NEL/REPORT 1429 ae u 6Zb1 1¥0dau/13N SEA-SURFACE TEMPERATURE ESTIMATION Time-series length necessary for long-term estimation of sea-surface temperature E. R. Anderson and C. J. Van Vliet ° Research Report -° 17 January 1967 U.S. NAVY ELECTRONICS LABORATORY, SAN DIEGO, CALIFORNIA 92152 mt VSS HOS! vo, ItZ4 ed PROBLEM Develop statistical, physical, and computer techniques for interpreting, summarizing, and extrapolating oceanic and meteorologic data for reliable esti- mation of the sound velocity distribution in the ocean. Specifically, determine the length of time-series necessary to produce reliable long-time estimates of sea- surface temperatures; and, as a corollary, find whether or not systematic varia- tions of sea-surface temperatures, over periods of several years, are to be ex- pected. RESULTS 1. Using autocorrelation and regression techniques, Six time-series of sea-surface temperature measurements were examined. 2. Plots of the 100R? statistic (percent variance explained by regres- sion) as a function of time-series record length for the six time-series records considered lead to the conclusion that record lengths of 8 to 10 years are neces- sary to obtain reliable long-time estimates of sea-surface temperature. This conclusion is supported by the behavior of the autocorrelation coefficients for the 40-year Scripps Pier record. 3. An examination of the annual average temperatures confirmed pre- viously published conclusions regarding the systematic year-to-year variability in sea-surface temperatures. In addition it showed that such long-term vari- ability is not unusual or unexpected. ADMINISTRATIVE INFORMATION Work was performed under SR 104 03 01, Task 0586 (NEL L40571). The report covers work from July 1961 to August 1964 and was approved for publica- tion 17 January 1967. The assistance of Dr. George W. es in providing motivation, en- couragement, and advice; of Mrs. J. M. Baker, J. S. Buehler, and H. W. Frye, for numerical analysis assistance; and of Mrs. G. L. ae for handling the many details essential to the success of such a study, is gratefully acknowledged. meses NANT MM 0 0301 OO40518 ited CONTENTS INTRODUCTION . . . page 5 TIME-SERIES LENGTH. ..5 SYSTEMATIC VARIATION OF SEA-SURFACE TEMPERATURE OVER PERIODS OF SEVERAL YEARS .. . 18 SUMMARY AND CONCLUSIONS . . . 27 REFERENCES . . . 27 ILLUSTRATIONS 1 R? statistic for Scripps Pier data for records of varying length . . . page 8, 9 2 Percent variance explained by equation (1B) as a function of record length for six long-time-series of sea-surface temperature measurements .. . 10 3 Autocorrelation functions for 5-year records of Scripps Pier data... 12 4 Autocorrelation functions for 8-, 10-, 20-, and 40-year records of Scripps Pier data... 14, 15 5 Average autocorrelation functions for various record lengths for Scripps Pier data... 17 6 Annual averages of sea-surface temperature for coastal and island locations .. . Annual and semiannual amplitudes and phases for all stations as a function of time... 22, 23 8 Percent variance explained and standard deviation of residuals for annual records for all stations . . . 24, 25 9 Autocorrelation coefficients for 1931 to 1935 Scripps Pier data with lags extended to 1400 days .. . 26 TABLES 1 Location of sea-surface temperature time-series . . . page 6 2 Variability in autocorrelation functions .. . 13 REVERSE SIDE BLANK \ ee i“ INTRODUCTION This study is the fourth in a series of studies concerned with the analysis of sea-surface temperature observations. The first study dealt with the effect of missing data in long time-series of sea-surface temperature measurements on cer- tain regression and autocorrelation analyses. The second examined the use of re- gression models for time-space interpolation of sea-surface temperature observa- tions.? The third presented the results of an autocorrelation, regression, and trend analysis of time-series of sea-surface temperature measurements made at six lo- cations representing different oceanographic conditions and considered the diffi- culties encountered in applying these techniques to oceanographic data samples.? This study considers the oceanographic aspects of the last of the above studies.? In particular, it examines the length of time-series necessary to pro- duce reliable long-time estimates of sea-surface temperature. In addition, it con- siders the corollary question of whether or not systematic variations of sea-sur- face temperatures, over periods of several years, are to be expected. TIME-SERIES LENGTH The length of time-series necessary to produce reliable long-term esti - mates of sea-surface temperatures interests oceanographers concerned with observational programs for obtaining information necessary for establishing aver- age sea-surface temperatures. The time-series of data used to obtain insight into this question are listed in table 1. Van Vliet and Anderson? concluded from their autocorrelation, regression, and trend analyses of these time-series that the following regression model, with k = 2, provided a good statistical fit to the observed daily sea-sur- face temperatures: Ps Bye » a, sin [2ni(D-0;)/365]+ « (1A) — a or expanding, R T= By + S [Bais Sin (27iD/365) + Bri cos (27iD/365)] + € (1B) n= ‘Superscript numbers denote references in the list at the end of this report. where D is time measured in days from some arbitrary origin, and T “is the fitted value of the surface temperature. Fitting equation (1B) to the ob- served surface temperature, T', using the method of least squares yields estimates of the regression coefficients, 8, and an estimate of the variance of ¢«. The amplitude a and phase @ can be obtained from the B’s. The quantity « is the random error of residual term. TABLE 1. LOCATION OF SEA-SURFACE TEMPERATURE TIME-SERIES Location Time Period Weather Ship PAPA BOON 145°W North Pacific 1/56 - 8/62 6 yr 7 mo Weather Ship ECHO 35°N 48°W North Atlantic 9/49 - 9/56 7 yr 1/35-1/61 21 yr (5 yr missing) Cape St. James D2°N 131°W North Pacific Triple Island 1/40- 1/61 54°N 131°W 21 yr North Pacific {Langara Island 1/41-1/61 54°N 133°W 20 yr North Pacific 1/21- 1/61 40 yr Scripps Pier 33°N 117°W North Pacific An integral part of any estimation problem is the determination of the reliability of the estimate as measured by the variability of observed data about the estimated values. As a measure of this variability consider the statistic R?, the fraction of variability explained by a statistical fit. Equation (1B) was fitted to the Scripps Pier data using samples within the 40 years of lengths 1, 5, 8, 10, 20, and 40 years. This resulted in the following samples: forty 1-year, eight 5- year, five 8-year, four 10-year, two 20-year, and one 40-year. Figure 1 summarizes the R? statistic for records of various lengths for the Scripps Pier data. In general, a single year’s data are expected to yield a higher R? than would several years of data, where year-to-year variations would give a poorer fit, although in the forty single-year fits there are some years with poorer fits than those for longer periods. To compensate for the few years of poor fits there are many years of excellent fits. The fact that for 33 years R* was greater than 0.81 and for 23 years was greater than 0.86 substantiates this con- clusion. To compare R2’s for the various record lengths, L, the mean R”’s for the available runs of each length have been computed and 100R?’s have been plotted on figure 2. As expected the mean R? is a decreasing function of the length of record, L. More unexpected is the actual shape of the curve. From L = | the curve drops off sharply to somewhere between L = 5 and L = 10, from which point on there is a negligible decrease in R?. The mean R? is plotted in preference to the mean R or to the mean of : 1 1+R Fisher’s Z = —lo isher’s 5 ea R tionship of R to R? in the range of consideration of R? is so nearly linear that a , since R? is easiest to interpret. In addition, the rela- plot of mean R with appropriate scale changes cannot be distinguished from that of mean R?. The relationship of Z to R? is such that the curvature of figure 2 would be even more emphasized if Fisher’s statistic were plotted. The distri- bution of R? is asymptotically normal for R 4 0.4 Because of the dependence of the average R? among the samples from which they were computed, and because of the autocorrelated residuals, the development of confidence limits for these average R2’s seems intractable. How- ever, as a rough estimate of their variability, one standard deviation of the mean R? is plotted as a vertical bar in figure 2. These standard deviations are given by the formula: A(R) = aR WER? N 2 (365) 2 They are computed under the assumption that repeated sampling over the same N = 40 year period at Scripps Pier is possible. In this conceptually possible but practically impossible situation, the deviations about the same mathematical model of regression are assumed to be independent among the repeated samples. Under these assumptions the confidence limits for the plotted points are narrow, and it is concluded that the sharp change in slope of the curve in the region 5 < L < 10 is real. Attention is called to the systematic change in the absolute magnitude of the average R2’s, for the data for PAPA, ECHO, Langara Island, Cape St. James, and Triple Island. This change appears to be associated with the exposure, or “‘continentality,’’ of the station — thus, PAPA and ECHO are typical of open- ocean locations; Triple Island is much like an open-ocean location, being a very small coastal island; and Scripps Pier is least like an open-ocean location with the observations being made at the end of a 1000-foot pier. Cape St. James and Langara Island have a ‘‘continentality’’ between Scripps Pier and Triple Island. EXPLAINED VARIANCE (R2) 1— YEAR 3 — YEAR 1.00 Sl = 80 )— 8 — YEAR 70 ole | | dod le 1930 1940 1950 1960 Figure 1. R? statistic for Scripps Pier data for records of varying length. EXPLAINED VARIANCE (R2) 1.00 90 80 -/0 we) =) So So co S vit ./0 10 — YEAR 20 — YEAR 40 — YEAR 10 Le | 1930 1940 1950 1960 Figure 1. (Continued) ‘sqUoWaMSPoOW oIMyeIadwW9} BdRJANS-eaS JO SalIaS-9UIT]-BUO] XIS JOJ yySuey plooea Jo uotouny ve se (GT) UoTyeNbe Aq poule[dxe soueeA JusdIog *% oINSIY (SYV3A) GYOOSY 40 HLONAT Or se 0 G2 02 SI 01 5 0 [ ] ] | a ee ee Sls $$ =p Yald Sddldos {—_. ¢ oo aNVISI lees Se, ‘A = PS SANE “LS alee oS eg ea ee Tee 1S Se aNv1S| SE a SL 08 Ve) [=s) (74001) GANIW1dX3 SONWINWA INS0YSd (=) D S6 O0l 10 The implications of figure 2 are: 1. A record of daily surface temperatures of 10-year length is adequate for fitting a regression curve to estimate long-term variability. 2. The unexplained long-term variability, that is, variability unexplained by the regression model, varies from about 23 percent at Scripps Pier, for a sample longer than 10 years; to less than 5 percent at PAPA and ECHO, both one-year samples taken at exposed open-ocean locations. Since R? is not de- graded by extending a record beyond 10 years, the estimates of regression co- efficients based on 10 years are as adequate as those that might be obtained from a longer record. In the same light, records of 5 years or less reflect shorter-term variability in temperature and thus give an improved fit as record length decreases. Additional information on the length of time-series necessary for obtain- ing long-term estimates of sea-surface temperature may be obtained from an exam- ination of the autocorrelation function available from the 40 years of Scripps Pier record. For the various samples of Scripps Pier data, the autocorrelation func- tions were determined for the time-series consisting of the differences of the ob- served surface temperatures and the temperatures estimated by the fit of combined annual and semiannual terms, equation (1B) (k=2). The functions were computed for lags at intervals of 5 days up to 900 days in most cases. Consider first the autocorrelation functions for the eight different 5-year samples of data plotted in figure 3. There is considerable variability among the functions. It would be desirable to compute some measure of this variability, and compare it with the corresponding variability of the autocorrelation functions for the 8-, 10-, and 20-year records of figure 4. Before computing any such measure, we have to make a decision as to the range of lags to use in the comparison. The value of the standard deviation of the nonsignificant autocorrelations, a, for the 40 years of Scripps Pier data is 0.0293.? For a 10-year record o, = 0.0586, and the 95 percent significance values are +0.115. For reasons previously discussed, a 10-year record of sea-surface temperature is needed in order to obtain reliable estimates of the long-term vari- ability. Thus it is not necessary to consider 5- or 8-year records in selecting the range of lags to use. If we assume the 40-year autocorrelation function is close to the true function, lags out to 145 days yield autocorrelations greater than 0.115 and can be used to compare the sets of functions. For the set of autocorrelation functions based on samples of length L years and for each lag ; = 5(5)145 days, we determine the following sum of squares: 40/L Q(7,L) = [Cj() - C()]? (3) J= ee 11 192] - 1925 -0.5 1931 - 1935 10° M5 8° S008. Peeooe e@eoe ee Sees, ® eo Of py 1941 - 1945 AUTOCORRELATION FUNCTION 1926 - 1930 1936 - 1940 1946 - 1950 1951 - 1955 100 LAG (DAYS) 1956 - 1960 oleae a; L 50 100 LAG (DAYS) Figure 3. Autocorrelation functions for 5-year records of Scripps Pier data. where Cj (x) is the autocorrelation coefficient for lag z of the j-th of 40/L sets, and C (7) is the mean of 40/L coefficients with the same lag 7. By analogy with normal distribution theory, the quantity Q (7,L)/o?(7,L) is like a chi-square vari- able with (40/L)-1 degrees of freedom, where o?(7,L) is the variance of C (7) for a sample of length L. Assume the Q(7,L)’s are independent, and that o7(7,L) is inversely proportional to sample length and the same for all 7. That is, o?(7,L) = ko?/L where k is any proportionality constant. Then kL Q (7,L)/o? Ta is like a chi-square variable with v = 29 [(40/L) — 1] degrees of freedom. For two different values of L, L, and L,, the ratio Ly », Q(7,Ly)/11 if F ———— lbp », OCs» is like an F-variable with v,,., degrees of freedom. The ‘‘mean square,’’ L » Q(7,L)/v, and the F’-ratios using the mean (f square for 20 years as the denominator, are shown in table 2. Assuming a robust F-test, none of these ratios is significant (though less than 1) and it is concluded that the variability in the autocorrelation functions is about as expected. TABLE 2. VARIABILITY IN AUTOCORRELATION FUNCTIONS Mean Square 13 14 AUTOCORRELATION FUNCTION 8 — YEAR RECORDS Ai 1921 — 1928 0.5 soe agell | ‘ ay rote, 0.5 ; ie 1929 - 1936 TBR ocors ecto Pn ee ee -0.5 ee 1937 — 1944 ; Oy ere i gee coche 0.5 ! we 1945 — 1952 0.5 ;° g[_*ftteaperrerererpecsen, | 0.5 Vie 1953 — 1960 OTe 3 =: a ae ses = 90 100 LAG (DAYS) Figure 4. Autocorrelation functions for 8-, 10-, 20-, and 40-year records of Scripps Pier data. AUTOCORRELATION FUNCTION 10 — YEAR RECORDS 1921 — 1930 1.0¢ 0.5 oe eo 0 ot pan eeees —0.5 oe 1931 — 1940 O5j- °*.,. 0 ot -0.5 tate 1941 — 1950 0.5 Ke 0 | + eo ocohicae! -0.5 1951 — 1960 0) © 0.56, 0 t t f— -f- | 0.5 | | 0 50 100 LAG (DAYS) Figure 4. 20 — YEAR RECORDS 1921 — 1940 t tt = 1941 — 1960 t = t 0 Sititigier® 40 — YEAR RECORD 1921 — 1960 near | | eel 0 30 100 LAG (DAYS) (Continued) 15 16 The investigation of the square of the multiple correlation coefficient suggested that about 10 years of record are sufficient for certain curve-fitting and estimation problems. It has just been concluded that there is no such break in the variability of the autocorrelation coefficient as a function of record length. Thus a decision as to the sample length necessary to obtain useful estimates of the autocorrelation function must be made on some absolute basis, or a cost func- tion must be introduced such that a combination of increasing cost and decreasing variability with sample size results in an optimization problem. Two additional comments are pertinent. First, figure 4 shows the auto- correlation functions for samples of 8, 10, and 20 years, respectively. Attention is called to the 10-year records. It appears that the autocorrelation functions agree well out to a lag of about 80 days. Second, autocorrelation functions for the same record lengths have been averaged by lags, and are shown in figure 5. There is a strong indication that autocorrelation functions from finite samples are biassed. Restricting the discussion to lags out to 80 days, and assuming the autocorrelation function for the 40-year sample is close to the true function, the mean functions for 5- and 8-year samples are badly biassed with little bias indicated in the 10- and 20-year mean functions. It is concluded that 10-year samples provide consistent and usable autocorrelation functions out to a lag of 80 days. 40 — YEARS 1.0 0.8 IN31014434500 NOILYT4ydOOOLAY 0.2 30 40 50 60 70 80 90 100 LAG (DAYS) 20 10 Average autocorrelation functions.for various record lengths for Scripps Pier data. Figure 5. 17 18 SYSTEMATIC VARIATION OF SEA-SURFACE TEMPERATURE OVER PERIODS OF SEVERAL YEARS In connection with the question of whether or not sea-surface temperatures vary significantly over periods of several years, four specific time periods are ex- amined here in some detail: 1. 1947 to 1956 which has been referred to as displaying *‘A uniform monotony of conditions in at least the eastern North Pacific that is scarcely sug- gested by any similar series of years in this century.’ 2. 1936 to 1956 where there are indications of a cooling trend. 3. 1957 and 1958 recognized as ‘‘the changing years.’’> 4. 1930 to 1935, another period, unique to this century, which appears to contain long-term oscillations. In the previous discussion on trends by Van Vliet and Anderson,? a variety of statistical considerations led to the general conclusion that no trend existed in the records for any of the locations examined and that quantities such as the annual average temperature (8,), annual amplitude (¢,), annual phase (6,), and percent variance explained by regression (100R?) all behaved as independent random variables are expected to behave. In addition it was pointed out that this conclusion does not deny the existence of real year-to-year differences in the ocean, but rather emphasizes that these differences are not unexpected from the viewpoint of statistics and thus are not considered unusual or improbable events. First the period 1947 to 1956 is examined in the light of the statistical parameters developed in this analysis. Figure 6 contains a plot of the average annual temperatures (8,) for four eastern North Pacific locations. During this decade, for 8 of the 10 years at all four stations, the B,.’s were below the median value and for 2 years were at the median or slightly above suggesting that on the average the decade was cooler than normal. Figure 7 contains parameters that determine the shape of the seasonal variation (a,, a, 9,, 0,). A study of these four parameters does not suggest anything unusual about the shape of the seasonal surface temperature variation during this decade. Figure 8 contains the percent variance explained by regression and the standard deviation, both parameters concerned with the variability. Again a study of these factors does no suggest anything unusual in the amount or degree of variability. Thus, this analysis suggests that the surface temperature variation during this decade was unusual as compared with other decades observed in this century in that the average temper- ature was slightly below the median value. Although too much after-the-fact analysis of data is contrary to statisti- cal philosophy, sometimes it is of interest to do such analysis. Specifically, for the Scripps Pier data shown on figure 6 there is a suggestion of a cooling trend for the years 1939 through 1956. Applying the theory of runs to this period? the following sequence is obtained: AAAAA BB AA BBB A BB A BB This sequence has eight runs. The critical number of runs at the 5 percent prob- ability level for 18 observations is six, and it is concluded that no trend exists even in this selected period of time. The applicable median {> is 16.60°C. The years 1957 and 1958 have been recognized from a consideration of several natural science parameters as the changing years.’ These years appeared to conclude the 1947-to-1957 decade -- a decade of below-median sea-surface temperatures. On figure 6, the B,’s for the succeeding years, 1959 to 1962 in- clusive, have been included for Scripps Pier. In 1959 the annual average tempera- ture continued to increase for the third successive year followed by an abrupt de- crease in 1960 and lesser decreases in 1961 and 1962. This same pattern, though less marked, occurred for the three island locations. Thus, it appears that in 1956 a long-term oscillatory variation, of period at least 6 years, began and that it was still in progress in 1962. In examining the Scripps Pier f’s in figure 6, we find that the period 1930 to 1935 also appears to contain an oscillatory term with a period of several years. In figure 3 are plotted the eight correlograms, obtained after removing the annual and semiannual oscillatory terms, for 5-year periods at Scripps Pier. Although these correlograms are plotted out to lags of only 150 days, they were computed out to lags of 900 days. A study of the eight 900-day lag correlograms shows one to be somewhat different from the remaining seven — the correlogram for the 1931- to-1935 period. The correlogram for this period for lags out to 1400 days (fig. 9) shows a peak in the autocorrelation coefficient of 0.46 at a lag of about 1170 days, or a period of about 3.2 years. Since the points are scattered in the neighborhood of this peak as well as in the neighborhood of the minimum at lag about 750 days, another estimate of period length is given by twice the difference in lags between the up-crossing at lag 960 days and the down-crossing at lag 265 days, or about 3.8 years. Either period confirms the intuitive conclusion reached from an exami- nation of figure 6. ; It thus appears reasonable to conclude that sea-surface temperatures will vary significantly over periods of several years and that these occurrences are not unexpected or improbable. REVERSE SIDE BLANK 19 TEMPERATURE (°C) SCRIPPS PIER = e : ° a 5 ACE Wake © Cepia RO ee 2 MEDIAN > 1b | IL | | | LANGARA ISLAND 10/— Baer 8.66 5 i Reitee MEDIAN 8.62-———— —e—- E ° 8 NEE itkoe hone ee STEN | | | | | ua TRIPLE ISLAND LE 10}- ° ome oL08 ° : [ee MEDIAN 9,08 ee ge mI ° | J | | | CAPE ST. JAMES 10 ° : " 9.17 ; LL MEDIAN 9.17’ ——,————*"-_, .—_______*-.___* “_« 1920 Figure 6. | | _| | IL 1930 1940 1950 1960 Annual averages of sea-surface temperature for coastal and island locations. PAPA CAPE ST. JAMES a 6 2 = ees Se ite ll LS ee (oe reine x * | Be ood Q LL ee 200 2 ie OF UP S 100L i = lis, Wi Qpeaom= @ x aS = He -109 LL | aa ECHO TRIPLE ISLAND 6 So fe eZ 4 eels —)) = le = => = = 0 aaehen | 200. jj jj JSS ee eentataman Hi ae we - _ =a = cc = q etd a a : i -yaujaxce £ oolweeesa (it Fen - a . : 7 UNCLASSIFIED Security Classification DOCUMENT CONTROL DATA-R&D (Security classification of title, body of abstract and indexing annotation must be entered when the overall report is classified) ORIGINATING ACTIVITY (Corporate author) 2a. REPORT SECURITY CLASSIFICATION Navy Electronics Laboratory UNC ESSE San Diego, California 92152 REPORT TITLE SEA-SURFACE TEMPERATURE ESTIMATION - Time-Series Length Necessary for Long-Term Estimation of Sea-Surface Temperature DESCRIPTIVE NOTES (Type of report and inclusive dates) Research Report July 1961 to August 1964 AUTHOR(S) (First name, middle initial, last name) E.R. Anderson and C.J. Van Vliet REPORT DATE 7a. TOTAL NO. OF PAGES 7b. NO. OF REFS 17 January 1967 27 5 Ba. CONTRACT OR GRANT NO 9a. ORIGINATOR'S REPORT NUMBER(S) Penis Sin LOL O8 Ol 1429 Task 0586 9b. OTHER REPORT NO(S) (Any other numbers that may be assigned (NEL L40571) this report) DISTRIBUTION STATEMENT Distribution of this document is unlimited SUPPLEMENTARY NOTES 12. SPONSORING MILITARY ACTIVITY Naval Ship Systems Command Department of the Navy ABSTRACT Time-series of sea-surface temperatures recorded in the North Pacific and North Atlantic over periods up to 40 years were analyzed by autocorrelation and regression techniques. It was found that record lengths of 8 to 10 years are necessary to obtain reliable long-term estimates of sea-surface temperature. The analysis also showed that year-to-year differences exist in annual average temperature and that such variability is to be expected. 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BIOLOGICAL LABORATORY ABERDEEN PROVING GROUND TECHNICAL LIBRARY ARMY RESEARCH AND DEVELOPMENT ACTIVITY ELECTRONIC WARFARE DIVISION ELECTRONIC DEPARTMENT REDSTONE SCIENTIFIC INFORMATION CENTER ARMY MISSILE COMMAND DOCUMENT SECTION ARMY ELECTRONICS RESEARCH AND DEVELOPMENT LABORATORY ARMY ELECTRONICS COMMAND MANAGEMENT & ADMINISTRATIVE SERVICES DEPT AMSEL-RD-MAT FRANKFORD ARSENAL HUMAN FACTORS LABORATORY SMUFA N6400/202-4 WHITE SANDS MISSILE RANGE STEWS-ID-E FORT HUACHUCA 52D USASASOC AIR FORCE HEADQUARTERS DIRECTOR OF SCIENCE AND TECHNOLOGY AFRSTA AIR UNIVERSITY LIBRARY AUL3T-5028 STRATEGIC AIR COMMAND OAST AIR FORCE EASTERN TEST RANGE AFMTC TECHNICAL LIBRARY - MU-135 AIR PROVING GROUND CENTER PGBPS-12 WRIGHT-PATTERSON AIR FORCE BASE (1) SYSTEMS ENGINEERING GROUP CRTD) SEPIR AIR FORCE SECURITY SERVICE ESD/ESG ELECTRONICS SYSTEMS DIVISION EsTI UNIVERSITY OF MICHIGAN OFFICE OF RESEARCH ADMINISTRATION NORTH CAMPUS COOLEY ELECTRONICS LABORATORY RADAR AND OPTICS LABORATORY UNIVERSITY OF CALIFORNIA-SAN DIEGO MARINE PHYSICAL LABORATORY SCRIPPS INSTITUTION OF OCEANOGRAPHY LIBRARY LIBRARY UNIVERSITY OF MIAMI THE MARINE LABORATORY LIPRARY MICHIGAN STATE UNIVERSITY LIBRARY-DOCUMENTS DEPARTMENT COLUMBIA UNIVERSITY HUDSON LABORATORIES LAMONT GEOLOGICAL OBSERVATORY DARTMOUTH COLLEGE RADIOPHYSICS LABORATORY CALIFORNIA INSTITUTE OF TECHNOLOGY JET PROPULSION LABORATORY HARVARD COLLEGE OBSERVATORY HARVARD UNIVERSITY GORDON MCKAY LIBRARY LYMAN LABORATORY OREGON STATE UNIVERSITY DEPARTMENT OF OCEANOGRAPHY UNIVERSITY OF WASHINGTON DEPARTMENT OF OCEANOGRAPHY FISHERIES-OCEANOGRAPHY LIBRARY APPLIEO PHYSICS LABORATORY NEW YORK UNIVERSITY 1) DEPARTMENT OF METEOROLOGY AND OCEANOGRAPHY TUFTS UNIVERSITY INSTITUTE FOR PSYCHOLOGICAL RESEARCH OHIO STATE UNIVERSITY ANTENNA LABORATORY UNIVERSITY OF ALASKA GEOPHYSICAL INSTITUTE UNIVERSITY OF RHODE ISLAND NARRAGANSETT MARINE LABORATORY LIBRARY YALE UNIVERSITY BINGHAM OCEANOGRAPHIC LABORATORY FLORIDA STATE UNIVERSITY OCEANOGRAPHIC INSTITUTE UNIVERSITY OF HAWAII HAWAII INSTITUTE OF GEOPHYSICS ELECTRICAL ENGINEERING DEPARTMENT AEM COLLEGE OF TEXAS DEPARTMENT OF OCEANOGRAPHY THE UNIVERSITY OF TEXAS DEFENSE RESEARCH LABORATORY ELECTRICAL ENGINEERING RESEARCH LABORATORY PENNSYLVANIA STATE UNIVERSITY ORDNANCE RESEARCH LABORATORY STANFORD RESEARCH INSTITUTE NAVAL WARFARE RESEARCH CENTER MASSACHUSETTS INSTITUTE OF TECHNOLOGY ENGINEERING LIBRARY LINCOLN LABORATORY LIBRARY, A-082 FLORIDA ATLANTIC UNIVERSITY DEPARTMENT OF OCEAN ENGINEERING THE JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY DOCUMENT LIGRARY INSTITUTE FOR DEFENSE ANALYSES DOCUMENT LIBRARY