, southeast Asia, Indonesia, and north tip of Australia (SR: all seasons), (4) southwest coast of North America, in- cluding portions of the North Pacific (SR: Feb- ruary, May, November), (5) Gulf of Mexico and Caribbean region (SR: May, August, November), (6) northwest coast of Africa and western Mediterranean (SR: May, August), (7) Antarctica (D: May, August). Area (1): Tropical west coast locations in the vicinity of 15°-22°N or S are affected annually by three or four latitudinal weather zones [Tre- wartha, 1961]. In winter the Dakar-Fort Lamy region is under the influence of dry anticyclonic Saharan air, but even at the time of low sun, the prevailing surface air movement is onshore from the southwest. The vertical depth of this maritime current is more shallow than in sum- mer, but during the early morning hours, the surface relative humidity is 80 to 90 percent compared with 40 to 60 percent in the dry sub- siding air above. Even with radiational cooling, the night temperatures throughout the marine layer (from 50 to 600 m thick) still remain over 20°C. This combination of temperature and hu- midity creates trapping conditions for frequen- cies below 300 Mc/s about 30 percent of the time in February (fig. C-31). The weather zones advance rapidly north- ward [Thompson, 1965], so that by July the Dakar-Fort Lamy strip is in the wet tropical regime associated with the fluctuating, unstable Intertropical Convergence Zone (ITC). The marine current of the westerlies becomes much deeper, but the ducting layers are shallower and can exist only intermittently between the turbulent, showery periods common to the re- gion. Figures C-22, C-24, C-26, and C-28 indi- cate that more than 30 percent of the ducting layers are over 100 m thick for all seasons ex- cept summer. Area (2): The coastal areas of Arabia ex- perience high surface humidities all year from monsoonal and sea-breeze effects, but during May and August these values are reinforced by temperatures above 25°C in a marine layer which may extend up to a height of 800 to 900 m before it meets the overrunning dry north- easterlies [Tunnell, 1964]. The percentage oc- currence of ducts at Bahrain seems much high- er than at Aden because all observations at Bahrain were taken at 0300 LST (when the surface humidity is at its maximum of 75 to 90 percent), whereas the Aden observations, taken twice a day, include as many observations at 1500 LST (when the relative humidity value is much less) as at 0300 LST. For instance, 50 of the 66 ducts recorded in August at Aden were from early morning observations. However, the fact that ducting gradients at Bahrain trap fre- quencies below 300 Mc/s over 75 percent of the time as compared to 5 percent at Aden (fig. C-37) is due to another factor: the thickness of the moist marine layer, when ducting is present at Bahrain, is greater than 300 m over half the time. Area (3): A moist surface layer is also found in the monsoonal areas. Its temperature is 25 to 30°C and, during occurrences of ducting, the surface relative humidity ranges from 85 to 100 percent, but the trapping incidence is much less than in either area (1) or (2). The surface layer is shallower and its gradient is less intense because the humidity decrease between it and the air mass directly above it is only 10 to 20 percent. Because brief periods of stable weather occur even between surges of the summer mon- soon, the ducting incidence remains over 10 per- cent for all of area (3). Area (4): Along the western coast of North America, from Southern California to Central Mexico, the most important month for unusual radio propagation due to surface conditions is February, when frequencies below 300 Mc/s are trapped 10 percent of the time. During the pe- riod studied, 30 percent of the superrefractive gradients were at least 300 m thick in all 4 months. Closer examination of the ducting structure in Mazatlan reveals that if near-sur- face layers (bases of 100-300 m) were included, the percentage of occurrence would be increased by 20 to 40 percent for all months except Au- gust. During February, May, and November the surface temperature of 20 to 30°C remains nearly constant through the ducting layer, but the relative humidity decreases from a surface value of 70 to 80 percent to values ranging from 20 to 40 percent. The dry air in the upper layer results from subsidence in the eastern margin of the Pacific high pressure cell, which shifts northwestward in August, thus decreasing the ducting incidence in Mazatlan but increasing it in lower California and the Hawaiian Islands (figs. C-21, C-28, C-25, C-27). Area (5): The center of most intense ducting in the Caribbean Sea and Gulf of Mexico changes with the seasons (figs. C-41 through C-56). The smallest percentage of superrefractive ground- based gradients is found in February, with the stronger gradients concentrated near the east coast of Central America. By May the super- refractive area has shifted eastward into the Caribbean and northward into Florida. In Au- gust it includes parts of the eastern U.S. but is still most intense in the Swan Island area, and in November the area encompasses all of the Caribbean. The ground-based superrefractive layers are thicker than 100 m approximately 70 percent of the year, but the ducting layers are never intense enough to exceed the 1-percent trapping level for 300 Mc/s. Area (6): The cause of superrefraction in the western Mediterranean and northwest Africa is very similar to that in area (4). During the summer season, subsidence along the eastern WORLD MAPS OF EXTREME N-GRADIENTS 15 edge of the Atlantic high-pressure cell superim- poses a dry layer over the marine surface layer ; during the winter season, the major subsidence area shifts southward, the temperatures throughout the surface layer are 5 to 10°C low- er, and the percentage of superrefraction and trapping incidence decreases. Area (7): During the long Antarctic night, in- tense radiation from the snow-covered ground keeps the surface temperature much lower than that in the air several hundred meters above. This temperature inversion of 10 to 25°C is the cause of all the ducting gradients in May and August, which trap frequencies <1000 Mc/s at least 40 percent of the time (see appendix E). 5.4. Discussion of Cumulative Distributions of Ground-Based Gradients Data from 22 representative stations were selected as a sample of the kinds of ground- based gradient distributions from the surface to 100 m which occur in various climates and locations throughout the world. Interesting similarities which exist among the gradient distributions imply that the re- fractivity climate of any station may be related more to the season or month of the year than to any particular latitudinal location. For in- stance, consider the interesting relationships between Bangui (a tropical station), Bordeaux (temperate), and Amundsen-Scott (arctic). The gradient distribution at Amundsen-Scott in February resembles that of Bordeaux in February, but its August distribution slope re- sembles that of Bangui in May. However, Ban- gui’s distribution slope and range in August resembles Bordeaux in May. Amundsen-Scott forms another interesting climatic triad with Saigon and Long Beach. In August the distri- bution slope and range of Amundsen-Scott is very similar to that of Saigon (a tropical sta- tion), but the negative gradient intensity is about 100 N-units greater at all percentage lev- els. However, Saigon in May, before the mon- soon, resembles Long Beach in February. It was expected that a pronounced diurnal effect would exist in the gradient structure near the surface, so two stations, Aden, Arabia, and Nicosia, Cyprus, where data were available at two thermally opposite times of day—2 and 3 a.m. and 2 and 3 p.m. (0000 and 1200 GMT*)— were studied. Figures C-57 and C-71 in appen- dix C show the diurnal differences in the cumu- lative distribution of initial gradients from 0 to 100 m for these two stations for the 4 months studied. Superrefractive conditions normally accom- pany nocturnal inversions. At Nicosia this 6GMT (Greenwich Mean Time) is the same as UT (Universal Time). 16 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY proves to be the case for all seasons, with a very definite maximum in August, when anticyclonic upper air circulation intensifies the humidity decrease aloft and radiational cooling lowers the surface temperature 15°C below that found during the day. Aden, a coastal station with less change in its diurnal temperature cycle than Nicosia (an in- terior valley station on a fairly large island), exhibits superrefraction day and night for all seasons. During August and November initial gradients have a wide range of values, with the largest variation occurring at 0000 GMT, but in February and May the nocturnal stability apparently is seldom destroyed by convective mixing, and the 1200 GMT (1500 LST) initial refractivity gradient may exceed the 0000 GMT (0300 LST) gradient. However, the early morning inversion is usually more significant from a radio-meteorological viewpoint because | ADEN — MAY —— 0000 GMT, Hy=197, H\=9.95 —— 1200 GMT, Hy=2.08,H,=9.47 km HEIGHT, -90 N-GRA -100 -110 -120 DIENT FIGURE 5. Five-year mean vertical refractive gradient profile: Aden. the refractivity gradient is much more intense from 250 m to 750 m, thus affecting more radio frequencies. This can be noted in figure 5, rep- resenting a 5-year mean of the vertical gradient observed from 0 to 4.5 km during May. Figure 6 presents the same data for Nicosia during a 5-year August period. Because scale heights are also a measure of stability and strat- ification, figures 5 and 6 not only give the differ- ences in the mean total N-gradient values in the lower atmosphere, but also the wet (Hw) and dry (HM) seale heights. Five-year mean values of N; at Aden in May were found to be 382 (surface wet term of 123) at 0000 GMT and 374 (surface wet term of 119) at 1200 GMT. The corresponding values at Nicosia in August were 341 (wet term of 83) and 310 (wet term of 62). The percentage of occurrence of subrefraction (N/km>0) is larger at night (0000 GMT) in February and November at both locations. However, this diurnal trend is much more pro- nounced at Nicosia, particularly in November when the percentage of night subrefraction is over 30 percent larger than the daytime per- centage of occurrence. 5.5. Reliability and Limitations of Ground-Based Refractivity Gradient Data Because ground-based gradients are so sen- sitive to local effects, such as terrain and land- water relationships, it was impossible to contour figures C-1 through C-56 for individual small areas. For instance, although Madrid (on the high interior plateau of Spain) experiences little ducting during the year, it is surrounded WORLD MAPS OF EXTREME N-GRADIENTS 17 by areas of high ducting incidence, and no at- tempt was made to delineate this small region of nonducting. Also, refractivity gradients cal- culated from radiosonde observation levels sep- arated by less than 20 m may be seriously affect- ed by instrumental errors, so ground-based lay- ers less than 20 m thick were not included in the analysis. Consequently, very shallow duct- ing (such as that found at certain times over oceans, under dense jungle canopies, and in NICOSIA — AUGUST 0000 GMT, Hy-l75, H,=973 —— 1200 GMT, Hy-2.16, H,=9.83 km HEIGHT, -10 - 80 -90 -100 -110 -120 N-GRADIENT FIGURE 6. Five-year mean vertical refractive gradient profile: Nicosia. mountain valleys) is not included in the con- toured data; however, such layers may be in- tense enough to create trapping conditions for frequencies down to 600 Mc/s [Jeske, 1964; Baynton et al., 1965; Behn and Duffee, 1965]. The time of day represented by the available observational data must also be considered for any variable which has a definite diurnal trend. Therefore, for a true comparison of worldwide gradient behavior, it would be desirable to use comparable data recorded at least twice a day at standard local or sun-referenced time. How- ever, because simultaneous data are needed for the preparation of synoptic maps, all stations in the U.S.A. and many in the European coun- tries schedule radiosonde observations at 0000 GMT and 1200 GMT. Many stations in other parts of the world take only one observation per day (usually at either 0000 GMT or 1200 GMT, but there are exceptions, e.g., 0600 GMT at Abidjan, Dakar, and Niamey). Even if all sta- tions had a common GMT hour for taking ob- servations, the diurnal problem would still exist because the local time for any designated GMT 18 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY time would be distributed throughout the day as one traversed the globe. For instance, the fol- lowing stations (in tropical areas where the occurrence of either subrefractive or superre- fractive layers is especially dependent upon the time of day) were used in this report: Station Localtime GMT *Aden, Federation of South 0300 0000 Arabia *Curacao, Netherlands 2000 0000 Antilles Fort Lamy, Republic of Chad 0100 0000 *Hilo, Hawaii 1400 0000 Lae, Territory of New Guinea 1000 0000 Majuro Island, Marshall 1200 0000 Islands Singapore 0700 0000 Those stations marked with an asterisk also take observations at 1200 GMT. However, when evaluating the apparently low level of duct oc- currence at some locations (e.g., Majuro) and high occurrence at others (e.g., Fort Lamy), and when checking the subrefraction occurrence at warm, dry continental locations, such as Ni- amey (where no midday observation is taken), the local time of the radiosonde ascent should be considered. In addition to the spatial and temporal limita- tions imposed by the use of available radiosonde data, there are instrument recording limitations (see sec. 2) which must be considered when evaluating N-gradients. Although the alternat- ing sequence system of observing the humidity and temperature can put a lower limit on the thickness and thus mask the true gradient of atmospheric layers which can be detected by radiosonde, the response of the radiosonde tem- perature and humidity elements is a more se- rious problem in the measurement of the in- tensity and number of superrefractive gradients at or near the surface. For example, in typical ducting situations during May in a tropical (Saigon) and in a temperate climate (Bor- deaux), correction of both humidity and tem- perature sensor time lags as suggested by Bean and Dutton [1961] would intensify gradients of —293 N/km (Saigon) and —212 N/km (Bor- deaux) to —377 N/km and —362 N/km, re- spectively. This type of correction also would have increased the percentage occurrence of superrefractive and ducting gradients in the majority of cases. Such extensive recalcula- tions were not possible in this study, but the possibility that more intensive gradients may occur in larger percentages at some locations (particularly in temperate, humid climates) should be kept in mind when applying values obtained from any of the figures in appendix C. Another limitation which applies primarily to the detection of subrefractive layers (figs. C-1 through C-12) in hot, dry regions is the high electrical resistance of the lithium chloride hu- midity element at very low humidities which causes open-circuit signals (‘‘motorboating’’). At stations such as Aoulef, Algeria, where the daytime surface temperature often exceeds 30°C, the relative humidity may be below the motorboating boundary at all levels from sur- face upward, and all relative humidity values (except the surface) are estimated, usually in values which are equal to, or less than, the sur- face value. However, it is quite probable in these highly convective conditions that the abso- lute humidity remains constant with height, instead of rapidly decreasing (as the estimated relative humidity values would indicate). If it did remain constant, fairly persistent subre- fractive gradients would be found in such areas during the hours of most intense solar heating. 6. World Maps of Mean Tropopause Altitudes Maps have been prepared of the mean tropo- pause altitudes which were calculated in the course of obtaining the mean N-profiles for the 112 station sample, as discussed previously. The maps, for the 4 “‘seasonal’”’ months, are shown in appendix D. The zone of maximum tropo- pause altitudes for each month seems to corre- spond quite well with the mean position of the Intertropical Convergence Zone. As stated earlier, the criterion for determin- ing the tropopause altitude for each radiosonde ascent was the altitude of the base of the first layer or layers which had a total thickness of at least 2 km and a temperature lapse rate of less than 2°C/km. The mean tropopause alti- tude for each station and for each month was 19 determined by a simple average of all of the individual values for the profiles in the sample (usually of 5 years’ length). The reliability or consistency of these maps is difficult to assess, since the results of determining tropopause alti- tudes depend to a great extent on the criteria used for selection of the first stratospheric layer. The criterion used here is the one in most com- mon usage [U.S. Weather Bureau, 1964], but other criteria may be applicable where the re- sults are intended for use in specific atmospheric problems. These maps supplement tropopause data presented in other reports and atlases [For example, Willett, 1944; U.S. Navy, 1955- 59; Smith, 1963; Smith et al., 1963; Kantor and Cole, 1965]. 7. Appraisal of Results 7.1. Accuracy of N(z) Maps The general accuracy of the 5-year mean values used in the N(z) study was checked by computing the standard deviation of the year- to-year monthly means and dividing by the square root of 5. This should be a good estimate of the rms (standard) error of the 5-year mean values as compared to the true long-term mean (assuming there are no trends in the data). Table 1 shows the estimated standard error of the 5-year mean N, values for 40 stations, ar- ranged by climatic classification. The percent- age errors should be similar for the N(z) pa- rameters (with the possible exception of scale heights) at various altitudes. The combined (rms) standard error of 5-year mean N, for the 40 stations for February and August was 2.37 N-units, or about 0.7 percent of the mean Ns. It is significant that even the standard 30-year period recommended (e.g., by the WMO) for standard climatological normals would have a nominal standard error of about + 1.0 N-unit (2.37 divided by \/6), or about 0.3 percent of mean N, values.’ The 30-year means would thus TABLE 1. Standard errors of 5-year mean values of monthly mean Ng for 40 stations. 12-month |12-month estimate Tms as Number | Febru- | August* | (rms of | percent- Climatic type of ary* (N-units)| Feb. and] age of stations | (N-units) Aug.) mean (N-units) Ns Arctic 2 1.6 0.6 iL 0.4 Subarctic 2 12, 2.8 Zell 0.7 Marine west coast 4 15) 1.9 Li 0.5 Marine (ships) 4 Dee, 1.5 1.9 0.6 Continental (cool) 2 na) 2.9 Zee 0.7 Continental (warm) and subtropical 3 Zep 126 1.9 0.6 Semiarid cool, high altitude 2 1.4 3.5 PO 1.0 Arid and semi- arid tropical 8 2.4 3.8 3.2 0.9 Monsoon 3 3.2 1.3 2:0 0.7 Equatorial 6 (seasons not 200 ORT applicable) All (rms of above) 40 —- — PeeNt| 0.7 *For stations in the Southern Hemisphere, months were reversed (February was combined with August for northern stations, etc.). _ 7 Thirty-year means are used because there are long-term trends in most climatological series; thus a standard period is desirable for comparison between stations. 20 have an advantage of only about 50 percent in rms error, as opposed to the 5-year means actually used. The overall accuracy of the three-part expo- nential model was checked in two ways. First, a check was made of the accuracy of recovering the AN values using the three-part exponential. Here the value of AN was calculated, using the wet- and dry-term tropospheric exponentials, and the value obtained was compared with the actual AN from the mean N-profile. Figure 7 shows the results of such a comparison for 95 of the 112 stations in the original sample for which coincident data of several types were available. The true value of AN from the mean N-profile is the dependent variable, and the value recovered from the wet and dry expo- nentials is the independent variable. The rms error in recovering AN was 9.2 N-units; how- ever, if those stations (points shown as crosses on fig. 7) which are in areas where the three- part exponential model is of questionable valid- ity (as shown in fig. A-30) are eliminated from the sample, the rms error is reduced to 6.4 N-units. The regression line shown in figure 7 is for this reduced sample. The deviation of the regression line from the 45° line (labeled ‘‘per- fect agreement” in fig. 7: zero intercept and unity slope) is significant at the 5-percent level; thus it would appear that this is not the best usage for the three-part exponential model. Use of the AN maps in appendix B is recommended rather than the N(z) maps, for this purpose. The second check was to use the N(z) maps to recover the values of N(z) for some of the actual station locations, at different heights above the surface, and compare these with the actual values of mean N(z). This would be a check not only on the three-part exponential model but also upon the contouring process. Table 2 shows the results of such an error an- alysis. Thirty-two of the original 112 stations were selected on an areal basis, and the correspond- ing three-part exponential model was construct- ed for each of these stations, for all 4 months, using the maps in appendix A. These exponen- tial models were then used to calculate N (z) for three heights (3, 8, 16 km) for each month, and the results were compared with the actual mean N-profiles. The mean and maximum values of the absolute errors thus derived are shown in table 2. Stations and seasons in this sample which are characterized by a high (tropical- type) tropopause showed larger errors at 16 km than at 8 km, the reverse of the usual trend 140 -- APPRAISAL OF RESULTS 21 in table 2. It is apparent from inspection of table 2 that errors in recovering N(z) at alti- tudes of 3 km or more are likely to be small. 120 }-— + - POINTS AFFECTED BY LOW-LEVEL SUBSIDENCE 110 ANm- MEAN N PROFILES 50 PERFECT AGREEMENT REGRESSION LINE ONm = 14.64 + 0.6437 AN, + 6.40 r= 0.795 ANe FROM 90 100 110 120 EXPONENTIAL MODEL FIGURE 7. Correlation of AN: monthly mean N-profiles versus monthly mean exponential model. TABLE 2. Absolute errors in recovering mean N from map contours for 32 stations (in N-units). 3 km 8km 16 km Month ———— Mean Max Mean Max Mean Max Feb. 1.0 2.1 1.6 3.6 0.6 1.7 May 1.3 3.2 1.4 3.3 0.9 2.3 Aug. 1.4 2.1 1.9 3.7 0.8 1.6 Nov. 2.0 4.0 1.3 3.0 12 3.0 Year 1.4 4.0 1.6 3. 0.9 3.0 The total variance, o7?, in using the maps of N(z) given in appendix A can be estimated in terms of the following error model: Oy? oe Ox? + Oyg?, (7) where ¢;” is the variance of 5-year mean values (as compared to long-term means), om? is the variance of errors in mapping N(z). Random instrumental errors are included in 7,7. The value of *; can be estimated at about 2.5 N-units (table 1), while “1 may be estimated at 1.5 P.E., where the probable error, P.E., is given approximately by the mean absolute errors in table 2. These would yield 2.1 N at 3 km, 2.4 N at 8 km, and 1.4 N at 16 km, for mw. A reason- able estimate for °™ at the surface (0 km) would be 1.0 N-unit. These estimates may be combined to yield approximate ¢’r values, as shown in table 3. Minimum and maximum val- ues were obtained by permutations of the values in tables 1 and 2. The standard errors of the 5-year means have been assumed to be a con- stant percent of mean N(z), independent of altitude. 22 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY TABLE 3. Approximate total standard errors, °T, for N(z) maps in appendix A. It is probable that the percent errors given in table 3 do not increase materially above 16 km; asymptotic values of 3, 2, and 4 percent for average, minimum, and maximum relative standard errors are probably good estimates for altitudes from 20 to 30 km, while above 35 km the standard errors are probably no more than 0.2 N-units. There are also undoubtedly some bias errors involved in the N(z) maps, although they are probably quite small. Sources of these bias er- rors would include the equation for N itself (Smith-Weintraub formula), the radiosonde in- 0 | 3km 8km 16 km In terms of N-units Average 2.6 1:9 1.8 1.0 Minimum 1-4 1.8 11} 0.6 Maximum 4.3 2.8 22, 1.3 As percent of mean N(z) Average 0.8 1.1 1.5 2 Minimum 0.4 0.7 1.1 1.6 Maximum 1.3 1.6 1.9 3.3 Mean N(z) 320 175 117 38 140 T 130 120) 110 -— 100 -— & 90 | 5 80 |— ry 10}-— 4 60 50 40 30 20 PERFECT AGREEMENT———a REGRESSION LINE ONm = - O11 +0998 ANws +5.20 r70914 ANws - WEATHER 10 20 30 40 50 60 70 80 90 100 110 SUMMARY DATA FIGURE 8. Correlation of AN: monthly mean N-profiles versus monthly mean weather summary data. strument (where the sensor lag would seem to assure a slight positive bias for all upper-air NV values, even over long-term means), and certain peculiarities in the mapping and curve-fitting procedures which might produce bias for some values of z and not others (e.g., the bias due to the sharp “knee” of the D, and D, exponential terms at the N-tropopause as compared with the smooth transition of real mean N-profiles ; note example in fig. 2). It should be noted that this last type of bias error was (automatically) included in the mean absolute errors given in table 2, since there was no easy way of separat- ing this type of error from the others. Hence table 3 includes an allowance for this particular source of bias error. 7.2 Accuracy of AN Maps As a check on the method used to calculate the AN values from which the maps in appendix B were derived, the AN values from the weather summary data were obtained for all 90 of the stations which were also contained in the mean N-profile sample; the AN values for the months of February, May, August, and November were then compared with the corresponding values from the actual mean N-profiles. However, the period of record involved in the mean N-profile study was not, in general, even partially coin- cident with the 1960-64 period used for the AN study. Thus it was expected that the variance, op’, of the differences between the weather sum- mary AN values (from which the maps given in appendix B were obtained) and the AN values from the mean N-profiles would have two com- ponents, °D rf = Gp" oe on, (8) where ¢pr” is the variance of the real difference in the two 5-year mean values of AN (because they are obtained from different time periods), and %¢? is the variance of the differences which are caused by the interpolation errors inherent in the method used to obtain the AN from the weather summary data. Since the real differ- ences (represented by °r) would be expected to have a near-zero mean for a worldwide data sample, a regression analysis of the two types of AN values should reveal any bias which had been produced by the interpolation method used in the AN study. Figure 8 shows the results of such a comparison, with AN from the mean N-profiles as the dependent variable, and AN from the weather summary data as the inde- pendent variable. There is no statistically sig- nificant bias shown, since the regression line is almost identical with the “perfect agreement” line (zero intercept and slope of unity). An evaluation of the relative sizes of ¢r and was made by calculating the rms difference be- tween the two types of AN values for a restrict- ed sample containing only those stations where the mandatory pressure level used to calculate the AN values from the weather summary data was within + 100 m of 1 km above the surface; the rms difference thus calculated was 3.9 N- units. Since any interpolation errors would be expected to be quite small for this restricted sample, it was concluded that the 3.9 N-unit rms represented essentially the value of °r as given in (8). The value of %p as given in figure 8 is 5.2 N-units; thus by (8) the value of cz is probably on the order of 3.5 N-units. This should be a good approximation to that part of the overall rms error in the AN maps which is assignable APPRAISAL OF RESULTS 23 to the interpolation method used on the weather summary data. The overall accuracy of the AN maps given in figures B-2 through B-13 depends on several factors: the accuracy of the interpolation meth- od, the variability of the 5-year mean period as compared with a standard WMO 30-year mean period, and the heterogeneous nature of the local observation times included in the data sample. The weather summary data were most- ly derived from observations taken at 0000 GMT, although many stations in different parts of the world supplied data taken at 1100 or 1200 GMT, while others supplied data averaged at two, or in a few cases four, times per day. In this study no attempt was made to correct for diurnal variations imposed by the fixed observa- tion times of the various stations; hence diurnal variability must be added to the sources of pos- sible error in the maps. It is reasonable to assume that the actual (unknown) standard error of the maps is not independent of the true value of monthly mean AN desired, but that it is more likely a certain percentage of the true value. Since the overall correlation between the contoured and true values is probably quite high, it is plausible to estimate the standard error of the maps as a percentage of the con- toured values. On figure 8 it is found that an allowance of + 10 percent from the perfect- agreement line (in the vertical) will exclude only 23 percent of the 360 data points (40 above the limits, and 44 below the limits) ; for a nor- mal distribution, 32.5 percent of the points should be excluded by the standard error limits. Therefore, allowing for some added error from diurnal variability, it seems reasonable to esti- mate the overall standard error of the maps of AN as approximately 10 percent of the con- toured values. This is equivalent to assuming an error model, Te == OF oe Ge ae AR (9) where the terms have the same meaning as those in (7) and (8), with 7,2 = ¢p? (possibly as low as 14 ¢r?) in (8), and where 7%; is the variance assignable to diurnal variations in AN. The value of 7; should be on the order of 2 to 4 N-units, which is an estimate based on inspec- tion of the CCIR maps [CCIR, 1965]. ae There is doubtless some bias error in the AN maps; the discussion given for bias errors in the N(z) maps is mostly applicable to the AN maps. Here the bias due to the radiosonde is relatively larger, because of the differencing used to obtain AN values. This mean bias error may be as high as 1 percent of the AN values, but data adequate for checking on this possi- bility are not available. 24 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 7.3. Accuracy of Gradient Maps It is very difficult to assess the probable errors in the maps of the different kinds of initial gradients given in appendix C. The primary reason for this is that no data are available ex- cept those used to prepare the maps. It is likely that the most serious source of discrepancies will arise because of the admixture of data taken at widely differing local times. In line with the results of the AN error analysis, an overall error of about 15 percent of the contoured val- ues seems reasonable, but may be higher or lower, depending on the area being considered. The maps in appendix C are probably best suit- ed for the depiction of climatic tendencies of subrefraction and superrefraction. 8. Conclusions It seems clear that the most significant con- elusion which can be drawn from this study (pertaining to future requirements) is that careful selection of data by local time of obser- vation, and the segregation of these data into types, e.g., nighttime and midafternoon, prior to analysis or mapping, is probably equally as 25 important as obtaining larger samples. The effects of such an analysis on the results given in this atlas would probably be slight in the case of the N(z) maps, somewhat larger in the case of the AN maps, and might well have a profound effect on the ground-based gradient maps of appendix C. 9. References Air Ministry, Meteorological Office (1961), Tables of Temperature, Relative Humidity and Precipitation for the World, vols. I-VI (Her Majesty’s Stationery Office, London). Baynton, H. W., H. L. Hamilton, Jr., P. E. Sherr, and J.J. B. Worth (1965), Radio climatology of a tropical rain forest, J. Geophys. Res. 70, No. 2, 504-508. Bean, B. R. (1954), Prolonged space-wave fadeouts in 1046 Mc observed in Cheyenne Mountain propagation program, Proc. IRE 42, No. 5, 848-853. Bean, B. R. (1959), Climatology of ground-based radio ducts, J. Res. NBS 63D (Radio Prop.), No. 1, 29-34. Bean, B. R. (1961), Concerning the bi-exponential nature of tropospheric radio refractive index, Beitr. Physik Atmosphare 34, Nos. 1/2, 81-91. Bean, B. R. (1962), The radio refractive index of air, Proc. IRE 50, No. 3, 260-2738. Bean, B. R., and B. A. Cahoon (1957), A note on the climatic variation of absolute humidity, Bull. Am. Meteorol. Soc. 38, No. 7, 395-398. Bean, B. R., and B. A. Cahoon (1961a), Limitations of radiosonde punch-card records for radio-meteorologi- cal studies, J. Geophys. Res. 66, No. 1, 328-331. Bean, B. R., and B. A. Cahoon (1961b), Correlation of monthly median transmission loss and refractive in- dex profile characteristics, J. Res. NBS 65D (Radio Prop.), No.1, 67-74. Bean, B. R., and E. J. Dutton (1961), Concerning radio- sondes, lag constants, and refractive index profiles, J. Geophys. Res. 66, No. 11, 3717-3722. Bean, B. R., and F. M. Meaney (1955), Some applications of the monthly median refractivity gradient in tropo- spheric propagation, Proc. IRE 43, No. 10, 1419-1431. Bean, B. R., and G. D. Thayer (1959), On models of the atmospheric refractive index, Proc. IRE 47, No. 5, 740-755. Bean, B. R., G. D. Thayer, and B. A. Cahoon (1960a), Methods of predicting the atmospheric bending of radio rays, J. Res. NBS 64D (Radio Prop.), No. 5, 487-492. Bean, B. R., J. D. Horn, and A. M. Ozanich, Jr. (1960b), Climatic charts and data of the radio refractive index for the United States and the world, NBS Monograph No. 22. Behn, R. C., and R. A. Duffee (1965), The structure of the atmosphere in and above tropical forests, Rept. No. BAT-171-8, Battelle Memorial Institute, Colum- bus, Ohio. Bunker, A. F. (1953), On the determination of moisture gradients from radiosonde records, Bull. Am. Mete- orol. Soc. 34, 406-409. CCIR (International Radio Consultative Committee) (1963), Influence of the atmosphere on wave propa- gation, Rept. 233, Documents of the 10th Plenary Assembly, vol. 2 (Geneva). CCIR (International Radio Consultative Committee) (1965), Propagation over the surface of the earth and through the non-ionized regions of the atmosphere, Rept. G.l.d(V), Conclusions of the interim meetings of Study Group V (Geneva). Dodd, A. V. (1965), Dew point distribution in the con- tiguous United States, Monthly Weather Rev. 93, No. 2, 113-122. Dutton, E. J., and B. R. Bean (1965), The bi-exponential nature of tropospheric gaseous absorption of radio waves, Radio Sci. J. Res. NBS 69D, No. 6, 885-892. 26 du Castel, F. (1961), Propagation Tropospherique et Faisceaux Hertziens Transhorizon, 140-141. (Editions Chiron, Paris.) Hutchings, J. W. (1961), Water vapor transfer over the Australian continent, J. Meteorol. 18, 615-634. Jeske, H. (1964), Transhorizon-transmission and height gain measurements above the sea with waves in the range of 1.8 cm to 187 cm under special consideration of meteorological influences, Proc. 1964 World Conf. on Radio Meteorology, Am. Meteorol. Soc., Boston, Mass. 458-463. Kantor, A. J., and A. E. Cole (1965), Monthly atmos- pheric structure, surface to 80 km, J. Appl. Meteorol. 4, No. 2, 228-237. Kerr, D. E. (1951), Propagation of Short Radio Waves, MIT Radiation Lab. Series, vol. 13. (McGraw-Hill Book Co., Inc., New York, N. Y.). Knoll, D. W. (1941), Climatology Asiatic Station, Hy- drographic Office, U. S. Navy, Washington, D. C. List, R. J. (1958), Smithsonian Meterological Tables, 204. (Smithsonian Inst., Washington, D. C.). Michaelis, J.. and E. Gossard (1958), Distribution of refractive layers over the North Pacific and Arctic, NEL Rept. No. 841, U. S. Navy Electronics Lab., San Diego, Calif. Misme, P., B. R. Bean, and G. D. Thayer (1960), Models of the atmospheric radio refractive index, Proc. IRE 48, No. 8, 1498-1501. Misme, P. (1964), A meteorological parameter for radio- climatological purposes, Radio Sci. J. Res. NBS 68D, No. 7, 851-855. Reitan, C. H. (1963), Surface dew point and water vapor aloft, J. Appl. Meteorol. 2, No. 6, 776-779. Rydgren, B. (1963), Proposal for a Swedish radio stand- ard atmosphere, Rapport A518, Forsvarets, Forsk- ningsanstalt, Stockholm. Saxton, J. A. (1951), Propagation of metre waves be- yond the normal horizon, Proc. Inst. Elec. Engrs. (London) 98, Pt. III, 360-369. Serra, A. (1955), Atlas Climatologico do Brasil, vol. 1. (Conselho Nacional de Geografia e Service de Meteor- ologia, Rio de Janeiro.) Smith, E. K., and S. Weintraub (1953), The constants in the equation for atmospheric refractive index at radio frequencies, Proc. IRE 41, 1035-1037. Smith, J. W. (1963), The vertical temperature distribu- tion and the layer of minimum temperature, J. Appl. Meteorol. 2, No. 5, 655-667. Smith, O. E., W. M. McMurray, and H. L. Crutcher (1963), Cross sections of temperature, pressure, and density near the 80th meridian west, National Aero- nautics and Space Administration, Washington, D. C. Thompson, B. W. (1965), The Climate of Africa (Oxford University Press, Nairobi). Trewartha, G. T. (1961), The Earth’s Problem Climates (Univ. of Wisconsin Press, Madison, Wisc.). Tunnell, G. A. (1964), Periodic and random fluctuations of the wind at Aden, Meteorol. Mag. 93, No. 1100, 70-82. UNESCO (1958), Arid Zone Research X, Climatology, eves of Research, 159 (Imprimerie Firmin-Didot, aris). U. S. Navy, Chief of Naval Operations (1955-1959), Marine Climatic Atlas of the World, vols. 1-5, NAV- AER 50-1C-528 through 50-1C-532, U. S. Govt. Print- ing Office, Washington, D. C. U. S. Weather Bureau (1964), Manual of Radiosonde Observations, Circular P, 7th ed., U. S. Govt. Printing Office, Washington, D. C. Waener, N. K. (1960), An analysis of radiosonde effects on the measured frequency of occurrence of ducting layers, J. Geophys. Res. 65, 2077-2085. REFERENCES 27 Wagener, N. K. (1961), The effect of the time constant of radiosonde sensors on the measurement of tempera- ture and humidity discontinuities in the atmosphere, Bull. Am. Meteorol. Soc. 42, No. 5, 317-321. Willett, H. C. (1944), Descriptive Meteorology (Aca- demic Press, Inc., New York, N. Y.). 10. Appendix A. World Maps of N(z) Parameters Data from the weather stations listed alphabetically in table A-1 were used to prepare the maps in this appendix. Each station is preceded by a number to identify its location on figure A—-1 and followed by a listing of surface refractivity values at the 1, 5, 50, 95, and 99 percent cumulative distribution levels for the 4 months of February, May, August, and November. The N(z) parameters (all referenced to sea level) which are necessary to calculate N at any altitude, z, in kilometers, are Do, Wo, H,, H», Hw, and 2. These are given in figures A-2 through A-25. The z chart for any particular month will determine which of the dry- term curves will be used. If the desired altitude (above sea level) of N(z) is below the 2t value at the specified location, use the tropospheric equation N (2) =Doexp {- =} + Woexp}- = A-l I = Doexp )~ , { o€XP ) A, (* ( ) D If the desired altitude is above the 2 value, use NG) SDs sa ra a. t Sep ‘a or | (A-2) All three scale heights are required for equation (A—2), whereas only two, H, and Hw, appear in the tropospheric equation. If the surface altitude of the location is greater than 1 km, it is suggested that the “standard atmosphere” value of 3 km be substituted for Hw (see sec. 3.3). To illustrate the step-by-step procedure for determining refractivity from the N (z) parameters, the following example (assuming heights of 2 km and 20 km above the surface at a location 200 m above sea level, at latitude 15°N and longitude 0°, in August) is given: (a) Refractivity at 2 km above the surface: (1) At the assumed location, interpolate linearly between contours on figures A-19 to obtain z (13.8 km) to see whether the altitude above sea level, z (2.2 km), is above or below the z value. It is below, so equation (A—1) should be used. (2) The map values at 15°N and 0° of the parameters needed to calculate (A-1) are: D, = 267.5 N-units (fig. A-14) W.= 105.9 N-units (fig. A-15) H, = 9.35 km (fig. A-16) lily) = PAM) Naan (fig. A-18) (3) If these values are substituted in (A-1), N(z) is found to be 249.9 N-units at 2 km above the surface. (Probable errors due to contouring and data restrictions would suggest the use of only three significant figures, i.e., 250 N-units.) (b) Refractivity at 20 km above surface: (1) Check to see whether the assumed altitude (z= 20.2 km above sea level) is above or below the z: value. Since it is above, (A-—2) should be used. (2) All the parameters are needed for this calculation. In addition to the four values listed in calculation (a) above, these are required: H,= 5.90 km (fig. A-17) Zz =13.8km (fig. 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8°96E L°O8é L'vS& 9°6FE PPIE 0°29E 8°O1€ 9°62E PEE 6 PLE SZ 0EE 9°30€ SObE V6CE £628 L°30P 2° 30E LSTé SESE oSeE 0°S98 POLE T0LE P9EE £068 este 0°6ZE 6°STE G'98E 6 16E 00 00 MOO MOO MOO MOE M00 a6 aps aes chase alt asp MPP aso cons ase arg M9S MGS avs 90 M9T M00 MOT aor M20 MGS MSG MTS MIE MET a6P aLsé cra ase are aLt aor MLYP cra MP0 ALS agp M9S aLe M6E MEZ MLI MIG aag aes MPE aLg alg ast ave NOO NOO NOO NOE N00 NSP Noo NLT NLO Nee NOT N¥S S9T N&Z@ Nt0 NOt S¥s NOP Nve Sar NIZ NEP NIP N9G NPP N6P Ng $20 SST S62 N&8TI NOE SLS NIT N@s N66 sos N60 N62 N6E NGE NST SsP Sst N9E N6>h NOT NET N6P NIT Sé¢ Ngo N8@ Sos N6>h S6F ssg *uaar6h uorpaaja on t "Diop fo ssvah § uDY) 883'Ty W dys “US'S'A HOysoaIpeyA “4 W'S'S 1. suRAoys18A WSS’ ‘ean oo ekQry “odi1y, “BleAySNY ‘aTjIASUMOT, "YStA ‘puRIsy ysooqey Seen uedep ‘ouaqey, “WS'S'N ‘uexysey Bieemstr tiers orjqnday Aseareyy ‘eateueury, WSS weyxAA pA sess" puelsyT UBMs Spurs] puvpyeq ‘Aejurys aes sad ae Waele ae ted e1odesuig sos ss +> -eysely ‘eAulaug oss use fapqeag “cw cg ‘uenpe ueg “-yyeO fosarq weg “> “WEN JIA ‘uUOsIeEg BI]ODG BAON ‘pueysy aqus pete [2eig ‘ayoay "purest [nowy “arly “3qUOJW ojeng “oa0010Jy ‘AaqneAT 410g Doses Seg fyaunasyatg “cc erpeaqsny ‘yqleg “* uapamsg ‘punsia3sC rss" “4s1I04HO. eyo "T'S" "eSS9PO sononnaee eyes puejsy WePla}SUY a][aANON verses ss ssnidkg ‘eIsooIN + ydaBIN ‘AoweIN bdudoo ‘AN ‘yI0 XK MIN “7 erpuy ‘1yyeaq MAN ooo sseyal Gayonquen, oss *sspurysy ity ‘Ipuen sorts ekuay ‘1Iqolreny "yc YDIPL ‘suauayD qunopy ATS'S'p ‘Moosopy fan Dolo ollejUQ ‘aauosoopy sete es ss = pueisy AVMPIPAL De eer qreEpy ielmieibes Oolxey ‘URVezZeyy pomunacacke pursy ude puna ae purysy oinfeyy Sood Deo AON ureds ‘pupey “cos *" pupisy atenbor yy poonoa owe AWS'Sy ‘AoAT PoaEHaoGES BOUTY 189M asen3nqiog ‘epuenyT “BOLW 4Seq asen3nq10g ‘sonbieyy oouamoy 32 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 13 = a ° 8 ! ay I og { eS fo ‘ei FIGURE A-1. Location of N(z) data stutions. (3) If these values are substituted in (A-2), N(z) is found to be 20.7 N-units at 20 km above the surface. Figures A—26 through A—29 are seasonal maps of the standard prediction error of the exponential fits to the mean wet-term profiles used in the N(z) parameter maps. An rms error in the wet term of more than 5 N-units was considered to be a reasonable limiting criterion for locations where the N(z) model should be used with caution, if at all. Figure A-30 delineates these areas; the cross-hatched areas indicate that the error was in excess of 5 N-units for 2 or more of the seasonal months (February, May, August, and November) and the single-hatching depicts areas where only 1 of the 4 months showed such large errors. Further discussion of the uncertainty in these areas can be found in section 3.4. APPENDIX A 33 Figure A-2. Mean sea-level dry term, Do: February. 150 FicureE A-3. Mean sea-level wet term, Wo: February. 34 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 60 90 a a Tia ke a2 80 «718 16 714 aN FiGuRE A-5. Dry-term stratospheric scale height in km, Ho: February. APPENDIX A 35 FIGURE A-7. Mean density tropopause altitude in km, z4: February. 36 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 150 T 296 FIGURE A-9. Mean sea-level wet term, Wo: May. APPENDIX A 37 FIGURE A-11. Dry-term stratospheric scale height in km, Ho: May. 38 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE A-13. Mean density tropopause altitude in km, z¢: May. APPENDIX A 39 90 120 Leis FIGURE A-15. Mean sea-level wet term, Wo: August. 40 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE A-17. Dry-term stratospheric scale height in km, He: August. APPENDIX A 41 150 S, 150 150 FiGurE A-19. Mean density tropopause altitude in km, 24: August. 42 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 90 120 ya mane 316. FIGURE A-21. Mean sea-level wet term, Wo: November. APPENDIX A 43 FIGURE A-23. Dry-term stratospheric scale height in km, Ho: November. 44 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE A-25. Mean density tropopause altitude in km, 2: November. APPENDIX A 45 FiGurE A-27. Standard prediction error of the exponential fit to the mean wet-term profile: May. 46 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE A-29. Standard prediction error of the exponential fit to the mean wet-term profile: November. APPENDIX A 47 150 LEGEND E24 rms WET TERM ERROR >5N FOR 1 MONTH ae rms WET TERM ERROR >5N FOR 2 TO 4 MONTHS + FIGURE A-30. Areas of doubtful applicability of three-part exponential model of N(z) for z < 6 km. 11. Appendix B. World Maps of AN. The weather stations from which data were obtained for this study are shown in figure B-1. Their locations, elevations and the 5-year mean surface refractivity for each month of the year are alphabetically listed in table B-1. The monthly AN values between the surface and 1 km above the surface are pre- sented in figures B-2 through B-13. If the AN at a specific location is desired for a cer- tain month and year for which a monthly mean surface refractivity value, Ns, is avail- able, the following relationships may be used: AN =b (N,—N,) + AN, (B-1) where N.=No EXDiae nce z = elevation above sea level in km. World maps of No (the yearly sea-level value of refractivity), b (the slope of the regression line (B-1) ),and AN (the mean annual value of the refractivity difference be- tween surface and 1 km) are presented in figures B—14 through B-16. If the AN value were required at a station with an elevation of 300 m and a location of 80°N and 30°E, here is the procedure which would be used. Available surface wea- ther reports indicate that the mean N, was 314 for a recent month for which a value of AN is needed. Therefore, at the assumed location, these values are interpolated linearly from the figures: Nee 320 (fig. B-14) AN = 48 N-units (fig. B—15) b= 0:60 (fig. B-16) N, = 820 exp -°-1-3) = 311 Using the value of 314 for Ns, AN is found to be 50 N-units. In some areas of the world (e.g., where the assumption of an exponential distribution of the wet term is largely invalid; see sec. 3.4), the use of the regression method to predict AN has definite limitations. To delineate these locations, figures B-17, B-18, B-19, and B-20 are presented. The first two figures are world maps of the correlation coefficient and the standard error of estimate of the regression line of AN versus N; for the 60 months of station data, and figure B-19 gives the percentage of this standard error to the AN value. Areas with correlation coefficients < 0.5 (fig. B-17), standard errors > 5 N-units (fig. B-18), and standard errors > 12 percent of AN (fig. B-19) are shaded, but the use of equation (B-1) for any location in these shaded areas may still be valid if: (a) a low correlation coefficient occurs with a small standard error (typical of sta- tions with a small seasonal range of variability in both N, and AN), or (b) a large error is found with a good correlation (typical of stations with distinct wet-dry seasons). However, if the coefficient is less than 0.7 (reducing the variance of AN to ~ 50 per- cent) and the standard error is greater than 10 percent of AN (as discussed in sec. 7), it would be reasonable to assume that the yearly dependence of AN upon N; is not sufficient to justify the regression prediction method. Areas represented by these criteria are shaded in figure B-—20. 48 APPENDIX B 49 TABLE B-1. Mean surface refractivity. Elevation Station (meters) | Latitude | Longitude | Jan. | Feb. | Mar.| Apr. | May | June| July | Aug. |Sept.| Oct. | Nov.| Dec. ASClEIY INR? ChESHs sogkaanouadiconadne 15 | 05 15N 03 56W | 383 | 389 | 389 | 387 | 387 | 382 | 377 | 373 | 380 | 384 | 387 | 333 Adelaide, Australia ob 11 | 34 56S | 138 35E | 316 | 321 | 321 | 321 | 322 | 323 | 322 | 319 | 318 | 313 | 313 | 314 Widen Arabialenacnesee ene nee 4] 12 50N 45 O1E | 365 | 364 | 371 | 380 | 385 | 386 | 379 | 378 | 376 | 372 | 366 | 369 Albrook (Balboa), Panama C.Z 9 | 08 58N 79 33W | 366 | 360 | 365 | 368 | 375 | 375 | 372 | 381 | 382 | 379 | 378 | 375 A buquerque;pNeyMiexse ery steers 4 1620 | 35 03N | 106 37W | 254 | 251 | 248 | 245 | 253 | 251 | 267 | 274 | 259 | 256 | 251 | 251 AN Gbiny WEESio ogou bongo oosconoonbdoas 680 | 58 37N | 125 22E | 297 | 296 | 289 | 284 | 283 | 292 | 305 | 304 | 293 | 287 | 291 | 297 Alert, Northwest Territories........ yee 62 | 82 30N 62 20W | 326 | 327 | 330 | 321 | 312 | 3138 | 313 | 315 | 310 | 313 | 320 | 324 ‘Alexander Bay, South Africa..... a0 22 | 28 34S 16 32E | 342 | 341 | 340 | 336 | 330 | 327 | 326 | 325 | 328 | 329 | 334 | 338 Alger/Maison, Algeria........... RoDE 28 | 36 43N 03 14E | 326 | 323 | 327 | 329 | 337 | 346 | 354 | 355 | 354 | 344 | 333 | 328 Alice Springs, Australia,................ 546 | 23 48S | 133 53E | 283 | 287 | 288 | 290 | 290 | 287 | 289 | 281 | 282 | 284 | 285 | 284 Allahabad) Iniayre gcc clccrssieispeiere chit orn) 98 | 25 27N 81 44E | 327 | 312 | 303 | 291 | 299 | 338 | 385 | 391 | 379 | 353 | 325 | 324 Alma Ata, U.S.S.R.. comer Sve 851 | 43 14N 76 56E | 284 | 283 | 285 | 287 | 293 | 294 | 299 | 295 | 286 | 285 | 285 | 284 Amundsen- Scott, Antarctica. 2800 | 90 00S 00 00 221 | 229 | 243 | 246 | 246 | 245 | 246 | 247 | 244 | 236 | 226 | 220 Anadyr, U.S.S. R.. 62 | 64 47N | 177 34E | 314 | 317 | 319 | 321 | 310 | 312 | 325 | 321 | 313 | 308 | 311 | 314 Anchorage, Alaska....... 40 | 61 10N | 149 59W | 307 | 307 | 305 | 306 | 308 | 318 | 324 | 324 | 316 | 306 | 307 | 307 An kara urke Vater voltekreisitekvaiels ccc 902 | 39 57N 82 53E | 284 | 283 | 283 | 284 | 290 | 292 | 290 | 286 | 287 | 288 | 286 | 287 Antofagasta, Chile....... pase 122 | 23 288 70 26W | 338 | 341 | 337 | 333 | 330 | 327 | 325 | 327 | 326 | 328 | 333 | 334 Aoulef, Algeria,........... DBRT 290 | 26 58N 01 O5E | 294 | 288 | 283 | 280 | 279 | 277 | 274 | 278 | 287 | 291 | 294 | 299 Argentia, Newfoundland BOOT 17 | 47 18N 54 00W | 311 | 310 | 310 | 313 | 320 | 323 | 335 | 339 | 331 | 320 | 318 | 312 Arichangelsk;(UIS:SoRen sn ee oe nice abbo 13 | 64 35N 40 30E | 312 | 312 | 311 | 311 | 315 | 324 | 331 | 333 | 324 | 316 | 314 | 312 Advensl WEE ticsossscadonddooanaene 230 | 37 58N 58 20E | 307 | 305 | 305 | 311 | 305 | 305 | 303 | 306 | 300 | 305 | 305 | 309 Aswan, United Arab Republic... . Bone 196 | 23 58N 32 47E | 299 | 287 | 280 | 280 | 278 | 276 | 285 | 288 | 291 | 294 | 299 | 305 AthensyGat eects p 54 246 | 33 57N 83 19W | 308 | 308 | 312 | 321 | 335 | 352 | 360 | 361 | 347 | 325 | 316 | 308 Athinai, Greece......... o oe 107 | 37 58N 23 43E° | 316 | 314 | 316 | 317 | 325 | 325 | 326 | 323 | 326 | 327 | 329 | 322 Auckland, New Zealand 49 | 36 51S | 174 46E | 339 | 345 | 341 | 339 | 239 | 331 | 327 | 329 | 328 | 326 | 329 | 334 Bahia Blanca, Argentina................ 72 | 38 44S 62 11W | 320 | 323 | 328 | 320 | 319 | 315 | 316 | 312 | 317 | 321 | 320 | 319 Bahrainglslandhees ieee eink tire 2 | 26 16N 50 387E | 338 | 339 | 342 | 348 | 361 | 365 | 381 | 386 | 382 | 370 | 353 | 341 Baker Lake, Northwest Territories. ..... 9 | 64 18N 96 0OW | 329 | 333 | 326 | 316 | 312 | 314 | 318 | 320 | 314 | 312 | 317 | 325 Bangkok ihailand penser eeisceacr 16 | 13 44N | 100 30E | 366 | 377 | 385 | 393 | 393 | 391 | 390 | 391 | 393 | 390 | 374 | 363 Bangui, Central African Republic........ 385 | 04 23N 18 34E | 348 | 347 | 359 | 362 | 361 | 363 | 361 | 362 | 361 | 363 | 362 | 354 IBArrowseA laskale wpe kroner ecpaccisisetae sets 4 | 71 18N | 156 47W | 323 | 325 | 325 | 315 | 313 | 316 | 318 | 319 | 315 | 811 | 318 | 324 Beer Ya Aqovy, Israel. . a 49 | 32 00N 34 54H | 324 | 322 | 323 | 327 | 332 | 339 | 351 | 855 | 347 | 337 | 324 | 323 B=Blan UES Sibert bere eiee aOHAE 23 | 46 57N | 142 43E | 312 | 312 | 311 | 311 | 314 | 326 | 339 | 341 | 331 | 317 | 312 | 311 Beni Abbes/Colomb, Algeria... ... Fine 498 | 30 08N 02 10W | 294 | 289 | 283 | 275 | 274 | 276 | 269 | 273 | 284 | 291 | 294 | 296 Beninambiby asa tenancy Cee 125 | 32 06N 20 16E | 323 | 322 | 319 | 324 | 324 | 338 | 350 | 349 | 343 | 339 | 331 | 326 Beograd wavugoslaviannc).csi-cees secs 139 | 44 48N 20 28E | 310 | 311 | 308 | 312 | 324 | 331 |. 335 | 331 | 320 | 318 | 317 | 312 Bismarck, N. Dak... . 506 | 46 46N | 100 45W | 296 | 296 | 294 | 289 | 296 | 306 | 313 | 308 | 299 | 293 | 294 | 295 Bjornoya Island.............. 14 | 74 31N 19 01E | 310 | 310 | 310 | 312 | 315 | 316 | 320 | 320 | 317 | 312 | 311 | 310 Blagoveshchensk, U.S.S.R..... Bloemfontein, South Africa Boise ld ahor yey tte oss se) emoqancheies. ane 871 | 43 34N | 116 13W | 284 | 284 | 279 | 280 | 286 | 285 | 283 | 281 | 278 | 283 | 285 | 285 Bombay, India... .. 11 | 18 54N 72 49 | 347 | 352 | 362 | 371 | 380 | 386 | 389 | 388 | 384 | 375 | 364 | 355 Bordeaux, France. 48 | 44 51N 00 42W | 820 | 823 | 322 | 323 | 330 | 337 | 343 | 342 | 343 | 332 | 324 | 321 Brest, France........ 103 | 48 27N 04 25W | 319 | 316 | 318 | 321 | 326 | 332 | 338 | 337 | 336 | 381 | 323 | 324 i i 41 | 27 28S | 153 02E | 351 | 357 | 351 | 343 | 328 | 323 | 319 | 320 | 325 | 326 | 337 | 347 137 | 50 16N | 127 30E | 318 | 311 | 304 | 299 | 304 | 325 | 386 | 339 | 318 | 305 | 307 | 314 1422 | 29 07S 26 11E | 283 | 284 | 288 | 277 | 270 | 264 | 265 | 259 | 264 | 267 | 279 | 285 Broken Hill, Zambia...... 1206 | 14 27S 28 28E | 319 | 322 | 315 | 309 | 293 | 285 | 282 | 278 | 279 | 282 | 305 | 317 Brownsville, Tex.............. 6 | 25 55N 97 28W | 337 | 344 | 346 | 359 | 366 | 377 | 375 | 377 | 370 | 357 | 345 | 339 Bruxelles, Belgium............ 100 | 50 48N 04 21E | 314 | 318 | 315 | 317 | 324 | 8383 | 338 | 337 | 384 | 328 | 321 | 318 Bukhta Tikhaya, U.S.S.R 6 | 80 19N 52 48E | 326 | 320 | 321 | 316 | 313 | 312 | 314 | 315 | 313 | 311 | 314 | 319 BulkhtaymiksiMUSSishReneene sat serene 8 | 71 385N | 128 55E | 332 | 327 | 324 | 317 | 312 | 316 | 319*) 320 | 314 | 311 | 322 | 327 Byrd Station, Antarctica...... 1500 | 80 00S | 120 OOW | 253 | 257 | 259 | 261 | 267 | 266 | 263 | 267 | 262 | 259 | 254 | 253 Cairo, United Arab Republic. . 68 | 30 O8N 31 24K | 314 | 312 | 313 | 313 | 318 | 329 | 341 | 347 | 338 | 334 | 331 | 322 GalcuttayIndia toes). 6e see 6 | 22 39N 88 27E | 337 | 338 | 346 | 366 | 381 | 389 | 394 | 395 | 394 | 379 | 346 | 341 Camaguey, Cuba. . pando 122 | 21 25N 77 52W | 351 | 353 | 357 | 363 | 370 | 377 | 378 | 379 | 379 | 376 | 365 | 360 (Gantonplslandmer.terici ehy erie cine 3 | 02 46S | 171 43W | 374 | 372 | 377 | 381 | 377 | 877 | 379 | 376 | 373 | 378 | 373 | 372 (CEYIS ISIETEEAGE ING (Oba Suave gpaanopesooos 3 | 35 16N 75 33W | 319 | 323 | 324 | 337 | 349 | 364 | 376 | 374 | 366 | 347 | 332 | 326 Caribou, Maine........... 191 | 46 52N 68 O1W | 307 | 305 | 303 | 305 | 310 | 325 | 332 | 330 | 323 | 318 | 308 | 306 Charleville, Australia ee 299 | 26 25S | 146 17E | 325 | 385 | 329 | 324 | 316 | 314 | 309 | 302 | 303 | 304 | 303 | 310 Chatham Island........... 00 49 | 45 58S | 176 33W | 330 | 339 | 336 | 330 | 327 | 323 | 322 | 324 | 322 | 327 | 329 | 332 Chiangmai, Thailand 313 | 18 47N 98 S9E | 338 | 332 | 336 | 349 | 368 | 371 | 375 | 377 | 376 | 369 | 358 | 345 CONTE WESHSEI. Bouss so gubosbossoaebons 671 | 52 O5N | 118 29E | 300 | 294 | 287 | 281 | 279 | 297 | 310 | 308 | 293 | 288 | 291 | 295 Christchurch, New Zealand. . o4 8 | 43 32S | 172 37E | 334 | 335 | 337 | 334 | 326 | 324 | 323 | 323 | 324 | 320 | 321 | 327 Clark Field, ‘the Philippines. . 170 | 15 O8N | 120 35E | 345 | 344 | 345 | 351 | 362 | 367 | 366 | 369 | 866 | 363 | 357 | 349 Cloncurry, Australia........ 188 | 20 40S | 140 30E | 338 | 339 | 333 | 311 | 310 | 305 | 307 | 299 | 295 | 300 | 308 | 321 Gocostislandeame cere relents ciscirenvee ee 5 | 12 05S 96 58E | 373 | 380 | 372 | 378 | 379 | 376 | 374 | 372 | 372 | 370 | 369 | 369 Golumbiayy Mow. eEeer enter eee ane cic 239 | 38 58N 92 22W | 305 | 305 | 306 | 312 | 327 | 343 | 353 | 352 | 330 | 316 | 306 | 306 Coppermine, Northwest Territories... ... 9 | 67 49N | 115 15W | 327 | 329 | 328 | 318 | 313 | 318 | 318 | 322 | 316 | 312 | 318 | 323 Coral Harbour, Northwest Territories... . 59 | 64 12N 83 22W | 324 | 324 | 322 | 312 | 310 | 313 | 317 | 319 | 314 | 309 | 313 | 317 Cordoba, Argentina 479 | 31 19S 64 18 828 | 331 | 332*| 321*) 318 | 305 | 303 | 298 | 300 | 314 | 318 | 327 Curacaopslslandanen saree 16 |} 12 11N 68 59W | 372 | 368 | 372 | 376 | 380 | 380 | 382 | 384 | 384 | 382 | 379 | 376 Dakaryoenegal ines yee rerac eater eer 22 | 14 44N 17 30W | 342 | 342 | 348 | 351 | 358 | 367 | 371 | 377 | 381 | 379 | 360 | 342 Dar Es Salaam, Tanzania. . 20 58 | 06 52S 39 16E | 376 | 376 | 382 | 379 | 370 | 362 | 359 | 357 | 361 | 366 | 372 | 375 Darwin, Australia......... 27 | 12 26S | 130 52K | 380 | 382 | 387 | 372 | 359 | 338 | 344 | 338 | 360 | 373 | 374 | 385 Denvert@olowegets.. ea va been 1625 | 39 46N | 104 53W | 251 | 249 | 250 | 249 | 257 | 259 | 264 | 269 | 256 | 252 | 252 | 251 D.F. Malan (Capetown), SouthwAitricate tain 2) Mee ae lrisn cave. snee 49 | 33 55S 18 36E | 337 | 341 | 337 | 333 | 333 | 330 | 329 | 327 | 328 | 380 | 380 | 334 (Dijarbakir whurkeyesy ee ten aciansiee ae 652 | 37 55N 40 12E | 293*) 292 | 293 | 297 | 298 | 289 | 289 | 282 | 276 | 284 | 297 | 294 Djakarta, Indonesia 8 | 06 11S | 106 50E | 382 | 383 | 382*/ 383 | 382 | 376 | 367*| 366 | 368 | 375 | 380 | 380 Dodge City, Kans........... = 791 | 37 46N 99 58W | 285 | 286 | 287 | 291 | 306 | 316 | 322 | 318 | 308 | 294 | 285 | 284 Douala, Cameroon... Dn 13 | 04 01N 09 48E | 382 | 382 | 383 | 382 | 382 | 380 | 379 | 379 | 380 | 879 | 381 | 382 Durban, South Africa 14 | 29 58S 30 57E | 365 | 367 | 365 | 356 | 345 | 333 | 388 | 336 | 343 | 349 | 357 | 360 50 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY TABLE B-1. (Continued) Elevation Station (meters) | Latitude | Longitude | Jan. | Feb. | Mar.| Apr. | May | June! July | Aug. | Sept.| Oct. | Nov.| Dec. frdmonton, Alberta wis acest mesieieysereelaieret= te 676 53 34N | 113 31W | 290 | 287 | 287 | 286 | 287 | 298 | 307 | 308 | 296 | 288 288 | 287 Egedesminde, Greenland.... ay 48 | 68 42N 52 52W | 311 | 311 | 310 | 309 | 310 | 314 | 318 | 318 | 311 | 309 | 307 | 306 El Adem, Libya 157 | 31 51N 23 55E 314 | 312 | 312 | 314 | 322 | 330 | 337 | 343 | 336 | 329 | 322 | 317 El Paso, Tex....... 1194 | 31 48N | 106 24W | 266 | 261 | 258 | 255 | 260 | 266 | 284 | 289 | 27z | 271 | 268 | 262 DR y SeIN GV ohana seed ortvnicieye, ela .atevesevs ne elniseste 1908 | 39 17N | 114 51W | 249 | 249 | 248 | 247 | 251 | 249 | 248 | 249 | 248 | 249 | 249 | 249 Hnteb bel ganic ae eit tele ier tayeeietene areal 1146 | 00 03N 32 27E | 320 | 321 | 323 | 325 | 326 | 323 | 321 | 320 | 322 | 320 | 318 | 319 Eureka, Northwest Territories. . 5 2 | 80 00ON 85 56W | 332 | 335 | 338 | 325 | 313 | 315 | 316 | 316 | 314 | 317 | 327 | 333 Ezeiza, Argentina.................- 20 | 34 50S 58 32W | 340 | 349 | 344 | 338 | 331 | 326 | 325 | 326 | 328 | 333 | 335 | 338 Fairbanks, Alaska.......... eh 138 | 64 49N | 147 52W | 314 | 309 | 304 | 301 | 304 | 315 | 322 | 322 | 311 | 305 | 308 | 314 Morrest,;Australia’. goc-ps isis enn ater suateraiate wets 160 | 30 51S 128 06E 311 | 319 | 319 | 318 | 315 | 313 | 313 | 316 | 317 | 309 | 300 | 310 BtolbamysiChad tac cc avers esac ime 300 | 12 O8N | 15 02E | 288 | 279 | 282 | 298 | 316 | 337 | 353 | 360 | 358 | 336 | 297 | 294 Ft. Nelson, British Columbia........ Rie 375 | 58 50N | 122 35W | 306 | 302 | 296 | 294 | 295 | 307 | 314 | 313 | 305 | 298 | 299 | 303 Ft. Smith, Northwest Territories... . 203 | 60 01N | 111 58W | 315 | 311 | 306 | 302 | 301 | 307 | 316 | 317 | 310 | 305 | 305 | 311 Ft. Trinquet, Mauritania........... ae 359 | 25 14N 11 37W | 302 | 301 | 300 | 304 | 305 | 311 | 308 | 313 | 312 | 310 | 309 | 303 Wunchal}-Madeira:ocictee 2 sc/esnc svelte avers 110 | 32 38N 16 54W | 329 | 329 | 324 | 329 | 336 | 345 | 352 | 352 | 349 | 344 | 335 | 330 Gauhati, India.......... 54 | 26 11N 91 45E 336 | 332 | 333 | 348 | 368 | 385 | 392 | 394 | 388 | 377 | 356 | 344 Giles, Australia......... 514 | 25 02S 128 18E 292 | 293 | 287 | 288 | 294 | 288 | 287 | 284 | 279 | 286 | 281 | 289- Goose Bay, Labrador....... 44 53 19N 60 25W | 310 | 310 | 309 | 308 | 309 | 315 | 324 | 324 | 315 | 310 | 308 | 309 Gough Island.............. ne 40 | 40 19S 09 54W | 334 | 334 | 332 | 328 | 328 | 324 | 324 | 324 | 322 | 324 | 326 | 332 Great Malls) Mion te rgeyetess,clene sis b/8est esis ater ere 1115 | 47 30N | 111 21W | 272 | 271 | 270 | 269 | 271 | 277 | 278 | 273 | 271 | 270 | 270 | 269 | | Green’ Bay, Wi8@si. 5c 25s sectors cues ns 210 | 42 29N 88 O8W | 306 | 305 | 306 | 308 | 316 | 329 | 338 | 342 | 327 | 316 | 306 | 306 Guryev, U.S.S.R.. rr rail 47 07N 51 55E 315 | 314 | 315 | 314*| 315 | 331*/ 325 | 326 | 320 | 320*| 317 | 316* Habbaniya, Iraq........ 45 | 33 22N 43 34E 320 | 318 | 317 | 317 | 311 | 302 | 303 | 306 | 310 | 309 | 321 | 322 Helsinki, Finland........ he 58 | 60 19N | 24 58E 809 | 311 | 311 | 312 | 316 | 325 | 333 | 384 | 327 |} 319 | 314 | 311 ilo; Hawaii: ssc aces cos tase seer ee abt 19 44N | 155 04W | 850 | 349 | 349 | 353 | 358 | 359 361 | 367 | 362 | 361 | 358 | 356 | | Hobart, Tasmania, Australia............ 54 | 42 53S 147 20E 319 | 323 | 323 | 317 | 315 | 313 | 314 | 314 | 314 | 312 | 312 | 319 Tonge Wong eneccan any a iettteem es 66 | 22 18N | 114 10E 331 | 334 | 348 | 363 | 378 | 385 | 391 | 391 | 383 | 360 | 348 | 334 International Falls, Minn. 860 | 48 34N | 93 23W | 301 | 300 | 297 | 296 | 300 | 318 | 323 | 323 | 311 | 303 | 298 | 299 Istanbul, Turkey......... P| 40 | 40 58N 29 05E 317 | 317 | 318 | 323 | 333 | 342 | 352 | 354 | 342 | 333 | 329 | 321 Tzmirg Dur key: sis eicvcje.ciene 6 cae, eyeigiasess/esadevels 25 | 38 24N 27 10E | 317 | 316 | 314 | 319 | 826 | 332 | 334 | 382 | 324 | 3828 | 325 | 322 DOCH PUL lial wterere cee lsaieeneare sia ate 224 | 26 18N 73 O1E | 301 | 290 | 292 | 284 | 297 | 339 | 355 | 368 | 359 | 310 | 297 | 303 Johnston Island............ 5 16 44N | 169 31W | 363 | 361 | 365 | 371 | 366 | 376 | 375 | 375 | 378 | 376 | 371 | 366 Karachi, West Pakistan. . 4 | 24 48N 66 59E 824 | 341 | 355 | 370 | 384 | 394 | 391 | 891 | 387 | 368 | 350 | 330 Karaganda, U.S.S.R...... ae 555 | 49 48N 73 O8E 296 | 297 | 295 | 295 | 296 | 304 | 312*| 311 | 296 | 294 | 296 | 297 Leet WES S acon endacosdae esos 75 | 54 583N | 23 538E 310 | 311 | 310 | 313 | 322 | 327 | 337 | 336 | 327 | 320 | 316 | 312 | | KMeflaviks iceland) cages a erosnieiesanee 50 | 63 59N 22 837W | 309 } 310 | 311 | 318 | 317 | 321 | 325 | 323 | 322 | 316 | 318 | 306 Khabarovsk, U.S:S.R.w sc cscs sceGe fee ae 72 | 48 31N | 185 10E | 316 | 311 | 306 | 306 | 310 | 329 | 346 | 346 | 328 | 310 | 307 | 313 Kharkov, U.S.S.R.......... 153 49 56N 36 17E 309 | 308 | 308 | 308 | 311 | 321 | 325 | 325 | 317 | 317 | 314 | 309 Khartoum, Sudan....... 385 15 36N 32 33E 287 | 284 | 283 | 285 | 286 | 305 | 328 | 341 | 329 | 302 | 296 | 293 Khatanga, U.S.S.R 24 | 71 59N | 102 28E 331 | 328 | 320*| 316 312 | 314*| 321 | 317 | 313 | 313 | 325 | 328 | | Karensk, USS:S:Rivc.scc esses 258 | 57 46N | 108 07E 818 | 312 | 306 | 299 | 301 | 314 | 324 | 324 | 311 | 304 | 306 | 314 Kobenhavn, Denmark.............. 6 55 38N 12 40E 314 | 314 | 315 | 316 | 319 | 325 | 333 | 333 | 329 | 324 | 321 | 316 eolpashevy US: Se bstre sal srejeissielstense a 76 | 58 18N 82 54E 316 | 313 | 311 | 309 | 308 | 317 | 333 | 330 | 320 | 311 | 312 | 314 Koror, Palau Islands...... Be 29 | 07 20N | 134 29E 385 | 383 | 383 | 387 | 391 | 385 | 388 | 387 | 388 | 386 | 388 | 387 KrasnolarsiglW so. Srkvesen eisai dese een 194 | 56 0ON 92 53E 309 | 305 | 304 | 301 | 300 | 310 | 327 | 327 | 315 | 305 | 304 | 310 Rustama ys Us oese beac ereteuntess ie cueieraneterstercis se 171 53 13N | 63 37E | 310 | 309 | 308 | 305 | 304 | 321*| 328 | 320 | 311 | 307 | 307 | 310 Kyev, U.S.S.R. ae 182 50 27N 30 30E 308 | 307 | 306 | 307 | 313 | 320 24 | 328 | 319 | 314 | 314 | 308 La Coruna, Spain....... 57 | 42 23N | 08 22W | 320 | 321 | 323 | 324 | 331 | 334 | 340 | 342 | 341 | 334 | 324 | 320* Lae, New Guinea......... Brest 8 | 06 44S | 147 00E 378 | 377 | 379 | 381 | 382 | 377 | 377 | 377 | 377 | 376 | 380 | 379 Meagoss Nageriay sms anrsremanisiarscoesnstaisadiay 40 | 06 35N | 03 20E 877 | 382 | 382 | 384 | 385 | 378 | 373 | 372 | 379 | 382 | 381 | 379 | 1 | MakeiCharles; Lak. jaccam selene cece ence 5 | 30 13N 93 O9W | 325 | 328 | 330 | 346 | 362 | 375 | 382 | 380 | 369 | 350 | 333 | 329 MasiVegas, NeVecs. ces -c ans se 664 | 36 O5N | 115 09W | 283 | 279 | 274 | 269 | 267 | 264 | 279 | 284 | 271 | 275 | 278 | 280 Leningrad, U.S.S.R. 4 | 59 58N 30 18E 312 | 312 | 312 | 312 | 318 | 327 | 334 | 334 | 327 | 320 | 315 | 313 Leopoldville (Kinshasa), Democratic | Republic of the Congo...... 290 | 04 19S 15 19E | 369 | 368 | 368 | 367 | 368 | 355 | 345 | 346 | 352 | 360 | 364 | 367 Lerwick, United Kingdom... 82 | 60 08N 01 11W | 314 | 315 | 315 | 316 | 320 | 324 | 329 | 330 | 329 | 324 | 318 | 313 Lima, Peru. Meee revit 135 | 12 06S 77 02W | 354 | 357 | 356 | 350 | 343 | 339 | 336 | 338 | 336 | 340 | 342 | 349 Lindenberg, East Germany. . 105 | 52 13N 14 07E S10 | S137 | 312° | 21301319 (325) 886") 3827) 326 | 322.) su7, eats Lisboa, Portugal........... 1038 | 38 46N 09 O8W | 325 | 328 | 328 | 324 | 329 | 330 | 335 | 336 | 341 | 330 | 326 | 327 Lourenco Marques, Portuguese East Africa 44 |} 25 55S | 32 34E 871 | 370 | 366 | 361 | 348 | 341 | 339 | 341 | 345 | 356 | 357 | 362 Luanda, Portuguese West Africa........ 70 | 08 49S 18 18E 375 | 374 | 377 380 | 369 | 356 | 351 | 352 | 359 | 367 | 375 | 374 | TUVONG Uc s9: Leena saypernues acmces mennerert ree 329 | 49 49N 23 57E 302 306 | 301 | 307 | 315 | 322 | 330 | 327 | 318 | 312 | 309 | 304 Macquarie Island. eee ae 6 | 54 30S 158 57E 320 | 319 | 319 | 317 | 316 | 314 | 315 | 316 | 315 | 314 | 312 | 320 Madras, India... . 16 13 00N 80 11E 364 | 363 | 368 | 379 | 379 | 367 | 369 | 376 | 380 | 384 | 379 | 366 Madrid, Spain. . . eeeoer ie ty 657 | 40 24N 03 41W | 296 | 296 | 296 | 291 | 300 | 304 | 298 | 299 | 305 | 304 | 300 | 301 WMiayuro: slander cesiveinrne cutee seein 3 | 07 O5N | 171 23E 383 | 378 | 381 | 385 | 387 | 381 | 383 | 383 | 383 | 383 | 384 | 383 Viale alssou daria acnvaccerstenttriertesy teense tety 389 | 09 33N 31 39E 298 | 292 | 299 | 320 | 337 | 353 | 358 | 362 | 363 | 358 | 329 | 304 Malye-Karmakuly, U.S.S.R.. ae 16 | 72 23N 52 44k 811*| 315 | 312 | 312 | 311 | 316 | 322*) 321*; — | 312*| 314*| 314* Maracay, Venezuela........ 442 10 15N 67 39W | 339 | 335 | 336 | 345 | 354 | 352 | 353 | 353 | 354 | 355 | 350 | 344 Marion Island sds. sese is sic pias 26 | 46 53S 37 52E 317 | 317 | 319 | 316 | 313 | 313 | 313 | 314 | 313 | 314 | 314 | 313 Maun south Atricacc cc nsivcls areletsteciee as 945 19 59S 23 25E 823 | 324 | 319 | 301 | 284 | 279 | 275 | 271 | 274 | 282 | 308 | 321 Mawson, Antarctica. <2. 2.0.5.0 0+055 a0 14 | 67 36S 62 53E 299 | 298 | 300 | 303 | 305 306 | 306 | 306 | 303 | 300 | 298 | 299 Mazatlan, Mexico.......... | 78 | 23 11N | 106 26W | 346 | 339 | 342 | 351 | 360 | 374 | 379 | 380 | 384 | 377 | 354 | 340 McMurdo Sound, Antarctica......... 45 | 77 51S 166 40K 302 | 301 | 310 | 307 | 310 | 311 | 312 | 315 | 308 | 304 | 299 | 299 Medford Ore gece mnie sents estsieieieve is cere arre 405 | 42 23N | 122 52W | 305 | 303 | 301 | 300 | 304 | 307 | 306 | 305 | 306 | 307 | 307 | 307 Melbourne, Australia................4.. 44 | 37 49S 144 58E 328 | 328 | 330 | 326 | 321 | 324 | 322 319 | 320 | 319 | 324 | 322 Merida, Mexicolrs.sccas ec acts aie -ciivates 22 | 20 58N 89 31W | 350 | 349 | 354 | 363 | 369 | 376 | 379 | 378 | 381 | 370 | 357 | 353 Mersa Matruh, United Arab Republic... 25 | 31 20N | 27 18E 319 | 319 | 321 | 323 | 336 | 348 | 361 | 361 | 345 | 340 | 334 | 323 ATi, Biase arene coe vii oak sae 4 | 25 49N 80 17W | 341 | 343 | 347 | 357 | 367 | 375 | 379 | 379 | 380 | 362 | 352 | 346 Milano, Dtaly vic. wesw scree ce 120 | 45 28N | 09 17E 313 | 315 | 315 | 319 | 330 | 340 | 346 | 348 | 340 | 329 | 319 | 316 Moscow, U.S.S.R.. 156 | 55 49N 37 37E 307 | 306 | 305 | 306 | 314 | 322 | 332 | 327 | 318 | 313 | 310 | 307 APPENDIX B51 TABLE B-1. (Continued) Elevation Station (meters) | Latitude | Longitude | Jan. | Feb. | Mar.) Apr.| May] June} July | Aug. | Sept.) Oct. | Nov.) Dec. Mould Bay, Northwest Territories....... 15 | 76 14N |} 119 20W | 331 | 330 | 332 | 322 | 314 | 314 | 316 | 317 | 312 | 313 | 323 | 328 IMurmansksU:S: Seber tree mcileihusit ete ers 50 | 68 58N 33 03E 309 | 309 | 308 | 308 | 310 | 315 | 324 | 325 | 317 | 313 | 311 | 309 Mys Cheliuskin, U.S.S.R.... 13 | 77 43N | 104 17E 328 | 326 | 326 | 318 | 314 | 315 | 317 | 316 | 314 | 312 | 320 | 321 Mys Kamenny, U.S.S.R.. . 7 | 68 28N 73 36E 319 | 321 | 318 | 314 | 313 | 316 | 321 | 327 | 314*| 314 | 315 | 322 Mys Schmidt, U.S.S. 7 | 68 55N | 179 29W | 322 | 326 | 325 | 318 | 313 | 317 | 318 | 318 | 316 | 312 | 315 | 320* INagphur india teserrer er erirtssrtie isles 310 | 21 06N 79 OTE 319 | 300 | 297 | 305 | 308 | 343 | 370 | 369 | 361 | 341 | 321 | 317 Nairobi Ken yaeinertiontelssljetenetfstsietetal «for 1798 | 01 18S 36 45E 277 | 278 | 277 | 289 | 289 | 282 | 281 | 280 | 277 | 276 | 284 | 286 INandisnijibislandserremperiteiel-cieci ree 16 17 45S 177 27E 380 | 382 | 381 | 379 | 373 | 367 | 364 | 362 | 367 | 367 | 371 | 373 Nantucket, Mass. 14 | 41 15N 70 04W | 311 | 312 | 312 | 318 | 327 | 340 | 355 | 354 | 347 | 330 | 320 | 313 Naryan-Mar; W2S.S-R.n)) 4-ciicls selec) s)- 7 | 67 39N 53 01E 313 | 313 | 313 | 312 | 313 | 315 | 325 | 325 | 318 | 313 | 313 | 315 Nashwvaillesmenn seers acilsrslefsierreieitss 184 | 36 07N 86 41W | 309 | 311 | 311 | 324 | 338 | 351 | 363 | 361 | 340 | 326 | 313 | 312 Naval Orcades Island 4} 60 45S 44 43W | 309 | 308 | 307 | 308 | 307 | 307 | 309 | 306 | 307 | 307 | 307 | 307 Neuquen, Argentina 270 | 38 57S 68 O7W | 296 | 303 | 305*) 310*| 304 | 300 | 304 | 302 | 299 | 300 | 297 | 296 New Delhi, India............ Bstid 216 | 28 35N 77 12E | 313 | 311 | 308 | 299 | 306 | 334 | 373 | 379 | 362 | 332 | 318 | 313 Niamey Niger nies ists eieisnetpvou 226 | 13 29N 02 10E | 286 | 281 | 285 | 308 | 331 | 351 | 362 | 369 | 369 | 348 | 308 | 294 INCOR Chow poooopasodseosegnconuns 218 | 35 09N 33 17E 817 | 315 | 313 | 314 | 315 | 325 | 326 | 331 | 325 | 318 | 320 | 319 Nitchequon, Quebec... 515 | 53 12N 70 3835W | 298 | 296 | 293 | 291 | 293 | 299 | 311 | 307 | 301 | 296 | 293 | 295 Nome, Alaska......... d 14 | 64 380N | 165 26W | 313 | 315 | 315 | 313 | 313 | 320 | 326 | 326 | 318 | 311 | 312.} 315 INorfolkplslandiigrctjctneteloe cities eietepelsteisi-tel 110 | 29 03S 167 56E 352 | 357 | 349 | 348 | 336 | 336 | 331 | 334 | 335 | 337 | 340 | 348 Norman Wells, Northwest Territories... . 64 65 18N | 126 51W | 322 | 321 | 317 | 310 | 308 | 314 | 327 | 325 | 315 | 309 | 315 | 318 INorthpelattesNebrecyriche-reiisctersivt-)-7- cpsie 850 | 41 O8N | 100 42W | 282 | 281 | 281 | 282 | 295 | 307 | 317 | 314 | 294 | 289 | 283 | 281 Norway Base, Antarctica....... 50 | 70 20S 02 0OW | 302 | 303 | 303 | 309 | 309 | 311 | 313 | 313 | 309 | 305 | 301 | 302 Nouvelle Amsterdam Island... . 28 | 37 50S 77 34E 340 | 339 | 386 | 335 | 332 | 324 | 327 | 328 | 325 | 328 | 330 | 339 Oakland, Calif. 6 | 87 44N | 122 12W | 328 | 324 | 324 | 326 | 329 | 334 | 337 | 337 | 335 | 330 | 323 | 321 Odessa, U.S.S. 64 | 46 29N 30 38E 312 | 313 | 312 | 315 | 327 | 334 | 337 | 336 | 326 | 323 | 321 | 315 Okhotsk mUrG-Sakecmterener ieeerasi cick: 7 | 59 22N | 143 12E | 316 | 314 | 311 | 309 | 312 | 320 | 332 | 337 | 321 | 307 | 310 | 314 OmskyjU:S!SiRi es. = 2 94 | 54 56N 73 24E 315 | 312 | 311 | 309 | 305 | 316 | 331 | 328 | 316 | 307 | 311 | 313 Onslow, Australia... . . 4} 21 40S 115 O7E 344 | 350 | 855 | 332 | 327 | 330 | 321 | 317 | 322 | 316 | 325 | 337 Ostersund, Sweden 309 63 11N 14 37E 299 | 299 | 299 | 299 | 301 | 310 | 313 | 314 | 310 | 304 | 303 | 298 Ostrov Chetyrekhstolbovoy, U.S.S.R...... 6 | 70 388N | 162 24E 825 | 327 | 8325 | 317 | 312 | 315 | 316 | 317 | 316 | 313 | 319 | 324 OstrovaDikson; U:S:S:Renee cs -icis se = 20 | 73 30N 80 14E | 324 | 323 | 318 | 315 | 313 | 315*| 320 | 320 | 314 | 311 | 316 | 320 Papeete, Tahiti Island................. 2 17 33S 149 37W | 375 | 376 | 378 | 377 | 375 | 368 | 366 | 362 | 363 | 368 | 373 | 375 Perth, Australia....... 2 60 | 31 57S 115 49E 330 | 330 | 333 | 330 | 327 | 326 | 325 | 328 | 323 | 324 | 320 | 324 Peshawar, West Pakistan 359 | 34 01N 71 35E 303 | 301 | 309*| 312 | 304 | 305 | 847*| 365*| 373*| 318 | 311 | 304 Petropavlovsk Kameatskij, U.S.S. 7 | 52 58N | 158 45E 803 | 304 | 305 | 305 | 309 | 320 | 326 | 381 | 320 | 311 | 306*) 303 Ponape, Caroline Islands................ 37 | 06 58N | 158 13E | 379 | 380 | 380 | 384 | 385 | 386 | 384 | 384 | 384 | 384 | 384 | 378 Rortblairpindiaseri eee 79 11 40N 92 43E 364 | 365 | 369 | 371 | 385 | 384 | 382 | 386 | 384 | 382 | 378 | 367 Port Elizabeth, South Africa... 61 | 33 59S 25 36E 351 | 350 | 350 | 339 | 331 | 328 | 329 | 328 | 332 | 335 | 339 | 346 Port Harrison, Quebec 20 | 58 27N 78 O8W | 819 | 318 | 317 | 312 | 313 | 315 | 321 | 322 | 317 | 313 | 310 | 314 Pretoria, South Africa 1368 | 25 45S 28 14E 301 | 300 | 298 | 287 | 275 | 270 | 270 | 266 | 276 | 282 | 294 | 299 RBuertowMonttn@hilemmancriraictcmicriies 3 | 41 28S 72 56W | 386 | 337 | 331 | 328 | 326 | 328 | 326 | 324 | 325 | 326 | 326 | 331 Quetta/Samungli, West Pakistan......... 1601 | 30 15N 66 538E 263 | 258 | 267 | 269 | 262 | 276 | 282 | 276 | 270 | 261 | 256 | 259 Raizet, Guadaloupe Island............... 8 | 16 16N 61 31W | 368 | 361 | 363 | 369 | 370 | 374 | 377 | 379 | 380 | 377 | 374 | 369 Raoullsland erecta teste ce 49 | 29 15S | 177 55W | 349 | 352 | 358 | 346 | 342 | 334 | 331 | 331 | 333 | 335 | 338 | 346 Resistencia, Argentina.................. 52 | 27 28S 58 59W | 356 | 362 | 362 | 358 | 346 | 332 | 329 | 332 | 337 | 345 | 342 | 354 Resolute Bay, Northwest Territories..... 64 | 74 483N 94 59W | 325 | 328 | 330 | 319 | 311 | 318 | 315 | 315 | 310 | 310 | 319 | 320 Roma Ltalyeheeieru scan srecvcereioralslcsie sustavaes 131 | 41 48N 12 36E | 314 | 317 | 316 | 319 | 328 | 334 | 338 | 339 | 335 | 326 | 321 | 318 Saigon, Viet Nam.. . 10 10 49N | 106 40E 363 | 362 | 369 | 375 | 384 | 386 | 384 | 386 | 384 | 383 | 373 | 370 Saint Paul, Alaska. 6 | 57 O9N | 170 18W | 313 | 311 | 311 | 312 | 314 | 320 | 326 | 326 | 322 | 315 | 311 | 312 Nalisburyprebodesiak piety crt 1480 17 56S 31 05E 302 | 305 | 295 | 288 | 279 | 277 | 271 | 270 | 274 | 275 | 288 | 300 Salt Lake City, Utah 1288 | 40 46N | 111 58W | 270 | 268 | 265 | 264 | 268 | 267 | 270 | 272 | 265 | 268 | 270 | 271 Samarovo, U.S.S.R. 37 | 60 58N 69 04E 316 | 312 | 311 | 309 | 310 | 321 | 330 | 329 | 318 | 311 | 314 | 315 Samsun, Turkey. . i 44 | 41 17N 36 20E 811 | 313 | 316 | 320 | 330 | 348 | 346 | 347 | 337 | 329 | 322 | 313 San Diego, Calif.......... abs 9 | 32 44N | 117 10W | 320 | 325 | 325 | 328 | 332 | 339 | 346 | 350 | 346 | 337 | 323 | 317 Santyuanwe whem beey entice icine enter 19 18 26N 66 O0OW | 358 | 358 | 359 | 363 | 371 | 376 | 378 | 378 | 378 | 374 | 370 | 365 Sapporo jJapanveierien aie lear 18 | 43 03N | 141 20E | 309 | 309 | 310 | 310 | 318 | 332 | 349 | 353 | 337 | 323 | 313 | 309 Saratov, U.S.S.R....... 135 | 51 34N 46 00E 304 | 306 | 303 | 305 | 307 | 315 | 322 | 318 | 314 | 307 | 305 | 305 Sault Ste. Marie, Mich.. 221 | 46 28N 84 22W | 307 | £06 | 304 | 305 | 309 | 325 | 333 | 332 | 323 | 315 | 307 | 306 Shemya, Alaska............ atch 37 | 52 43N | 174 06E 308 | 808 | 311 | 315 | 318 | 321 | 327 | 327 | 324 | 316 | 310 | 307 Singapores apemerecrncliri cashes 18 | 01 21N | 103 54E 375 | 377 | 382 | 385 | 387 | 384 | 383 | 382 | 382 | 382 | 380 | 381 Sodankyla, Finland...... 179 | 67 22N 26 39E 306 | 306 | 306 | 304 | 305 | 309 | 319 | 320 | 313 | 307 | 307 | 306 Stanley, Falkland Islands. . 53 51 42S 57 52W | 317 | 316 | 318 | 316 | 313 | 312 | 313 | 311 | 312 | 312 | 314 | 314 Stockholm, Sweden....... 52 59 21N 18 04E 811 | 310 | 310 | 311 | 311 | 318 | 328 | 329 | 324 | 321 | 315 | 312 Stuttgart, Germany p60 315 | 48 50N 09 12E 303 | 303 | 304 | 306 | 318 | 319 | 322 | 325 | 319 | 312 | 306 | 302 BWEnclloyaliss WHSHSH ShoconnudosonodeseuoeT 284 | 56 50N 60 38E | 306 | 303 | 302 | 300 | 302 | 314 | 324 | 319 | 309 | 303 | 305 | 304 Swanwlslandigreeciscryeve sree eutecieteere 10 17 24N 83 56W | 368 | 364 | 371 | 377 | 382 | 886 | 387 | 387 | 388 | 383 | 375 | 371 Syktyvkar, U.S.S.R....... 96 | 61 40N 50 51E | 311 | 309 | 308 | 308 | 310 | 317 | 325 | 324 | 318 | 312 | 311 | 311 Tacubaya, Mexico........ 2306 19 24N 99 12W | 249 | 247 | 242 | 247 | 252 | 263 | 265 | 266 | 265 | 258 | 254 | 251 Maipel; Taiwan..s....-... Sten 8 | 25 02N |} 121 31E 338 | 342 | 349 | 357 | 372 | 381 | 384 | 384 | 380 | 364 | 357 | 343 Tamanrasset, Algeria.................-. 1378 | 22 48N 05 32E 246 | 248 | 244 | 248 | 248 | 252 | 251 | 252 | 255 | 251 | 252 | 250 Tananarive, Malagasy Republic. . 1310 18 54S AT 32E 312 | 311 | 312 | 307 | 299 | 295 | 291 | 289 | 290 | 294 | 305 | 311 Mashkent;U:S!S°RI). 4) 9.0. dees: 478 | 41 20N 69 18E 296 | 295 | 298 | 304 | 308 | 301 | 302 | 304 | 297 | 297 | 300 | 300 Tatoosh Island, Wash... .. 26 | 48 23N | 124 44W | 318 | 321 | 317 | 321 | 328 | 332 | 336 | 340 | 336 | 328 | 324 | 321 GMs WASHSH 4 cocboaRe aq6 404 | 41 43N 44 48E 299 | 297 | 298 | 305 | 314 | 320 | 331 | 330 | 321 | 311*| 308 | 303 ynhesPasManitobager nie criieinities 272 | 538 58N | 101 06W | 310 | 305 | 302 | 301 | 304 | 316 | 326 | 326 | 312 | 305 | 303 | 305 phourane;pvieteNamccs erie iii 7 16 02N | 108 11E 365*| 366 | 372*| 382*| 385*| 383*| 385*| 390*| 389*| 386*| 376*| 363* Townsville, Australia 4 19 15S 146 46E 376 | 378 | 371 | 363 | 351 | 339 | 333 | 336 | 339 | 356 | 361 | 366 Trivandrum, India.......... 64 | 08 29N 76 57E 362 | 364 | 370 | 380 | 385 | 382 | 382 | 380 | 379 | 380 | 378 | 368 Tromso, Norway......... 9 | 69 42N 19 01E 307 | 309 | 311 | 310 | 316 | 321 | 328 | 328 | 321 | 313 | 312 | 308 ARIE, WESHEE YS 5 popdooodsunodegDeUS 147 | 64 16N | 100 14E | 334 | 324 | 307*| 304 | 301*| 313 | 324 | 318 | 310 | 306 | 316*| 329 52 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY TABLE B-1. (Continued) Elevation Station (meters) | Latitude | Longitude | Jan. | Feb. | Mar.| Apr. | May | June} July | Aug. |Sept.| Oct. | Nov.} Dec. Turukhansk; U.S-S:. Reise. sere 55 37 | 65 47N 87 57E | 324 | 318 | 312 | 308 305 311 | 323 | 324 | 316 | 310 | 316 | 324 Ushuaia, Argentina........ 6 54 488 68 19W | 311 | 312 | 307 | 308 | =08 | 310 | 310 | 308 | 309 | 305 | 305 | 303 Valentia, United Kingdom. . 14 51 56N 10 15W | 320 | 319 | 321 | 322 325 8332 | 337 | 336 | 335 | 329 | 324 | 320 Valparaiso, Chile... 0.04: .« 41 33 01S 71 39W | 345 | 346 | 343 | 334 | 332 | 333 | 331 | 332 | 332 | 334 | 335 | 338 Vera Cruz; IMexiCO! 21. ferns /ercternieioe's @n1s ee 16 19 12N 96 OSW | 361 | 367 | 370 | 380 | 382 | 386 | 386 | 387 | 383 | 379 | 368 | 361 Werlchoyansky, OU: S:Gabwess ce po citetslersvcsnialess 135 | 67 33N | 133 23E 345 | 341 | 320 | 307 | 301 | 308 | 316 | 314 | 307 | 310 | 331 | 345 Vishakhapatnam, India...) 3.6.86. eens 3 17 43N 83 14E 357 | 356 | 369 | 391 | 392 | 391 | 384 | 386 | 389 | 380 | 358 | 353 Wiadivostolk. WULS {SiR iisrecars:a ian aoiens nie meterei ese 1388 | 43 O7N | 131 54E 308 | 305 | 304 | 307 | 314 | 329 | 347 | 350 | 332 | 311 | 304 | 306 Wiologdts Uecss Sake ca ies steintaevevesetetar tiara ceustcte 118 59 17N 39 52E 309 | 308 | 307 | 308 | 312 | 325 | 335 | 330 | 319 | 314 | 311 | 309 Weajima, Japani.e . cc cere cs eune eee ee eee eae iG 87 23N | 136 54E 314 | 314 | 316 | 322 | 332 | 348 | 369 | 371 | 355 | 336 | 324 | 318 Wake Islan Ginko ait cvetelerarait a aiaracirevelanecheoviene sn 4 19 17N | 166 39E 356 | 359 | 363 | 367 | 371 | 378 | 380 | 384 | 383 | 380 | 373 | 368 Washington, D. C.... 20 | 38 51N 77 02W | 310 | 311 | 309 | 320 | 328 | 342 | 354 | 352 | 343 | 328 | 316 | 313 Whitehorse, Yukon 698 | 60 43N | 135 04W | 291 | 287 | 284 | 282 | 282 | 287 | 292 | 293 | 289 | 284 | 284 | 289 Wien /Hohe-Warte, Austria.............. 203 | 48 15N 16 22E 306 | 307 | 307 | 309 | 316 | 325 | 332 | 333 | 322 | 317 | 312 | 308 Wilkes Stn., Antarctica.............. : 12 66 15S 110 35E 301 | 302 | 300 | 303 | 308 | 306 | 307 | 303 | 303 | 301 | 299 | 302 Windhoek, South-West Africa........... 1728 | 22 34S 17 06E 263 | 269 | 267 | 265 | 248 | 247 | 245 | 241 | 237 | 240 | 256 | 250 Ship A.. t | 62 0ON 33 0OW | 307 | 312 | 312 | 315 | 317 | 323 | 326 | 324 | 320 | 314 | 311 | 306 Ship B.. t | 56 30N 51 OOW | 310 | 310 | 312 | 312 | 318 | 321 | 326 | 325 | 320 | 315 | 311 | 309 Ship C t | 52 45N 35 30W | 317 | 315 | 315 | 321 | 322 | 328 | 332 | 333 | 330 | 323 | 319 | 315 Ship D t | 44 00N 41 00W | 327 | 323 | 324 | 328 | 334 | 340 | 356 | 360 | 348 | 336 | 330 | 330 Ship E ft | 35 0ON 48 0OW | 339 | 336 | 337 | 341 | 351 | 366 | 374 | 374 | 368 | 357 | 349 | 346 SD Dg Loererecacyees tees ata anstenainbenevaanrel intense casyaye ane ar t | 59 OON 19 0OW | 315 | 315 | 316 | 319 | 321 | 327 | 330 | 328 | 324 | 320 | 318 | 315 STAT Ue cceerccyte Suoyestate ca teaaiene ola'ra) sweseseraivive: ctarret ays ft | 52 30N 20 0OW | 322 | 319 | 321 | 323 | 324 | 333 | 337 | 337 | 333 | 327 | 326 | 319 SBM Gere runner cs eyeneteueeve rea ienavaleiniessteusneas ~ | 45 00ON 16 OOW | 329 | 322 | 327 | 329 | 335 | 342 | 348 | 348 | 345 | 338 | 330 | 332 BSE poe Ae oi stposaees caves royey aren onb dane ve svete jess wnyeuenege ft | 66 0ON 02 00E 312 | 314 | 315 | 316 | 319 | 321 | 327 | 327 | 326 | 318 | 318 | 312 SELB Nleeievage aah sauanentenacays)avatevate te sepeiece osoee a! ¢ | 30 OON | 140 00W | 340 | 339 | 335 | 338 | 340 | 344 | 349 | 351 | 350 | 348 | 345 342 STB etna sere eve ote c as enorelageetersvtapn atch (ene t | 50 OON | 145 0OW | 316 | 318 | 318 | 317 | 324 | 328 | 333 | 336 | 335 | 325 | 319 317 SI GV is anes ena vas sta cpa sero: exwkovsVesmansue de ayeusione t | 34 OON | 164 00E 328 | 331 | 335 | 340 | 350 | 359 | 379 | 381 | 369 | 366 | 355 | 337 * Less than 3 years of data. t No elevation given. : |e Dice” ina: FIGURE B-1. Location of AN data stations. 90 120 150 APPENDIX B_ 53 FIGURE B-3. Monthly mean AN: February. 54 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE B-5. Monthly mean AN: April. APPENDIX B55 —j—— = N6 150 FiGuRE B-7. Monthly mean AN: June. 56 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 150 FIGURE B-9. Monthly mean AN: August. APPENDIX B 57 FIGURE B-11. Monthly mean AN: October. 58 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE B-13. Monthly mean AN: December. APPENDIX B_ 59 120 FIGURE B-15. Annual mean of refractivity gradient between surface and 1 km, AN. 60 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FiGuRE B-17. Correlation coefficient of AN versus N,. APPENDIX B_ 61 FIGURE B-19. Standard prediction error of the regression line of AN versus Ng as a percent of AN. 62 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE B-20. Areas of doubtful applicability of using N, to predict AN. 12. Appendix C. World Maps and Cumulative Distribution Charts of Gradients of Ground-Based Atmospheric Layers Initial gradient data, obtained (see sec. 5) for 99 of the 112 stations listed in table A-1, are presented in groups of seasonal world maps which illustrate various aspects of the percentage distribution of gradients in ground-based layers. The specific map groups are given below. Figures C-1 through C-4: Percent of time gradient = 0 (N/km). Figures C-5 through C-12: Gradient exceeded 10 and 2 percent of the time for 100-m layer. Figures C-i3 through C-20: Percent of time gradient = -100 (N/km) and percent of superrefractive layers > 100 m thick. Figures C-21 through C-28: Percent of time gradient = -157 (N/km) and percent of ducting layers > 100 m thick. Figures C-29 through C-40: Percentage of time trapping frequency is below 3000 Mc/s, below 1000 Mc/s, and below 300 Mc/s. Figures C-41 through C-56: Lapse rate of refractivity (N/km) exceeded 25, 10, 5, and 2 percent of the time for 100-m layer. Cumulative probability distribution charts were prepared for 22 climatically diverse locations for the months of February, May, August, and November (figs. C-57 through C-78). The alphabetical listing of these stations in table C-1 includes seasonal median and minimum trapping frequency values when these were available. Distribution data for two separate times of day at Aden and Nicosia are shown in figures C-57 and C-71. The nega- tive gradient of 50 N-units/km, which is generally considered to be a good normal value for ground-based layers, has been indicated on each of the distributions by a dashed line to provide a common reference for the vertical scale. The circled value on the distribution line represents the mean ground-based gradient (of any layer thickness greater than 20 m) for each month. 63 64 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 50 150 FIGURE C-2. Percent of time gradient > 0 (N/km): May. APPENDIX C_ 65 FIGURE C-4. Percent of time gradient > 0 (N/km): November. 66 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 150 FIGURE C-6. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: February. APPENDIX C 67 FIGURE C-8. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: May. ; 68 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE C-9. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: August. 90 120 150 + -20 | Coy | FIGURE C-10. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: August. APPENDIX CG 69 FIGURE C-12. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: November. 70 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE C-14. Percent of superrefractive layers thicker than 100 m: February. APPENDIX C 71 150 FIGURE C-16. Percent of superrefractive layers thicker than 100 m: May. 72 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 150 FIGURE C-18. Percent of superrefractive layers thicker than 100 m: August. APPENDIX C 73 A | 150 120 FIGURE C-20. Percent of superrefractive layers thicker than 100 m: November. 74 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE C-22. Percent of ducting layers thicker than 100 m: February. APPENDIX C 75 FIGURE C-24. Percent of ducting layers thicker than 100 m: May. 76 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE C-26. Percent of ducting layers thicker than 100 m: August. APPENDIX C 177 150 90 120 150 as FIGURE C-28. Percent of ducting layers thicker than 100 m: November. 78 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE C-30. Percent of time trapping frequency < 1000 Mc/s: February. APPENDIX C 79 FIGURE C-32. Percent of time trapping frequency < 3000 Mc/s: May. 80 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 150 FIGURE C-34. Percent of time trapping frequency < 300 Mc/s: May. APPENDIX C 8&1 FiGurE C-35. Percent of time trapping frequency < 3000 Mc/s: August. FIGURE C-36. Percent of time trapping frequency < 1000 Mc/s: August. 82 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 150 FIGURE C-38. Percent of time trapping frequency < 3000 Mc/s: November. APPENDIX C 83 150 FIGURE C-40. Percent of time trapping frequency < 300 Mc/s: November. 84 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 150 180 150 80;— T 75 70) 150 FIGURE C-42. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 100-m layer: February. APPENDIX GC 85 500 ar F-hi— 4 00 150 FiGuRE C-44. Lapse rate of refractivity (N/km) exceeded 2 percent of time for 100-m layer: February. 86 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE C-46. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 100-m layer: May. APPENDIX C 87 150 FIGURE C-48. Lapse rate of refractivity (N/km) exceeded 2 percent of time for 100-m layer: May. 88 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 50 FIGURE C-50. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 100-m layer: August. APPENDIX C 89 FIGURE C-51. Lapse rate of refractivity (N/km) exceeded 5 percent of time for 100-m layer: August. FIGURE C-52. Lapse rate of refractivity (N/km) exceeded 2 percent of time for 100-m layer: August. 90 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 150 pe 00 Ee @) ines = FIGURE C-54. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 100-m layer: November. APPENDIX C 91 120 100 150 FIGURE C-56. Lapse rate of refractivity (N/km) exceeded 2 percent of time for 100-m layer: November. 92 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY TABLE C-1. Median and minimum trapping frequency (Mc/s) of ducting layers. hae Feb. May Aug. | Nov. Med Min Med Min Med Min | Med Min Aden, Arabia 0000 GMT......... 478 82 865 40 898 41 485 114 12002GIME soos 475 82 | 767 51 | 582 41 522 122 Amundsen-Scott, Antarctica......... 2503 ey 531 266 495 272 | t Balboa (Albrook), Panama C. Z...... 743 311 676 270 | 663 242) || 657 314 Bangui, Central African Republic... . 280 66 569 247 265 176 369 294 Bordeaux, Hrancei. a.ne eas eee 1300 190 | 565 99 402 89 442 186 Dakar, Senegal................... | 328 56 409 43 848 162 378 35 Denver) Colom. sees ae eee | t = le St fe | a t Hizeiza, Argentina.................. 681 123 | 181 - | 383 * 1403 * Fort Smith, Northwest Territories... . 872 9 430 z 145 43 | 1345 * ER OSE Va Weal xen ee tencisne eee eeeis 862 482 801 346 | 467 253 1035 496 Long Beach, Calif.................. fame i eee t Lourenco Marques, Portuguese East Africa........... 554 245 ii 2334 < 2404 430 Nandi, FijiIslands................. t | + ae: * t * New YorkjiNiYun 92.222: 5 ans esas ee t - t | t = t * Nicosia, Cyprus 0000 GMT...... | 1143 * 307 125 iY 44 398 s 1200 GMT..... .| t 290 * | 680 122 1223 ef Ostersund, Sweden................. | + t | + | f+ Perth, Australia.................... | 4864 Se 09 a TA) ad 649 Ss Saigon) VietNam. «eqns ces seen | 582 15 5e ee olO 326 904 403 5389 264 Sandan ber Eveen ae aee eee nee .| 671 51 631 41 595 227 | 535 53 Shipys tations Cie ere are | 320 * 843 1028 sa 308 + Mashkentss Use skvos ieee eee | 7 a 334 i 774 i Vladivostok (US S:Rass 920s eee ens | t 499 * | 584 50 1253 bs * Less than 5 ducting layers during month. t+ No ducting. t Trapping frequencies not computed. APPENDIX C 93 ‘(ivy ‘hivnigay) viqoiy ‘uapy :lahn) wW-OOT pasng-puno.b tof Yp/Np fo suoyngrysrp Ayyiqnqosd aaynjnwng (VD) “LG-O AXODIy 0303399X3 SI JLVNIGHO 3WIL JO LN3943d 030339X3 Si JLVNIGHO 3Wil 4O LN39DH3d 666 966 S66 66 86 S6 06 @ iM 09 OF Oh Of 02 oO S 2 [$0 20. ‘10 666 966 S66 66 86 | S68 m Oo 8 O& O Of 02 os 21 $0 20 10 = | a Va 001 - SS = icra 009- + + + + 009- 005 - - + a +— 008- 00 - 1F | =I 0b - oog- i +— 00 - 002- ae 002- 2 “oak 2 sel ol- $ — oo - = Do a He he sesehe | = oe q 4 all pt = 0 x (2 fo} 2 ool om + =p 001 a 2 on ie = 002 a3 at Tt of 00» 4 = 00% 006 eee + 005 009 LWD 0000 seeeee | | 009 LW9 002) Auwnue34 'N30V OL Ef eet fea foel @le 001 oz so 1 2 5 oO 2 Of OF 6 8 Ob 06 86 «S68 GSCBG SKB (66 BEE (Yard Se lat $l 2 8 O 6 8 Of OF 06 «S68 KS BG (966 GE 030339X3 LON SI 31vNIGHO 3WIL 3O LN3943d 030339X3 LON SI! 3LVNIOHO 3WIL 40 LN3943d Y3BL3WOTIN Y3d SLINN-N A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 94 *(daqueandony snbny) viqoiy ‘uapy :1ahn] W-OQOT pasng-puno.b tof Yp/Np fo suowynqrysrp Ayrprqngosd aarjojynuny (q) *LG-D AMNSIY 030339X3 Si 3LVNIQHNO 3Wid JO 1N3DH3d 030339X3S! 31viiduO 3WiL JO 1N3DY3d 666 965 S66 65 98 | GOOG mw of 0 &% OF OF 02 o 6S 2 1 $0 20 10 666 965 S66 66 HSB ® OL 09 & Ob OF oO o 2 1 $0 20 10 + 006 - TI 7 SP Ses ooL- 000- + tp + 7 Pala o0l- 7 + ——— + ons- 006 - 3 S s a 43L3WNOTIN Y3d SLINN-N o os sa eae +—— avs 0b +—+——+—_}+——}+ LIND 0000 eeeeeee aL ——+ 009 a ee pod OL fo 20 SO ot 2 S ol 2 Of OF 0 0 w OF 06 S6 06 «66 S66 166 566 fo 20 $0 1 2 $ i} 02 Of «Ob 0 609 Ok 06 S6 96 66 S66 066 666 930339X3 LON SI 31VNIGHO 3WI1L JO 1N3943d 030339X3 LON SI JLVNIOQNO 3WIL 40 LN30N3d SLINN-N Y3L3WOTIM Y3d APPENDIX C 95 “DIYIIDIUY “YOIS-uaspunwy :1ahv] W-OOT pasvg-puno.b sof yp/Np fo suoynqrysip Ayyrqngqosd aayvjnwng “gc-9Q aUNdIA 030339X3 S| 3LVNIQHO 3WIL JO LN39H3d 666 966 S66 66 06 $6 06 08 oO 09 0S Oh OF ek ol S [es SIE ¥3BN3AON '1109S-N3SONNWY ie « 009: 00s- 00p- 00f- 002- 001- 001 —l —| J 992 m20 su 1 2 sol 0 OF 0 09 Of OF 866 SGC S66 EE Ge 030339%3 10N S| 3LYNIGYO 3WIL 40 1N30U3d 030339X3 SI 31vNIGYO 3WIL JO 1N30N3d 66 96 SBE 66 6 GOSS os 201 90 wi ‘fa aa == —T +— ou- — 4 + sa. + 008- + + + ws- op ee + o0f- T oz- + 4! } Hd ool Tiel ae =r = =] lial 0 T = = col AVR '11095-N3SOHONY LI SE [esciea Es ell 1} | Jog m2 so 1 2 sO 2 OF OF 0S % OF 06 S6 6 66 S86 Gbe6bs 030339X3 LON Si 31vNIGYO 3WIL JO 1N3943d Y3L3WOMM Y3d SLINN-N Y3L3WOTIN 43d SLIND-N 030339x3S! J1VNIGYO 3WIL JO 1N39N3d 666 766 S66 66 86 $6 06 oe Ok 09 «0S sh Sk vl] $ | so 20 10 a == —= O0L- | 1 E dt = 005- 000- 002 - 001 [E = 4SMony '1109S-N3SONNA (LJ les =e o0z m2 so 1 2 soo 2 Of oO OS Of oO (OP 6 6 SBE GE 666 030339X3 LON SI 31VNIOQYO 3WI1L 40 LN39DN3d 030339X3 S| 31VNIGNO 3WIL JO LN3943d 606 966 S66 66 AE $6 06 oe 6k 8 Se oO S$ Se + —.—_- Ti 009- 002- -- AuWNYGRs '1109S-N3SONNAY | so ot 2 $ a @ Of OF 6 09 oO w 6 $6 030339X3 LON SI 3LVNIO¥O 3WIL JO 1N3043d BBLIWOTIN 43d SLINN-N BILIWOTIN Y3d SLINN-N 96 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 66 O86 Ses ZO vupnuvg ‘(yoo1g)y) voqwg :4ahkv)] W-OOT pasng-punoib sof Yp/Np fo suorynqgriysip Aprpqoqgoud aayoynuny °6G-Q AUNDIY $6 @30339X3 SI JLVNIGHO 3WIi JO 1N39943d 06 oe OL 09S o S ee 009- 00s- aaa 00f- 002- nels w38W3A0N ‘vos ive 00! ol @ Of Ob OS 09 OL oF 6 56 030339X3 LON Si 31vNIONO 3WIL JO 1N3083d 030339x3 SI 3LVNIOGHO 3WiL 40 LN3DY3d tt 002 66 S66 (ABE Gt 365086 S65 66 6 SEE op 09 OS ke os z 1 so 20 SS OL — -——+— 009- 00s- i me oe + + 001 AUN 'vOR Iya. | | = ees | a [|| m0 20 «$0 2 S ol oO 8 OF 6 08 Of OF 06 $6 % «66 S66 966 66 0303393 LON S! 31VNIGHO 3WIL 40 LN3983d YBL3WOTIM Y3d SLINO-N Y3L3WOTIN 43d SLINN -N 030339%3S) JLVNIGHO 3WIL JO LN30H3d 666 066 SB6 66 86 6 6 oF OL 09 O- OP Of 02 ol S z 1 $0 2010 i a ie wile T 00l- ie aT a 003- + 005- = + hr 00! lL asnony ‘voewwe a ee ete ibs, yo 20 sD ot z S Oo Of oO OF 09 O OF 6 $6 66 SEE ORE OBE 030339X3 LON Si 31VNIGYO 3WIL 40 LN30U3d 030339X3S! 3LVNIGYO 3WIL 4O 1N39043d 66 EEG SHG 66 86 S6 06 oo Ok FOS 02 ol $ #: 1 $0 20 10 Ts Goa wm: tne nt O0- oor one Ol- 0 Auwnue34 ‘vOR Ive S oO 02 OF «Ob OS 08k 6 @30339X3 LON Si 31LVNIGYO 3WIL 40 LN3083d $6 001 6 SHS TR ES B3IL3NOTIM Y3d SLINN-N YILINOTIM Y3d SLINN APPENDIX C 97 ‘oygqnday unify pay ‘nbung :1ahv, w-ooT paspg-puno.ib 1of Yp/Np fo suoyngr.ysip Apryprqnqoid aayopnwny °09-9 aUNDdIA 0303393 SI 3LVNIOWO 3WiL 40 LN3943d Cr at a a | | ~ 009- | | | Hu =| t 1 as | | | | ey a oe SS rs ro | | | | | | | + oo- en | | T r- 1 Ool- | | ne > +—o bl oo == —|{oni | B3GN3AON ‘NONE | [eae | a a eT: 030330X3 LON $1 31vNiGO 3WIL 40 1N3DuI0 03033943 S1 3LvillO¥O 3WIL 40 1N3283d Sones ses os eshte ws a : =—— = —- 5S = + oo ie cop CSS SSS ES ESSENSE J 902 mew st 6 ww oO fm ou wm ow MW w sw sme 030339X3 LON S| 31VNIONO 3WI1 30 1N3DH3d SLIND-N Y3L3NO7 43d -N 3L3N0TIM 43d SLINN 030339X3 S| 3LVNIOHO 3WIL 40 1N39N3d Oma se Oe a 0 we mH © Cn 21 so 20 to Tee = a alae on + + os - 0 08 = 002 - oo- ° he = 01 Asnony ‘inowve we om 1 2 sO _ a CF oe GF Oo ww oo 8 wo @ Se re oe 030339X3 LON S| 31VNIQHO 3WIL 40 LN3DURd 030339%3 Si J1VNIOHO JWI JO LN30N3¢ CC eC Ce ee ee} ~~ Ts ee Ser Ot) 2 fF mF me hw a 6 oe oe 030339X3 LON SI 31VNIGHO 3WIL JO 1N39N3d W3L3NONM 83d SLINN-N WB1L3NOTIN 43d SLIND- 98 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY ‘audigy ‘anvap.og ‘afin W-OOT pasng-puno.b 4of Up/Np fo suowyngrysrp Ajyrqoqoid aayojnuny “19-9 AUMNdIY B3ILINOTIN 43d SLINN-N 030339X3 SI 3LVNIGHO 3WIL JO LN30N3d 030339X3 S| 31VNIGHO 3WIL 40 LN39H3d 665065 S65 66 06 GOB om OL 09 OF a os 21 $0 20 10 666 066 S65 66 6 GO mH 1 8 mH OW (ness zt $0 20 10 a 009 - ee —— 9 - | | 4 | tog - + + =} + + wr oof- 2 c z 5 o- 9 2 m D wi- 2 °o z a 3 mn . > in 01 ie } we oz U3BN3AON ‘xnv30H08 | isnony 'xnv30808 <2 | alle oor et aa m7 so 1 2 sO 2 OF OF 0 6 56 M6 66 Sb6 «986 666 Oe Om SOS ae sO 2 OF OF % 0 hw Cr Ly 0303393 LON Si 3LYNIGHO 3WiL 4O 1N39N3d 030339%3 LON SI 31VNIGHO 3WIL 40 LN3D¥3d 030339X3 S! 3LVNIQYO WIL 49 1N30N3d 030339X3 S| 3LVNIOHO 3W|1 40 1N30N3d 665 065 SHE 66 SSE ot 09 Oe o os 2 1 $0 20 10 666065 965 66 HS S66 Ce os 21 $0 20 10 i — — > 009 - 9 = aE + os i — —t 4 | |} p95 oor - + + oor om- > 00% - 2 = oo2- 7 on - mR DD x oo- = wi- z 3 a (ark t) oo t 001 + oz 1 + +. + 02 AVA 'xnv30808 Aumnue34 "xNV30408 [eet i (LEI eal le Si Lee EAL Ss ee 00 to 2 so 1 2 sol 2 © OF &% 09 Oo % 6G OSHS: COE ‘oz so ot 2 a a a | a ne ee ee 030339X3 LON S| 31VNIGHO 3WIL 40 LN39N3d 030339X3 LON SI 3LVNIQHO 3WIL 40 LN30N3d MILIMOTIN Y3d SLINN-N APPENDIX C 99 “(Avpy ‘huvnigay) wbauag fo ayqnday ‘unynq :4ahv) W-00T pasng-puno.6 1of yp/Np fo s woynqrysip Azyprqvgoid aayojnwuny (D) *Z9-D AUN 030339X3 SI 3LVNIGHO 3WIL 40 LN3943d 606 966 S66 66 86 sé 06 8 d 09 OS OO HK ol $ 2 50) zor | AV ‘HV 00I!- 0001- 006- 008- 001- 009- 00s- 00E- 002- 0!- 00! 0b 00S 009 lo 20 S0 1 2 S$ ol 02 oO Ob 0 09 OL 08 6 56 86 66 SEE FBG 666 030339X3 LON SI 3LVNIGHO 3WIL 40 LN30Y3d Y3LIWOTIN Y3d SLINN-N 030339X3 S! SLVNIGHO SWIL 30 LN39Y3d 666 866 SEG 66 8 sé 6 08 OL 09 OG Oh OF 02 O $ 00II- 006- 008- 00S- | Auvnue34 ‘Yvtvd @ S 0 02 «(OF Ob OS 09 OL 0 06 66 @30339X3 LON S! 3LVNIGHO 3WIL JO LN30Y3d Ela Lan ae OOb- 002- 0l- Y3L3WO1IM Y3ad SLINN-N 100° A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY “(daquaaon Ysnbny) jobauag fo syqnday ‘wwynq :4ahv, w-OOT pasng-puno.b sof UP/NP fo suoyngriysip Ayuypqnqgoid aaynjnuny (Q) *Z9-—Q ANNDI 030339X3 S| SLVNIGHO SWIL 4O LN3043d 666966 SEG 66 S606 oe oo 09 6 & HK UW} $ 2 | G0 2010 t AON ‘dV VO 0021- OOll- 00I- 006- 008- 00I- 009 020 SO 1 2 S ol 02 & 0 6 OW M Of 0 $6 86 66 S66 6666 030339X3 LON Si 3LVNIGHO 3WIL 4O LN3ON3d Y3ILAWOTIM Yad SLINN-N 666866 S66 66 030339X3 SI SLVNIGHO 3WIL 30 LN3JON3d 86 S606 08 OL 09 OS Ob M& 2 ol S$ 2.1 S00 10 isnony ‘svava O0II- 00! 2 $s (Ol 2 Of OF 6 OO Oh 06 =S6 86 66 SES 96 65 Q30339X3 LON SI SLVNIGHO SWIL 40 LN39043d Y3L3WO1IN Y3d SLINN-N APPENDIX C_ 101 ‘0]0) ‘daauagl :4ahv) W-OOT pasng-puno.s6 sof yp/Np fo suoynqgrysip Aqzyrqngosd aayonwuny “E9-Q TANS 030339X3 SI JLVNIOQYO 3WIL 40 LN30Y3d 666 966 S86 66 96 S66. ov of _09 0S OF OF 02 oO S$ 2 so of 001 qT a i B3IGNIAON 'H3ANIO ed aL Jot Oz UNS OM ain can Seo © OF & 0 MW © OF Sb 86 66 SH’ 65 666 G303390X%3 LON S!I 3LVNIGYO 3WIL JO 1LN30Y3d 030339X3 S! 3LYNIO¥YO0 3WIL JO 1LN3943d 656 056 S86 66 05 = SB GSS OSS Oe eG) ay 0) TT > 009- rt 006 - + oor O0f- 002- 001 002 mo 20 W3AN30 — td so 1 2 S ol 0 «Of «Oh 60S 09 OP 06 0303393 LON SI 3LvNIQHO 3WIL 30 1N39N3d sé oof 6 66 S66 96 666 Y3L3WOTIN 43d SLINN-N Y3L3WO1IM 3d SLINN-N Q30339X3 S! 31VNIOHO 3WIL JO LN39043d 666 966 SBS 66 N66 ov 09 OF os 21 $0 0 asnony '43AN30 Of 020 so | 2 S ol O OF Oh 0 OF Ok OP 6 (56 66 SS EE OOS 030339x3 LON SI! 3LYNIGHO WIL 40 LN3083d 030339X3S! 3LVNIGHO 3WiL 30 1N3983d 666 065 StG 66 56 6 ob OL «09 0S Ob OO o Q (eee) ed mo 7m so 1 2 GF OO 8 &% Of OF % 08 @h BW CBC BS 6 B SH ce ots 030339x3 LON S! 31V¥NIQNO 3WIL 40 1N3983d BILIWONA Y3d SLINN-N B3L3NOTIM 3d SLINN-N 102. 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JLVNIGNO 3WIL 40 1N39N3d 6 HBL3NONN Y3d SLINN-N BILINOTIM H3d SLINN-N APPENDIX CG 103 “DPDUDA ‘Sa1UOjLllaf, JSaNyyLoN 030339X3 S| 3LVNIQYO 3WIL JO LN3943d Seb vee S66 66 WG 96 ob] Sh OS 80, | : L + 5 = 0s + i oy + + + 00g 001 1001 002 W3BW3AON ‘HLINS LHOS iE 00£ 4020 So | 2 $ Oo 02 Of Ob 0S 09 W 08 06 $6 6 66 S66 966 666 0303393 LON S| 31VNIGHO 3WIL 3O 1N30Y3d 030339X3 SI 3LVNIGHO 3WIL 4O 1N3943d 666 966 S66 66 06 sé 06 op =f 09 «(0S «=(Ob Ok 02 oO S 2 1 $0 20 10 ero eo re r Le 7 = ons {Lt + Or L| T tel 008 IE IL + 4 o02- | T }001- ill ie 0 + | IL 001 i a i LI +—— + 002 AVM 'HLINS 1803 LIEN | eal | 00f 020 So 1 S ao 02 Of «oO 0S 09 OL OF 06 6 SS SH 966 666 030339X3 LON SI 31LVNIGHO 3WIL 40 1N3983d M3BL3WOTIN 43d SLINN-N M3BL3WOTIN B3d SLINN-N 666 866 S66 030339X3 SI JLVNIGHO 3WIL 40 LN39Y3d 66 06 $6 06 @ Oo 09 OF Oo oF “YLUS 140y 2 4afiv] W-OOT pasng-puno.b of Yp/NP fo suoryngrysip hyyrqngoid aayojnwung ¢9-9 BANDIT no 1 eae |e |i asnony 'HLIMS 1YOs | 008 voz so 1 2 § OF O Of O OS 09 O© OF 06 G6 06 66 S66 066686 030339X3 LON Si 3LVNIQUO JWI 30 1N3983d 030339X3S! 3LVNIGHO 3WIL 40 1N39830G 666 06 se6 66 06 $6 06S ees, rT to3-4 4 IL 4 005- O0f- 002 i 10 2 $0 Auynue34 “HL | WS 1uOs i) z $ or Oo OF om OB 8 030339X3 LON Si 3iVNIGHO 3WiL 30 LN3043d 06 sé 100 % G Se 0 566 B3L3WO1IN Y3d SLINN-N -N H3L3NWOTIM 43d SLIND 104 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY “DMD ‘oj :4ahv] w-O0OT pasvg-puno.b tof Yp/Np fo suoynqrysrp fipapiqnqgold aaryojnwnyg “99-9 AUNDI 030399X9 Si'dLvNIOwO Swi 40 INaoBad 030330x3 S| FLVNIOHO 3WIL 40 LN39H3d we ReS S65 66 e668 of OL 09S Ob Se oS 2 1 50 20 10 665966 SB5 66 86 S66 a: a os 2 1 $0 20 10 c SSS 009- Ae aI =a [ae Lt | + i—a6 o0s- OK fa or 00y- T of 0 - ¢ z a 02 - 002 » a D x OOl- = o- ° = m oy : ae Olen f) ool ual 001 4 = vd — HEE we WIBNIAON ‘OH asnony ‘OH or jj jj oor 2 so 1 2 sO o OF «Ob Os 09 OW nn MC TT 3 ie sot 2 $l 2 Of Oo 0S OF Of OF 06 «56 GCG OSHS (SE OBE 030339%3 1ON Si 3LVNIOHO 3WIL 40 LN39N3d 030330X3 LON S| 3LVNIGHO 3WIL 40 LN3OH3d 030339X3 S| 3LVNIOHO 3WIL 40 1N30H3d 030339X3 S| 31VNIOHO 3WIL 40 1N3943d See 065 S66 66 M6 | 6B H of 0 G O OF 0 os 2 1 $0 20 10 666 065 S65 66 86 | SG OB mH of 8 OB om OF fees 2 1 90 wi [ 1 009 ~ == 9- os - t+— 005 - aan 000- B3BL3NOMM 83d SLINN-N B+ mz AVA 'OTIH AuynuGad ‘OTH EEE SS ——— 1__1_] o9¢ ise 008 920 sO I z § a 2 Of O 8 Ow a «6 $6 1 68 S88 OES ETS 20 $0 1 2 g o 2 Cf & 6 Oo ow «6 s eo 6 Sw ree 030339X3 LON Si 3LVNIOHO 3WiL 40 1N3943d 0303393 LON SI 3LVNIGHO 3WIL 40 1N3DN3d MILIWOTIN 3d SLINN-N BIL3IMOTM Y3d = SLINN-N APPENDIX C105 “fyog ‘yovag buoy :4ahv)] W-OOT pasg-puno.b tof yp/Np fo suoryng2tsip Ayyriqogoid aaynjnung °*L9-O AUNdIA Q30339X3 SI! 666 8b6 S66 66 6 $6 06 8 BLVUNIQNO 3WIL JO 1N390U3d oL_09 0S oF of 02 oO Gof Q30339%3 1ON SI 656 766 S66 66 86 $6 06 oF ZLVNIGQNO 3WIL 4O IN390U3d 09% OF OF i. 8 z 4 $0 20 10 009- M3L3NOTIM Y3d SLINN-N M3L3NOTIM Y3d SLINN-N ‘-- 009- loan soe 00s- ws: 009- 0P- 00f- oof z é 2 5 o0e- 4 02- m » x oOl- FE O0i- °o = a hy} m Oe ° 001 oo! 002 oz BIBWIAON ‘HV3G ONCT asnonw 'H2v38 9NO7 Se gor ees oof m7 oo 1 2 § oF 02 Of O OS 09 © OP 06 S6 % 66 SUE 6666 1 2 § Of O& Of O O& 09 O WW 06 S6 6 66 SEG meb6tE Q30339%3 LON S! 3LVNIGQUO 3WIL 4O ILN30U3d G30339X%3 LON SI JLVNIGHO 3WIL 4O LNIDN3Rd 030339X3 SI 3LVNIOQYO 3JWIL 30 1N39Y3d 030339X3 S! 3LYNIGHO 3WIL 40 1N39U3d 686 086 S66 5 UGG OBS]! a 686 oS S66 66 86 G6 eS kD T 009- I 009- os 009- oof- ¢ z 5 oz % v m 2 oo- 2 ° =z a a 0 » 0! 002 ANYNHG34 'HIV3B ONC) 10 20 0 cor J 1 2 S$ Of & OF O OG 09 OL OF OF G6 86 66 S86 oe6 666 ive sor 2 Ss oO o Of O O& 09 O OF 0 <6 06 Gf G86 06 Q30330X3 LON SI 3LVNIGYO 3WIL JO LN3D4N3d 030339X3 LON S! 3LVNIQYO 3WIL 3O LNIDYN3d 106 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY ‘DILYY Iso asanbnjy10g ‘sanbpy ooainoT :4ahv] W-OOT pasvg-punos6 tof yp/Np fo suoynqgiysrp Apypqoqoid aayojnuny *g9-9 ayn 030339X3S! JLVNIGHO 3WiL 40 LN3943d 030339X3 S| 31VNIGHO JWI 40 1N39U3d 666066 S66 66 96 Sb 06 8 OL 09 0S Ob OF 02 or S 2 1 $0 20 ‘4 666 T66 S66 66 06 $6 06 iJ OL 09 0 O OF 02 or S z 1 $0 20 uk = alee == Saat 1 7808 — 06 _ | {+ oor - pot 4 oor Y3L3WOTIX Y3d SLINN-N W3BN3AON = 'ODNIHNO) asnony '09N3HNOT _}_hoy le " fo 20 $0 1 2 s ol 02 % OF % 09 OF OF 06 $6 96 66 S66 866 666 4020 So | z 7 o 02 Of OF O% 09 OP 06 56 06 66 566 956 666 0303393 LON SI 31VNIGYO 3WI1 JO 1N3943d 030339X3 LON Si 31VNIGHO 3WiL 30 1N3943d 030339X3 SI 3LVNIGNO 3Wid JO LNSou3d 030339X3 SI 3LVNIGHO 3WiL 40 1N3983d Ce a 666 We S66 6 SBS SLOSS Sk | mca ito 1a) an aS 5 ha Ws- ih ietenlratt | tia fe 005- ala - 2 + t ~{00¥ At + I + ite +—+— ooe- r + +—> “| eas t ¢ = =| a m 3 4 x 2 z a i m 2 [ale |_| m | — } 1 oo Av 'ODN3HN07 AuWnE®: __| eee Ne | | | ema 7 mm sv 1 2 5 «(OF OOOO Ch OOS ON COPSSCSC«G:SC«éa SCC ozo sD 1 2 $ OF Of Of Ov OS OF © O OF O WW Gl St wes ets 030339X3 LON SI 31VNIONO 3WiL 40 LN39N3d 030339X3 LON Si 3LVNIGHO 3WIL 40 1N3983d B3L3N0TIN B3d SLINN-N B3LIMOTIM 3d SLINN-N APPENDIX C 107 ‘spunpsy wry ‘ypunyry :safin, W-OOT paspg-puno.6 4of yp/Np fo suorngrysip Ayyrqngoid aaynjnuny) ~69-D AANA 030339X3 S| 3LVNIONO 3WIL JO LN3943d 030339%3S! 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Appendix D. World Charts of Tropopause Heights Five-year mean tropopause heights obtained in the process of computing N (2) parameters for February, May, August, and November at the 112 stations listed in table A-1 were plotted and contoured. Maps of these heights, given in figures D-1 through D-4, represent the average of all of the individual altitudes which marked the base of the first layer which had a thickness of at least 2 km and a temperature lapse rate of less than 2°C/km (see sec. 6). FIGURE D-1. Tropopause heights (km), based on temperature lapse rate: February. 117 118 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY FIGURE D-2. Tropopause heights (km), based on temperature lapse rate: May. 150 FiGuRE D-3. Tropopause heights (km), based on temperature lapse rate: August. APPENDIX D 119 150 FIGURE D-4. Tropopause heights (km), based on temperature lapse rate: November. 14. Appendix E. Sample Listing of the Computer Output for San Juan, P.R., and Amundsen-Scott, Antarctica A sample listing of the complete computer output of the mean N-profiles for February, May, August, and November at a subtropical and an arctic station is given in the first section of this appendix. For instance, at station 11636 (San Juan, P. R.) in February (table E-1), the heading gives the number of pieces of data used to compute two types of tropopause height (based on two types of temperature criteria) : (a) the mean heights where the extreme minimum temperature occurred in 320 indi- vidual profiles was 17.56 km + a standard deviation of 0.65 km, (b) the mean height of the bottom of the lowest atmospheric layer with a thickness => 2 km and a temperature gradient = —2°C/km in 307 temperature profiles was 16.44 km + 0.96 km. This February profile also gives refractivity information at 40 height levels ranging from 0 to 30 km. Following each height level is a listing of the values of total refractivity, gradient, dry and wet terms, and their respective standard deviations at that height above surface. For example, at 1 km, 383 radiosonde profiles were examined, and the refractivity was found to be 306.7 with a standard deviation of 8.22 N-units; the gradient at that level was —47.66 N/km with a standard deviation of 17.38 N/km; the dry term was 242.4 + 1.19 N-units, and the wet term was 64.3 + 8.52 N-units. The correlation coefficients for the data fit, within various height ranges, of the wet term (W) and the tropospheric (D,) and stratospheric (D.) dry terms to the line represented by a computed regression equation are found at the bottom of each month’s listing. In this example, for the dry term equation (D,), with a surface value of 271.9 and an exponential decay coefficient of —0.1088, the correlation coefficient is 0.999 and the standard deviation is 3.01 N-units. (These figures were based on 5 years of data from the surface to 15 km.) At station 90001 (Amundsen-Scott, Antarctica) the wet-term value is so small at all heights during the months studied that the regression equation from 0 to 3 km becomes meaningless. Because the South Polar region is not shown on the ground-based gradient maps (figs. C-1 through C-56), a complete computer listing for Amundsen-Scott is included in this appendix. This also illustrates the form of the original station data which were used to plot the various values needed for the gradient maps. All gradients are in N-units/ km and all frequencies are Mc/s. The “profiles skipped” represents the number of profiles which had gradients > —100 N/km. 120 APPENDIX E 121 08 = H= LI LSTSES'OF (HOTIZLT'0—)dxq 19°299 = %q G88666'0 82= H= 91 ZLOLGP OF (HPELZLT'0— )dxq gg1z99 = *q 019666'0 or =r —10 OLETIO'EF (H8LL801'0— )dxq 1611z = 1G £66866'0 tI=H=0 ZEE00r' SF (HGOTIOT 0O—)dxq zzs9z = Iq £68666'0 € =H=0 6IPPS0'9F (H8S9899'0— )dxq 26°66 = M 2982860 €& =H=0 PSI900'EF (HOSEEEFO—)dxq SOTIT = M 1964660 = sia = a (WM) asuey uorjenbay UOTTeIaIIOD, (WM) aduey uorjenby UOIzE[IIIOD, a]yolg [etyusuodxg a[yolg [etyuauodxy 0- 0 900 6s z0'0 = T9°0— 900 68 € 000'08 = 0 FIO g'g 400 B8"0— FIO 3 g'g 9 000°8z i0= 0 600 Fg 400 = L8"0— 60°70 oS 61 000°82 = 0 ITO) G'h 10°0 = Ta"I— ITO 39 S"2 cg 000°92 iO 0 V0 PL 90:0 §=. 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S'EZt | 29T OF9T— | 6aT s'éZ~ 86 00¢'F W= 0 IV's FO {OWT tise) o1eeh 19 000°F T2°0 00 Ter Laer | 26:0 esi9t— | ser sitsr 86 000°F W= 0 SI'S S00 9A2T= “\eSise. “SsLer 19 009° Le°0 T0 Sol SOFT | 28:0 e8LI— | Lal v0FT 86 00g" Z1'0 0°70 ira 260 = GL°8T—_—- | 82'S S0ST ra 6 000° 9¢°0 £0 VET Z6rt | 18:0 S8'8I— | SEI Ss 6rT 86 000° 420 0 98% 6:0 10'02— | Zee GO9T 29 00¢°% €L'0 9°0 8VI 98ST | 801 I66T— | 821 Z 6ST 86 008° 8e°0 0 v's TWIT 8 bG1Z— | LES GOLT POL 000°% 98°0 isa SOT G'89T | 201 S6:0Z— | OST 4'69T 86 000° 8r'0 £0 SoZ Ae WU as aE FOL 00ST 10'T VI SST O'6LT | FET 99'%Z—- | O9T G-08T 86 00¢°T €s°0 0 £9°% 90° 98S¢— | LeS Zs P6I POL 000°T 62 Lait 907% G06T | 20'S LOvZ— | GOT ZZ :26T 66 000°T ¥S°0 0 es 99% = 90°L4— 1002 POL 0gL°0 Te 8ST 10% L961 | $8°3 PBS2— | SLT $'86T 66 0g2°0 L¥°0 £0 18% 130 Vee 0°802 POL 00S°0 60°T GT eS ¥80% | 2eL 9018z— || 80° O7¢0z 66 00¢°0 82'0 20 Gee LGTZ 8T'0S— VLIZ POL 0620 18°0 8°0 sO" =VITZ | 29°6 18'se— | OL:e E'2tZ 66 0¢2°0 O1'0 x) 91'S L299 Z8'SZI—| STS +622 POT 0010 92°0 T0 49°§ 9812 | 0808 *S'29- | ESE L'STZ 66 00T'0 0°0 0°70 18'S Iv'69 Le LPI— | 08S G98% POL 0S0°0 61'0 TO OS 2s | GLE IL'S9- | 98h Zs :Ze2 66 0g0°0 = 00 16°9 62°96 L6P9I—| L69 9FFZ Lae 0 ST 00 evs §6gc2z | 68°ce Le99— | Beg g'e2z 66 0 Mas JLaM |aqs Aud | DGS 4P/NP | Nas N dequinn | (NM) 3451I0H MGS LAM | ads Aud | Das yYpP/NP | NaS N JequnNn | (AM) 4310 H ‘OL'OF GEG esde'T ‘00'GE 99°GT “dway, “ul 3481I0F] osnedodory, “gg asnedodoiy, SuryepnojeD ul pasy) sayyorg jo Jaquinny “Col G WIUOTW ‘T0006 BONIS ‘a[yO1g-N uray ‘IG'OF 86°F esdeyT “480 8e'Gg ‘dway, ‘uy qystey asnedodoiy, 06 esnedodoly, Suyenojey ul pas~) safyorg jo Jaquinyy “06 Z WOW ‘T0006 woes ‘a[Yoig—-N urayy ‘DayovjUy “yoog-uaspunwy :sapfoid-N uvapy 2-H ATAV J, ANS la ()6 TS8669'OF (HEEPSST'O— )dxq 12°682 = ®a PPE666 0 Vat 10 1¥9209°'3F (HPTLOST‘0— )dxq 96022 = ‘a 6991660 § = EH ="0 T60&zS'OF (HSL09LE°0 )dxa cto = M FSSEES 0 CAM) eury uojenby uoTyR]aIIOD, ajyoig [etjuauodxg Ome 0 O- 6T 10 = S6.0— 50 = 6:5 T 000°2E 6. = Hi = or Z2106E'OF (HSOSLLT'O—)4xq 09'eze = a STL666°0 0= 0 920 G3 c0°0 3e0—- 92°0 ard ¥ 000°08 MY eS 8b) POPLIL'9F (HOFP9ET'O—)4Xq 02622 = 'd 0619660 20= 0 G0 6% 600 &r'0— S20 6% 8 000°82 Wy Seb 10) 000000° I+ (H ‘0 )dxd 0OOT = M 0 Ome 0 80 (OTF 90°0 39,0= 8F°0 OF TZ 000°94 = = ass Os 0 99'0 99'S 90°0 ¢3°0—- 99:50 9g GP 000°FS (INM) esuey uonenby uoTye[alI0() L0= 0 180) Lh 80°0 (ie T8'0 Lh gs 000°%a alyorg [ejueuodxg T= 0 88°70 «SOT 60°0 eo T— 88°0 SOT TL 000°04 nO 0 160 = €'aT 80°0 €6°T— 160 as 9L 000°6T 00> 0 0— 66:1-— ‘O— 8 OT T 000°6T = 0 68°0 FFT €1'0 FAS 68°0 VPl 98 000°8T Ath 0 80°0 OF o— 62°0 TI Ot 000°8T ‘O- 0 $80 «26 '9T F10 Bice r8'0 6°91 0oT 000°LT Ole 0 60°0 g6ig— 360 6ST ¥% 000°LT t= 0 ws'0 L'6T 810 1 ee 80 L6. Sot 000°9T = 0 &1'0 GVilb ae 66°0 0°61 IF 000°9T 10> 0 T8'0 = =-T'&6 02°0 Cet T8'0 1&3 611 000°ST = 0 L410 L0°¥— 9F°0 8°2S o9 000°ST 0= 0 840 «TLS 620 LE 8L'0 TLS 921 000°FT 0S 0 120 NT Fo LS°0 BLS 16 000°FT We 0 oso «6 TE TO ye 08°0 6 TE 621 000°&T Bthere 0 Te'0 09 = 020 faa 6IT 000°ET A= 0 gs'0 GLE 9F°0 909m s8°0 GLs ZéL 000°CT US 0 Lv°0 Soo = 88°0 PSE TI 000°2T BOs 0 960 FFP T¢'0 TEL 96°0 PPP Sel 000°TT 0S 20; $90 89 LOT ooh Sst 000°TT RO 0 PTT Gas 09°0 T8°8— PIT Ges T¥I 000°0T i= 0 99°0 G6°8— cet L'8S 89T 000°0T AUS 0 ost TLS €L°0 0956 Oe'T TLS TFL 009°6 0= 0 02°0 08°6— 8o'T vss OLT 00S°6 A 0 srt 129 9L°0 Lv0I— SPT Teo eFI 000°6 a= 0 SLO so0T— 68'T g"89 SLT 000°6 i= 0 6L°T 9°L9 86°0 LV'TI—. | &L°T 9°L9 PPL 00g°8 0= 0 280 [Stk= 90°% T'69 PLT 006°8 nO 0 66T SEL 66°0 08 oI— 66°T G’8L 6hI 000°8 50 0 82°0 LUGI— 92°% O°SL GLI 000°8 Ome 0 Sao 0°08 S8'l 8o°sT— Gos 0°08 oST 00c*L A 0 810 06°3I— 98S S18 LLY 00GS*L = 0 Ors OL8 OEE tA Bo Ors 0°L8 SST 000°L Os 0 180 8esl— SLT 000°L 10= 0 866 a6 8eT hae 80% oF6 PST 00g°9 0= 0 180 LAU A tae o8T 00g°9 10= 0 FOG =O TOT Pt 16 FI— FOS 9 TOT 9ST 000°9 = 0 ¥8°0 CO A le ost 000°9 0= 0 OL'T = =T'60T Als &2'ST— OL'T T'60T 8ST 00g°¢ i Orme 0 16°0 OCS hee 2st 00g°¢ 10= m0) opt = 8°9TT TET 68°ST— oP't BOTT O9T 000°S 50> 0 £0°T Costs €8T 000°¢ nOes 0 OST Pal €0°T 08°ST— Oa T GPO TOT 00g'P AU 0 £0'°T Se9t— €81 00S" ‘O- 0 STI 9°38. 06°0 T9°9T— Sir 9ET EST 000°P Om 0 c6°0 UA yD te €8T 000°F rag) 0°0 OST TIFT 68'0 6eLT— OaT TTPT Lon 009°6 Oe 0 00'T 06°LT— E81 00g°6 92°0 T'0 Tet 6°6PT €6°0 Sv'8t— 6a T 6°6PT WOE 000°6 A= 0 oll 89°8T— SBT 000°6 Lv'0 €'0 6FT 6ST 28°0 rO:6l= IPT P'6ST LOT 009°% TO 0°0 90°T PL6T— G8T 009°% 99°0 9°0 69°T T°69T BLT 60° T3— 9cer 9°69T TOT. 000°% 610 00 LOT Abel de Sst 000°% 080 80 68°T =L°6LT 66°0 Te°co— LL'T GO8sT T9T 00g'T 00 T0 oo'T GST 00¢°T 96°0 OT 90% 8 T6T 18% LSF3— 10'S €c6T T9T 000°T TE'0 10 82'S $9'Se— Sst 000°T c0'T OT 79% =O LOT 8S Go'9e— CES 9°86T LOE 0SL°0 620 v0 L9°% LO Le— S8T 0cL"0 66°0 OT G83 6b FOS | GAP Ig‘8o— 8h'% F906 T9T 009°0 92°0 TO €F'9 9 T8— Sst 00¢°0 S80 L°0 SVE 9S e990 LP LE— OLs% f els T9T 0S2°0 FIO 00 00°L2 §=L0°eg— S8T 0S2'0 Sao v0 9L§ 861% | 90°62 sat9— SL's 8616 T9T 00T'O 90°0 0-0 61°09. 8a Feat — S8T 00T'0 810 0°0 Leb 22% | OSE F9'99— PEP ZEsS TOL 0¢0°0 = 00 o8°L9 FL L9T— Sst 0¢0°0 Lv'0 00 FTG L966 GESCE 6 E'G9— Its L'9@% T9T 0 = 00 6L°8L 3S°SLT— Sst 0 MadS LHM | ads Aud Das 4P/NP NdS N yoquinn | (NM) I441I0H MGS LOM AUd Dds 4P/NP Nas N jaquinn | (NM) 1431eH Z8OF TL'G osdeyT ‘get st, “dway “uA Wsey esnedodosy IT YI4OW ‘T0006 YORIS TILT Tes asdey ‘ogi sept dway uw wsey esnedodoiy, 8 YIUOW ‘T0006 “OeIS AS & “get asnedodoiy suyjepnojeg ul pasy) seTyolg jo Jaquinny “aTYOIg-N urayy "6SL ‘og asnedodoiy, Surje[nojeD ul pass seyyolg jo Jaquinn ‘a[YOIg-N uray 124 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY (panuyuoy) ‘Z-4 ATAVL APPENDIX E 125 TABLE E-3. Cumulative distribution of ground-based gradients: Amundsen-Scott, Antarctica (gradient followed by percentage level). 0-50 METERS, STATION 90001, MONTH 2 —20.7 0.51 —20.8 1.52 —22.3 2.53 —23.1 3.54 —24.8 4.55 5.56 —28.7 6.57 —28.9 7.58 —30.5 8.59 —31.0 9.60 —32.8 10.61 —35.0 11.62 12.63 —40.0 13.64 —41.6 14.65 —42.9 15.66 —43.3 16.67 —44,2 17.68 —44.2 18.69 19.70 —=46:9 20.71 —47.1 21.72 —47.7 22.73 —49.7 23.74 —50.1 24.75 —50.2 25.76 26.77 —51.0 27.78 —51.3 28.79 nly 29.80 —52.8 30.81 —53,8 31.82 —54.0 32.83 33.84 —56.0 34.85 —56.6 35.86 —57.5 36.87 —57.6 37.88 —61.5 38.89 —61.7 39.90 40.91 —63.0 41.92 —63.5 42.93 —63.6 43.94 =63:9 44.95 —65.4 45.96 —67.6 46.97 47.98 —67.8 48.99 —68.3 50.00 . —68.4 51.01 —69el 52.02 —70.7 53.03 7136) 54.04 55.05 —73.5 56.06 —73.8 57.07 —74.1 58.08 —74.5 59.09 —75.3 60.10 —75.7 61.11 62.12 —716.7 63.13 =T7:9 64.14 —79.5 65.15 —81.1 66.16 —81.9 67.17 —82.8 68.18 69.19 —84.0 70.20 —84.4 71.21 —85.1 72.22 —85.4 13123) || —85.8 74.24 —86.1 75.25 76.26 —89.2 TT.27 Os} 78.28 —93.7 79.29 —95.6 80.30 tics) 81.31 —101.0 82.32 83.33 —104.4 84.34 —107.2 85.35 —107.4 86.36 —107.5 87.37 —107.5 88.38 —109.7 89.39 90.40 —114.6 91.41 —123.7 92.42 —125.1 93.43 —133.7 94.44 —187.2 95.45 —146.9 96.46 97.47 —166.7 98.48 Ales) 99.49 0-50 METERS, STATION 90001, MONTH 5 —33.8 0.30 —41.3 0.91 —42.4 1.52 —43.9 2.13 45.1 2.74 —45.4 3.35 —45.6 3.96 —47.4 4.57 —48.7 5.18 —52.5 5.79 —53.3 6.40 —56.4 7.01 —57.4 7.62 = 5925) 8.23 —63.1 8.84 —64.8 9.45 —65.2 10.06 —66.6 10.67 —68.1 11.28 —68.3 11.89 —69.5 12.50 —70.2 13.11 —70.8 13.72 —72.3 14.33 —73.1 14.94 — iol 15.55 —78.2 16.16 Sf) 16.77 —80.6 17.38 —80.8 17.99 —82.0 18.60 —83.0 19.21 —83.7 19.82 —83.7 20.43 —89.5 21.04 —91.5 21.65 —92.0 22.26 =93.7 22.87 = O41 23.48 —94.6 24.09 =97-.6 24.70 SHEL®) 25.30 —102.3 25.91 —102.4 26.52 —105.8 27.13 —108.9 27.74 —110.4 28.35 ——t a Kees 28.96 —111.4 29.57 —111.5 30.18 =—113°9 30.79 —116.0 31.40 —116.7 32.01 —118.3 32.62 33.23 —119°7. 33.84 —120.7 34.45 aoe) 35.06 —131.9 35.67 —132.2 36.28 —132.2 36.89 37.50 —134.4 38.11 —135.6 38.72 —137.1 39.33 —137.2 39.94 —137.4 40.55 —187.8 41.16 41.77 —139.0 42.38 —140.7 42.99 —141.2 43.60 —144.5 44,21 —146.1 44.82 —147.3 45.43 46.04 —150.3 46.65 —150.3 47.26 —150.4 47.87 —152.4 48.48 —153.3 49.09 49.70 50.30 —156.6 50.91 —157.1 51.52 —159.6 52.13 —164.0 52.74 —164.7 53.35 53.96 54.57 —166.7 55.18 —167.0 55.79 —168.4 56.40 —168.7 57.01 —170.5 57.62 58.23 58.84 —173.5 59.45 —173.6 60.06 —174.6 60.67 —175.1 61.28 —176.7 61.89 62.50 63.11 —178.5 63.72 —179.6 64.33 —180.1 64.94 —180.5 65.55 —183.4 66.16 66.77 67.38 —186.3 67.99 —186.9 68.60 —187.3 69.21 —189.7 69.82 —193.8 70.43 71.04 71.65 — LO ON 72.26 —204.3 72.87 —204.9 73.48 —207.6 74.09 —208.9 74.70 75.30 75.91 —210.9 76.52 =211.2 77.13 —214.2 77.74 —214.4 78.35 —216.3 78.96 79.57 80.18 —226.8 80.79 227.7 81.40 —229.2 82.01 —232.7 82.62 —233.4 83.23 83.84 84.45 —241.1 85.06 —244.1 85.67 —247.3 86.28 —247.6 86.89 —251.5 87.50 88.11 88.72 —263.3 89.33 —268.2 89.94 —270.8 90.55 —270.9 91.16 —275.0 EDR 92.38 92.99 —285.1 93.60 —297.8 94.21 —299.3 94.82 —299.8 95.43 —310.0 96.04 96.65 97.26 —341.2 97.87 —383.6 98.48 —429.0 99.09 —481.2 99.70 0-50 METERS, STATION 90001, MONTH 8 —37.9 0.27 —39.3 0.81 —45.5 1.35 —47.9 1.89 —48.4 2.43 —49.5 2.97 —49.6 3.51 —58.3 4.05 —59.3 4.59 =59:3 5.14 —69.3 5.68 —74.6 6.22 ith 6.76 —74.9 7.30 Mosk 7.84 —80.4 8.38 —80.7 8.92 —82.9 9.46 —85.7 10.00 —89'9 10.54 =O0s8 11.08 =92°.6 11.62 —94.9 12.16 —95.4 12.70 =96.2 13.24 —96.6 13.78 =EEL) 14.32 —100.4 14.86 —100.5 15.41 —100.6 15.95 —101.7 16.49 —105.6 17.03 —106.5 17.57 —108.2 ST LOT 18.65 —112.4 19.19 —112.9 19.73 —114.7 20.27 —114°9 20.81 —115.1 21.35 —115.2 21.89 | —1'6:0 22.43 —117.5 22.97 —118.6 23.51 —121.6 24.05 —123.9 24.59 —126.3 25.14 —126.3 25.68 —129.2 26.22 —129.6 26.76 —130.8 27.30 1319 27.84 —132.3 28.38 =182:7, 28.92 —134.3 29.46 —134.8 30.00 —135.2 30.54 —135.5 31.08 —135.9 31.62 — 187.3 32.16 —187.5 32.70 —137.6 33.24 —138.0 33.78 —138.7 34.32 —139.1 34.86 —139.3 35.41 —141.7 35.95 —143.6 36.49 —143.7 37.03 37.57 —146.0 38.11 — 146.6 38.65 —147.1 39.19 —147.2 39.73 —147.5 40.27 —149.8 40.81 41.35 —151.4 41.89 —152.6 42.43 —154.7 42.97 —156.6 43.51 —156.8 44.05 —156.9 44.59 45.14 —161.1 45.68 —163.3 46.22 —164.4 46.76 —164.6 47.30 —166.7 47.84 —166.9 48.38 48.92 —169.2 49.46 —170.1 50.00 fale} 50.54 51.08 —171.8 51.62 —173.0 52.16 52.70 —176.0 53.24 —176.2 53.78 —176.3 54.32 54.86 —178.4 55.41 —180.4 55.95 56.49 —180.9 57.03 —180.9 57.57 —183.8 58.11 58.65 —184.4 59.19 SEE) 59.73 60.27 —186.4 60.81 —189.5 61.35 —190.2 61.89 62.43 —191.8 62.97 —192.0 63.51 64.05 —193.8 64.59 =197.1 65.14 —198.3 65.68 66.22 —200.8 66.76 —203.1 67.30 67.84 —206.0 68.38 —208.6 68.92 —208.8 69.46 70.00 —211.8 70.54 —212.0 71.08 71.62 —215.4 72.16 —216.1 72.70 —216.1 73.24 73.78 —218.3 74.32 —219.3 74.86 d 75.41 —221.7 75.95 —225.2 76.49 —225.7 77.03 T7.57 —230.6 78.11 —230.7 78.65 —232.5 79.19 —238.2 79.73 —239.9 80.27 —240.9 80.81 81.35 —242.6 81.89 —244.8 82.43 —245.4 82.97 —246.9 83.51 —249.6 84.05 —252.3 84.59 85.14 —254.6 85.68 —262.2 86.22 —265.4 86.76 —266.3 87.30 —267.0 87.84 =267 21 88.38 88.92 —272.4 89.46 —275.7 90.00 =277.6 90.54 =—279.1 91.08 =—279:2 91.62 —280.0 92.16 92.70 —290.5 93.24 —290.7 93.78 —292.7 94.32 = 293827 94.86 —296.5 95.41 —298.7 95.95 96.49 —305.8 97.03 —306.4 97.57 —314.4 98.11 —336.0 98.65 —346.2 99.19 —419.4 99.73 126 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY TABLE E-3. (Continued) 0-50 METERS, STATION 90001, MONTH 11 Sai 0.31 —16.1 0.93 ise 1.55 oie Asli —22.5 2.80 —24.9 3.42 25.1 4.04 25.8 4.66 =27.8 5.28 27.6 5.90 —27.6 6.52 27.7 7.14 TES 7.16 28.0 8.39 —28.2 9.01 ~29.1 9.63 = 3107 10l25 —35.1 10.87 —37.2 11.49 = 39:40) duit —38.6 12.73 —39.5 13.35 —39.6 13.98 —39.7 14.60 —40.4 15.22 —40.7 15.84 —42.7 16.46 —43.6 17.08 —443 17.70 —45.4 18.32 —45.5 18.94 —46.5 19.57 —46.5 20.19 —46.6 20.81 —46.7 21.43 —47.3 22.05 —48.3 22.67 —49.1 23.29 —49.3 23.91 —49.3 24.53 —49.8 25.16 —49.9 25.78 —50.3 26.40 = 51 ta wm 2702 —53.7 27.64 —53.7 28.26 —53.9 28.88 —54.6 29.50 —55.1 30.12 —55.4 30.75 —55.7 31.37 —55.9 31.99 —56.1 32.61 —56.3 33.23 —56.4 33.85 —56.8 34.47 —58.3 35.09 —58.6 35.71 —58.7 36.34 —58.8 36.96 —59.1 37.58 —59.7 38.20 —59.9 38.82 —60.2 39.44 —60.5 40.06 —61.1 40.68 —61.4 41.30 —61.9 41.93 —62.0 42.55 —62.3 43.17 —62.8 43.79 —62.8 44.41 —63.2 45.08 —64.1 45.65 —64.5 46.27 —65.0 46.89 —66.3 47.52 -66.3 48.14 -66.5 48.76 —67.2 49.38 —67.5 50.00 -67.7 50.62 —67.8 51.24 —68.2 51.86 —68.5 52.48 —68.6 53.11 —69:2 53.73 —69.7 54.35 —70.2 54.97 —73.0 55.59 —73.2 56.21 —73.4 56.83 —73.9 57.45 —74.2 58.07 —74.3 58.70 -74.6 59.32 —76.0 59.94 —76.3 60.56 -76.8 61.18 =7a10§ 61,80 -17.2 62.42 —77.2 63.04 -17.4 63.66 —17.8 64.29 —78.7 64.91 —79.0 65.53 —79.0 66.15 -79.5 66.77 —79.8 67.39 —80.5 68.01 —81.6 68.63 —81.8 69.25 —82.3 69.88 —84.0 70.50 Zn plats —84.7 71.74 —85.1 72.36 —86.0 72.98 —86.0 73.60 —86.0 74.22 -86.1 74.84 —86.6 75.47 —86.8 76.09 -87.3 76.71 =R707 TSS —88.9 77.95 —89.8 78.57 -90.2 79.19 —91.3 79.81 —92.4 80.43 —92.6 81.06 —96.0 81.68 97.6 82.30 —98.9 82.92 —99.2 83.54 —99.4 84.16 100.0 84.78 —100.7 85.40 100.8 86.02 —100.9 86.65 102s SVT -102.3 87.89 —102.9 88.51 —103.7 89.13 —105.3 89.75 —106.1 90.37 108.4 90.99 —110.2 91.61 -1118 92.24 111.9 92.86 -119.1 93.48 —119.1 94.10 -119.3 94.72 —122.4 95.34 —123.8 95.96 —124.1 96.58 —128.8 97.20 —139.4 97.88 147.0 98.45 —155.9 99.07 —161.3 99.69 0-100 METERS, STATION 90001, MONTH 2 —20.6 0.51 —20.7 1.52 Syl 2.53 —23.0 3.54 24.7 4.55 —28.4 5.56 —28.6 6.57 —28.8 7.58 a0 8.59 —30.4 9.60 —32.7 ~ 10.61 —34.7 11.62 SRyal iba —39.8 13.64 —41.1 14.65 —42.6 15.66 —43.0 16.67 —44.0 17.68 —44.0 18.69 —45.1 19.70 —46.6 2.71 —46.7 21.72 —46.8 22.73 —47.4 23.74 —48.9 24.75 —49.9 25.76 —50.0 26.77 —50.7 27.78 —51.0 28.79 —51.4 29.80 —52.5 30.81 —53.5 31.82 —53.6 32.83 —53.8 33.84 —55.6 34.85 —56.2 35.86 —57.1 36.87 —57.3 37.88 —61.1 38.89 61.3 39.90 —61.3 40.91 —62.6 41.92 —63.1 42.93 —63.1 43.94 —63.5 44.95 -64.9 45.96 -67.1 46.97 —67.2 47.98 —67.3 48.99 —67.8 50.00 —67.9 51.01 -68.5 52.02 —70.2 53.03 -71.1 54.04 -71.9 55.05 —72.9 56.06 —73.2 57.07 —73.5 58.08 —73.8 59.09 —74.7 60.10 -75.0 61.11 —76.0 62.12 -76.1 63.13 —77.3 64.14 —78.8 65.15 | —80.3 66.16 Rises 6717 —82.0 68.18 —S3.1 69.19 —83.2 70.20 —83.6 71.21 —84.3 72.22 | -84.6 73.23 —85.0 74.24 —85.3 75.25 —86.2 76.26 SEG 4ieoyi —90.4 78.28 —92.7 79.29 —93.0 80.20 —94.6 81.31 | —96.8 82.32 —99.9 83.33 —101.3 84.34 —103.2 85.35 -106.2 86.36 —106.2 87.37 —106.2 88.38 —108.4 89.39 —112.2 90.40 —112.8 91.41 —113.1 92.42 —122.1 93.43 —1318 94.44 -135.2 95.45 —144.6 96.46 —151.8 97.47 —163.8 98.48 —168.8 99.49 | 0-100 METERS, STATION 90001, MONTH 5 —42.2 0.30 487 0.91 | 44.9 1.52 —45.2 Pay —45.4 2.74 47.2 3.35 —49.2 3.96 —56.1 4.57 —56.6 5.18 | —57.1 5.79 —59.1 6.40 —62.7 7.01 —64.7 7.62 —66.1 8.23 —67.6 8.84 —70.2 9.45 —70.8 10.06 —71.8 10.67 = DES -74.0 11.89 —74.2 12.50 =e) aaa -17.6 18.72 -79.9 14.33 —80.1 14.94 —83.0 15.55 —83.6 16.16 —86.1 16.77 -87.1 17.38 —90.3 17.99 —90.6 18.60 -91.1 19.21 -92:7 19.82 —93.2 20.48 —93.7 21.04 —96.6 21.65 —97.0 22.26 —98.0 22.87 —101.2 23.48 —101.3 24.09 —104.6 24.70 107.6 25.30 —109.1 25.91 —109.5 26.52 -109.7 27.18 Swi yee" -110.2 28.35 -110.8 28.96 —112.6 29.57 —114.7 30.18 —114.7 30.79 —115.0 31.40 -115.3 32.01 -116.8 32.62 118.2 33.23 118.2 33.84 —119.2 34.45 126.3 35.06 130.0 35.67 130.4 36.28 —130.4 36.89 —131.0 37.50 —132.6 38.11 —134.6 38.72 -134.9 39.33 -135.2 39.94 —135.5 40.55 —135.9 41.16 —136.2 41.77 —138.7 42.38 —139.1 42.99 -141.0 43.60 —142.5 44.21 —144.0 44.82 -146.0 45.43 —146.5 46.04 —148.0 46.65 -148.1 47.26 —149.4 47.87 —150.0 48.48 —150.9 49.09 —151.2 49.70 153.9 50.30 —154.6 50.91 -157.7 51.52 —158.8 52.13 | —160.5 52.74 | —161.2 53.35 —162.1 53.96 —162.3 54.57 —163.7 55.18 -163.8 55.79 —165.7 56.40 —165.8 57.01 | —166.3 57.62 —168.2 58.23 —168.3 58.84 —169.6 59.45 —170.5 60.06 -170.6 60.67 A718 61.28 -171.5 61.89 -172.1 62.50 =1730s) 63311 -173.5 63.72 -1742 64.33 -174.9 64.94 —175.3 65.55 -176.4 66.16 217712) 66/77 —180.1 67.38 180.3 67.99 —180.4 68.60 182.8 69.21 | -183.4 69.82 —183.7 70.48 —186.1 71.04 —186.2 71.65 —186.3 72.26 —193.4 72.87 —194.5 73.48 —195.1 74.09 | 195.7 74.70 —196.1 75.30 -197.3 75.91 -197.4 76.52 —198.8 77.18 —200.1 77.74 200.7 78.35 | 203.3 78.96 —204.9 79.57 205.5 80.18 —206.7 80.79 208.9 81.40 —209.6 82.01 209.7 82.62 —210.2 83.23 —212.0 83.84 —212.5 84.45 —217.3 85.06 —220.1 85.67 —221.1 86.28 221.7 86.89 —222.6 87.50 —225.0 88.11 227.5 88.72 227.9 89.33 —229.4 89.94 —230.3 90.55 | 234.0 91.16 —238.6 91.77 —239.8 92.38 245.3 92.99 —248.1 93.60 250.6 94.21 —252.3 94.82 —252.8 95.43 263.5 96.04 —264.9 96.65 —270.7 97.26 —271.5 97.87 277.4 98.48 —288.5 99.09 ~290.9 99.70 TABLE E-3. (Continued) APPENDIX E 127 0-100 METERS, STATION 90001, MONTH 8 —37.7 0.27 —39.1 0.81 —45.2 1.35 —AT7.7 1.89 —48.2 2.43 —49.4 2.97 —57.9 —74.0 4.05 —714.2 4.59 —74.3 5.14 —719.5 5.68 =—719.7 6.22 —80.0 6.76 — 82.2 —84.2 7.84 —84.9 8.38 —89.1 8.92 —89.9 9.46 —91.7 10.00 —93.9 10.54 —94,4 —95.6 11.62 =97.9 12.16 —99.4 12.70 —99.6 13.24 —99.9 13.78 —105.3 14.32 —107.0 —108.9 15.41 —109.1 15.95 —111.1 16.49 —113.3 17.03 —113.6 17.57 —113.7 18.11 —113.8 —114.6 19.19 —115.1 19.73 —116.1 20.27 —116.9 20.81 —117.2 21.35 —120.1 21.89 —122.3 —124.7 22.97 —124.7 23.51 —126.8 24.05 —127.4 24.59 —127.9 25.14 —130.2 25.68 —130.4 —131.9 26.76 —132.1 27.30 —132.4 27.84 —132.9 28.38 —133.3 28.92 —133.6 29.46 —134.0 he —135.4 30.54 —135.5 31.08 —135.5 31.62 —135.6 32.16 —136.7 32.70 —136.7 33.24 —1387.1 3 —139.6 34.32 —139.8 34.86 —140.8 35.41 —141.5 35.95 —141.6 36.49 —141.7 37.03 —143.9 d —145.0 38.11 * =—145.1 38.65 —145.3 39.19 —147.0 39.73 —147.6 40.27 —149.1 40.81 —150.3 r —150.6 41.89 —152.2 42.43 —154.0 42.97 —154.5 43.51 —154.6 44.05 —156.0 44.59 —157.4 o —158.1 45.68 —158.5 46.22 —161.1 46.76 —161.6 47.30 —161.8 47.84 —163.6 48.38 —164.1 : —165.2 49.46 —166.3 50.00 —168.6 50.54 —169.3 51.08 —169.9 51.62 —170.7 52.16 —170.7 b —170.9 53.24 —171.4 53.78 —172.9 54.32 —173.3 54.86 —175.5 55.41 —176.2 55.95 —177.2 . —177.3 57.03 —177.7 57.57 —180.4 58.11 —180.5 58.65 —181.1 59.19 —182.5 59.73 —182.8 60.27 —182.9 60.81 —183.4 61.35 —183.5 61.89 —185.7 62.43 —185.9 62.97 —186.2 63.51 —186.5 64.05 —187.9 64.59 —188.4 65.14 —188.5 65.68 —189.2 66.22 —190.0 66.76 —190.4 67.30 —192.7 67.84 —192.8 68.38 —192.8 68.92 —196.1 69.46 —196.7 70.00 —198.7 70.54 —199.2 71.08 —203.3 71.62 —204.3 72.16 —204.4 72.70 —206.6 73.24 —207.4 73.78 —207.4 74.32 —207.6 74.86 —208.0 75.41 —210.6 75.95 —210.8 76.49 —211.4 77.03 —211.5 T7157 —213.7 78.11 —214.4 78.65 —215.8 1919) —216.1 79.73 —218.3 80.27 —218.9 80.81 —219.3 81.35 —219.4 81.89 —220.0 82.43 —221.1 82.97 —221.2 83.51 —221.2 84.05 —222.5 84.59 —225.3 85.14 —231.0 85.68 —232.9 86.22 —233.5 86.76 —235.2 87.30 —235.6 87.84 —236.3 88.38 —238.5 88.92 —243.9 89.46 —244.8 90.00 —248.1 90.54 —252.9 91.08 —253.2 91.62 —255.4 92.16 —257.3 92.70 —260.0 93.24 —260.1 93.78 —260.2 94.32 —261.8 94.86 —262.9 95.41 —265.7 95.95 —271.5 96.49 —279.1 97.03 —279.7 97.57 —282.1 98.11 —293.5 98.65 —319.1 99.19 —328.5 99.73 0-100 METERS, STATION 90001, MONTH 11 —9.0 0.31 —9.9 0.93 -17.9 1.55 —21.3 2.17 —22.5 2.80 —25.0 3.42 —25.7 4.04 —27.2 4.66 —27.5 5.28 —27.6 5.90 —27.7 6.52 —27.9 7.14 —28.1 7.76 =D 8.39 —29.8 9.01 —31.6 9.63 —34.9 10.25 —37.0 10.87 —38.2 11.49 —38.4 12.1 —39.4 12.73 —40.3 13.35 —40.5 13.98 —42.4 14.60 —44.1 15.22 —45.2 15.84 —46.3 16.46 —46.3 17.08 —46.3 17.70 —46.4 18.32 —48.1 18.94 —48.8 19.57 —49.0 20.19 —49.0 20.81 —49.5 21.43 —49.6 22.05 —50.0 22.67 —51.0 23.29 —51.1 23.91 —51.3 24.53 —51.4 25.16 —53.4 25.78 —53.4 26.40 —53.6 27.02 —564.3 27.64 —54.6 28.26 —54.7 28.88 —55.1 29.50 —55.3 30.12 —55.8 30.75 —56.0 31.37 —56.0 31.99 —56.5 32.61 —57.3 33.23 —57.9 33.85 —58.3 34.47 —58.4 35.09 —58.7 35.71 —58.8 36.34 —59.3 36.96 —59.5 37.58 —59.8 38.20 —60.1 38.82 —60.7 39.44 —61.0 40.06 —61.5 40.68 —61.6 41.30 —61.8 41.93 —62.4 42.55 —62.4 43.17 —62.8 43.79 —63.7 44.41 —64.0 45.03 —64.5 45.65 —65.6 46.27 —65.8 46.89 —66.0 47.52 —66.7 48.14 —67.0 48.76 —67.3 49.38 —68.1 50.00 —68.7 50.62 —69.2 51.24 —69.6 51.86 —72.4 52.48 —72.8 53.11 —73.3 53.73 —73.3 54.35 —713.5 54.97 —73.6 55.59 —73.7 56.21 —74.8 56.83 —75.4 57.45 —15.7 58.07 —76.1 58.70 —76.4 59.32 —76.5 59.94 —76.6 60.56 —76.7 61.18 —77.0 61.80 —78.0 62.42 —78.1 63.04 —78.3 63.66 —78.3 64.29 —78.8 64.91 —79.2 65.53 —19.8 66.15 —80.7 66.77 —80.8 67.39 —80.9 68.01 —81.1 68.63 —81.6 69.25 —83.2 69.88 —83.2 70.50 —83.8 T7112 —84.0 71.74 —84.1 72.36 —84.2 72.98 —84.3 73.60 —85.2 74.22 —85.2 74.84 —85.2 75.47 —85.3 76.09 —85.5 Geral —85.8 717.33 —86.0 17.95 —86.0 78.57 —86.5 79.19 —86.9 79.81 —88.0 80.43 —88.9 81.06 —89.3 81.68 —89.8 82.30 —90.4 82.92 —91.6 83.54 —95.1 84.16 —96.6 84.78 —96.8 85.40 —98.2 86.02 —98.3 86.65 —98.9 87.27 —99.6 87.89 =99.7 88.51 —100.7 89.13 —100.9 89.75 —101.2 90.37 —101.7 90.99 —102.5 91.61 —104.1 92.24 —104.9 92.86 —107.1 93.48 —107.4 94.10 —110.4 94.72 —110.6 95.34 —114.8 95.96 117-6 96.58 —120.7 97.20 —120.8 97.83 —122.1 98.45 —182.5 99.07 —158.5 99.69 TABLE E-4. Analysis of ground-based superrefractive and ducting layers: Amundsen-Scott, Antarctica. FEBRUARY 87 Profiles Skipped 99 Profiles Read Number of Ducts 2 Number of Superrefractive Layers 10 CUMULATIVE DISTRIBUTION OF DUCTING GRADIENTS, STATION 90001, MONTH 2 —157.500 25.00 —164.815 75.00 CUMULATIVE DISTRIBUTION OF DUCT THICKNESSES, STATION 90001, MONTH 2 0.120 25.00 0.108 75.00 CUMULATIVE DISTRIBUTION OF TRAPPING FREQUENCIES, STATION 90001, MONTH 2 3693.683 25.00 1191.104 75.00 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER GRADIENTS, STATION 90001, MONTH 2 —100.000 5.00 —101.626 15.00 —103.922 25.00 —103.960 35.00 —117.391 45.00 —118.033 55.00 | —124.793 65.00 —129.054 75.00 —138.182 85.00 —148.182 95.00 128 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY TABLE E-4. (Continued) CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER THICKNESSES, STATION 90001, MONTH 2 0.148 5.00 0.123 15.00 0.122 25.00 0.121 35.00 0.110 45.00 0.110 55.00 0.104 65.00 0.102 75.00 0.101 85.00 0.092 95.00 MAY 45 Profiles Skipped 164 Profiles Read Number of Ducts 79 Number of Superrefractive Layers 40 CUMULATIVE DISTRIBUTION OF DUCTING GRADIENTS, STATION 90001, MONTH 5 —157.396 0.63 | —158.605 1.90 | —158.92 3.16 | —159.596 4.43 | —159.873 5.70 | —160.156 6.96 | —161.207 8.23 —164.045 9.49 | —165.248 10.76 | —165.306 12.03 | —165.789 13.29 | —166.667 14.56 | —167.290 15.82 | —167.480 17.09 —167.925 18.35 | —169.595 19.62 | —170.732 20.89 | —172.414 22.15 | —173.810 23.42 | —174.737 24.68 | —175.163 25.95 —175.694 27.22 | —176.119 28.48 | —176.774 29.75 | —176.800 31.01 | —180.952 32.28 | —182.906 33.54 | —183.511 34.81 —187.629 36.08 | —187.850 37.34 | —188.095 38.61 | —192.035 39.87 | —192.105 41.14 | —192.241 42.41 | —196.241 43.67 —196.522 44.94 | —198.742 46.20 | —200.962 47.47 | —202.778 48.73 | —202.857 50.00 | —203.175 51.27 | —205.263 52.53 —206.542 53.80 | —210.204 55.06 | —210.959 56.33 | —214.019 57.59 | —219.231 58.86 | —220.192 60.13 | —221.429 61.39 —224.107 62.66 | —224.762 63.92 | —231.633 65.19 | —231.818 66.46 | —232.824 67.72 | —233.333 68.99 | —237.903 70.25 —239.583 71.52 | —240.230 72.78 | —244.706 74.05 | —246.808 75.382 | —250.000 76.58 | —259.375 77.85 | —263.265 79.11 —266.279 80.38 | —266.327 81.65 | —272.500 82.91 | —275.000 84.18 | —276.389 85.44 | —284.615 86.71 | —285.981 87.97 —298.947 89.24 | —302.410 90.51 | —306.383 91.77 | —351.282 93.04 | —375.000 94.30 | —377.586 95.57 | —405.128 96.84 —422.414 98.10 | —789.655 99.37 CUMULATIVE DISTRIBUTION OF DUCT THICKNESSES, STATION 90001, MONTH 5 0.215 0.63 0.196 1.90 0.188 3.16 0.178 4.43 0.171 5.70 0.169 6.96 0.164 8.23 0.159 9.49 0.157 10.76 0.155 12.03 0.153 13.29 0.152 14.56 0.148 15.82 0.144 17.09 0.141 18.35 0.134 19.62 0.133 20.89 0.131 22.15 0.128 23.42 0.126 24.68 0.126 25.95 0.126 27.22 0.125 28.48 0.124 29.75 0.123 31.01 0.120 32.28 0.117 33.54 0.116 34.81 0.116 36.08 0.116 37.34 0.115 38.61 0.113 39.87 0.112 41.14 0.112 42.41 0.108 43.67 0.107 44.94 0.107 46.20 0.107 47.47 0.107 48.73 0.107 50.00 0.106 51.27 0.105 52.53, 0.105 53.80 0.105 55.06 0.104 56.33 0.104 57.59 0.104 58.86 0.099 60.13 0.098 61.39 0.098 62.66 0.098 63.92 0.098 65.19 0.097 6 0.096 67.72 0.096 68.99 0.095 70.25 0.095 71.52 0.094 72.78 0.087 74.05 0.086 0.085 76.58 0.083 77.85 0.078 79.11 0.076 80.38 0.076 81.65 0.075 82.91 0.073 0.072 $5.44 0.066 86.71 0.060 87.97 0.058 89.24 0.058 90.51 0.049 91.77 0.048 0.047 94.30 0.039 95.57 0.039 96.84 0.029 98.10 0.028 99.37 CUMULATIVE DISTRIBUTION OF TRAPPING FREQUENCIES, STATION 90001, MONTH 5 2429.613 0.63 2325.397 1.90 2227.842 3.16 1903.914 4.43 1718.862 5.70 1450.565 6.96 1439.236 8.23 1108.889 9.49 1107.595 10.76 1063.192 12.03 1054.213 13.29 1040.684 14.56 1037.927 15.82 980.129 17.09 961.749 18.35 957.395 19.62 954.692 20.89 913.759 22.15 847.641 23.42 838.695 24.68 820.140 25.95 816.260 27.22 810.394 28.48 777.463 29.75 764.293 31.01 712.759 32.28 710.191 33.54 662.327 34.81 646.673 36.08 610.797 37.34 603.274 38.61 592.484 39.87 586.278 41.14 582.761 42.41 573.161 43.67 563.175 44.94 553.068 46.20 538.194 47.47 534.227 48.73 530.990 50.00 528.221 51.27 522.541 52.53 515.639 53.80 512.172 55.06 507.723 56.33 506.381 57.59 502.051 58.86 494.731 60.13 484.498 61.39 483.106 62.66 482.604 63.92 482.287 65.19 481.507 66.46 476.108 67.72 472.742 68.99 465.027 70.25 450.911 71.52 449.615 72.78 449.164 74.05 448.447 75.32 445.739 76.58 439.734 77.85 437.183 (9-12 435.225 80.38 424.335 81.65 412.328 82.91 394.994 84.18 390.938 85.44 390.912 86.71 387.054 87.97 370.600 89.24 340.803 90.51 338.249 S177 302.637 93.04 299.088 94.30 290.004 95.57 287.878 96.84 282.840 98.10 266.106 99.37 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER GRADIENTS, STATION 90001, MONTH 5 —103.333 1.25 | —103.347 3.75 | —103.791 6.25 | —106.587 8.75 | —106.667 11.25 | —111.515 13.75 | —111.892 16.25 —112.632 18.75 | —113.333 21.25 | —114.465 23.75 | —115.741 26.25 | —120.084 28.75 | —123.478 31.25 | —125.253 33.75 —126.087 36.25 | —126.214 38.75 | —127.700 41.25 | —127.835 43.75 | —128.144 46.25 | —130.000 48.75 | —130.337 51.25 —130.469 53.75 | —133.110 56.25 | —133.588 58.75 | —134.375 61.25 | —135.714 63.75 | —138.967 66.25 | —139.655 68.75 —139.662 71.25 | —140.845 73.75 | —141.799 76.25 | —142.938 78.75 | —143.158 81.25 | —147.183 83.75 | —147.619 86.25 —148.980 88.75 | —150.649 91.25 | —155.208 93.75 | —155.556 96.25 | —155.797 98.75 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER THICKNESSES, STATION 90001, MONTH 5 0.300 1.25 0.299 3.75 0.240 6.25 0.239 8.75 0.239 11.25 0.237 13.75 0.213 16.25 0.213 18.75 0.213 21.25 0.211 23.75 0.206 26.25 0.198 28.75 0.189 31.25 0.185 33.75 0.184 36.25 0.178 38.75 0.177 41.25 0.174 43.75 0.167 46.25 0.167 48.75 0.165 51.25 0.159 53.75 0.154 56.25 0.147 58.75 0.142 61.25 0.138 63.75 0.131 66.25 0.128 68.75 0.126 71.25 0.120 73.75 0.115 76.25 0.108 78.75 0.098 81.25 0.097 83.75 0.096 86.25 0.096 88.75 0.095 91.25 0.095 93.75 0.090 96.25 0.080 98.75 THICKNESS AND GRADIENT OF SUPERREFRACTIVE LAYERS OVER 300 METERS THICK 0.30000 —106.66667 APPENDIX E 129 TABLE E-4. (Continued) AUGUST 31 Profiles Skipped 185 Profiles Read Number of Ducts 104 Number of Superrefractive Layers 50 CUMULATIVE DISTRIBUTION OF DUCTING GRADIENTS, STATION 90001, MONTH 8 —157.059 0.48 | —157.639 1.44 | —157.843 2.40 | —158.268 3.37 | —159.236 4.33 | —160.759 5.29 | —161.881 6.25 —162.667 7.21 | —163.265 8.17 | —166.355 9.13 | —167.262 10.10 | —167.532 11.06 | —168.033 12.02 | —170.149 12.98 —170.248 13.94 | —170.408 14.90 | —172.308 15.87 | —172.727 16.83 | —172.727 17.79 | —173.404 18.75 | —173.984 19.71 —175.510 20.67 | —175.949 21.63 | —176.056 22.60 | —176.111 23.56 | —176.190 24.52 | —176.786 25.48 | —177.165 26.44 —177.922 27.40 | —181.250 28.37 | —182.292 29.33 | —182.895 30.29 | —182.993 31.25 | —184.127 32.21 | —184.536 33.17 —185.185 34.13 | —185.315 35.10 | —186.170 36.06 | —190.816 37.02 | —191.919 37.98 | —194.286 38.94 | —196.939 39.90 —198.889 40.87 | —200.000 41.83 | —200.000 42.79 | —200.769 43.75 | —202.062 44.71 | —202.069 45.67 | —202.083 46.63 —202.778 47.60 | —203.738 48.56 | —205.517 49.52 | —205.674 50.48 | —208.571 51.44 | —210.526 52.40 | —211.111 53.37 —211.702 54.33 | —214.286 55.29 | —214.943 56.25 | —216.239 57.21 | —218.584 58.17 | —218.750 59.13 | —220.619 60.10 —221.552 61.06 | —222.078 62.02 | —225.000 62.98 | —230.645 63.94 | —232.174 64.90 | —232.941 65.87 | —234.694 66.83 —236.842 67.79 | —237.113 68.75 | —239.175 69.71 | —243.434 70.67 | —243.750 71.63 | —243.966 72.60 | —245.977 73.56 —252.885 74.52 | —255.652 75.48 | —256.701 76.44 | —257.843 77.40 | —258.491 78.37 | —264.935 79.33 | —266.154 80.29 —266.279 81.25 | —267.257 82.21 | —268.817 83.17 | —271.429 84.13 | —277.586 85.10 | —278.261 86.06 | —281.395 87.02 —281.633 87.98 | —282.558 88.94 | —284.058 89.90 | —284.211 90.87 | —288.298 91.83 | —289.189 92.79 | —290.566 93.75 —295.349 94.71 | —298.507 95.67 | —302.941 96.63 | —326.923 97.60 | —332.653 98.56 | —408.000 99.52 CUMULATIVE DISTRIBUTION OF DUCT THICKNESSES, STATION 90001, MONTH 8 0.204 0.48 0.202 1.44 0.201 2.40 0.189 3.37 0.180 4.33 0.170 5.29 0.168 6.25 0.168 7.21 0.158 8.17 0.158 9.13 0.157 10.10 0.154 11.06 0.152 12.02 0.147 12.98 0.145 13.94 0.145 14.90 0.144 15.87 0.144 16.83 0.144 17.79 0.144 18.75 0.143 19.71 0.143 20.67 0.142 21.63 0.141 22.60 0.134 23.56 0.130 24.52 0.127 25.48 0.127 26.44 0.126 27.40 0.126 28.37 0.124 29.33 0.123 30.29 0.122 31.25 0.121 32.21 0.117 33.17 0.116 34.13 0.116 35.10 0.116 36.06 0.115 37.02 0.115 37.98 0.113 38.94 0.113 39.90 0.108 40.87 0.107 41.83 0.107 42.79 0.106 43.75 0.106 44.71 0.105 45.67 0.105 46.63 0.104 47.60 0.102 48.56 0.099 49.52 0.099 50.48 0.099 51.44 0.098 52.40 0.098 53.37 0.098 54.33 0.098 55.29 0.098 56.25 0.098 57.21 0.098 58.17 0.098 59.13 0.098 60.10 0.098 61.06 0.097 62.02 0.097 62.98 0.097 63.94 0.097 64.90 0.097 65.87 0.097 66.83 0.096 67.79 0.096 68.75 0.096 69.71 0.095 70.67 0.095 71.63 0.094 72.60 0.094 73.56 0.094 74.52 0.094 75.48 0.093 76.44 0.090 77.40 0.087 78.37 0.087 79.33 0.086 80.29 0.086 81.25 0.086 82.21 0.086 83.17 0.085 84.13 0.084 85.10 0.078 86.06 0.077 87.02 0.077 87.98 0.077 88.94 0.075 89.90 0.075 90.87 0.069 91.83 0.068 92.79 0.067 93.75 0.065 94.71 0.065 95.67 0.064 96.63 0.057 97.60 0.046 98.56 0.037 99.52 CUMULATIVE DISTRIBUTION OF TRAPPING FREQUENCIES, STATION 90001, MONTH 8 4257.656 0.48 2532.044 1.44 2411.305 2.40 2246.992 3.37 1828.382 4.33 1707.076 5.29 1536.587 6.25 1452.918 7.21 1329.146 8.17 1250.981 9.13 1105.087 10.10 1094.283 11.06 1054.796 12.02 1015.818 12.98 963.952 13.94 959.732 14.90 898.651 15.87 836.497 16.83 793.512 17.79 788.227 18.75 774.572 19.71 173.430 20.67 762.783 21.63 748.876 22.60 689.477 23.56 686.622 24.52 679.791 25.48 667.047 26.44 665.675 27.40 645.208 28.37 612.670 29.33 608.351 30.29 605.451 31.25 590.511 32.21 586.965 33.17 585.818 34.13 583.745 35.10 581.916 36.06 576.303 37.02 571.701 37.98 557.462 38.94 555.015 39.90 554.666 40.87 552.839 41.83 550.368 42.79 536.565 43.75 535.535 44.71 511.758 45.67 509.624 46.63 508.428 47.60 507.829 48.56 496.512 49.52 493.191 50.48 491.071 51.44 486.291 52.40 484.535 53.37 456.489 54.33 455.120 55.29 454.290 56.25 450.911 57.21 441.075 58.17 439.567 59.13 439.536 60.10 434.024 61.06 433.891 62.02 429.227 62.98 422.651 63.94 420.691 64.90 418.707 65.87 413.111 66.83 412.601 67.79 410.449 68.75 400.787 69.71 398.659 70.67 396.399 71.63 394.077 72.60 393.639 73.56 387.321 74.52 385.794 75.48 383.099 76.44 374.394 77.40 369.326 78.37 365.419 79.33 363.385 80.29 362.590 81.25 361.955 82.21 360.014 83.17 355.321 84.13 351.509 85.10 347.118 86.06 341.917 87.02 323.797 87.98 322.623 88.94 321.650 89.90 321.337 90.87 320.504 91.83 317.181 92.79 308.929 93.75 306.892 94.71 298.083 95.67 298.050 96.63 292.426 97.60 274.025 98.56 272.166 99.52 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER GRADIENTS, STATION 90001, MONTH 8 —104.545 1.00 | —105.093 3.00 | —106.000 5.00 | —109.016 7.00 | —109.502 9.00 | —110.687 11.00 | —111.111 13.00 —112.500 15.00 | —113.978 17.00 | —118.902 19.00 | —120.257 21.00 | —120.556 23.00 | —122.609 25.00 | —124.409 27.00 —126.894 29.00 | —127.362 31.00 | —127.368 33.00 | —127.962 35.00 | —128.090 37.00 | —128.495 39.00 | —129.583 41.00 —130.380 43.00 | —131.250 45.00 | —131.902 47.00 | —132.903 49.00 | —133.047 51.00 | —133.444 53.00 | —133.673 55.00 —134.343 57.00 | —135.052 59.00 | —136.598 61.00 | —136.813 63.00 | —137.288 65.00 | —138.378 67.00 | —140.217 69.00 —140.645 71.00 | —142.553 73.00 | —144.253 75.00 | —144.324 77.00 | —144.545 79.00 | —146.012 81.00 | —146.980 83.00 ele caee 85:00 —151.402 87.00 | —152.222 89.00 | —153.247 91.00 | —155.224 93.00 | —156.081 95.00 | —156.164 97.00 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER THICKNESSES, STATION 90001, MONTH 8 0.311 1.00 0.307 3.00 0.302 5.00 0.279 7.00 0.264 9.00 0.244 11.00 0.240 13.00 0.233 15.00 0.232 17.00 0.224 19.00 0.221 21.00 0.220 23.00 0.219 25.00 0.216 27.00 0.211 29.00 0.200 31.00 0.198 33.00 0.194 35.00 0.186 37.00 0.185 39.00 0.185 41.00 0.184 43.00 0.182 45.00 0.180 47.00 0.178 49.00 0.177 51.00 0.174 53.00 0.166 55.00 0.164 57.00 0.163 59.00 0.163 61.00 0.158 63.00 0.155 65.00 0.155 67.00 0.149 69.00 0.148 71.00 0.146 73.00 0.134 75.00 0.131 77.00 0.127 79.00 0.117 81.00 0.115 83.00 0.110 85.00 0.107 87.00 0.098 89.00 0.097 91.00 0.095 93.00 0.094 95.00 0.090 97.00 0.077 99.00 130 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY TABLE E-4. (Coutinued) THICKNESS AND GRADIENT OF SUPERREFRACTIVE LAYERS OVER 300 METERS THICK 0.31100 —120.25723 0.30200 —133.44371 0.30700 —127.36157 NOVEMBER 144 Profiles Skipped 161 Profiles Read Number of Ducts 0 Number of Superrefractive Layers 17 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER GRADIENTS, STATION 90001, MONTH 11 —100.000 2.94 | —100.000 8.82 | —100.000 14.71 | —101.786 20.59 | —102.970 26.47 | —106.504 32.35 | —109.804 38.24 —111.972 44.12 | —113.592 50.00 | —115.254 55.88 | —118.750 61.76 | —120.690 67.65 | —122.000 73.53 | —131.667 79.41 —137.255 85.29 | —151.852 91.18 | —152.252 97.06 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER THICKNESSES, STATION 90001, MONTH 11 0.142 2.94 0.123 8.82 0.120 14.71 0.112 20.59 0.112 26.47 0.111 32.35 0.103 38.24 0.101 44.12 0.100 50.00 0.081 55.88 0.070 61.76 0.060 67.65 0.059 73.53 0.058 79.41 0.051 85.29 0.051 STIs 0.050 97.06 | | yy VY U.S, GOVERNMENT PRINTNG OFFICE: 1967—O 222-613 oan ae _ ae } ey i Seta baie : oe eine ae ties ; Mie init) vay hs Hes }4)¢3 - ; f Pelee riot) Sethe oe he talthce' ratte) i sit inated if Hy Of Bae belie) . va Vad i ONS A ao eee Me : swinsie de tar) r peed ss) Denman A bien peeeaieeaty ota ty Sse STS eeecee i a4 U ee it ac ‘ Nk - e Sees shane Mees deh ik ay ‘ Hes it i! X Ty Hite uy ies al Pity AAT BNA yy f ) dn Dt } RRM R AID rae ee ey ai , y af dy By ee) ABs eo