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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.
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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. A-19)
30 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY
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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
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13. 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
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122 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY
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APPENDIX E 123
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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 | |
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