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JPL PUBLICA rlON 85-63 

~TAR 0 6JAN.1 R 1986 

Satellite-Derived Sea 

Surface Temperature: 

Workshop-III 

February 22-24, 1984 

Pasadena, California 

October 15, 1985 

NJ\SJ\ 

National Aeronautics and 

Space Administration 

Jet PropulsIon Laboratory 

California Institute of Techr,ology 

Pasadena, California 

118t.-16ESS 

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986-168(2 

CSCL OBJ UnClas 

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JPL PUBLICATION 85·63 

Satell ite-Oerived Sea 

Surface Temperature: 

Workshop-III 

February 22-24, 1984 

Pasadena, California 

October 15. 1985 

NI\SJ\ 

National Aeronautics and 

Space Administration 

Jet Propulsion Laboratory 

California Institute of Technol09Y 

Pasadena. California 

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ThIS publlcallOn was prepared by the Jet PropulSIon Laboratory. CalifornIa InStitute 

01 Technology. under a contract wIth the National Aeronautics and Space 

Admlnl$trahon 

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ABSTRACT 

ThiB is the third report tn a series of three workshops, sponsored by the 

Sational Aeronautics and Spacf' J.dmh.lstratlon, to investigate the state of the 

art tn global sea 8urface tempfor .. ture mea8Urt~ments from spa("C'·. T1.ree workshop .. 

were neceHsary tQ process and anal!ze sufficient dat3 from Wllich to draw conclu~ion8 on the accuracy and reliability ni the satellite ~eaHure~cnts. In 

thiK workshop (~orkshop Ill), the final two (out of a total of four) months of 

satellite and In situ dllta chosen for study were processed and evaluated. ite-

~u1t5 fro::l the AVURR, IURS, S!1.'1R, and VAS tiensorn, in comparison with In situ 

data from ships, XBTs, and buoys, conflr~ed satellite rms accuracies In the 

',.5 to I.Oue range, but wIth varIable bisses. These accuracies may degrade 

under adverse conditions for specific sensors. A variety of color maps, plots, 

and statistical tables are provided for detailed study of the individual sensor 

SST measurements • 

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FOREWORD 

This report documents the proceedings of a Satellite-Derived Sea Surface 

'Cemperature Workshop, held at the Jet Propulsion Laboratory, Pasadena, 

California, on February 22 to 24, 1984. The workshop was the third and final 

of a series designed to compare directly SST measurements from existing 

'satellite sensors, thereby evaluating their global accuracies and enabling 

informed decisions to be made concerning future sensor de .. elopment. Motivation 

for the cDmparisons arose from revorts of approximately 1°C accuracy In SST 

measurement by fuur different satellite sensors, and from the need for oceanographers and climate scientists to understand conditions under ~hich better or 

worse performance could be expected from each Sensor type. The workshops were 

convened under the sponsorship of the Oceanic Processes Branch, Office of Space 

Science and Applications of the National Aeronautics and Space Administration. 

A first planning me~ting for the workshop series was held at the 

NASA/Goddard Space Flight Center on April 16, 1982, at which the principal 

workshop participants met to discuss approaches to the proposed satellite and 

in situ data comparisuns. Subsequent workshop organization took place at JPL 

to facilitate archiving and processing of the satellite and in situ data sets 

on the JPL Pilot Ocean Data System. 

A substantial effort was required to assimilate, process, and analyze 

several months of satellite and in situ data of divergent sampling densities, 

spatial resolutions, and formats. The first workshop, held January 27 to 28, 

1983, WAS thus limited to a single satellite sensor (SltMR) and one month of 

data (November 1979), and to evaluation of approaches developed to compare the 

satellite data sets with each other and with climatology, ship, XBT, and budy 

observations. The second worksho~, held June 22 to 24, 1983 included data from 

the SMMR, HIRS, and AVHRR, for November 1979 and December 1981. Finally, the 

third workshop was held February 22 to 24, 19H4, and included data from the 

SHMR, HIRS, AVHRR, and VAS, for the additional month9 of Harch and July 1982. 

An improved set of display products for analysis was developed for t~is 

workshop • 

. Sin~e the completion of these workshops, increasing attention has been 

paid by the scientific research community to the question of utilizing satellite SST dcta in larl:e-scale ocean and atmosphere observing programs. A Tropical Oceans and Global Atmosphere (TOGA) Workshop on Sea Surface Temperature and 

Net Surface Radiation was held in La Jolla, California. March 28 to 30. 1984, 

and a Committee for Space Research (COSPAR) International Workshop on 

Satellite-Oerived Sea Surface Temperat.ures for Global Climate Applications was 

held in Washington, D.C., Hay 29 to 31. 1985. Ensuring the long-term availabillty and accuracy of satellite SST measurements is now a high priority for 

ocean and climate research applications. 

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Part1clpatlon 111 the three NASA/JPL Workshops wa!> varied. 'fllose wno particIpated 1n \.'orkbllop III and cOlltribl:"co to lhis report are listl'(J in ,\ppcn-

~1)( U. "Ie eltort'" 01 aU those 101110 cOlltributed to the organ1zation ot till' 

workt:nops ilre brateful1y aCknowll'(]bl'(J. Special tllanks ,1re lIUl' to Jetl IHlland 

l\ nl1 the tHa 11 0 t the J1'L l' !lot Ocean lla ta System I tllrou~h wllost.' tl re less 

effort", tile Wille array oi processed SST comparison prodl. ':s was generatell anll 

IJdlle avctl1aole tor the work6110l's. 

t::nl G. Njokll 

Workshop (;Il.llrman 

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TASLE OF CONTENTS 

( . SUtlMAKY (E. Njoku) • • . . . . . . . . . . . . . . . . . 11. DATA 5E1'::' -\NO I'HOl)U(;TS (J. hlliand lind E. Njoku) • . . . . . I lI. AUVANC£O VEHY HIGH KESOLUTION RADIOMETER (AVIlHH) SENSOK 

c.:ONPAI<ISUN CttAI<ACTEI{IS1'lCS (E. P. IkClaln) •••••• 

IV. KETKIEVAL 01-' SEA-SUKl-'Ac.:E 1'1::MI'ERATUlU::S FIWM HlKS:l/rISU 

(J. SUSSKind and D. Ket.t~r) ••••••• . . . . V. SEA SUIU'ACt; 1UIl'EKAl'UIU;S FROM VAS NSI OAf A (J. J. Hatt:s) 

Vi. ISLt:NUED SST Ht:Ll>S USING COMBINED SHHK/SHII' DATA (T. Wl1hel t 

and U. Han) . . . . . . . . . . . . . . . . . . . . . . . . VIl. Hi'1'EKCOMI'AHISON OF GLOMAL SST FIELDS Ui:;KIVEIJ FKOM SATELLl1't: 

VilL • 

i\PI't:NIHCES 

SENSUKS AND SHIP UMSEHVATIONS (S. E. Pa~an) . . . . . . . . 

REQUIREMENTS FOR SEA SURFACE TEMPERATURE GROUND 'l'RUTH IN TIlE 

I~~NESIAN REGION (J. Penrose) ••••••••••••• 

HEfEKE~CES • • • • • • • • • • . . . . . . . . 

1\. COKltELAT ION 'f AHLES • • • • . . . . . . . . . . . . . . . . . EKKUi<.-I'AIUTCION ING TAHLES . . . . . . . . . . . . . . . . . c.:. c.:ULOK IMAGES • • • . . . . . . . . . . D. LIST OF PARTICIPANTS • . . . . . . . . . . to: • GLOSSARY • • • • • • • . . . . . . . . . 

4-1. Retrievals were rUII in the IOlrmest and second W'arrnest 

12~ x 125 krn quaarantsof the 250 x 250 Km grid ••• 

6-1. Generalized analysin algorithm which is tile basic bulldint; 

olock ot the !Jias rer.lOval analysis technique • 

b-Z. Application ot the oasic building block in tne bias removal 

analySis techllique • • • • • • • • • • • ••••• 

7-1. Spatial dlstr1outions of grid points used in ppp 

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2-1 

3-1 

4-1 

S-1 

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7-1 

H-I 

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A-I 

B-1 

C-I 

0-1 

E-l 

4-4 

6-2 

6-4 

COlnl)arisOlls •••••••••••••••• • • • • •• 7-~ 

f'HEG(D1N(J PAGE: BLJWK NOT f(LMED 

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TABLE OF CONTENTS (Continued) 

fIGURES (Continued) 

C-I. In situ data distribution for November 1979 showing the 

number of SST measurements per month per 20 latitudelongitude bin ••••••••••••••••••••• · . C-2. SST anomalies for November 1979 · . . . . . . . . . . . . .. C-3. SST anomalies for December 1981 · . . . . · . . . . . C-4. SST anomaliea for March 1982 •• · . . . . . . . . . c-s. For July 1982: (a) AVlfRR SST data distribution, (b) AVlfRR 

SST anomalies, (c) ship SST anomalies •••• • •••• 

C-6. HIRS SST anomalies . . . . . . · . . . · . . . C-7. SST anomalies for July 1982 · • • . · . • · • · · • · · • . C-8. SST anomalies for March and July 1982 in the VAS observation 

region • . . • • • . . • • . • · • . · . • • • · · • • · • • 

C-9. AVHRR SST anomaly differences, day minus night · · · · · 

C-1U. SST anomalies for July 1980 · · · · · · · · · · · 

TABLES 

2-1. Sensors Evaluated During the SST Workshops and Resolutioll of 

Derived SSTs • • • • • • • • • • • • • • • • • ~ • • • • 

2-;.! • Analysis and Graphical Display Teclmiques Applied to 

Satellite-Derived SSTs • • • • • •••••••• 

2-3. Data Analysis Products Spatial and Temporal Length 

· . . . 

Scales • • . . . . . . . . . . • • • e.. • • • · . . . . 2-4. Time-Space Windows Used for Merging Sensor Data to Data from 

Other Sensors Listed in the Table ••••••••••••• 

2-5. Data Sets Available for Workshop Processing ••• 

2-& • SST Workshop Color Maps (Global) 

1-1. Sensor Sea Surface Temperatures C~~oared With Pazan Screened 

2-l 

2-6 

2-8 

2-10 

2-10 

2-11 

Ship Temperatures ()5 per bin) • • • • • • • • • • • • • •• 3-) 

3-2. AVHRR Multichannel Sea Surface Temperature.: Compared With In 

Situ Temperatures . . . . . . . . . . . . . . . . . . . 3-5 

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TABLE OF CONTENTS (Continued) 

TAbLES (ContLnupa) 

3-3. 

3-4. 

4-1. 

4-2. 

4-3. 

4-4. 

5-1. 

AVHRR Multichannel Sea Surface Temperatures Compared With 

Pazan Screened Ship Temperatures ()5/8in) by Day and by 

Night ••••••••••••••••••••••• 

Pazan Screened Ship Temperatures ()5/Bin) Compared With 

'Lranspac XBls and AVHRR Multichannel Sea Surface 

• • 

Temperatures • • • • • • • • • • • • • • • • • • • 

HIRS2 and MSU Channels · . . . . · . . · . . . . · . . Comparison of Monthly Mean Anomaly Fields with Ships 

)5/Cell. December 1981 · . · · • • · · · · · • · · • · 

Comparison of Honthly Mean Anomaly Fields with Ship~ 

)5/Cell. March 1982 • • · · · · · · · · · · · · 

Comparison of Monthly Mean An~maly Fields with Shipa 

>5/CeLl. July 1982 . . · • · · • · · · · • · • . . . · · · · 

Cross Correlations of Satellite SST Estimates Versus Ship 

SST Estimates for March 1982 •••••••••••• · . . . 5-2. Cross Correlations of Satellite SST Estimates Versus Ship 

3-& 

3-7 

4-3 

4-11 

4-12 

4-13 

5-3 

SST Estirndtes for July 1982 • • • • • • • • • • • •• 5-5 

7-1. Results of PPP Intercomparisons of Ship Injection Temp~ratures T With an Artificial Data Set T', Where Each Temperature T'(*) - F(~,a) + T(*) • • • •• 7-3 

7-2. Number of Space Grid Points · . . . 7-3. Pool Permutation Procedure, SlTES Statistics on 20 

YHnned Data . . . . . . . 7-4. SITES Statistics on 100 Binned Data ••••••• ~ • 

7-5. Pool Permutation Procedure, SPRED Statistics on 20 BinneJ 

Data 

7-6. SPRED St&tistics on 100 Binne~ Data 

A-i. Statifitical Comparisons Between 2° Latitude-Longitud~ 

BLnned SST Anomaly Fields •••••••••••••• 

A-2. Statistical Comparisons Between 20 Latitude-Longitude 

Binned SST Anomaly Fields with 3 x 3 Cell Spatial 

Smootlllng •••••••••••••••••••••• 

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7-4 

7-5 

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7-11 

7-12 

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TABLE OF CONTENTS (Continued) 

TABLES (Continued) 

A-3. Same as Table A-2 Except HIRS and HIRS Version 2 Rr,piaced 

by HIRS Version 2 Weighted and UIRS Version 2 Weig"ted 

Smoothed • • • • . • . • • . • . . • • • • . • . • • • • 

8-1. Partitioned rms Error Beteen Trtplet Combinations of the 

2o-Binned SST Anomaly Data Sets: AVHRR, SMHR, HIRS, and 

Climatology . • • . • • • • . . . • • • • • . • • . • . 8-2. Partitioned rms Error Between Triplet Combinations of the 

2o-Binned SST Anomaly Data Sets With 3 x 3 Cell Spatial 

• • 

• . 

Filter Applied: AVIlRR, SMHR, HIRS, and Climatology •• 

B-3. Average Partitioned rms Error for Each Sensor ••••••• 

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B-18 

B-34 

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SECTION 1 

SUHlI.ARY 

E. Njoku 

Jet Propulsion Laboratory, California Institute of Technology 

In the series of three Satellite-Derived Sea Surface Temperature (SST) 

Workshops, held at the Jet Propulsion Laboratory (JPL) between January 1983 

and Feurua~y 1934. satellite measurements of SST were reviewed and evaluated 

in detail. The emphasis was on global-scale evaluations. to complement the 

many investigations pr~viously carried out using high-resolution or regional 

data. and to address the SST measurement objectives of large-scale ocean and 

climate programs. 

Four satellite sensors and their associated retrieval techniques were 

reviewed: the Advanced Very High Resolution Radiometer (AVHRR). the HighResolution Infrared Sounder/Microwave Sounding Unit (HIRS/MSU). the VisibleInfrared Spin-S~an Ra~iometer Atmospheric Sounder (VAS). and the Scanning 

Multichannel Microwave Radiometer (SMMR). Data from these sensors were compare~ with each other and with in situ data fro~ ships. expendable bathythermographs (XBTs). and drifting buoys. Four months of data were studied: ' 

November 1979, December 1981. M)rch 1982. and July 1982. 

Principal investigators for each sensor provided JPL with "raw" SST data 

to be processed on the JPL Pilot Ocean Data Syst.em (PODS). A variety of display products was generated from these data so that results from all sensors 

could be compared in a common format. The display products included color 

maps. histograms, scatterp10ts. and tables of romparison statistics. Data 

were compared either by point-to-point match-ups (raw data comparisons) or as 

monthly averaged fields on a 20 x 20 latitude-longitude grid (binned data 

comparisons). WorkHhop tnvestigators then examined the display products and 

sought to draw conclusions as to the accuracy and error characteristics of the 

sensor SST measurements. 

Conclusions and recommendations arising from Workshops I and II have been 

documented in the reports of those workshops (JPL 1983 and 1984) and in the 

report of n subsequent TOGA Workshop on Sea Surface Temperature and Net Surface 

Radiation (WCP 1984). Conclusions and recommendations from Workshop III have 

been summarized by Njoku (1985) and detailed results from the workshop series 

will be published as a special collection of papers in the Novembe~ 1985 issue 

of the Journal of Geophysical Research (JGR O~eans). This report. therefore. 

serves mainly to provide details of the new data and analysis methods used in 

Workshop III and to supplement discussions appearing in the JGR issue. 

As a very broad and general conclusion. which will be expanded upon in the 

following sections. the workshops have shown that present satellite sensors can 

measure global SST with rms accuracies in the range of 0.5 to 1.OoC. Future 

emphasis must. however. be placed on improved validation and monitoring techniques to understand the nature of residual spatial and temporal bias variatIons. The present accuracies and geographical distributions of in situ 

sensors are inadequate for this purpose. Fortunately. large-scale ocean/ 

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atmosphere experiments (TOGA and WOCE) of the World Climate Research Program 

now· provide a focus for continuing effurts to improve satellite SST accuracies 

beyond the O.SoC level (COSPAR 1985). 

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SECTION II. 

N86"16856 

DATA SETS AND PRODUCTS 

J. Hilland And E. Njoku 

Jet Propulsion Laboratory, California Institute of Technology 

A. BACKGROUND 

Since 1981, the Advllnced Very 1Iigh Resolution Radiometer (AVIIRR) on the 

~OAA satellites has made SST measurements in the infrared portion of the spectrum using a multi-channel technique developed by HcClaln et ale (1983). In 

order to detercine SST more accurately under partially cloudy conditions. 

infrared soundings from the High-Resolution Infrared Sounder (UIRS) And microwave soundings from the Hicrowave Sounding Unit (HSU) , also on NOAA satellites, 

have been combined in a scheme described by Susskind et ale (19H4). Another 

instrument, the Visible-Infrared Spin Scan Atmospheric Sounder (VAS), has provided SST retrievAls from geostationary orbit (Sliith and Woolf 1982). The 

ocean surface has also been viewed in thn microwave portion of the spectrum by 

the Scanning Hultichannel Microwave Radiometer aboard Niabus-7. "'llhe1t et 

ale (1983) have described the SMMR SST retrieval techniquco 1n detail. 

In contrast to satellite methods, in situ data collected from ships, expendable bathythel~ographs (XBTs), and moored or drifting buoys have provided 

direct bulk measurements of SST. These plDtforcs have long served as oceanographers' primary tools. Hence, a large body of knowledge has been compiled 

on in situ accuracies, with estimates in the range 0.2 to 1.00C (Saur 1963; 

Tabata 1982). 

Against this background of spacecraft and in situ measurements, particip~nts at the JPL workshops sought to review the sensor performances (including 

calibration problems). understand the different SST retrieval algorithms, 

eva1~ate the sensor 5ST accuracies, and discuss directions for future sensor 

development. The issue of utilization of satellite 5STs in climate, air-bea 

interaction, and ~esoscale oceanography studies was not the main focus of the 

workshops, but did have a bea.ing on the reco~endation8 that arose from the 

discussions. workshop planning and initial results from SHHR were discussed 

in JPL (1983). Workshop II resull~ were more comprehensive as a result of 

refinements in the analysis procedures. In addition, more data were available 

for analysis with the acquisition of AVHRR (HeSST), IlIRS/H5U, and VAS data 

sets as described in JPL (1984). In Workshop III, the subject of this report, 

the acquisition and analysis of data were completed, recommendations for future 

research and sensor development were discusaed, and plans were made for eventual publication of results from the workshop series In the open literature. 

B. DATA SET CHARAC1ERISTICS 

Each sensor collected data in a unique manner, due to resolution and scanning methods, as well as a result of sensor processing and duty cycles (the 

percentage of time the sensor provided data satisfactorily relative to the 

total time). Therefore, the dal~ distribution varied greatly. Table 2-1 Gummari~C8 per,inent sampling parameters for each sensor. The comparison shows 

2-1 

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Table 2-1. Sensors Evaluated During the SST Work8hopa and 

Resolution ~f Derived SSTs 

Platform Sensor 

TlaOS-H} {"MRR 

NOAA-7 HIRS/HSU 

Nimbus-7 SH.'iR 

GOES VAS 

Ships Thermometer 

XST Thermistor 

FGGE Thermhtor 

Duty Cycle Spatial Resolution 

(X) of Derived SST (km) 

100 25 

100 125 

30 150 

75 50 

Coverage 

Global 

Global 

Global 

N.W. Atlantic 

S.E. Pacific 

Global (L1Qstly 

northern heCiisphel'e) 

N. Pacific 

Southern hemisphere 

that the high resolution, nearly continuous duty cycle of the visible-infrared 

sensors yields an enormous number of discrete radiances. Spacecraft-~e8sured 

radiances were averaged as part of the conversion process to geophysicolly 

meaningful temperatures before delivery to JPL, thus somewhat reducing the data 

volume. Details of the sensor modes of operation were reported in JPL (1983 

and 1984), but will be summarized below for reference. 

1. AVHRR 

Global, d~y/night coverage across 11 2,500 km wide swath at 4 km 

resolution characterizes the fundamental global sampling of AVIIRR instruments 

aboard the NOAA satellites. Prior to mid-November 1981 the Global Operational 

S~a Surface Temperature Computation (GOSSTCOHP) provided 50 ~~ resolution SST 

retrievals using a single window (centered at It ~m) algorlth~. After this 

~ate the improved five-channel instrument waG used to derive SST from the 3.7, 

II, and 12 ~m windows, utilizing the triple-window technique known as the 

multi-channel sea surface temperature (HCSST) algorithm. A spatial resolution 

of 25 km was retained. The standard NOAA GOSSTCOHP and HCSST products 

provided geolocatcd SSTs and supporting parameters such as platform source, 

data quality, and day/night status. 

2. HIRS/HSU 

The HIRS and MSU instru:nents flown on TIROS-N and NOAA-7 served as 

sources for derived SSTs. The large number of Infrared and microwave channels 

are combined in a physical algorithm to produce surface tempcrntureo under 

clear or clouJy conditions. Arrays of IIIRS soundings (instnntaneouo field of 

view 17.4 km at nadir) are averaged across the 2,300 km wide swath to form 

2-2 
·_.= sr' r b III I. Is __ .......... • • 

S5Ts at a spatial resolution of 125 km. Becausc thc rctrievals arc spatially 

averaged. points within 60 km of land tend to be contaminated by warmer land 

tcmpera!'!:ea. For workahop purposcs. opace/till!e location and quality parametera vere p.ovided eo that information could be segregated for variou8 8tudy 

aonths. regions. and 8ampling conditions such as day/night and clear/cloudy. 

3. SttfR 

Dual polarizcd microwave radiancc measurements at 6.6, 10.7, 18, 

and 21 ellz vere the fundamental input to SHMR SST algorithms. The SHMR aamples 

along a nadir-centered 780 km wide owath with a spatial resolution of 150 km at 

6.6 Cltz. Various quality control criteria were applicd to the data by the 

Nimbus-7 Algorithm development team, but the cost obvious and influential. with 

regard to workshop processing, was a land proximity mask. All dnta within 600 

km of land. including large islands, were eliminated due to possible antenna 

8idelobe contamination. Furthermore, in order to distinguish the highest 

quality values, data aent to JPL werc flagged for day/twilight/night status 

and ccll (1-5) locat!on in the swath. The instru:ent was turncd on and off 

every other d.~y due to spacecraft/power limitations. Furthermore, the end 

cells in the swath were deemed unreliabile due to polarization correction 

errors. Thus, the overall duty cycle was reduced to 30:. 

4. VAS 

The e~ostationary Operational Earth Satellite (COES), carrying VAS. 

provided a stable platform for scanning the full disc. Dayti~e-only IR and 

VIS dat3 were collected as part of the normal operations. These data were 

screened for cloud-free areas as part of the SST derivation scheme. Three of 

the twelve thermal bands sensed by VAS were used to dcrive SSTs at a spatial 

resolution of -50 km. Finally, retrievals from the eastern tropical Pacific 

and northwestern Atlantic were provided for evaluation. 

5. In Situ 

The primary surface data set consisted of ship intake temperature 

measurements collected from radio reports by the Fleet Numerical Oceanography 

Cente~ (FNOC). Typically, intake temperatures are accurate to thc nearest 

1°C. Additionally, biases on the order of ten tho of a degree Celsius have 

been reported (JPL 1983). However, these data are the sole source of global 

in-situ measurements and ~t best provide spotty spatial coverage in the 

southern hemisphere. The temporal resolution of most reports is six hours. 

Marine reports were closely scrutinized for pathological errors related to 

erroneous ship locations and extreme temperatures. 

Compl~m~ntary dntn sets consisting of XBT drops across the northern and 

tropical Pacific and measurements from ddfting buoys in the southern Pacific 

provided ~dditional validation i~formntion. XBT drops between North America, 

Japan, Haw!l1i. Tahiti, and thc Panama Canal are made about every 200 km, 

jielding 400 to I ,DOD observations during any month. Reported accuracies are 

O.loC with biases of about +0.2oC relative to salinity-temperature-depth 

(STD) instruments. 

2-3 

.\! 

! 

I 

I 

t· 

I 

-

; 
,~ , 

\ 

...... , -----':.....--.. -- ~ 

Drifting buoys launched during the First GARP Global Experiment (FGGE) 

supplemented ship observations in the southern hemisphere from 20 S to 65 s. 

Hore than 100 buoys reported SSTs at roughly 6-hr intervals throughout each 

study month. In an examination of the buoy program (Garrett 1981). comparisons 

made with ship measurements within 1 hour and 100 km of the buoy observation 

yielded a worst-case standard deviation of 1.4SoC and an average bias of 

+0.2SoC relative to the ships. Allowing for buoy temperature sensor stabilization reduced the otandard deviation and bias to 1.lSoC and TO.7SoC when 

compared to intake temperatures and 0.560C and +O.lSoC. respectively. when 

compared to bucket measurements. It should be noted that these statistics 

were determined for buoy SSTs taken 24 hours after the ship recording. 

C. ANALYSIS TECHNIQUF.S 

Very different spatial and temporal sampling characteristics, as well aa 

a low signal-to-noise ratio. characterized the data. Henc~. analysis 

procedures were designed to reduce the noise by forming monthly, 2° latitude 

by 20 longitude average SST anom31y fields. Noise levels were determined 

from point-to-point or "spot" comparisons of SST anolla11es. An anomaly is 

defined as the departure of absolute SST from climatology. Anomalies were 

computed by linearly interpolating, in space and tille. a 10 by 10 climatology generated by Reynolds (1982) to the irregularly spaced satellite or 

surface point and subtracting the climatology from the measured temperature, T. 

The resultant value. hereafter referred to as a "raw" anomaly. ~T, was used 

as the funda~ntal aignal rather than the absolute SST. Thus, a picture of 

ocean variability was depicted by each sensor. 

A variety of statistical and display routines, summarized in Table 2-2. 

was used to portray raw-anomaly quantitative results and spatial features. 

First and second momento were computed in the usual Gaussian sense: 

nnd 

n 

rlf - lIn 2: t.Ti 

i-I 

n 

~Trmsd - (lIn 2: (~Tl i-I 

where n is th~ total number of points. ~Ti is the raw anomaly located at 

latitude y and longittJd~ x at time t, and 6Trmsd is the root-mean-square 

deviation about the mean. 

2-4 

(2-1) 

(2-2) 
~ .. __ .'7" 

, ~"\1 

.... , "t , ........ , ............ : ' ........ _. --. ': .-... .:... '~. _-'-:-'~''':''';'''P'....;.'~ _________ ... "______ ·, .. ' .... ).,1.. 

~ . . -, 

'f • ~ I 

. ,. 

" ,_\1 ~ •. , 

t· • , 

· \ \' ... " , 

• 

i '. lit · , · , . 

I 

• 

", 

I 

! £' I 

; ... 1 

:;...~ 

, I r I t" I 

!:" ! 

(1 

~ c I 

- I ~ 

Monthly average fields were formed from raw anomalies by averaging all 

points that fell within a 20 by 20 cell centered on odd latitudes and 

longitudes. The average temperature or "binned" anololaly for latitude. j, and 

longitude, i, is Simply expre3sed as 

(2-3) 

where m is the total number of points in cell j, i, and Tyx is the kth raw 

anomaly located at latitude y and longitude x within the cell centered at j, i. 

Statistics of the binning process were retained for the purpose of comparing sampling charateristics, temperature extremes, and data dispersion within 

each cell. Raw anomalies exceeding +5.750 C were e:iminated before binned 

anomaly fields were formed, because the natural variability of the ocean is 

typically much less than this magnitude. It follows that any signal ~f this 

intensity is the result of poor sensor perfor.mance or algoritltm deficiencies, 

except perhaps in the case of a strong £1 rano. No further quantitative editing was performed on SST anomalies. However, the data were stratified into 

latitude/longitude bands and segregated based on a qualitative interpretation 

of the status flag associated with each observation. Table 2-2 summarizes the 

screening procedures applied to each data set. Field-data statistics were 

calculated as in Eq~. (2-1) and (2-2) by substituting the mean anomalies for 

the raw values. 

Sensor and algorithm performance were measured relative to climatology and 

to each sensor. Statistics of the relationships quantified the bias. standard 

deviation. and correlation. The correlation between any two sensors (climatology was treated as a sensor) is given by: 

(2-4) 

where subscripts 1 and 2 refer to sensor pairs and subscripts nand m denote 

!:he cells common to both sensors. 

2-5 
.. 

., 

... 

:~ 

·1 

.. 

Table 2-2. Analysis and Craphical Display Techniques Applied to 

Satellite-Derived 55Ts 

Data Type ~nalyzed 

Raw Raw Binned 

Techniques Absolute Anomaly Anomaly 

Compute raw anomalics by interpolating 

climatology to eatellite point and subtracting 

Form 20 latitude x 20 longitude monthly 

averages (binning) 

Calculate absolute SST by adding binned 

ancmalies to 20 binned climatological SST 

Prepare a histogram of SST and summary 

statistics; mean, standard deviation, and n~ber of observations 

Prepare a contour map of binned absolute SST 

Prepsre a thematic map of binned anomalies 

Prepare a thematic map of anoClaly differcnces 

Draw a thematic map of number density within 

a 20 cell 

Hake a scattcr diagram of binned anonaly SST 

versus Reynolds climatological SST: summary 

statistics, bias, standard deviation about 

x 

x 

x 

bias, correlation and number of observations X 

Prepare cross correlation tables: statistics, 

correlation, bias, standard deviation about 

bias, and number of 1° cells 

Calculate error partitioning tables: statistics 

and overall r~s error contributed by each sensor 

and rms error for each sensor combined with two 

other sensors 

Make a scatter diagram of sensor versus 

sensor: statistics, correlation, bias, 

standard deviation about bias, and number of 

20 cells 

Make a scatter diagram of binned anomaly 

differences for a aen~or pair versus number 

of observations in 2° cells for either sensor 

2-6 

x 

x 

x 

x 

x 

x 

X. 

X 

X 

x 

x x 

x 

---.,~ 

" 
, . . 

.~. " ,. I' 

.. .~ i 

,. , 

, , 

. , 

,oj 

, 

.{ 

\. 

i 

t 

\ 

~ -, :J 

I 

t 

. 

.. 

• ••• 

I.nothcr information cxtraction technique, error partitioning, was used to 

ascribe a measurement error to each sensor. This method employs sensor triplets in a set of three simultaneous equations which can be solved for each 

sensor's contribution to the total error in an rms sense. The mean-square 

differ~nce bctween binned anomalics from two sensors 1s expressed as: 

N 

lIN L(fln - T2n)2 

n-l 

where < > indicates the sample mean computed over all binned points, n, 

that are common to the sensor triplet, and Tl and T2 are the binned SST 

anomalies. The error in the measured anomaly is 

where Tk is the measurement from sensor k and Tt is the true anomaly. 

Then, it is possible to express Eq. (2-5) as: 

(2-5) 

(2-6) 

(2-7) 

It has been assumed that sensor errors are uncorrelated; hence, the mean crossproduct of the errcrs is zero. Similar expressions may be derived for D13 

and D23 • thus forming a set of three equations that can be ~olved for the 

sensor errors. <ck>. k a 1.2,3 [JPL (1983), Appendix GJ. 

In this manner. error estimates for sensors forming the triplet can be determined. For H sensors there will be (H2 - 3M + 2)/2 possible triplet combinations containing a given sensor. An overall error estimate can be obtained 

for each sensor by averaging the partitioned error for that sensor in each 

triplet combination. 

These analytical techniques were applied to the binned anomaly fielda and 

raw anomalies. Results were displayed from field data on monthly global and 

regional scales and from raw data in the for:n of "spot" comparisons on spatial 

and temporal scales commensurate with sensor sampling and geophysical variability. The space-ti~e scales used for preparing analysis products are presented 

in Table 2-3. 

2-7 

I 

I 

I 

,

I .. t 

' .. 

I 

I . "i- I , , 

I 

! 

! 
. ---------,,-

Table 2-3. Data Analysis Products Spatial and Temporal Length Scalesl 

Analysis Product Global2,6 

Anomaly histogram X 

Absoluta SST contour map X 

Thematic map of binned 

anomalies X 

Thematic map of anomaly 

differences 

Thematic map of number 

density 

Scatter diagram of anomaly 

SST ver~us climatological 

X 

x 

S~ X 

Cross correlation table 

Error partitioning table 

Scatter diagram sensor 

versus sensor 

Scatter diagram anomaly 

difference versus numerical 

observe 1n 20 cell 

X 

X 

North 

Pacific3 

X 

x 

X 

X 

x 

x 

X 

x 

South 

Pac1fic4 

X 

X 

X 

X 

x 

X 

X 

x 

North 

Atlantic5 

X 

X 

X 

X 

X 

X 

X 

X 

Monthly 

Average 

X 

X 

X 

X 

X 

X 

X 

x 

Spot merge 

+6, +12 hr; 

20, Too km 

1. ior some products the Pacific study area was separated into 3 regions of 

latitude to separate the tropics and extratropics. These regions were latitudes: (1) below 20°5, (2) between 20°5 and 200N, and (3) above 200N. 

2. Global study area; 60° 5 to 600 N, 0° to 3600 E. 

3. North Pacific study area: 00 to 600 N. 1000 to 2900 E. 

4. South Pacific study area: 6Go S to O~, 1000 to 2900 E. 

5. North Atlantic study area: 00 ~o 600 N, 2900 to 3600 E. 

6. Global and regional thematic maps and scatter diagrams within 200 

latitude bands extended to 60° latitude. All other products terminated 

at 5So latitude to eliminate possibly Rp~rious points due to sea ice. 

2-8 

-----.- - ._-----,- . 

... 

:' ;: 
, • 

. .' J .. 

~ 

·r-

~ .. 

,-- i 

• • 

.. , J~ .• 

, j 

:.r. 

.t\ 

$ 

H 

.~ A..==w" ....... 

__________________ .u~ __ ~.". __ .. __ ~ _______ ~ 

Spot comparisons of the raw data were made using a number of different 

space-time "windows" in which sensor data points were matched within a given 

time tolerance (+ hr) and radial distance (km). Table 2-4 provides the window 

specifications for matching any given sensor to the sensors. listed in the 

Table. 

D • WORKSH0P III RESULTS 

Detailed analysis results from Workshop III have been compiled into a 

special collection of papers to be published in the November 1985 issue of the 

Journal of Geophysical Research (JGR Oceans). In this report. some additional 

sets of data products are provided. Tables of correlations and other statistics can be found in >pendix A. Error partitioning rcsults are provided in 

Appendix B. and selected color images of SST anomaly fields are shown in 

Appendix C. A lengthy set of histograms and scatterplots were also generated 

for the Workshop and can be individually copied and supplied by request to the 

authors. Individual summaries of findings from the tlorkshop data are provided 

in Sections III to VIII. 

1. Data Sets 

Table 2-5 shows the satellite and in situ data sets processed during the three JPL workshops. In Workshop III, data from all sensors for the 

months of Hp.rch and July 1982, and alao HIRS data for ~ccember 1981, were 

evaluated. Details of the procedures used to derive the SST data can be found 

in Sections III to VI, or in previous workahop reports (JPL 19~3 and 1984). 

Prior to Workshop III some changes were made in the HIRS SST algorithm to 

improve its performance. These changes included a higher-resolution climatology to define land points, tightened criteria for internal consistency checking, and retention of more data samples in each cloud analysis area (see Section IV). This new version of HIRS data was named "Version 2." In addition. 

both HIRS versions were provided with data quality weights which could be . 

applied in the binning process. Those SST data with weights applicd werc 

referred to as HIRS "weighted." 

Another data set unique to Workshop III was provided by T. Wllheit. This 

"SMMR/ship" data set consisted of a blended monthly averaged SST field on a 

20 x 20 grid, for the Pacific Ocean region, comprising original raw SST 

data from the SMMR and from FNOC ships. The mechanism for blending the SMMR 

and sldp data is outlined in Section VI and, in essence, uses the ship data, 

where available, in an objective anal),sis scht!me to remove spatial biases in 

the SHMR SST field. 

2. Analysis Products 

A great improvement in Workshop III was the availability of color 

images, or "thematic maps," to d1.splay global SST anoma.' , ficlds and data 

distributions. Table 2-6 lists the complete set of global color maps ':hat 

were produced, including retrospective proccssing of maps for November 1979 

and December 1981. Those maps, which have beer. referenced by discuss~ons in 

Sections III through VI, are shown in Appendix C. The maps are selfexplanatory. 

2-9 

i 

I' 

I 

I, 

! 

! 

I 

I 
i 

I --- i --- h 

" .. 

H 

t. 

,-

.' 

.. 

...... "--.""" .. ; •• ' o ..... :,~~ .... - -: .or 

-- .. -.... -.--.-.;.. . ~ 

Table 2-4. Time-Space Windows Used for Merging Sensor Data to Data from 

Other Sensors Listed in the Table 

Sensor 

AVHRR 

Ships 

XBT 

FGCE Buoys 

" 

... 

Time 

(!hr) 

12 

6 

12 

12 

Radial Distltnce 

(km) 

100 

100 

20 f 100 

20 

Table 2-5. Data Sets Available for Workshop Proce~sing 

SMMR 

AVHRR 

HIRS/MSU 

VAS 

FNOC Ship 

FGGE Buoy 

TRANS PAC XBT 

November 

1979 

1 

2· 

2 

1 

2 

2 

Key: 1 m available for Workshop I 

2 - available for Workshop II 

3 - evailable for Workshop III 

December 

1981 

2-10 

2 

2 

3 

2 

2 

March July 

1982 1982 

3 3 

3 3 

3 3 

3 3 

3 3 

3 3 

.... ~ 

-----

I 

'l 

I 

I 

I 

A 

\. ; 

i 

I 

-, 

. 

i 

.. 

, 

,} 

i 

.. 

.~ ., 

1 

I 

j 

l 

I 

i 

! 
---

~ .. or ' " , I 

l • , " 

,. 

, , I 

; \ Table 2-6. SST Workshop Color Maps (Global) 

I.:. 

· , 

· '-

~ ~.I Nov 79 Dec 81 Mar 82 Jul 82 , , 

, . . '." 

I J 

'( ; Data Distribution: .... 

r,'; AVHRR -I -I -I -I rJ i 

HIRS -I -I .; .; I t SHMR -I -I -I .; 

r. VAS 0 0 .; .; I I Ship .; .; .; .; 

1 XBT 1 .; .; .; .; \ .; 

j 

FGGE Buoy 0 0 0 

, S~T Anomalies: 

· AVHRR -I 1-. .; .; .; 

~I HIRS .; .; .; .; ! SHMR .; .; .; .; 

SMMR/Ship 0 0 .; .; r;1 VAS 0 0 .; .; r 1] Ship .; .; .; .; 

XBT .; -I .; .; I [I FCCE Buoy .; 0 0 0 i 

~ I 

I 

r· SST Anomaly Differences: t-

! I AVHRR (day-night) 0 .; .; .; 

HIRS-AVHRR .; J .; .; I, 

SMMR-AVHRR .; .; .; .; I' d SMMR/Ship-AVHRR 0 0 .; .; I, 

VAS-AVHRR 0 0 .; .; 

HIRS-SMMR .; .; .; .; 

AVHRR-Ship .; .; .; -I 

HIRS-Ship -I .; .; .; .' 

I SI1HR-Ship .; .; .; .; ! ~ 

I SMMR/Ship-Ship 0 0 .; .; , 

i VAS-Ship 0 0 .; .; i' , 

I XBT-Ship .; .; .; .; 

XBT-AVHRR .; .; .; .; 

Key: .; Available 

o Not available 

\ 

i 

1 

1 

;~ ,. 

;'~ 

~;J ~, 

, t 

,. ~. J 

r~ 2-11 

~ __ 

-

d 
\ 

\ 

, 

\ 

i \ - 0; ~ 

\ 

\ 

, ! 

..... j 

The tables in Appendix A provide statistics (as discussed in Section II.C) 

fpr pair-wise sensor comparisons of binned data for all months. All data have 

been mask(:~ up to 600 km from land to conform to the SMMR areal coverage. 

Another benefit of the masking is that high-gradient coastal current regions, 

which could cause sampling errors in the ZO-binned analyses, are largely 

eliminated. For each month, there is one global set of statistics and another 

set for each of four ocean regions. This allows the contrasting of statistics 

for tropics and extratropics, or for North Atlanti~ and North Pacific. A 

second table (A-2) was generated llsing the same data as for Table A-I, but 

with a 3 x 3 cell two-dimensional spatial filter applied. The weights for the 

filter were as follows (normalized by a factor of 16): 

1 

2 

1 

2 

4 

2 

1 

2 

1 

The motivation for applying the filter was to investigate any reduction in the 

standard devidtion or increase in the correlation therefrom. (Climate-scale 

models can accommodate data on ZO x 50 or even 50 x 50 spatial scales.) 

However, care must be taken in interpreting the statistics of Table A-2 since 

the ZO-binned sensor data are no longer strictly independent after the 

spatial filter has been applied. Finally, Table A-3 has been included to show 

the effects of using the HIRS algorithm qual.ity weights on the March 1982 data 

(see Section IV). 

The error-partitioning tables in Appendix B follow from the discussion in 

Section lIC. Only the four data sets with truly global coverage have been 

included. Results could be significantly skewed by the addition of ship data, 

unless all data were restricted to the North Atlantic or North Pacific. 

Separate tables for these regions were not produced, however, at the 

Workshop. As 1n Appendix A, separate tables were generated for unamoothed and 

smoothed data sets. The HIRS data used was Version 2. For each sensor, the 

partitioned error from each of three possible triplets (or triads) was 

averaged to give an overall average rms error. These average errors have been 

collected together for convenience in Table B-3. 

E. DISCUSSION 

The data products (maps, plots, statistics) generated for the Workshops 

led to very detailed discussions concerning sensor calibration, algorithm 

performance, errOL characteristics, error sources, validation problems, and 

future research. Parti~ipants were cnco~raged to submit summaries of their 

evaluations for the Workshop report. These investigator summaries are 

provided in Sections III through VIII. (Some investigators chose to postpone 

publication of their analyses until the JGR Oceans special issue.) 

2-1Z 

I 

,. 

! 

. . I • 

;-, ; . " 

., 

. -. • r"'''' s.n ........ """, ..... ?e ". -

, 

i '" ~; 

\ . 

01 

I 

i 

.. i 

I 

, 

"'-". , - , :: I .... , 

l· ! •• 1 oJ 

,ojf I . i 

- I 

~~ 

A. 

N86-1685? 

SECtION III 

ADVANCED VERY HIGH RESOLUTION RADIOMETER (AVHRR) 

SENSOR COMPARISON CHARACTERISTICS 

E. Paul McClain 

National Oceanic and Atmospheric Administration 

INTRODUCTION 

Multi-channel sea surface temperature (MCSST) techniques make use of the 

AVHRR on the NOAA satellites (Schwalb 1978), which delivers i.1-km and 4-km 

resolution measurements in four (or five) channels: 0.58-0.68, 0.725-1.10, 

3.55-3.93,and 10.3-11.3 pm (also 11.5-12.5 pm for five-channel AVHRRs). 

Var1:>us combinations of the four or five AVHRR data channels are used in the 

important initial cloud-filtering stages. Thresholds of the bi-directional 

reflectance in theo visual and reflected-IR channels, and of brightness 

temperature in the thermal-IR channels, have been established for cloud-free 

conditions. Homo&eneity tests take advantage of the low radiometer noise 

levels and the high degree of spatial uniformity in the ocean surface reflectance and temperature fields in the absence of clouds. Details of the cloud 

tests are given in McClain et ale 1983. and in Part 11, pp. 1-8, in the SST 

Workshop II Report (JPL 1984). 

The brightness temperatures measured in 0' given window channel of the 

AVHRR are corrected for atmospheric attenuation by use of the brightness temperatures from two or all three of the atmospheric windows, each of these 

spectral bands being characterized by a different atmospheric transmittance. 

The relationships between the atmospheric correction and various combinations 

of brightness temperature differences is linear with exceptionally smaLl scatter, as was determined from simulation data bases (McClain 1981). Satellite 

and buoy measurements matched ~o within 25 km and 24 hours have been used to 

derive a small but significar.t temperature-dependent bias corrOection teL"Ill for 

the simulation equations. Further details of deriving multichannel sea 

surface temperatures from AVHRR meoasurements are given in McClain et ale 1983. 

B. ADVANTAGES OF THE AVHRR (MCSST) TECHNIQUE 

The principal advantages of the AVHRR-derived sea surface temperatures 

arc high resolution, broad geographic coverage, general consistency, and good 

accuracy. The MCSSTs have been produced on an operational basis since November, 1981, and they are available globally from NOAA/NESDIS as a monthly mean 

(65N-65S, 2.50 grid) or as a weekly composite (70N-70S, 100-km grid), and as 

selected regional (5Q-km grid) or local charts (14-km grid). The basic retrievals are obtained from 2 x 2 arrays of AVlIRR data over an ar~a nominally 

8 km on a Side, then on~ or more of the retrievals per 25-km box are reaolved 

onto the various grid intervalS listed above. MCSSTs can be obtained within 

10-20 km or a land, ice, or cloud edge; and except in regions of extremely 

persistent and continuous cloud cover, most areas of the wo~ld are sampled at 

least once on a lOO-km grid every 5-10 days. 

3-1 

... __ • _______ • _______ o~ 
Iii -----.----------.... --...... ~- ...... ...-... -...... , ~ .... -, R . ...... -~':'-. - -;-

~ 

i i 

~ j 

'I 

~I 

· i 

H 

II 

~ ! 

,I 

I! 

t 

· . 

;. 

• 

~ / 

I 

I~ II r [1 

I 

r 

I, 

~ I 

~. ! 

In comparison with tn situ senaor9. particularly thos~ on ships of 

opportunity. the AVIiRR with its nearly continuous onbollrd calibration 

generates an internally uniforc sct of brightness ttmperature measurements 

orbit after orbit. day after day. Except for quite unusual circumstances. 

such as the E1 Chichon cn:ptions and the el~ctrlcal interference problems that 

have plagued the 3.7 U~ data during certain periods (discussed in next 

section), the operationally derived HCSSTs genernlly comprise a spatinlly and 

temporally consistent data base. The few changes that have been made in the 

uperational algorithms have affected the root Dean squa~e (rms) differences 

with respect to drifting buoy temperatures only at the <0.2SC level. Recent 

drifting buoy spot comparisions over a wide range of temperaturep, r,eographic 

area. and seasons consistently indicate biases of <O.lC and ~ms diJferences 

(or scatters) of 0.5-0.bC (Strong and HcClain 1984). Comparisons ~ith 

screened ship observations, after removal of a coemon ship-based climatology 

to derive anomalies, are summarized for the various periods and regions in 

Tahle 3-1 along with the statistics for the other sensors studied during the 

several JPL SST Workshops. It is evident that the AVHRR. compared with the 

other sensors. was almost always and everywhere oer the globe characterized by 

the largest number of matchups. the lo~est bias and scatter. and the highest 

correlation relative to the Pazan set of screened s~lp observations (the 

prncipal exception to this occurred in July 1982 in connection with the El 

Chlchon eruption effects). 

Table 3-1 gives the various &t~tistics for ench sensor with respect to 

ship catchups more than 600 km from any land or ice surface. The use of Q 600 

km mask is nece3sary In order to equalize the coverage for the AVHRR. 

HIRS/HSU. and VAS. which can obtain observations near coastlines and icc 

edges, with that avallable for the SHMR. which is constrained to operate ~t 

least 600 km away. Furthermore. it should be noted that theloe are no 

measurements from the S~~ over the North Atlantic in November 1979. and the 

VAS coverage is li~ited to two areas In March and July ot 1982. one about 

250 latitude by 300 longitcde on a side in the southwestern North Atlantic 

and the other about 40P latitude by 400 longitude in the extreme eastern 

equatorial and northeastern South Pacific. 

. Table 3-1 also enables comparison of sensor statistics for ship matehups 

)600 km from land or ice and those for the.same matchups after a special 3 x 3 

weighted smootherl is applied. This proced~re was used on all available 

2° latitude-longitude bins )bOO km from land or ice. A sharp drop in saople 

size resulted from loss of outer rows and colu~ns of bins and from no computation being made ror arrays where inadequate in aitu o~ satellite data resulted 

in no bin average being computed for one or more bins of th& 3 x 3 array. This 

was a particularly acute problca in the case of the FGGE buoy set; e.g •• the 

unsmoothed global data set of N-400 for the AVHRR matchups was reduced to N-1 

for the smoothed set. 

1A smoothing set of we1~hts Is applied to the 

monthly menn SST anomalies (T I • T2 ••••• T9) 

in overlapping 3 x 3 arrays of the two-degree bins. 

3-2 

I 

! 

I 

! 

l 

I 
I 

" 

I 

" 

--~--------------------~--------------------------~ .. ~ 

Table 3-1. Sensor Sea Surfa~a Temperatures Compared With Pazan Screened Ship 

Tcmperaturp.s (>5 per bin). Values in Parentheses Are After a 3x3 

Center-Weighted Smoother Was Applied (A· AVlDlR; S • SHMR; V • VAS, 

H • HIRS/MSU). 

........ f 

... ~,. 

CleM I 

"rt~ he Ifle UC).~I/ 

III.-, .. "Ie U_101) 

.... l~ , .. lfle uC).»a 

11M Co'" .1_ "i.u".) 

Glokl 

"rt~ , .. Ule 

...... '..,Ule 

Su..,.,.o.. ... ," 

(iCauer) 

Cl0NI 

lrorUII Pac It II' 

CloMI 

So<ol~ raclflc 

" ru 

UlO) 

Hr 

(11., 

)4 

(4) 

I 

(0) 

270 

(lU) 

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(10) 

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(lit) 

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(4) 

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(u.) 

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(lU7) (n61 

61 

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(117) 

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(101) 

A 

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(ZIO) 

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(0) 

,., 

(n)) 

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d ... 

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.. , 

-
The effect of the smoother on the bias is mixed at best, but there is 

definite (often dramatic) improvement in the scatter (i.e. standard deviation) 

and cross correlation statistics in nearly every case for every sensor. This 

is noteworthy in particular, for it demonstrates just how important a factor 

i8 the uneven quality of the in situ temperature observations when one is 

using them in an dttempt to validate satellite-derived temperatur~s. 

Table 3-2 displays the statistics for AVHRR/ship matchups >600 km from 

land or ice with various types of in-situ data (viz., Pazan ships, >5 and >20 

per cell; FGGE buoys; and Transpac XBTs); and Table 3-3 shows AVHRR/ship 

matchup s~atistics separated into day and night. Table 3-4 enables comparison 

of two types of in situ observations, viz., Pazan ships the Transpac XBTs. 

c. LIMITATIONS OF AVHRR (MCSST) TECHNIQUE 

Perhaps chief among the limitations of the MCSST, or any other infrared 

method, is lack of retrievals in areas of persistent and essentially continuous 

cloud cover. The relatively high resolution of the AVHRR does enable more 

retrievals to be made in areas of patchy cloud cover than can be done wth the 

other sensors. The cloud detection tests appear to work well in that retrievals 

are seldom made using cloud-contaminated data, including contamination by thin 

cirrus or sub-resolution cumulus fields. 

Severe volcanic eruptions or dust storms can produce extraordinary gerosol 

loadings in the atmosphere and thereby greatly increase attenuation of the infrared Signal reaching the satellite, as well as interfere with those cloud detection tests that depend on visible-band measurements. El Chichon, because of 

the large mass of H2S04 droplets found at very high altitudes in the atmosphere, was particulary severe in its impact on the MCSSTs, especially in the 

Northern Hemisphere tropics and subtropics. Daytime retrievals were virtually 

eliminated between 5N-30N for up to six months, and nighttime retrievalb were 

biased too low by up to 2-4C from April to October 1982 (Strong et ale 1983). 

Recent research indicates the v~ry real possibility of using a different 

formulation of the triple-window MCSST equation, one that appears to be nearly 

insensitive to the concentration of EI Chichon type a~rosols (Walton 1985). 

Furthermore, rlaytime visible band data from the AVHRR can probably be used to 

obtain a point-to-point measure of the aerosol loading, thus leading to other 

possibilities for correctior. of the retrieval temperatures (Stowe 1984). 

Use of any triple-window equaticn obviously needs noise-f 'ee measurements 

in all three IR-window channels. The noise level has been exceptionally low 

<0.1 K, in the 11 and 12~m channels. The 3.7 ~m window data, however, tend to 

have an acceptable noise figure «0.2 K) during the first 12 months or so after 

each satellite launch, but ~hen become increasingly ~ontaminated by electrical 

interference thereafter. Fortunately, so-called "outgassing" procedures that 

were implemented recently2 sur.cessfully reduced the complex but coherent noise 

in the 3.7 ~m data to levels comparable to those measured immediately after 

l/~nch. Hore recent experience indicates that outgassing must probably be repeated every 6-12 months to control this problem. Although the noise level in 

this channel was moderately high in July 1982, this was reflected primarily in 

a loss of nighttime MCSSTs from failure of the MCSST Intercomparison Test 

(see HcClain in Part II, pp. 1-8, JPL 1984) rather than a decrease in accuracy. 

20n 6/15/83 for NOAA-b, on 9/27/83 for NOAA-7, and on 9/8/83 for NOAA-B. 

3-4 

'- . .. , 

1 
....... i'. r ; . 

, 

..... I . '. , 

j (I 

: I Table 3-2. AVHRR Multichannel Sea Surface Temperatures Compared Uith In Situ 

Temperatures. Valueo in Parentheses Are After a 3x3 Center-Weighted 

Smoother Was Applied (P5 • Pazan Screened Ships. >5/Bin; P20 - Pazan 

:, 

Screened Ships. >20/B1n; F • FGCE Buoys; T • Tr4Qspac XBT's). 

_IUt Doc INI ..... ~ IH2 J.I, If" "'aM, .f 

·f. ii ............ " .19 • T ., PM T " .19 T " '20 T 

tl- i ,. C;1.1 72) IU 400 .,7 1%t Ut 216 7" ZU 407 ... 211 n. i f (ll&) U) (I) (0) (un (2) (I)) ,Me) u)) (II) (274) 0) ()) ! i •• t~ ... 1I1e )97 nl "1 .. I 4)4 nl )20 .It I 

(20-,..) (1111 (H) (lU7) (m (ZIO) (II) (117) U) I 1I14-... ItI. ,. U IH 41 000 to ,. I .. n lit I i u_ZOS) (4) (01 (I) U) (0) U) (0) (0) (0) 

"',h , .. 111. I 11% 0 U Q II .. / uo-"., (D) (0) (0) (0) (0) 

.... ,' AlI_U. no %n 2U UI (0-,..) (I") (Ie" (Ul) (m) 

I •• , (I •• U_ t er.;. .. ,oUlto) j CloMl -O.1t -0.11 -o.Zl -0. It oO.:IO oO.Z7 oO.:IO oO." .0.,. oO.1I oO." oO." 00." I 

(-0.%4) (00.)1) (-0.19) (00.") (OO.S)) (00.") (00.10) (00.14) (oO.n) (oo.u) (.e.101 ) 

....... 'Kille -0.21 -0.21 00." .0.4, OO.SO 00.:10 00.)7 (oil'''! 1 (-0.27) (-iI.21) (+0.'" (-0.6)) (00.,4) (0(,.14) (00.111 (+0.10 , 

! 1ll,., .. UI. -iIt.17 +0.10 -0.17 +0.27 -0.0% oO.Z1 -0.0% +1.0) oO.71 t (oO.OS) (+0.41) (oO •• )) 

.... 'h 'oclll. -0.04 -o.C2 -0.40 oO.1I I (.) 

"rl" Acl_tlc -iI.17 .. 0 • ., oO.lt oO.Sf (-0.20) (00. It) 000 000 (00.:10) (+0.'" .t ..... ,.. Oit.lact •• I lli!ili!1 

C .. kl J 0." O.SO 0." 0.70 O.SO O.U o.n O.S' O.S) 0.70 0.79 .71 1.01 

(o.nl (o.m (O.)l) (1).'1) (0.»)) (O.lt) (0.,1) (0.1s) (o.n) (0.43) (0.171 1 .. 1 

"rth 'ocltl. 0.6' 0.71 O.SO 0.7' 0." o.n o.n 1.26 .. (O.lll (O.UI (O.,t) (0.]11 (0.291 (O.nI 10.62) (0.171 

1I1'·,oc It I. 0.41 0.90 0." D." O.S) O.lt 0 ... t 0." 0." (0.041 (O.ZII (0.101 tt it _,~ 'ocUI. 0.67 0.60 0.71 0.1' i 

, > 

0.40 • 

! .. rtll AlI .. tte 0.S7 0.41 0.42 • (0.,., (0 ... , (0.21) (0.371 

r Cr ••• Corr.ht 10. 

01 ... 1 D • ., 0.40 o.n 0.72 0.76 0.72 0.7] 0.67 0.64 O.S' 0.12 0.71 o.n (0.111 (0.111 (0.711 (0.'" (0.7]1 (0.17) (o.nl (0.101 (0.10) (0.'01 (-iI ... ' 

"rt~ 'ocUle D.,. on 0.11 0.7S 0.S2 O.S2 O.~' o.n (O.IS) (0.'" (0.911 (0.7]1 (0,", (0.701 (O.ll, (-0."' 

1I14., .. UI_ 0.54 O.~4 0.)' 0.42 0.4' 0." O.S! O.H 0.61 

(O.H) (0.1" (-1 (0.141 

""'cia , .. Ule 0.71 0.07 0.00 0." 

",U, Acl .. Uc '0.43 0.7 .. 0.67 0.66 (0.4., (0.'" (0.80) (0.801 

3-5 

""'"" ~ " , 

~ 

; 

-, 

. 

:' Table 3-4. Paz an Screened Ship Temperatures (>S/Bin) Compared With ~ Transpac XBTs and AVHRR Multichannel Sea Surface Temperatures. 

Values in Parentheses Are After a 3x3 Center-Weighted ~ther 

Was Applied (A • AVHR.R; T • Trandpac XBTs). 

IlO'l 1'7' Dec 1911 Karel. 1982 Jill, 1912 

..... lIer of 'I. 

llate""p. A T A T A T A T 

Global 7U 212 729 lS. 7ts 2'2 .. 4 1S4 

(l24) (29) (2))) (I) (361) (1) (274) (2) 

Iortll .... Ulc 3" 223 376 ass ,.3 227 320 146 

(2o-SU) (176) (2') (127) (I) (210) (21) (117) (" 

1tU-'ecUIc ,. t 41 3 31 as 27 • U<*-201) (4) (0) (6) (0) (S) (0) (0) (0) 

IoIItll 'aclUc 1 0 12 0 II II 0 0 , (20-361) (n) (0) (0) (0) 

Iortll AtlaUc 270 0 255 0 267 0 2S1 0 

(o-SU) (144) (02) (153) (157) 

'I .. hhl' al_ i 

OilMr) 

Global -0.19 -0.11 +0.30 +0.03 +0.36 +0.27 +0.4' +0.2% 

(-0.24) (-o.as) (+0.))) (-0.24) (-0.44) (+0.47) (+0.35) (-0.06) 

I IIorth , ... IUc -0.21 -0.12 +0.44 +0.04 +0. SO +0.29 +0.37 +0.2] 

(-0.27) (-o.IS) (+0.43) (-0.24) (+0.54) (+0.47) (+0.17) (-0.06) 

Ki'-'ecUlc -0.17 -0.09 +0.27 ~.36 +0.21 +0.05 +1.03 .0.03 

(-0.05) (+0.41) (+0.43) 

I -'" 

IoIItll 'ac,Uc -0." -0.40 -0.11 

)1 

Iorth AU ... uc -G.17 +0. IS +0.29 +0.57 

(-G.20) (+0.19) (+0.'0) (+0.41) 

St.Mard D .. ,.UOD 

(Scatter) .:) 

Global 0.58 0.79 0.50 0.84 O.SI 0.89 0.79 0.94 ~ (0.'5) (0.34) (0.28) (0.27) (0.29) (0.35) (0.52) (0.21) 

IIorth •• dUc 0.6. (I.ao 0.50 0.84 0.48 0.91 0.93 0.96 .:~ 

(0.33) (0.~4) (0.29) (0.27) (0.29) (0.35) (0.62) (0.28) 

I Ki4-Paciflc 0.47 0.3' 0.54 0.7) 0.39 0.'0 0.49 0.52 

(0.04) (0.21) (0.10) 

t SouLh 'adUc 0.63 0.71 0.1' 

t Iorth AtlanUc 0.57 0.41 0.42 0.60 

, (0.34) (0.18) (0.21 ) (0.37) 

i Cro •• Correlation 

\ Clobal 0.69 0.53 0.76 0.63 11.67 0.39 0.62 0.58 I 

1 

(0.7.) (0.68) (0.91) (0.50) (0.77) . (0.70) (0.70) (-0.99) 

IIorth PadUc 0.74 0.62 0.77 0.63 0.52 C.38 0.59 0.55 

,I (0.8» (0.68) (0.92) (0.50) (0.58) (0.70) (0.63) (-0.99) 

;~ "l4-PtcUlc 0.54 C.79 0.42 -0.24 0.38 0.42 0.53 0.11 

:; (0.99) (0.71) (0.a4) 

South Pac Hie 0.17 0.00 0.86 

j' "j ,. 

.-, lI<>elh AtlanUc 0.43 0.74 0.67 0.66 

I (0.41) (0.9]) (0.80) (0.84) 

tj 

1/ 3-7 

~ r-~ 
:, 

,. 

i 

( 

; 

• 

\·0 

; ! 

I 

, 

; 

, 

f.-

1::. 

r 

~;: 

~, t~ 

~. 

-, !: 

, 

~ ,. 

r' 

,-, 

... , 

""" U ;'j ,. 

Ii 

rf 

~ 

Although the brightness temperature at 3.7 ~m can be significantly elevated above th. emission value by even small amounts of specularly reflected 

solar radiation (>1.7% normalized bi-directional reflectance) and pre8~nt 

operational HeSST procedures use only split-window, i.e., 11 and 12 vm 

brightness temperatures in thp. daytime for this reason. recent tests indicate 

that no detectable solar contamination exists at satellite zenith angler 

greater than 5-100 on the anti-solar side of the satellite subpoint track. 

Thus dual-window (3.7 and 11 ~m) or triple-window MCSST equations can be 

us'ed 1n the daytime over large areas of the globe. 

Concern has been raised over use of the standard MeSST equations, particularly the split-window one, when steep temperature inversions nre present in 

the atmosphere just over a water surface (see Condal et ale in Part II, pp. 

29-31, JPL 1984). Such conditions are prevalent at times over the Great Lakes 

1n late spring and early sucmer, and can occur in any mid-latitude coastal 

waters during this season if offshore winds are blowing. Recent investigations 

of this with buoy data from the Great Lakes in 1982 and 1983 found that the 

operational split-window MeSST equation actually performed rather well except 

under the most extreme inversion conditions. No reliable methods for satellite 

detection of the presence of these extreme conditions, and correcting for their 

effects on the temperature retrieval, have been found to date. 

The old problem remains of skin-versus-bulk temperature, as satellite IR 

techniques yield skin temperatures at depths of less than a millimeter an~ in 

situ methods of observation give bulk temperatures at depths ranging from a 

few centimeters (towed therciators), to a oeter or two (buoys), to three to ten 

meters (ship intakes). The use in the MCSST metho\ of a temperature-dependent 

bias correction derived from satellite/buoy matchup data presumably incorporates some sort of average skin-vs-l m depth temperature adjustment, but this 

effect is almost always negative (i.e., the skin is cool) and generally amounts 

to 0.1-0.5e in magnitude (Robinson et ul. 1984). It is not uncommon for the 

top few t~ns of em of the water surface to become heated under low amounts of 

cloudiness and when very light winds result in little mechanical stirring; 

this h38 been termed the "diurnal thermocline" (Robinson et ale 1984). 

D. ABILITY TO SATISFY USER NEEDS 

Obviously the higher the temporal and spatial resolution, and accuracy, 

and the more complete the coverage under all meteorological conditions, the 

more user needs will be satisfied by the satellite-derived SST measurements. 

Lesser resolu~ion data in time and space presumably can always be derived froo 

the original observations. ~ith its capability to produce relatively high 

resolution daily to weekly observations very close to coastlines or ice edges, 

the AVHRR can satisfy many users, both oceanographic (including fisheries) and 

meteorological, altho~gh extensive clcud cover may be constraining in some 

areas and times. Good global HGSS! coverage for the relatively low-resolution 

&onthly mean charts is virtually always available for climatological lIsers. 

3-8 

i 

, 

/ 

i 

-" ".1 

I 

I 

, : 

I 

I 

, 

I 

! 

\ 

J 

. 

j 

( 

i 

J 

b 
E. ERROR CHARACTERISTICS OF MCSST'S FROM STATISTICAL TABLES 

In addition to the various statistics summarized in Table 3-1, which all 

refer to the Pazan set of screened ship data, and where all anomalies were derived using the Reynolds' climatology (Reynolds 1982), numerous. ship and satellite anomaly fields, and associated difference fields, were also produced in 

connection with the workshops. The ship anomaly fields suffer from the traditional lack of observational cove~age in the Southern Hemisphere and some other 

areas. The Transpac XBT fields are severely constrained by limited geographical coverage. With this in mind, a discussion of each data period follows. 

1. November 1979 Data Period 

This first data period is different from the other three in that no 

MCSSTs were yet available, the NOAA operational product at that time being an 

improved AVHRR/HIRS version of the earlier GOSSTCOMP (Global Operational SST 

Computation), which was bAsed on data from the SR/VTPR-instruments on the---

pre-TIROS-N generation of NOAA operational polar satellites (Walton 1980). 

Compared with the SR/VTPR data, the AVHRR/HIRS measurements are of better 

quality and higher spatial resollJtion. Consequently, although still based on 

a single A~fRR window channel, the satellite retrievals based on the GOSSTCOMP 

procedures from 1979 through mid November of 1981 were generally of higher 

quality than those in 1978 and earlier. 

Table 3-1 indicates that AVHRR biases are comparable or a bit larger than 

those for HIRS/HSU, but significantly smaller than those for SMMR. AVHRR 

scatter and correlation values are ~uch better than for HIRS/MSU and very much 

better than for SMMR. 

Table 3-1 also demonstrates the very substantial improvement in scatter 

and cross correlation figures for all three sensors and in nearly ev~ry ar~a 

that results from the 3 x 3 weighted smoothing procedure (the mid-Pac1fl.c 

values are of dubious rel1ahllity because of the small sample): tli~ A'IHRR 

statistics are especially impressive (scatter of 0.33-0.38C and cross 

correlation of 0.41-0.85), whether on an absolute basis or compared with the 

. other sensors. Biases generally \o.'orsen somewhat for all sensors when the 

3 x 3 smoother is used. 

In Table 3-2 (AVBRR only) coJmparisons by in situ data type are limited by 

sampling constraints, especially after the 3 x 3 smoother is applied, so not 

all regions are represented. There is very little difference in bias from one 

type of in-situ data to another, but scatter values are clearly worse relative 

to FGGE buoys3 and a bit worse rela~ive to Transpac XBTs. Cross correlation 

comparison are mixed. Scatter and correlation figures generally improve substantially when the 3 x 3 smoother is used. 

3Thls was unexpected as previous studies have found lower bias and scatter 

relative to buoys than to ships (Strong and McClain 1984). An independent 

study of the FGGE buoy data set for Nov. 1979 for another purpose by a NOAA/ 

NESDIS contractor found that so~~ of the FGG~ buoy observations were seriously 

in error. This is significant because neither the buoya nor the XBTs were 

given thc samc kind of extensive screening as were the Pazan ships. 

3-9 

r 

/ 

I 

11.& 
/ / 

, 

I 

i 

-------------~-----~ , 

2. Dec~mber 1981 Data Period 

This was the first full month of operational HCSST p·:ocessing; and 

a8 in the Nov. 79 period, Table 3-1 indicates that AVHRR biases were comparable 

to HIRS/HSU ones and significantly better than those associated with SHHR, and 

the AVHRR biases tend to be better with the 600 km mask in place. Likewise, -scatter magnitudes arlo! much better for AVHRR than for ~URS/MSU, tne latter 

being significantly better in turn than for SMHR. Cross correlations are generally comparable for HIRS/HSU and SHMR (0.13-0.33), being much lower than for 

AVHRR (0.74-0.77). Scatter and cross correlation figures almost always improve 

substantially when the 3 x 3 smoother is used, but the relative rankings given 

above still hold. The 3 x 3 weighted smoother statistics for the AVHRR are 

quite respectable indeed, being 0.18-0.29C for scatter and 0.91-0.93 for cross 

correlation. 

Table 3-2 again has adequate AVHRR bin matchup sampling in only some of 

the regions especially after the smoother is applied, and there are no FGGE 

buoys. Bias and cross correlation values are comparable for all the in situ 

data bets, but scatter is somewhat worse for the Transpac XBT than the Pazan 

ships, although even this reverses when the 3 x 3 smoother is used. As before, 

the 3 x 3 smoother greatly improves the AVHRR/ship comparisons of scatter and 

cross correlation (see remarks on Table 3-1 just above), but generally worsened 

the biases somewhat. 

3. March 1982 Data Period 

This is the last full month of operational HCSSTs prior to the eruption of El Chichon and about the beginning of the period when the 3.7 lim 

noise level began to climb ~ignificantly. The statistics in Table 3-1 indicate 

generally lower biases for the AVHRR than for the HIRS/~~U. SMMR, or VAS. The 

AVHKR scatter values are superior to those of the other sensors, and they are 

particuarly noteworthy when the 3 x 3 smoothtr ia used, viz., 0.21-0.29C (see 

Table 3-1). The AVHRR cross correlation values are comparable with those of 

VAS and Gubstantially better th~n those for HIRS/MSU and S~1R. and as with 

previous periods, those correlations associated with the 3 x 3.smoothing are 

higher (reaching 0.58-0.84 for the AVHRR). 

Table 3-2, which has the same coverage restrictions as in the previous 

period, shows somewhat lower bias values for Transpac XBTs than fOl' Pazan 

ships, and again has about comparable cross correlations and higher Hcatter 

values relative to the Transpac XBTs. 

4. July 1982 Data Period 

By this month the El Chichon volcanic aerosol cloud had girdled the 

Earth several times, but generally had remained just north of the equator. and 

this severely reduced the numbers 'of daytime AVHRR retrievals in the region of 

the aerosol cloud. Nighttime MCSST observations in that same zone exhibit 

large positive biases (AVHRR lower than in si~u data because of aerosol attenuation in the high stratosphere). Chief effects of the increased 3.7 ~m noise 

level are reauced observational denSities &nd somewhat higher scatter, the 

latter from occasional erroneous passing of the uniform low stratus test, both 

at night. Table 3-1 reflects these factors in the large positive AVHRR bias 

3-10 

" 

.. 

" ~ 
• 

.O' 

.. 

,r' 

,. 

, . 

:. " 

\ , 

.-" \ 

values. Unfortunately, the 3 x 3 smoothing procedures results in a zero sample 

in the Mid-Pacific zone. AVHRR scatter is adversely affected also, making it 

higher than the VAS, comparable to the HIRS/MSU, but still rather better than 

the SMMR. AVHRR cross correlations are poorer than in the previous periods, 

but still comparable to or even better than for the other sensors. 

Table 3-3 showo AVHRR statistics are comparable relative to all the in 

situ data, nnd that all are generqlly worse than for the previous months 

studied. As with the other periodb. scatter and cross correlation statistics 

are 8ign~.ficantly improved by the 3 x 3 smoothing scheme, whereas the effects 

of the smoother on the biasing is mixed. 

5. Diurnal Variations 

Table 3-3 summarizes the AVHRR/ship comparisons for the three data 

periods when both day and night statistics are available. Excluding for the 

moment the month of July 1982, in which El Chichon had a strong influence that 

differed by day and night, there appears a systematic diurnal difference in the 

bias in the March period of 1982, larger positive (ship SST higher thdn MCSST) 

at night than in the daytime, that is not evident in December 1981. Standard 

deviations are somewhat low~r, and cross correlations somewhat higher, at night 

for all data periods. Both these statistics generally improve substantially 

when the 3 x 3 weighted smoother is used. The rather drastic change from a 

mcderate to a large negative bias in the daytime to an even larger positive 

biaB at night during June 1982 reflects the impact of El Chichon. Monitoring 

of drifting buoy/MCSST matchups during the first half of 1982 also detected a 

nightti~e positive bias of about 0.4C in the NOAA operational product. This 

bias was effectively removed after mid-September 1982 by re-derivation of the 

temperature-dependent bias correction (see discussion in Sec. F.5). 

6. Summary 

With the partial exception of the EI Chichon impacted July 1982 period, 

the error characteristics of the operational AVHRR-based SSTs as exhibited by 

the statistical measures given in Tables 3-1 and 3-2 are predo~1nantly as 

follows: biases are generally a few tenths of a degree and positive in sign 

(AVHRR lower than in situ temperatures) except for the earliest data period 

when the GOSSTCOMP instead of the MCSST method was in use. Scatter (i.e. 

standard devlation) magnitude tended to be in the range 0.5-0.6C, and when the 

latter are subjected to a 3 x 3 weighted smoothing scheme, the scatter further 

decreases to a remarkable 0.2-0.41 Cross correlations, except in the tropics 

where the extremely small range in surface temperature generally mediates 

against high values for this type of statistic, generally fall in the range 

0.3-0.7; use of the 3 x 3 smoothing results in a further increase to 0.5-0.9. 

It helps to put the foregoing discussion of the AVHRR/MCSST matchup comparisons with the Pazan ship and Transpac XBT data sets into better perspective 

if one looks at the statistics for the matchups between the Pazan screened ship 

observations and the Transpac XBT measurements (see Table 3-4). The biases 

associated with the Transpac XBTs tend to be one to three tenths of a der,ree 

smaller than those found with the AVHRR, but the standard deviations and cross 

correlations of the AVIIRR are concistently and significantly better than those 

relative to the XBTb. As has been noted in the previous remarks, the 3 x 3 

3-11 

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weighted smoother has variabl~ results on 'he bias values for both data sets, 

generally worsening them somewhat. while substantially improving both scatter 

and cross correlation figures. The XBT standar~ deviations tend to have larger 

reductions than the AVHRR when the smoother is applied. thus rendering them 

quite comparable in magnitude. This was particularly the case in the July 1982 

data period when the scatter for the XBTs became smaller than for the AVHRR. 

presumably because of the adverse effects of El Chichon on the MCSSTs. 

The foregoing onc~ again emphasizes 'th~ uncertainties in assessing the 

"true" accuracy of the satellite-derived SSTs when the correlative in situ 

measurements evidently contain significant but unknown errors of their own. It 

would be desirable to cross compare the Pazan ship, Transpac XBT, and FGGE buoy 

data sets in this connection, but th~ number of ship/buoy, and XBT/buoy bin 

matchups is Na l and N-14, respectively. The statistics for the latter are not 

particularly impressive: B - -0.49, S - 1.11, and C - -0.03, but confidence in 

them ia low with such a small sample. 

Unlike the favorable effects on the other ~tatistical measures for all 

data periods and regions, use of the 3 x 3 smoothing had mixed rp.8ults on the 

b1as values, gener3lly tending to worsen them slightly. The notable exception 

is the July 1982 data period when the El Chichon eruption cloud belt adverf;ely 

affected all the AVHRR statistics generally. Even in this case the large 

positive biases were decreased somewhat by the smoother. Use of only thos~ 

two-~egree bins having more than 20 ship observations appeared to have only a 

slight, and mixed, effect on all the statistical parameters. 

F. REGIONAL AVHRR ERROR CHARCTERISTICS FROM GLOBAL ANOMALY CHARTS 

When USing the global charts of SST anomalies or .1nomaly differences between AVHRR and in situ data sources, it is necessary to be reminded that valid 

comparisons can be made only in areas where there is an adequate distribution 

of both types of observations. Whereas the density of AVHRR-based 5STs was 

generally good to excellent on a world-wide basis (the exception being the Nov 

1979 GOSSTCOMP distribution. which is poor south of 455 and in parts of the 

tropical belt. particularly the zone from 90E to 18DE). the ship SST distribution for any of the data periods is of adequate density only in parts of the 

North Atlantic and North Pacific and a few narrow tracks elsewhere (Figure C-l 

is an example. see Appendix C). The Transpac XST data coverage is even poorer 

than that of the ships. being concentrated along a few heavily travelled shipping lanes. and only a few of the two-degree bins have )4-6 observations for a 

given month (Figure C-l is an example). The data distribution for the ~GGE 

buoys (Nov. 1979 only) is all in the Southern Hemispere, but it provides some 

spotty coverage in regions rarely visited by ships (see Figure C-l). 

1. November 1979 Data Period 

There is general agreement between AVHRR- and ship-deriv~d anomaly 

patterns. but some differences in amplitude are evident (Figure C-2). General 

agreement with the anomalies derived from Transpac XBTs and from the FGGE buoys 

(Figure C-2) is apparent also. though again not on a bin-by-bin basis. Some of 

the )3.5C positive anomalies seem suspect, particularly those on the edge of 

the AVHRR data void in the Southern Hemisphere. Parts of the South Atlantic 

area also appears too warm in the AVIlRR. 

3-12 

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2 • December 1981 Data Period 

The coverage and density of AVHRr./MCSST in this data period is 

better than it wa3 with the AVHRR/GOSSTCOMP in November 1979, particularly in 

the central and western North Pacific, central and eastern South Pacific, and 

the high latitudes of the Southern Hemisphere generally. There is still a 

paucity ~f observations in the deep tropicR of the western Pacific (Indonesia/ 

tU,cronesia areas). As in the November 19H period, if one looks at the larger, 

more coherent anomaly features, there is good general agreement b~tween the 

satellite-derived pattern and the ship-derived pattern (Figure C-3), although 

the overall amplitude of the MCSST anomaly field tends to be greater. The 

strength of the large positive MCSST anomaly northeast of New Guinea, as well 

as a weaker one northeast of Madagascar, and a strong ship-based anomaly southeast of the tip of Africa, all appear suspect. 

3. March 1982 Data Period 

Although MCSST data densities during this period are poorer in the 

northern and eastern North Pacific than in December 1981, they are higher in 

the Indian, South Atlantic, and Southern Oceans. As previously, the Indonesia/ 

Micronesia area is relatively poorly observe!. Once again, the larger scale 

patterns generally match, but the amplitudes of the MCSS! maxima are generally 

somewhat greater than the ship ones (Figure C-4). The positive MCSST anomaly 

in the Indonesia/Micronesia area and westward appears to have no counterpart 

in the ship-based pattern. 

4. July 1982 Data Period 

The drastic impact of the El Chichon aerosol cloud and the increased 

3.7 ~m noise on MCSS! observational densities is seen in Figure C-5. Excellent coverage remains, however, in the central North Atlantic and in the 

Southern Hemisphere down to at least 45S. If one disregards the "El Chichon 

negative ao,omaly" stretching around the globe between roughly 5N-30N, there is 

yet again reasonably good conformance bet~'een the MCSST-based and ship-based 

(to'igure C-5) anomaly fields, although the amplitUde of the MCS/iT negative 

anomaly in the mid North Pacific is somewhat too large. 

5. Diurnal Effects 

Global AVHRR day-minus-night charts (Figure C-9) were produced for 

the Dec. 1981, March 1982, and July 1982 data periods. Although some systematic difference patterns are eVident, their interpretation or explanation is 

hampered because no separate day and night anomaly charts were produced. 

Unfortunately, little recourse can be made to the separate day and night 

tabulations in the statistical tables (e.g., see Table 3-4), as they are 

broken out only by rather large reglons. 

A pronounced positive day-minus-night difference of several degrees extends around the Eart.:, between about 25-40S in the December 1981 data period. 

This positive diffe4ence appears in a far weaker, more irregular or interrupted 

form and generally at somewha~ lower latitudes of the Southern Hemisphere in 

the March 1982 period. It is absent there altogether in July 1982, but appears 

3-13 

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in similar latitudes of the Northern Hemisphere that month. This seasonal behavior, probably associated with northward shift of the most intense solar 

heating from the Southern Hemisphere at the time of the sucmer solstice there 

to the Northern Hemisphere at the time of its summer solstice, is consistent 

with a widespread "diurnal thermocline" developing in the weak wind regimes of 

the central subtropical anticyclone belts. 

The other major day-to-night difference that appears during all three periods is a positive one extending uestward from the Indonesia area into the central Indian Ocean near the equator. In December 1981, this is clearly related 

to too large an atmosphere correction from a quadratic term in the operational 

daytime split-window equation (see discussion in Sec. G.2). In March and July 

of 1982, however, a linear form of the split-window equation was in daytime 

use, so an alternate explanation must be sought for what appears to be a persistent (''1ercorrection for very high moisture. When the coefficients of the 

operational daytime and nighttime equations were adjusted slightly in October 

1982 on the basis of a larger and more representative sample of buoy/MCSST 

matchups (see McClain in Appendix C, pp. C-l to C-16, JPL, 1983), one of the 

results was to diminish this tendency toward a negative daytime and positive 

nighttime bias in those regions of the tropics that are extremely moist, and 

thus to lessen diurnal d1fferenceo of the type noted here. 

6. Summary 

The larger-scale MCSST anooaly patterns are in fair to good agreement with the corresponding ship-derived fields almost everywhere and during 

every data period where there was adequate common coverage. Looking only at 

the small-scale, bin-to-bin values, it is difficult to see the relatively 

large degree of pattern similDrity that really exists overall. In isolated 

areas there are suspect anomalies during the several data periods, and the 

amplitude of the AVHRR anomaly field seems somewhat larger overall than the 

ship field, but generally there is surprisingly good correspondence in view of 

the probable errors inherent in both fields. These errors combine (bdding or 

subtracting) in a variable and unknown way in their difference field. The 

diurnal variations apparent in the global day-minus-night charts are sometimes 

difficult to account for in the absence of separate day and night anomaly 

fields, but the prevalence of positive day/night differences can often be 

attributed to the "diurnal thermocline" effect in areas ,.here lack of wind has 

inhibited mixing of the uppermost layer. 

G. POSSIBLE CAUSES FOR ERRORS IN THE AVH~~-BASED ANOMALY FIELDS 

The general subject of sources of error in SSTs obtained from the AVHRR 

using the GOSSTCOMP or MCSST methods Is addressed in Section C, but a few 

additional remarks directed specifically to p0ssible errors noted in the above 

discussion of anomaly fields are in order. 

1. November 1979 

The tendency for a positive anomaly rll!ll1ling the edge of the data at 

45-50S (Figure C-2) is probably something peculiar to the GOSSTCOMP mer.hod i as 

it docs not appear in any of the three MeSST data periods. On the other h~nd, 

the rather scattered two-degree billS with ship-based ~nomalies do tend to 

support the presence of war~er than normal water in that part of the southern 

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Indian Ocean (Figure C-2). The relatively cooler water extending from near the 

southwest tip of Africa northwestward into the central South Atlantic is supported in the AVHRR/GOSSTCOMP anomaly field (Figure C-2) only by a tongue of 

relatively less warm water. This could be a "diurnal thermocline" effect, as 

this area of the subtropical Atlantic was characterized by anticyclon!c conditions with light winds at the surface and lc~ amounts of cloudiness in 

November 1979. There is no obvious explanation for the lac~ in the AVHRR of 

any real indication of the strong negative anomaly evident in the ship data 

some distance to the east of the southern part of South America. 

2. December 1981 

The large positive anomaly northeast of New Guinea, and to a lesser 

extent the weaker ones just northeast of Madagascar and along the northeast 

coast of Australia (Figure C-3) do not appear to be supported by the ship-based 

anomaly field (Figure C-3) , although ship data were very scarce in the first 

area mentioned. The Australian case would seem to be a manifestation of the 

"diurnal thermocline" in the IR-based SSTs. This area was under weak mean 

monthly pressure gradients near sea level (light winds), and there was a mean 

monthly albedo of <20%, indicating very little cloudiness. The New Guinea 

and Madagascar cases may also have an element of this ~ffect, but the situation 

there was aggravated by the use of a quadratic term in the daytime split-window 

MCSST equation (see Sec. F.5). This term was found to produce erroneously high 

MCSSTs, but only 1n areas where atmospheric water vapor was exceedingly large 

(i.e. precipitble water )~ ~m), a characteristic of the region extending 

westward from Micronesia/Indianesia into the central Indian Ocean. Further 

confirmation of a moisture maximum there is afforded by a SMMR-derived precipitable water chart for December 1981 provided by NASA/GSFC. The positive anomaly southeast of South Africa seems rather too large in magnitude in the shipderived field, but there is a large amplitude and equally dubious negative 

anomaly in the MCSST field just to the south of it in a large region devoid of 

ship data. There is no obvious explanation of either of these. 

3. March 1982 

Aside from the previously noted tendency for greater amplitude in 

the MCSST-derived anom~lies than in the ship-based ones (see particularly the 

negative anumaly features in the North Pacific and North Atlantic (Figure C-4) , 

the only unsupported feature is the positive anomaly in the far western Pacific 

and Indian Oceans centered roughly on the equator. This corresponds 

climatologically to the moistest portion of the tropics, and this is 

corroborated by the SMMR-based precipitable water charts supplied by 

NASA/GSFC. A siuilar, but rather stronger, positive anomaly in December 1981 

was largely accounted for by use of a quadratic daytime MCSST equ&tion, 

discontinued in February 1982. The fnct that an apparently erroneous positive 

anomaly persists in March and July 1982 il:dicates that the limited set of buoy 

mstchups used in deriving the original temperature dependent bias corrections 

to the MeSST simulation equations evidently still did not adequately account 

SThis information obtained or inferred from charts of monthly mean pressure 

at sea level from NCAR. monthly mean SMMR wind speeds (NASA/GSFC), and of 

monthly mean albedo and outgoing long wave radiation from NOAA/NESDIS. 

3-15 

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for the atmosphere/ocean conditions in this region. This positive anomaly 

vanished when re-derivcd bias corrections, baaed on a auch lnrgar and more 

representative buoy matchup data bllse, were incorporat.ed in the operational 

equations in October 1982. 

4. July 1982 

The large belt of negative anomaly stretching around the Earth from 

roughly 5-30N is, as discussed previously, almost entirely a consequence of the 

volcanic aerosol from eruptions of El Chlchan'in Mexico in enrly April 1982. 

The cold aerosol cloud almost eliminated dayt1cll~ MCSSTs in thls belt and 

severely attenuated the emitted rlldlotion froa the Earth's surface. The difference between monthly mean SSTs derived solely from satellite MCSSTs and 

solely from ship and other in situ measurements hao been used by Strong et ale 

(1983) to track the month-to-month coverage of the volcano cloud during 1982. 

His charts indicate a southward transport of the aerosol near central West 

Africa, and a northward transport in the west central North PacifiC, whlch is 

consistent with distortion in the negative anoaaly belt in those regions (Figure C-5). The ship-based anomaly field (Figure C-5) also shows negative 

anomaly features in those two areas, but the greater extent and amplitude 1n 

the AVHRR chart is attributed to tho added influence of tlle volcano cloud. The 

source of the large negntive MCSST anowaly along a large part of the extreme 

southern edge of the chart, especially southwest of Australia. 1s 6uopected to 

be deficiencies in the climatology there (the ship-derived anooaly chart has 8 

data void all through that region). The NOAA/NESDIS anomaly charts, which are 

based on the Robinson/Baur climatology, show only two small (in area) negative 

anomalies (ma.'(imum of -I.SC) anywhere in this zonal belt, O!lC southwest of 

Australia at about 50-535 and another southeast of New Zealand near 55-58S. 

The general tendency. in all data periods, for the amplitude of the AVHRRbnsed anomalies to be somewhat greater than the ship-derived anomalies, is posoioly a consequence of to factors: (I) the sparsel and more irregular sampling 

of sh~p data going into the bin averagesi. and (2) the skin temperature. 

H. POSITIVE FINDINGS 

Despite the difficulty of comparing satellite sensors with differing spatial resolution and geographical coverge, and matching ench of these in turn 

with common in situ data sets that are themselves highly irregular in density 

of coverage in many regions and srI.' of variable quality (furthemorc. they are 

"spot" Ireasuremcnts at depths of one to several meters, whereas the AVIIRR 

senses a kind of "sk.in" temperature over an area about ten kilometers on a 

side). there is surprisingly good correopondence in the sign and location of 

the major anomaly features; the correspondence in the amplitude of the anomaly 

maxima ts only fair. 

As expressed by the bias, scatter, and croBs-correlation statist1.:~ for 

the globe and for the North Pacific, Hid-:.cific, South Pacific, nnd North Atlantic regions, the operational NOAA/NESDI~ AVIlRR product, the ~ICSST. ~encrally 

compares better with the Pazan screelled ship data set than do any of the other 

satellite sensors. This is especially true In the case of the stalldard deviation (scatter) Ilnd the cross correlation, with the AVHRR hnvinR ht!cn lOlo'er 

scatter Ilnd hl~her correlati.on \oIlth respect to the Pazan shipl> tl\lln do the 

3-16 

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'rranspac XBTs. Thls, and the fact that th(: AVlIRR simUarly has better statlstics rolative to the Pazan screened ships than it has relative to either the 

Transpac xurs or the FGGE buoys, suggests that the la~tcr two data sets should 

have had an equally com~~ehen8ive screening to delete incorrect ob~ervations. 

When a modest.timount of additional spatial smoothing (using ihe 3 x 3 

center-weighted smoother) is done, the AVltRR/HCSST figures for scatter and 

cross correlation become impressive evcn on an absolute basis, attaining 

valucs of O.2-0.4C for thc former statistic and 0.7-U.9 for the lattcr. Thi" 

high a corr~lation is notcworthy when one is reminded that it rcfers to • 

anomaly values, which have a much smaller range than the tempcrature value~ 

from which they were derived. 

1. RECOHMt:NlJATlONS FOR HIPROVI-:O COMPARISONS OF SI-:NSOR At:O IN SITU DATA 

In view of the substantial improvcment in statistics that resultcd froo 

.pplication of thc 3 x 3 center-weighted smoother, with ito implications aboul 

the noise level remaining in the bin averagcs of ship and some of thc sensor 

~ata, thrcc recommcndations are made: (1) Find a way to include) x 3 arrays 

having up to a total of say four side and/or corner bins missing from the 3 x 3 

array; this would cut down the erroneous reduction tn sample size that results 

from the pr~scnt way of doing the 3 x 3 smoothing (i.e., no bin can be miss1ng). (2) Try using 4 x 4 deg. lat./long. bins instead of 2 x 2 in the basic 

monthly meun anomaly chart. (3) If the effect on the statistics Is salutory 

after dOing e't~er ot the above, produce new global anomaly charts, either 

color-coded or contoured. 

If global day-minus-nlght charts arc to be properly interpreted, then it 

is necessary to produce separate daytime and nighttime global anomaly charts. 

Otherwise, one cannot tell if a feature in the difference field comes about 

because the daytime values are hIgh, or the nighttime values arc low, or both, 

or vice versa. 

3-17 

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N86-16858 

SECTION IV 

RETRIEVAL OF SEA-SURFACE TEMPERATURES FROM HIRS2/HSU 

J. Susskind and D. Reuter 

Goddard Laboratory for Atmospheric Sciences 

The methods used at the Goddard Laboratory for Atmospheres (GU\) to 

retrieve sea surface temperatures from HIRS2/MSU data for the four months of 

the NASA sea surface temperature intercomparison workshop arc described. 

Results are shown comparing anomaly fields produced using data from shipo. 

AVHRR, HIRS2/MSU, SHMR and VAS for the last three of these months. Fields 

from AVHRR and HIRS2 show the highest accuracy compared to ship fields. 

Errors in the HIRS2 fields appear more random while AVHRR data ahows large 

area, spatially coherent errors. The random errors in the HIRS2 fields can be 

further reduced by performing the retrievals at a higher spatial resolution. 

A. OVERVIEW OF THF GLA RETRIEVAL SCHEME 

HIRS2 and HSU are the 20 channel infrared and 4 channel microwave passive 

sounders on the oF~rational, low earth orbiting satellites. They monitor emissions, arising primarily from the earth's surface and the atmosphere up to the 

mid stratosphere. These, together with the SSU, a three channel pressure modulated infrared radiometer which monitors emission from the mid-upper stratosphere, comprise the TOVS (TIROS Operational Vertical Sounder) system. 

The TOVS data are analyzed operationally by NOAA NESDIS to produce vertical temperature-humidity profiles USing a method based primarily on statistical 

regression relationships between observed radiances and atmospheric parameters 

(Smith 1980). The approach used at GLA is fundamentally different from the 

current operational approach. Rather than rely on empirical relationships between observations and meteorological conditions, we attempt to find surface 

and atmospheric conditions which, when substHuted in the radiative ttansfer 

equations describing the d~pendence of the observations on the meteorological 

conditions, match the observations to a specified amount. A physically based 

retrieval scheme has a number of advantages over a statistically based scheme. 

The single most important advantage is the ability to correct for th~ effects 

of auxiliary factors such as surface temperature, surface emissivity, surface 

elevation. reflected solar radiation. satellite zenith angle. and most significant of all, clouds on the obs~rvations. All of these parameters are either 

solved for, or directly accounted for, together with the atmospheric temperature profile, in an iterative scheme. As a result of this, the data analyzed 

produce not only global fields of atmospheric temperature profiles. which are 

necessary for the initialization of atmospheric models for numerical weather 

prediction, but also other auxiliary fields. These icclude the following 

monthly mean fields: sea/land surface temperature and their day-night difference which, over land, is related to soil moisture; fractional cloud cover, 

cloud-top temperature, and cloud-top pressure and their day-night differences; 

and icc and snow cover which is derived from combined use of the surface emissivity at 50.3 GHz and the ground temperature. Another important advantage of 

the physical retrieval is the abiliey to identify those areas where a satisfactory solution to the radiative transfer equations cannot be found, in which 

case the retrieved parameters are flagged as questionable. 

4-1 

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The details of an earlier version of the GLA retrieval algorithm, as well 

as rcsults of analysis of data from January 1979, are given in Susskind et ale 

(19H4). Th~ GLA processing system conoists of the following main steps: 1) 

the forward calculation of radiances as a function of atmospheric and surface 

conditions; 2) accounting for cloud and surface radiative effects on the observKtions; 3) determining atmospheric temperature profiles by the inverse solution of the radiative transfer equation; and 4) determination of auxiliary 

"meteorological parameters. Steps 1-3 are performed iteratively until convergence is reachcd. The effects of clouds on the infrared obscrvations must be 

accounted for before either ground or atmospheric temperatures can be determined. The cloud-filtering algorithm utilizes MSU channel 2, which has a 

transmittance of about 0.1 at the sutface and is sensitive to the surface 

emissivity. Therefore, after the calculation of radiances, microwave surface 

emissivity is calculated next in the iterative scheme, using MSU channel I, 

follelwed by the cloud correction, the retrieval of ground temperature, and 

finally, the update of atmospheric temperature profile. If sufficient agreement between observed and calculated radiances is found, the procedure is 

terminated and step 4 is performed. Otherwise, the iterative procedure is 

continued with recalculation of radiances, surfac~ emiSSivity, etc. 

Table 4-1 shows the channels, centers, and peaks of the weighting functions ~t/dtnP, and radiance contribution function Bdt/dtnP, and other relevant 

intormation, for the channels on MSU and HIRS2. The current analysis docs not 

employ the SSU observations. Those channels utilized by GLA in analysis of 

the data ana their primary function are indicated. 

The months of data for the sea-surface temperature workshop were November 

1979, December 1981, Dnd March and July of 1982. HIRS2/MSU data for the first 

month were taken from TIROS-N. The remaining data were from NOAA-7, on which 

the channels of HIRS are slightly but significantly different from thos~ on 

TIROS-N. Consequently, one change had to be made to the processing system 

described in Susskind et ale (1984) for the NO~\-7 data. Other changes and im-

~rovements in the processing were also made during the course of the workshop. 

The data from November 1979. the first month of the workshop, would have 

been analyzed exactly as described in Susskind et ale (1984), but essentially 

one half of the month of data were misSing including a big gap from Novcober 10 

to November 17 and a number of smaller gaps. Therefore. in order to have more 

data points and cut down the randor.: noise component of the monthly mean fields, 

retrievals were run in both the warmest (least cloudy) and second warmest 

(second least cloudy) 125 x 125 km quadrants of the 250 x 250 km grid (see Figure 4-1), rather than in only the warmest quadrant as had been done earlier. 

Significant changes were made to the processing system used to analyze the 

data for December 1981 and March 1982. One change. affecting the clear column 

radiance algorithm. was made primarily because of the change in characteristics 

of channel 13 and channel 14 of HIRS2 on NOAA-7 from those of the same channels 

of HIRS2 on TIROS-N. The improved algorithc, shown in the next section. has 

been found to be superior even with TIROS-N data. and has now been incorporated, together wtth other changes, for use in Lhe re-analysis of TIROS-N data. 

The second change involved use of the 11 pm window data. in addition to the 

3.7 and 4.0 pm window data. in the retrieval of ground temperatures. In 

addition, inrlividual soundings were assigned weights to be used in the generation of monthly mean fields. After studying the December 1981 and March 1982 

4-2 

J 

"4 

1 

1 

I 

I 

i 

..1 

," ,~ 
I 

. .. 

: , 

'r. ~ !, " ' 

" 

.~. ""1 , I , 

! - ~ 

: I 

.. I ~ : 

t 

, " I~ 

.--

------- ------~·1·, 

Table 4-1. HIRS2 and MSU Channels 

Peak of Peak of 

v(cm- 1) 

dT/d1np BdT/dlnp 

Channel A (\.1m) (mb) (mb) 

HI 14.96 668.40 30 20 

H2a 14.72 679.20 60 50 

1t3 14.47 691.10 100 100 

H4a ,c 14.21 703.60 280 360 

HSc 13.95 716.10 475 575 

H6c 13.65 732.40 725 875 

H7c 13.3& 748.30 Surface Surface 

HSb,c 11.14 897.70 Window, sensitive to water vapor 

H9 9,73 1027.90 Window, sensitive to 03 

HI0 8.22 1217.10 Lower tropospheric water vapor 

Hll 7.33 1363.70 Middle tropospheric water vapor 

H12 6.74 1484.40 UPFer tropospheric water vapor 

H13a ,d 4.57 2190.4G Surface Surface 

Hl4a ,d 4.52 2212.60 650 Surface 

H15a 4.4& 2240.10 340 675 

H16 4.39 2276.30 170 425 

H17 4.33 2310.70 15 2 

H1Sb 3.9S 2512.00 Window, sensitive to solar radiation 

H19b 3.74 2671.80 Window, sensitive to solar radiation 

M1e 0.516* 50.30** Window, sensitive to surface emissivity 

Hzd 0.55S* 53.74** 

H3a 0.546* 54.96** 

H4a 0.51S* 57.95** 

aused by GLA to compute temperature profiles 

bused by GLA to compute surface temperature 

cused by GLA to compute cloud fields 

dused by GLA in cloud correction 

eused by GLA to compute surface emissivity 

"',. in cm 

**v in GUz 

500 

300 

70 

sea surfece temperature fields in Workshop Ill, a number of other changes were 

made to further reduce noise in the field. The newest system, called HIRS 

version 2 in the workshop, was used to reprocess March 1982 data and to 

process July 1982 data. For this reason, results for July 1982 and the 

reprocessed data for March 1982 are better indicators of the cnpabilities of 

HIRS2/MSU for retrieval of surface temperature than those of the earlier 

months. Modifications to Susskind et £II. (1984) used in the dnalysis of 

workshop data for uecember 1981 and March 1982, and in the analysis of July 

1982 and re-analysis of March 1982, are described in the next two sections. 

4-3 

. 

,. 

j 

, 

" 
I 

~ 

I 

~ 

------- "-:- .---

~ 

,~ ....... 

\ ' ' ............. ~~ I 

"-..... ) \ \ 

\ 

SUB ORBITAL TRACK 

t ----t-~ 109.3km 

HIRSI2 CALIBRATION PERIOD 

Figure 4-1. Retrievals were run in the warmest and second warmest 125 x 125 km quadrants 

of the 250 x 250 km grid. 

.I 

"---. 

L 

---------------.-~ , (. •• -f' .... c...: ... . , 

'--....... 

'. 

.' .... " .. 

\ 

:--. 
i . 

!. , ~. I 

.. , .. 

r," 

:.. " 

: .1 ' .. 1 

I, " 

B. IMPROVEMENTS TO THE SYSTEM USED IN ANALYSIS OF DECEMBER 1981 AND MARCH 

1982 DATA 

HIRE2/HSU data for December 1981 and March 1982 were analyzed exactly as 

in Susskind et ale (1984), except for modifications which were made to improve 

the clear column racUance :.ilgorithm and the sea/land surface . temperature 

algorithm. These are described below. 

1. Improved Estimation of Clear Column Infrared P~diances 

Infrared observations are highly sensitive to the presence of clouds in 

the field of view and, hence, accounting for their effects on'the observations 

becomes p~rhaps the most important step in the retrieval process. If one looks 

at an otherwise homogeneous but partially cloudy scene with cloud fraction a, 

then to a reasonable approximation, one can write the radiance observed in 

channel i to be 

Rl • (I-a) Ri,CLR + a ~i,CLD (4-1) 

where Ri,CLR is the radiance one would observe in a cloud-free area and RisCLD 

is the radiance one woulr. observe in a completely cloud-covered area. Ri,CLD 

depends not only on the atmospheric variables but also on the detailed propertiea of the clouos. Rather than assume or attempt to determine the cloud properties simultaneously with the determination of atmospheric and surface pro- pertie~, the method attempts to estimate, or "reconstruct" from the ouserved 

radiances, the clear column radiances which would have been observ£d if no 

clouds were present. These reconstructed clear column radiances, Ri, are used 

in determiation of the atmoapheric temperature profile as well as in the determination of sea surface or ground tereperature. The cloud field parameters are 

determined only after a complete atmospheric and surface solution is obtained. 

This method of treating clouds is fundamentally different from the approach 

used in analysis of AVHRR data, in which high spatial resolution is used to 

attempt to identify clear spots. Sea-surfsce temperatures are not determined 

in AVHRR spots thought to have any cloud contamination. 

A two field-of-view approach, s1u.ilar to one originally introduced by 

Smith (1968), is used to_extrapolate obs~rved radiances to obtain reconstructed 

clear column radiances, Ri. We express Ri as 

(4-2) 

where Ri,l is the observed radiance for channel i in the field of view having 

.the larger 11 ~m radiance, and Ri,Z is the observation of channel 1 in the 

second field of view. 

If Ri,eLR is known for a given channel, then n can be solved for 

according to 

4-5 

i 

! 

I .• 

I 

i 

~ f 

: 

I 

, 

~ 

1 

I 

I , 
/, 

I 

/ .' 

, f: 

" >" ,. / 

I 

/ 

~ , 

( 

.... ,. 

, 

,'/~" 

/', \ 

\, ,~ 

, ',,'/. -":.------.. ~ .. 

(4-3) 

Since" - al/(a1 - a2)' it should not be dependent on the channel used. In 

Susskind et ale (1984~. " was determined by combined use of IlIRS2 cr.:o.mel 13 

and HSU channel 2. R1 3,CLR is computed in each iteration (N) u~lng the Nth 

iterative temperature profile and ground temperature. If the Nth guess is 

too warm (or cold), ki~~R would be too large (or small). The effect on the 

computed brightness temperature of chan~el 13 of a bias in the temperature 

profile in the troposphere is accounted for, to first order, by modifying the 

bl'ightness temperature according to 

(4-4) 

where e~3,CLR is the equivalent brightness temperature to R~3,CLR' GM2 1s 

the observed brightness temperature in HSU channel 2. which is sensitive to the 

average tropospheri~ temperature, and e~2 is the computed brightness 

temperature in HSU channel 2. This procedure works reasonably well even though 

channel 13 is sensitive primarily to radiation much closer to the surface than 

that of MSU channel 2. Then, "13 is computed according to 

~ 

B13 [913 ] - Rl3 ,1 

Rl3 ,I - R13 , 2 

(4-5) 

where n(9~), the black body function of the corrected equivalent brightness 

temperature, is the corrected estimate of the clear column radiance. While 

results using equations (4-2) through (4-5) are quite good, it has been found 

that in cases where the initial guess has a lapse rate error, improved results 

are obtained by defining "14 in an analogouo manner to "13 in equations 

(4-4) and (4-5) and setting" equal to the average of nI3 and "14 weighted 

by the square of the difference in radiances for each channel in each field of 

view: 

n - (4-6) 

Including channel 14 radiances in the determination of " has the effect of utili~ing a Single infrared channel with a broader weighting function, more in 

line with that of the microwave channel, to correct for cloud effects. Combined usc of channe18 13 and 14 wao especially necessary for NOAA-7 data bc4-(, i 

'. 

, ~ 

. 

I r. 
/ 

:/ . I I , I 

I 

I ;' 

/ 

I 

/' 

• i 

I 

i 

I 

/ 

I 

I 

d 

~ I 

·1 h 

~ I .. __ e M'--

! 

I 

., . 

I 

/. . ...... , ,/. . 

cause the weighting functions for both channels 13 and 14 were shifted lower in 

the atmosphere relative to TIROS-N and channel 13 provided even a poorer match 

with HSU channel 2. The procedure gives improved results with TIROS-N data as 

well. 

2. Improved Determination of Sea-Surface Temperatures and Ground 

Temperatures 

-N Once n is obtained in any iteration, reconstructed clear column radiances, Rt, are obtained for each channel using equation (4-2). These radiances are used to get the updated estimates of ground temperature and atmospheric temperature profile. One can solve for ground temperature at night 

from any window channel observation (8, 18, or 19) in a straightforward manner 

given the clear column radiance, the estimated temperature-humidity profile, 

and an estimate of the surface emissivity. During the day, solar rediation 

reflected off the ground contributes significantly to the observed radiances in 

the short-wave window channels 18 and 19. A sea surface or ground temperature 

can be obtained during the day by simultaneous use of the two shortwave channel 

observations, with the additional assumption that the surface bidirectional 

reflectance of T.adiation from the sun, in the direction of the satellite, is 

the same at both 4.0 and 3.7 um (Susskind et ale 1984). 

In Suoskind et ale (1984) only the two short wave channels were used to 

obtain both day and night global surface temperatures. The 11 ~m channel was 

not used because attenuation due to water vapor becomeo very significant in 

humid atmospheres. The differences between the retrieved January 1979 monthly 

mean day and night sea surface temperature were almost all less than IOC as 

expected. This gives evidence that the procedures used to correct the short 

wave observations for solar contamination are valid. 

While the results using this method were quite good, several new improvements have been made to the system. The first change involves including radiances in the 11 ~m window channel 8 in the estiruate of sea/land E;.Jrface 

temperature. As alluded to earlier, under very humid conditions, significant 

attenuation of radiation leaving the surface by water vapor absorption at 

channel 8 frequencies produces a large potential source of error in the determination of ground temperatures both becaus~ of uncertainties in water vapor 

distribution, and even more significantly, low response of the radiances to 

changes in surface temperatures. Nevertheless, inclusion of 11 um radiances 

introduces additional surface temperature infQrmation, which becomes 4uite 

accurate at low and moderate water vapor conditions. In addition, during the d~y, under some conditions, the long wave observations are superior to short 

wave observations which have a potential error source due to the reflected 

aolar radiation. In order to determine the proper mix 0f channels to be used 

to get ground temperatures in a given situation, ground temperatures were retrieved for all channels, that is, three independent estimates, Ts8' Ts18' 

and Ts19 ' were made at night, and two independent estimates, Ts8 and Ts18.19 

were made during the day. Each estimate was given a weight, Qi' which decreased according to the magnitude of the cloud correction and atmospheric and 

solar radiation corrections that had to be made to obtain the ground temperature TBi from the observed brightness temperatures. The weight was defined 

as 

4-7 

-----~-----------.--.-.--. 

---

r , 
·~-, 

,'Co .: " 

.' -

, 

'. -..'.' 

'. j ,: 

I 

, 

___ ~ ..... ~'~c... ~.:;l. -=--":-:::':".~'-;';",;,,,._-,;,-__ ..... ,-...... - .... -•• ___ :.:_. _.~ ____ .. 1 . 

(4-7) 

where 0i - 8i 1 is the difference between the reconstructed brightness temperature and ~hat observed in field of view 1 for channel i, and 0i - Tsi 

1s the difference between the reconstructed clear column brightness temperature 

used to obtain a surface temperature and the surface temperature obtained. The 

first term represents the cloud correction and the second factor represents the 

.atmospheric and solar radiation correction. 

In the case of daytime retrievals, the brightness temperature of channel 

18, which is less affected by solar radiation than that of channel 19, is used 

in equation (4-7) together with T18,19' to get QI8,19. For a given sounding, an estimate of surface temperature is given a zero weight if its weight as 

obtained from equation (4-7) is not at least half the average of the rest of 

the weights. In addition, any weight less than 1/30 was set equal to zero. No 

surface temperature is retrieved 1f all weights are zero. Otherwise, the surface temperature TN is taken as the weighted sum of Ts,i 

(4-8) 

The entire sounding is also given a weight, W, which rp.flects the number of 

independent e8tim~tes of sea surface temperature 8S well as the weight of each 

estimate 

Tain weighted by W 1n g~nerating monthly mean fields. Given a set of oeasurface telllveratures Tj and weights Wj for a grid box k, the (weighted) 

-w monthly mean sea surface temperature anomaly field Ak should be produced 

according to 

(4-9) 

(4-10) 

where TCL!M,j is the Reynolds (1982) sea surface climatology interpolated in 

time and space to the location of the sounding j. As a consequence of equat10ns (4-7) throu~h (4-10). more optimal use is made of the three window channels for a gIven sounding. and relatively clear and/or dry soundings are 

weighted more than cloudy or humid ones in generating monthly mean fields. In 

addition, night-time soundings are gIven more weight than day-time soundings 

(not necessarily a good result) because night soundings have 3 independent 

estimates instead of 2, which tends to increase W, and also because the nighttime values of Q tend to be higher i" shortwave channels than the day values. 

4-8 

i' 

i 

i 

~ ! 

j 

. .. 

I i'. , 

I 

I 

-... 
. , . i 

\ 

\ 

j

:.: ~!:."""~.!I".·UB!IIIl"!);::t.:·!i:·,,a...:.""'")"_)"'J"!.~"~iIf4jo •• I_UIll_.."i ......... _''''n:1ll ... ~A>'''' ... j~tI!''''·f.,.,,'1."ft'JiliBl'!.C_~ji~ritii"ft'll~.:zZJl5itr:tlI4ifti_::wrlms:z;· ~::I::t.'f~ •• rll'!I'! .. ~.!~!~~'!_.'\~.·~~i~~.~!'~?i,p.~ .. '?:.!2t%:l. ~t:~?~:1r·::gr.:~:!2.:!'!·~,:::!j~}'!"···!!'J4r~~· 'tir:~.~:1-.~ ~ 

~t ~ 

;~ 

, } 

. , 

, 

, 

1 

w, the weight for each sounding, equation (4-9), was sent to the workshop 

along with the corresponding Ts ' equation (4-8), so that weighted anomaly 

fields, equation (4-10), could be prGduced. Unfortunately, the workshop did 

not initially utilize the weights prov!tled to them in generating the monthly 

mean anomaly fields and treated all soundings as having equal weight. 

Figure C-3 (see Appendix C) shows the anomaly fields derived by the workshop from data from ships, AVHRR, HIRS and SMMR for December 1981. The small 

scale noisy nature of the HIRS field is evident. In addition, large spurious 

warm anomalie9 are observed in the "IRS field in the vicinity of land and 

especially in the Gulf of Mexico, the Sea of Japan, and the Yellow Sea. 

Similar characteristics are found in the March 1982 anomaly field generated 

uoing this method. The errors close to land were found to be the result of 

the inadvertent use of a 40 x SO topography which was used to define land 

and water in the retrieval program. This land-water flag is used to sreclfy 

the surface emissivity, which is taken as higher over ocean than over land. 

Using an emis~ivity with too low a value in analyeis of data off the coasts of 

continents resulted in spuriously warm retrieved surface temperatures. It was 

also observed 1·. Workshop III that the small scale noise in the "IRS fields is 

greatly reduced by applying a 9 point smoothing to the anomaly fields 

(4-11) 

where grid points 1 3re adjacent to k and SkI is a smoothing matrix. As a 

result of this, the workshop generated some statistics for smoothed anomaly 

fields derived from all sensors and ships. This produced SOme interesting 

findings which will. be shown later. 

C. MODIFICATIONS MADE TO REPROCESS I{ARCH 1982 AND PROCESS JULY 1982 - "IRS 

VERSION 2 

As a result of the findings on the "IRS fields originally produced for 

December 1981 and March 1982, a number of further modifications were made to 

the program. The first change involved simply replacing the 40 x SO topography by a 10 x 10 topography. This eliminated the large errors near 

the coasts. The second change involved the processing of more data to reduce 

the effects of rando~ noise. It was found that retrievals performed in each 

of the three quadrants having the largest brightness temperatures (see Figure 

4-1) produced geophysical parameters of comparable accuracy to those obtained 

only from analYSis of data from the warmest quadrant as seen by the 11 ~m 

window. This tripling of the data density did little to change the sea-surf8~e 

temperature anomaly patterns, but greatly reduced the random noise component. 

In addition, improved fields of ground temperature and ice 6nd snow cover 

resulted from the increased data denstty_ 

A consistency check was also added to the sea surface temperatures Ts,i 

obtained for a given sounding with channel i. Ir the case of three estimates 

of sea surface temperature for a given sounding, Qi was set equal to zero if 

Tsi was different from Ts ' computed in equation (4-8) uning Qi' by more 

than 1.SoC. In this case, Ts was recomputed using equation (4-8) with 

4-9 

)1 

J 

I 

f 

I 

\-

I 

,. 

I 

I 

j 

1 

i 

, 

C 

! 

~ 

i 

j. 

- J 

i 

1 

\ 
. ; 

~ I • I 

."-i 

" I ....-1 I 

~ I 

" 'I i;',l 

I I 

F I 

f 

B 

__________________ ftr,· .,

Qi - O. In the case of two estimates of To,i for a given sounding, the 

less reliable sounding WRS eliminated if the difference between the two soundings was greater than 30C. The less reliable sounding was defined as the 

one differing the most froc climatology. This does not eliminate large differences from climatology as long as the result is supported by more than one 

channel for a given sounding. This system, called HIRS version 2, was used to 

reprocess March 1982 and process July 1982 data. As in the system described 

in Section A, equations (4-1) through (4-9) were used and temperatures and 

weights were sent to the workshop. Comparison of results for December 1981, 

March 1982, and July 1982 with those produced from ship data are shown in the 

next section for HIPS fields and other fields in the works~,p. 

D. COMPARISON OF RETRIEVED ANOMALY FIELDS WITH THOSE OBTAINED FROM SHIP DATA 

One way of judging the accuracy of sea surface temperatures retrieved by 

various sensors is to compare characteristics of the monthly mean anomaly 

fields derived from the sensors and from ohip measurement. Climatology is 

Also treated as a sensor in this comparison. Tables 4-2 to 4-~, taken from 

values generated by the JPL workshop, show: C, the correlation coefficient; 

B, the bias (sensor-ship); S, standard deviation; and N, the number of grid 

point samples (20 x 20 grid) comparing anomaly fields from a number of 

sensors with ship fields. Statistics are given for global colocations and 

also for those in the North Pacific and NOLth Atlantic Oceans. Results are 

given for the unsmoothed field, and also for 9-point smoothed fields, in which 

case, both the ship field and retrieved field were smoothed and then compared 

to each other. All 8 points surrounding a grid point were needed to perform 

the smoothing. Therefore, only points for which unsmoothed anomalies were 

available for all surrounding points were included in the smoothed statistics. 

As a result of this, many ship and XBT observations were dropped from the 

smoothed fields because these fields have more data gaps, especially in the 

tropics and southern oceans. 

To interpret the significance of standard deviation from ships, an anomaly 

field produced by a sensor can be considered skillful if its standard deviation 

irom ships is at least as low as that of climatology, which is representative 

of the ocean signal. Statistical results for Dec~mber 1981 are shown in Table 

4-2. It is interesting to fee that climatology differs from ships with a 

standard deviation of about 0.6oC when no smoothing is applied and about 

0.4°(, when smoothing is applied. This drop in "signal" is most likely due 

to noise in the unsmoothed ship field. It is also possible that further 

deviations from climatOlogy exist in those grid points which are excluded from 

the statistics because ships did not report in all the surrounding grid points, 

but it is unlikely that this would explain all the reduction in standard 

deviati(JO. 

4-10 

! 

I 

I 

I 

I 

~ 

I 
'Ef~r·n~~:!"~""··t~ .. ,-, ~~-... ' '.. _. . ' . hi I rz,' •• %t ,. -- • .,. ~I ~ ,. •• " •• N01JCifiJifit!il'llli.tliJ EGC!i&IZR42C1m£t.Jat;DJ4Piii'!fA?iW!it£'ilEJ& .f.A'i ~i)L,"Hif(.HQf!t.*t .. JEt: '~'~ 

1 

, 

" 

.. 

1 

·1 

~ , 

t 

Table 4-2. Comparison of Hrlthly Mean Anomaly Fields with Ships> 5/Cell, 

, . December 1981 

• I 

Unsmoothed Smoot.hed I. 

Global N. Pac. N. Atl. Global N. Pac. N. Atl. 

C 0.16 0.11 0.14 , 0.91 0.93 0.93 

AVHRR B -0.30 -0.44 -0.15 -0.33 -0.43 -0.19 

S 0.50 0.50 0.41 0.28 0.29 0.18 

N 129 376 255 235 121 102 

C 0.21 0.29 0.13 0.45 0.56 0.04 ! 

I HIRS B 0.13 0.31 0.10 0.21 0.21 0.24 I 

S 0.88 0.89 0.11 0.42 0.l15 0.39 i . N 129 376 255 235 127 102 I I, 

I 

I 

C 0.21 0.22 0.33 0.40 0.34 0.59 j 

j SMMR B 0.12 1.08 0.42 0.11 0.95 0.47 i 

S 1.17 1.10 1.15 0.79 0.72 0.76 

N 611 361 221 226 126 96 I I 

! 

i 

C 0.63 0.63 C )0 0.50 I, 

XBT B -0.03 -0.04 0.24 0.24 1 

S 0.84 0.84 0.27 0.27 i 

N 158 155 8 8 1 

1 

~ 

• C 0.00 0.00 0.00 0.00 0.00 0.00 • 

~ CLIM B 0.03 0.18 -0.14 0.08 0.12 0.01 , " S 0.61 0.61 0.55 0.38 0'.41 0.35 --. " 

N 129 376 255 235 127 102 l .,.:. 

I, 

4-11 
• 

--~ 

~ l 

"""" 

.i , , 

J 

\: I 

1'1 

~) 

'~. ~ I 

5/CeH. '\ Table 4-3. Comparison of Honthly Hean AnoMaly Fields with Ships > t 

r.: . ttllrch 1982 J 

J 

" 

Unsmoothed Smoothed 

i Global N. Paro. N. Atl. Global N. Pac. N. Atl. 

I 

!. 

t C 0.67 0.52 0.67 0.77 0.58 0.80 

AVHRR B -0.36 -0.50 -0.29 -0.44 -0.54 -C.3U 

S 0.51 0.48 0.42 0.29 0.29 0.21 

'! N 795 434 267 368 210 153 

" 

'j 

, C 0.10 0.13 -0.01 0.40 0.39 0.43 >, "IRS B 0.30 0.47 0.16 0.29 0.42 0.12 

S 0.92 0.95 0.84 0.4'! 0.41 0.35 

N 795 434 267 368 210 153 

C 0.55 0.30 0.66 

I "IRS B 0.29 0.36 0.21 

Version 2 S 0.31 0.34 0.22 

N 368 210 153 , 

C 0.24 0.37 -0.05 0.15 0.54 -0.09 

SMHR B -0.21 0.05 -0.76 -0.17 0.13 -0.77 

Night S 1.11 0.99 1.19 0.79 0.67 0.69 

N 690 392 213 300 200 95 

C 0.58 0.57 0.75 0.75 .1 

SHHR/Sh1p B 0.04 0.03 0.07 0.07 

COlllDosite S 0.47 0.46 0.25 0.25 

N 438 394 207 203 '. 

C 0.40 0.50 0.79 0.79 

VAS B 0.90 0.89 0.91 0.91 

S 0.56 0.52 0.26 0.26 

N 109 106 51 51 

C 0.39 0.38 0.70 0.70 

XBT B -0.27 -0.29 -0.47 -0.47 

~ S 0.89 0.91 0.35 0.35 \ N 242 227 18 18 

C 0.00 0.00 0.00 0.00 0.00 0.00 

CLIH B 0.09 0.27 -0.05 0.13 0.29 -0.10 

~. S 0.52 0.48 0.42 0.35 0.32 0.27 

N 795 434 267 368 210 153 

4-12 

~ 

-----
" , 

......... , 

- ,-

Table 4-4. Comparison of Monthly Mean Anomaly Fields with Ships > 5/CcH, 

July 1982 

Unsmoothed Smoothed 

Global N. Pac. N. Atl.· Global N. Pac. N. Atl. 

C 0.62 0.59 0.66 0.70 0.63 0.84 

AVHRR B -0.48 -0.37 -0.57 -0.35 -0.17 -0.46 

S 0.79 0.93 0.60 0.52 0.62 0.37 

N 644 320 253 274 117 157 

C 0.49 0.43 0.51 0.78 0.52 0.85 

HIRS B -0.07 0.01 -0.08 0.09 0.14 0.04 

Version 2 S 0.69 0.72 0.62 0.38 0.39 0.36 

N 662 337 259 327 170 157 

HIRS C 0.52 0.45 0.56 0.79 0.45 0.88 

Version 2 B -0.37 -0.31 -0.37 -0.25 -0.23 -0.27 

Weighted S 0.69 0.74 0.61 0.38 0.43 0.10 

N 662 337 259 327 170 157 

C 0.46 0.54 0.32 0.55 0.62 0.66 

SMMR B -0.43 -0.22 -0.88 -0.69 -0.39 -1.07 

Night S 0.97 0.87 0.93 0.60 0.48 0.51 -- N 522 278 193 230 127 103 

C 0.76 0.74 0.86 0.86 I "' I SMMR/Ship B -0.04 -0.06 -0.10 -0.10 .. .'~ 

i S 0.53 0.54 0.27 0'.27 t . I 

~ '~ t 

N 316 282 137 137 '. , 

t 

[ C 0.49 0.46 0.42 0.42 t VAS B 0.48 0.50 0.55 0.55 

f S 0.46 0.45 0.22 0.22 I N 92 88 38 38 

f 

I 

t C 0.58 0.55 

~ XBr B -0.22 -0.23 

s 0.94 0.96 

N 154 146 

r· 

I' 

.. C 0.00 0.00 0.00 0.00 0.00 

~ CLIM B 0.67 0.26 0.70 0.96 O./~O 

S 0.73 0.69 0.63 0.50 0.64 

!! 

N 338 259 336 179 157 

4-13 c~ 

.. 

~ ~ 

... 
./ 

" 

~-.-. 

,. 

One reason the global deviation from climatology is small is that extensive areas have deviations very close to zero. On the other hand. large spatially coh~rent areas of anomalies> O.SoC also exist, as evident in Figure 

C-3a showing the ship anomaly field for December 1981. A much better indicator of skill is the correlation of the sensor anomaly field with that cf ships. 

This correlation is high if the anomalies lie in the right place and are of 

the right sign. Tha correlations are reduced by random noise. especially if 

the noise is of comparcble magnitude to the signal. In addition. large areas 

of small anomaly will also reduce the correlations because the magnitude of 

the Signals in these areas is comparable to noise. Climatology. by definition. has zero anomaly correlation and zero skill. 

As seen in Table 4-2. smoothing has reduced the noise and increased the 

correlation coefficients for all cases (except XBT which contains only 8 pointa 

in the smoothed tield). Part of this is due to random noise reduction in the 

ship field and part due to noise reduction in the field being compared to 

ships. The AVHRR field shows skill both in the standard deviation sense and 

In the correlation sense in the unsmoothed fields. while both the HIRS and 

SKHR fields show ponr skill in the unsmoothed fields. It is interesting to 

note the XBTs also appear noisy in the unsmoothed fields with regard to standard deviation. but not correlatior. coefficient. This may be in part due to 

noise. but it is also due to the fact that the portion of the North Pacific 

Ocean measured by XBTs had a larger anomaly than the North Pacific Ocean as a 

whole. In the smoothed fields. AVHRR shows remark~ble skill in both categories. In addition. the statistics for "IRS have improved greatly so as to 

show moderate skill in correlations and noise level. It should be noted that 

these statistics are for the unweighted 1I1RS2 field. The weighted field (not 

shown) provides better visual agreement '~ith the ship field, but statistics 

were not initially computed USing the weighted field. The SMMR field statiDtics also improved to show some skill in anomaly correlation, but the noise 

levels are high compared to the signal. 

While AVHRR appears extremely good statistically, some aspects of the 

anomaly field, shown in Figure C-3b, are disturbing. In particular. the region 

from about 750 E to 15SoE, SON to 50 S, sho~s a coherent warm anomaly of between 

0.50 and 2.SoC. which is not supported by the few ship measurements in the 

area. In fact this area did not enter into the statistics for two reasons: 

first. the scarcity of ships in the area, and secondly because of the "SMMR 

Mask" (s~e Figure C-3d) which was applied to statistics of all fields so similar areas could be compared statistically. This mask is applied to SMMR data 

because accurate SMMR retrievals cannot be obtained less than 600 km from land. 

A second problem 1s that the AVHRR data has regional biases. Table 4-2 shows 

a cold bias in both the Atlantic and Pacific Oceans. with the Pacific Ocean 

bias being about 0.20 larger. Figure C-3b shows the biases to be largest in 

the high latitudes, which represent the coldest, driest air. On the other 

hand, the western Pacific tropical area previously mentioned, which is the on~ 

with the highest water vapor content. was spuriously warm. It is possible 

that the algorithm used to correct the AVHRR soundings for water vapor undercorrects the effect for low water vapor content and overcorrects in the case 

of high water vapor content. This is consistent with theor~tical studies 

showing that the effects of water vapor content o~ brightness temperature grow 

in less than a linear fashion. 

4-14 

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Statistical results for March 1982 are shown in Table 4-3. 1his table 

includes results of the reprocessed March 1982 HIRS data. dS dcscrihed in Section B. called HlKS version 2 in the table. Only statistics for the smoothed 

version 2 data were generated. Also included in the table are statistics for 

the field derived using SHMR data and shi p data simult<lneously. and for the 

VAS retrievals. which were primarily in a small area in the North Atlantic 

Ocean. The SMMR/SIlIP composite field was produced only in the Pacific Ocean 

because of c<llibration difficulties with the S}~ in the Atlantic Ocean. 

Comparison of ships with climatology shows smaller anomalies in March 1982 

than in December 1981. Indeed. the signal in thp. North Pacific and North 

Atlantic Oceans is on the order of o.Joe. Stati5tlcs for AVHRR in March 1982 

are similar to those of December 1981, but the correlations are reduced somewhat. most likely because the signal ia reduced. Statistics for the HIRS2 are 

again similar to those of December 1981. It should be noted that HIRS version 2 is significantly better than HIRS. both with regard to standard deviation and anomaly correlation. 

Smoothing has reduced the noise and increased the correlation coefficients 

for all cases. Part of t'lis is due to random noise reduction in the ship field 

and part due to noise reduction in the field being compared to ships. The unsmoothed AVHRR field in March 1982. shown in Figure e-4b, shows marginal skill 

in the standa~d deviation sense and good skill in the correlation sense. Statistics for the unsmoothed March HIRS version 2 fielJs were not generated at 

the workshop. It is int~resting to note the XBTs also appear noisy in the unsmoothed fields with regard to st<lndard deviation and corr~lation coefficient. 

In the smoothed fields. AVHRR shows increased skill in both categories. 

The statistics for the smoothed HIRS2 fields are almost as good as those of 

AVHHR with regard to standard deviation, but the correlations are considerably 

lower. It should be noted that these statistics are for the unweighted HIRS2 

field shown in Figure C-6. The weighted field, shown 1n Figure C-6b, provides 

better visual agreement with the ship field, but statistics were not computed 

using the weighted field. The weighted S}~R field is much more noisy, but 

shows some correlation skill in the North Pacific Ocean. An additional field, 

produced using both S~~ data and ship data, shows good agreement in the 

vicinity of ships as expected. Surprisingly. the statistics are only marginally better than those of AVHRR or HIRS2. which did not have the benefit of 

including the ship data used to verify the fields. VAS shows good results for 

that portion of the North Atlantic Ocean where observations wer~ made but has 

a disturbingly large bias of O.90 e too warm. 

Figure e-4a indicates that March 1982 had only small areas showing more 

than 0.50 deviation from climatology. especially in the North Atlantic Ocean. 

In the North Pacific Ocean, there are small cold anomalies centered at about 

IbO<>W. 350 N and 17SoE, 2SoN and a small warm anomaly about 1150\~. 20oN. The 

weighted HIRS2/MSU field likewise shows small anomalies in the North Pacific 

Ocean. of the correct sign, centered at the appropriate locations. The anomaly 

centered at 3SoN is weaker than in the ship field, and is less apparent in 

the HIRS2 field because it is hetween 0 and -0.5°, showing up as white in 

the picture, but on the cold side of the 00 contour. The small anomalies in 

the Atlantic Ocean are in almost perfect agreement with those in the ship 

field. The AVHRR field. on the other hand. indicates extensive areas of cold 

4-15 

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anomaly in both the North Atlantic and Pacific Ocean, sometimes exceeding 1.50 

in magnitude. This feature is consistent with the cold bias of O.30 C to O.SoC 

indicated in Table 4-3 for AVHRR. The weighted HIRS2 field, like the ship 

field, shows a warm anomaly in the Bay of Bengal, which is absent in the AVHRR 

field, and an area of negligible anomaly along the equator from 750 E to 1550E. 

In contrast, the AVHRR field has a very large spurious warm anomaly in this 

region. Agreement of all fields in the southern hemisphere is reasonable, but 

the AVHRR warm anomalies are larger than those indicated by HIRS2/MSU or the 

ships. The unweighted HIRS2 field is somewhat warmer than the weighted field 

and has essentially no anomalies in the North Pacific Ocean. It is not surprising that small correlation coefficients were found in the North Pacific 

Ocean for the unweighted HIRS2 version 2 field. 

Statistics for July 1982 are shown in Table 4-4. For this month, statistics were gel,erated for both the wei~hted and unweighted HIRS2 version 2 

fields. As observed from looking at the statistics comparing climatology with 

ships, July 1982 was much more anomalous than other months in the workshop 

with a standard deviation of about O.60 C about a cold bias (Table 4-4 shows 

climatology-ships} of O.96oC in the North Pacific Ocean and O.40C in the North 

Atlantic Ocean. Therefore Signals are Idrge and one expects larger correlations between retrieved anomaly fields anci ship anomaly fields. Correlations 

for AVHRR are simlar to those in March 1982, but the standar1 deviations are 

considerably larger than in the other month~. HIRS2 statistics show improved 

correlation over March 1982, as expected, with standard deviations slightly 

degraded over those of March 1982. Statistics for the weighted HIRS2 field, 

relative to the unweighted field, show the weighted soundings have become 

colder on the average by about.O.3SoC. Results have improved slightly in 

the Atlantic Ocean and degraded somewhat in the Pacific Ocean. SMMR results 

have improved over previous months, both in the standard deviation sense and 

10 the sense of error correlation. The SMMR/ship field, as expected, shows 

good agreement with the ships used to produce the field and shows more of an 

improve~ent, with regard to standard deviation over the HIRS2 field, which did 

not have the benefit of including ships, than in March. VAS continues to have 

good agreement with regard to standard devtation in the small region of the 

North Atlantic Ocean where colocations with smoothed ships exist. VAS 

retrievals also continue to have a large warm bias, which is about O.40C less 

than in March 1982. 

Figures C-7a, b, and c and C-bc show the retrieved anomaly fields determined from ships. AVHRR. HIRS, and weighted HIRS data. llIRS a6ain shows very 

good agreement with the ship anomaly fields, wh!~~ this time are quite large 

. and extensive in both the North Atlantic and North Pacific Oceans. The HIRS 

field does have a few areas of spurious cold anomaly in July, located mainly 

within a thin latitude band running from about lSoN to 2SoN over most of the 

ocean, and off the west coast of South America from 150S to about the equator. 

These spurious anomalies, which are mostly of the order O.So - l oC, may be due 

to the effects of aerosols put into the atmosphere by.the eruption of El 

Chichon which occurred in Mexico 1n April 1982. The errors are amplified in 

the weighted field, which is colder in general. The effects of this eruption 

on the determination of sea surface temperatures are much more evident in the 

AVHRR anomaly pattern which shows a large block of Gpurious cold anomaly from 

lo"N to 3SoN running acre·ss the entire oceanic area, with magnitudes of the 

order of 20 e. 

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As shown in Figures C-6 and C-7, the weighted HIRS2 fields tend to be 

slightly cooler than the unweighted fields. The same result was found for 

December 1981. The wcighted fields show bctter agreement with ship ficlds in 

December 1981 and March 1982. -July 1982 is a special case where spurious cold 

anomalies already existed, possibly due to effects of the El- Chichon eruption, 

and results were slightly worse in thewcighted fields. 

Weighted fields count clear cases more than cloudy ones and night somewhat more than day. A simple explanation for weighted fields being colder 

would be the heavier weight placed on nighttime soundings. To test this, 

weighted fields were produced using only nIghttime data and using only daytime 

data and compared to the unweighted fields at the appropriate time of day. 

This comparison showed the weighted fields again to be cooler than the 

unweighted ones in both cases, with the effect being larger at night. It 

appears then that sea surface temperature soundings in an area are warmer 

under cloudy conditions than under clear conditions. This implics the effects 

of clouds are somewhat over-corrected in the systcm by the use of equations 

(4·'2) through (4-6). 

E. SUMMARY AND FUTURE PROSPECTS 

Prospects for improved sea surface temperature fields obtained from 

sounders such as HIRS2/MSU appear bright. The current test shows HIRS2/MSU 

fields to be statistically comparable to those of AVHRR in March 1982 and 

superior in July 1982, where the EI Chichon eruption had drastic consequences 

on the AVHRR data, but much smaller effects on HIRS2/MSU data. Errors in the 

AVHRR data tend to be systematic and spatially coherent, providing spurious 

warm anomalies in the most humid areas of the tropics and possibly spurious 

cold anomalies at high latitudes. HIRS2 errors tend to be more random in 

nature and resultant fields are improved considerably by averaging more data. 

Our plans in the near future involve reprocessing all data at a roughly 60 km 

spatial resolution, corrLsponding to 4 x 4 sets of ~IRS2 spots broken up into 

2 x 2 quadrants. We have currently completely vectorized the retrieval code 

for use on the CYBER 205 computer and one-day global retrievals at 60 km resolution takes only 10 minutes CPU time. We feel this is the finest resolution 

with which retrievals can be done using HIRS2 data because of the need for at 

least four spots in a quadrant to allow for distinct separation of radiances 

into fields of view for the purpose of cloud filtering. These higher resolution retrievals should produce much better anomaly fields both because of the 

reduction of random errors by a larger sample, and also because the individual 

retrievals will become more accurate due to increasp.d homogeneity in th~ 

sounding a~ea both with regard to clouds (assumption of a single cloud formation is implicit in the cloud filtering algorithm) and the sea surface temperature itself. 

Hardware modifications in the near future will also improve results. The 

HIRS2 to be flown starting on NOAA-H (-1987) will havc a modiHcation in it~ 

channel 17, now centered at 2360 cm- 1• The current channel monitors emission 

from the upper stratosphere and is highly affected by non-local thermodynamic 

equilibrium. Consequently little use has been made of observations in this 

channel either operationally or experimentally. The new frequency will be at 

2420 cm- 1 and can be used together with channel 18, at 2500 em-I, to get 

4-17 

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improved sea surface temper~tures during the day. The new combination is 

supe~ior to the current channel 18,19 combination for several reasons. The 

new channel 17 is less sensitive to solar radiation ch .. n 19; therefore, the 

effect of solar radiation is smaller. ~~80, channel 17 is insensitive to 

water vapor (atmospheric N2 is the biggest absorber), while channel 19 has 

moderate sensitivity to water vapor, which sometimes causes problems in tropical areas during the'day. The spurious cold anomaly in the central Pacific 

Ocean in March, derived from'HIRS2~data, in fact appeared only in daytime 

soundings. This problem will not occur with the new channel. Another significant improvement in the new channel 17 is that its bandpass is about 50 cm-1 

rather than 200 cm- 1 in channel 19. This makes the concept of "effective 

channel central frequency," which is used in converting back and forth from 

brightness temperature to radiance, more meaningful. The broader bandpass of 

channel 19 intr~duces an extra source of noise in the data. 

Perhaps the ultimate in measurement of sea surface temperatures will come 

trom an advanced infrared sounder, AHTS, described in Chahine et ale (1984), 

which may fly on polar orbit in the 1990's. This instrument has a number of 

"super-window" channels with atmospheric transmittance "'0.98, even in ver,] 

humid atmospheres, and 10 km spatial resolution with contiguous coverage. 

Simulation studies show soundings with about a 40 km =esolution can be performed in up to 90~ cloudiness with instantaneous accuracy of about 0.40C. 

It is expected that monthly mean fields with accuracies of the order of 0.20C 

can be obtained at a spatial resolution of about 50 km. This will go a long 

way toward meeting the needs of monitoring sea surface temperatures, at least 

in the climatological sense, and will malt.e possible detailed studies relating 

the effects of sea surface temperatures anomalies in the tropics on atmospheric circulation. 

4-18 

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N8GSECTION V 

SEA SURFACE TEMPERAnIRES FROM VAS HSI DATA 

J. Bates 

University of Wisconsin 

Madison, Wisconsin 

SST INTERCO~~ARISON WORKSHOP 

1. Introduction 

168'59 

The results of the SST intercomparison workshop series (JPL 1983) 

are the first examination of monthly mean SSTs derived from HSI data provided 

by the VAS inatrume~t on tho GOES series satellites. While VAS instruments 

are currently only on the U.S. geostationary satellites, limiting coverage to 

the western ~A~isphere, it is hoped that the success of VAS will encourage the 

European Space ABency, Japan, and India to consider installing a VAS instrument on their future geostationary satellites. Because the procedure to 

derive SSTs from VAS data is atill in the developmental stage, several changes 

in the procedure were made betwean the processing of data f~r March 1982 and 

processing the data for July 1982. The most significant chanSe was the use of 

tho ttree window channel algorithm (3.9, 11.0, and 12.6 ~) in the 

processing of the July data as opposed to the use of only the t~o window 

channel (11.0/12.6 ~) algorithm for the March data. Initially only the 

two channel algorithm was used in order to extend the analysio of SST into 

areas of sunglint in the tropics. However, the analysis of the March data showe~ that little additional data was gained by doing this. In addition. 

further satellite/buoy lM.tches indicated that the triple window channel algorithm showed a smaller standard deviation tl~n the two window channel algorithm and was less sensitive to the effects of volcanic aerosol contamination 

and low level inversion conditions. This is due to the smaller brightness 

tempecature attenuation by aerosols and water vapor at 3.9 ~ than at 11.0 

and l2.6~. Thus. the decision was made to use the best product (i.e •• 

the three window channel algorithm) for processing the July data. 

2. March 1982 Results 

lYo larle regions were chosen for analysis of VAS data from GOESEast. on~ 1n the western North Atlantic and one in the eastern Tropical 

Pacific. Since ship observations of surface layer temperature provide the 

only long-term climatology of SST, Reynolds (1982) climatology has been used 

as a atandard from which satellite SST monthly mean anomaly fieldS were produced. Data from all sensors were binned on a two by two degree latitude/ 

longitude grid for each month. Sr-:MR data were required to be more than 600 lcm 

from land in order to avoid ~ontamination from land. Thematic contour charts 

of seusor anomaly fields from climatology for March are shown in Figure C-S 

(see Appendix C). VAS. AVHRR, and ship data all show a pattern of cold to 

warm to cold to warm proceeding southeast off the U.S. east coast; however the 

5-1 

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VAS data have 8 warm bias of 0.5 to 1.O·C. In the South Pacific. the VAS data 

show'onlya slight warm bias and a6ain ar.e highly correlated with the AVHRR, 

ships, and XBTs. In particular, the VAS and AVHRR thematic contour anomaly 

charts show similar patterns with varm water along the coast·from 20° to 30 0 S 

and extending to the west along 30·S, 4 pool of cold water along the co~st 

from 0 to 10·S, another cold anomaly offshore, and near normal cunditions 

elsewhere. The BIRS data show generally 11e4ter anomaly patterns and 4 warm 

bias near the coastlines due to problems in accurately specifying the land/ 

water boundaries (Susskind, personal communication). The BIRS data do show a 

warm anomaly along 30·S in the eastern South Pacific and a large warm anomaly 

in the western North Atlantic. Little correlation in patterns is found 

between the VAS and the SMMR product. 

Table 5-1 summarizes the cross correlation statistics for each satellite 

verse. ship-of-opportunity measurements for March 1982. VAS estimates of SST 

show a wurm bias relative to ships for all regions ranging from +0.35 to 

1.73·C. The largest biases (1.73·C and 1.OS·C) are found with the lowest 

numbers of matches (21 and 53) and also occur at the largest satellite zenith 

angles (North Pacific region 20-S6°N and South Pacific regions 20-56·S). This 

indicates that the cagnitude of the warm bias for the two channel algorithm 

may increase with increasing satellite zenith angle but also suggests that 

noisy ship data may be partly responsible for some of the bias. 

The uniform warm biae in all regions, however, indicates a diurnal sampling bias and a possible bias in the matches used to tune the p.mpirical algorithm. Satellite/buoy ~tches are continuing to be collected in order to 

ensure that a seasonally and geogrnphically diverse set of matches is used to 

up1ate the coefficients for the empirical algorithms. It does appear though 

that the diurnal sampling of VAS data is largely responsible for the warm 

biaa. VAS data were gener.al!~ pr~ce8sed at 1530 and 1830 GMT (1030 and 1330 

LST at the GOES-East subpoint) and only cloud-free obse~ations were used. 

Thus, VAS SSTs might be expected to have a warm bias relative to estimates of 

SST that average day and night data. Diurnal heating of the ocean skin temperature 8S observed by satellite infrared data has also been reported by 

Strong (1984) and by Deschamps and Frouin (1984). Future intercomparisons 

must take into account possible diurnal sampling biases of each sensor. 

Additional cross correlation statistics for ~~rch show VAS with a scatter 

rnlative to ships of 0.79-1.24°C. The statistics show VAS well correlated 

with ships, and shows regional correlations very similar to those of the 

AVHRR. Tho one exception is the far South Pacific region (20-56·S). This 

again is the region of fewest matches and thus should be given little weight. 

3. July 1982 Results 

In the thematic anomaly charts for July (Figure C-8). the effects 

of the El Chichon volcanic aerosol are very evident in the AVHRR data as a 

zonal band of cold anomalies from 10-30·N. VAS data, however, do not show an 

analagous anomaly in those latituaes. This result is due to differences in 

the spectral channels of the VAS and AVIIRR, differences in the processing 

5-2 

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Table 5-1. Cross Correlations of Satellite SST Estimates Versus Ship SST Estimates for March 1982 

Number of Matches Bias Standard Deviation ,Croas Correlation 

... WRR SMMll BIRS VAS AVRRR SMMR BIRS VAS AWRR sma BIRS VAS AVBRR sma BIRS VAS 

Global 4322 1972 425 -0.06 -0.01 ~.63 0.81 1.20 0.96 0.58 0.25 0.59 

North Pacific 1563 815 127 -0.26 -0.05 ~.52 0.67 0.99 0.92 0.64 0.29 0.61 

(O-56"H) 

North Pacific 1033 529 1054 53 -0.39 -0.01 ~.54 +1.05 0.65 1.03 1.07 0.89 0.60 0.36 0.28 0.63 

(20-56"N) 

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Tropical Pacific 837 412 858 165 ~.10 -0.22 ~.23 ~.20 0.73 0.87 0.93 0.90 0.13 0.08 0.03 0.10 

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South Pacific: 535 202 541 21 +0.24 +0.55 ~.05 +1.73 0.78 1.19 1.11 1.24 0.42 0.08 0.28 -0.39 

(20-56'5) 

South Pacific 984 328 112 ~.26 ~.17 ~.52 0.80 1.1'· 1.20 0.29 0.11 0.23 

(0-56 OS) 

V}~ Pacific Region 178 81 181 ~.06 -0.16 ~.35 0.89 0.91 1.06 0.23 -0.08 0.03 

(l4"N-30"S) _ 

Clobal AVliRR 2214 1088 2229 211 +0.03 ~.12 +0.20 +0.55 0.86 1.11 1.04 1.12 0.48 0.29 0.20 0.41 

El Chichon Mask 

North Atlantic 715 315 186 :.0.33 -0.92 +0.76 0.61 1.18 0.79 0.58 -0.02 0.65 

(O-56"N) 

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algorithms, and differences in the average viewing geo~try. The VAS, SMHR. 

and ahip data all show a warm anomaly in the eastern tropical Pacific. 

In the North Atlantic VAS region~ the VAS data appears to be slightly 

warmer than the ship data, but again ahowa similar patterns. The VAS ari1 

AVBRR dnta show some correlation near the coast of the U.S., but meaninbfu1 

comparisons between the two are hampered by the volcanic aerosol contaminstion 

in the AVHRA data. The anomaly patterns are much the S811:8 in a comparison of 

the VAS/SMMR data. however. the SMMR data is contaminated by "cold" instrument 

warmup noise in much of the North Atlantic (Milman. personal communication). 

In the VAS region of the Pacific. the VAS, SMMR, and ship data all show warming. Bere, the VAS and SMMR data show a high correlation with a pattern of 

warm anomalies along the coast and extending westward along the equator. In 

contrast, the BIRS data, while not showing any consistent bJac in the El 

Chichon region, does show a large cold anomaly in this region. 

Crols correlation statiRtics for July 198Z are summarized in Table 5-Z. 

VAS SSTs again show a slight warm bias in all regions. The very large warm 

bias at large local zenith anglcs evident in the ~tarch 198Z data. however, has 

been eliminated by the use of the three window ct~nnel algorithm. Little bias 

is evident in the region of the El Chichon volcanic aeros~l (approximately 

lO-30-H). In this region, the AVBRR data ahow a cold bias of O.50-Q.7S·C 

relative to ships. The VAS standard deviations are also generally smaller in . July than in March due to the use of the three window channel algorithm. The 

cross correlations of VAS data with ship data, however, ar6 much weaker in 

July than March. 

After SST Intercomparison Workohop III. additional cross correlation 

tables were generated to try to answer some of the questions raised during the 

workshop. Most important to the interpretation of VAS data vas the stratification of AVHRR data into day and night 80 that the daytime only VAS data 

could be directly compared to daytime only AVHRR data. Although the new cross 

correlation tables are masked to include only data greater than 600 km from 

land (to normalize the comparison between SMl'iR and tl'.e other senSOl°S. but 

greatly reducing the number of VAS/ship matches). some trends are clearly evident. In March 1982. AVHRR shows a global average day minus night difference 

relative to ships of +O.43·C. This reduces the VAS minus AVHRR day bias to 

+O.23·C. The VAS verses ship biases remain unchanged since ships measure SST 

at some depth beneath the surface and are relatively insensitive to diurnal 

heating of the ocean skin. In July. on a global basis, the AVERR day product 

is O.43·C warmer than ships while the AVHRR night product is 0.7Z·C colder 

than ships. There is no discernable bias between AVllRR day SSTs p.~d VAS SSTs 

outside the El Chichon zone (i.e •• in the South Pacific and North Atlantic). 

while within the EI Chichon zone (the mid-Pacific) AVHRR day is O.69·C colder 

than VAS and O.SO·C colder than ships. These data clea~ly show that the 

diurnal heating of the ocean skin is being detected by VAS and AVHRR. and 

demonstrates that most of the VAS warm bIas relative to the other sensors is 

due to this diurnal variability. 

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Table 5-2. Cross Correlations of Satellite SST Estimates Versus Ship SST Estimates for July 1982 

Number of Y~tches Bias Standard Deviation Cro,s Correlation 

AvnRR SI'UiR BIRS VAS AVBRR SMliR HIRS VAS AVHRR SMMn RIRS VAS A",!!II~ SlflR BIRS VAS 

Global 3962 1826 437 -0.54 -0.18 +0.60 0.90 1.08 0.85 0.44 0.38 0.26 

North Pacific 1368 70B 116 -0.69 +0.26 +0.91 0.95 0.89 0.80 0.41 0.46 0.17 

(O-56'N) 

North Pacific 514 221 26 -0.18 +0.11 +0.40 0.64 1.13 1.25 0.50 0.24 -0.10 

(20-56'N) 

\J1 Tropical Pacific 779 366 165 -0.69 +0.10 +0.77 0.83 0.83 1.00 0.30 0.27 0.07 

I (20 0 N-20'S) 

\J1 

South Pacific 958 480 51 -0.54 -0.31 +0.61 0.98 0.92 0.50 0.46, 0.44 - 0.11 

(20-56'5) 

r South Pacific 883 359 126 -0.23 +0.06 +0.50 0.67 1.03 LOS 0.41 0.26 0.00 

II (0-56'5) , 

'\ 

VAS Par.ific Regiun 162 104 174 -0.38 +0.16 +0.68 0.81 1.10 1.08 0.25 0.05 0.01 

(14 ~N-30'S) 

I Global AVI!RR 2305 1112 216 -0.20 +0.02 +0.49 0.77 1.08 0.90 0.49 0.41 0.26 

I 

£1 Chichon l".aslt 

North Atlantic 695 324 195 -0.81 -1.06 +0.49 0.87 1.06 0.67 0.38 0.21 0.40 

(O-56'N) 

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. ---------

B. EVALUATION OF OTHER PRODUCTS 

1. AVHRR 

The AVERa MCSST is the only operational satellite SST analysis currently and is the most accurate and consistent product evaluated at the workshop series. As with all SST data sources, care and understanding must be 

used ~hen evaluating and applying this data. Studies such as that by Legeckis 

and Pichel (1984) are particularly useful in interpreting the weekly MCSST 

analyses. Users must also understand the nature and variability of ocean surface skin temperature measurements as opposed to ship bulk surface layer measurements. For example, the MeSST analysi8 for March 1962 has been criticized 

for showing a warm aaomaly along the equator from the western Pacific into the 

western Indian Ocean; an area where ship climatology shows little monthly 

variability. The AVHRR day-night thematic contour analysis (Figure C-9), however, shows that this warm anomaly may be the result of diurnal warming of the 

ocean surface. In fact, the AVHRR day-night analyses show a distinct diurnal 

pattern of solar heating from December 1981 to March 1982 to July 1982. In 

December 1981, a consistent zonal band of warm davtime SST anomalies is found 

from about 30-50·5, in the southam (summer) ~emisphere. In March 1982, the 

wa~ anomaly has become more diffuse and ohows the largest anomalies on the 

equator. By July 1982, the warm anomaly evident as a zonal band in the northern (summer) hemisphere. Diurnal variability of tile oceans skin is being measured by satellite sensors, as is evident from the analysis of AVHRR ~ay-night 

measurer:lents. 

2. SMMR 

The problema with the SMMR SST product are largely due to instrumental difficulties. The SMMR calibration biases are large and vary in time 

and space, and sidelobe interference requires observations to be greater than 

600 km from land. In spite of these difficulties, S~n1R analyses of the 

Pacific and Indian Oceans appear reasonable. Unfortunately, the calibration 

problem makes it diffi~ult to evaluate the problem of microwave emissivity 

changes of the ocean surface with wind speed, while the land mask restricts 

analysis of the impcrtant boundary currents. The SMMR/ship product is an 

improvement on SMMR alone, but it does not take full advantage of all the 

different sensors for measuring SST. 

3. HIRS/MSU 

Evaluation of the HIRS/MSU product is difficult because of changes 

in the product from one time to the next and because the data were presented 

late. The HIRS/MSU anomaly patterns generally look noisy bnd weaker than the 

anomaly patterns of the other sensors. In March 1982, the HIRS/MSU shows no 

correlation with any of the ether products and a standard deviction from climatology of about 1°C. The July 1982 statistics are better, but the anomaly 

patterns are inconsiatent, showing an overall ~ool bias. Particularly troublesome is a cool anomaly in the eastern Equatorial Pacific where all the 

other sensors show a warm anomaly. 

5-6 

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c. RECOMMENDATIONS 

1 • Improvements in Infrared Sensors 

Recent theoretical and empirical studies of the infrared portion of 

the earth's spectrum have revealed that neither VAS nor AVHRR have the optimal 

channel s~lection for SST detection. Studies ~re now underway to determine 

which window regions using a filtered radiometer would yield the most accurate 

SSTs. In th~ long term, though, an infrared spectrometer interferometer 

instead of a filtcrt.!d radiometer will be a much better instrument since. it 

would permit !lse "f aU portions of the infrared window regions to be utilhed. 

2. A tc~bined Product 

Efforts should begin on a combined satellite SST product that takes 

advantage of the benefits of each sensing system discussed in the workshop 

series. Such an approach should use the raw data from each instrument, not 

just the finished products such as the SMMR/Ship composite. Tho ~1cIDAS system 

has the capability of processing raw data from all censors used in the workshop series. It is time to begin a program to produce an operational SST 

analysis • 

3. Research Panel on SST Sensins 

A research panel to Ret research program goals, evaluate present 

systems. and recommend areas for further study should be set up under the dire~tion of NSF or other appropriate agency. This panel should coordinate 

efforts between ongoing ocean research programs and the remote hensing community. This panel could also serve as the focus for the development of a combined SST product • 

5-7 

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N 86-168,60 

SECTION VI 

BLENDED SST FIELDS USING COMBINED SriMa/SHIP DATA 

(SUMMARY OF TECHNIQUE) 

A. INTRODUCTION 

T. Wilheit and ~. Han 

NASA/Goddard Space Flight Center 

Creenbelt, KD 

All remote sensors have bias problems which vary in space and time. albeit 

rather slowly. The SMMR can in this regard be considered a pathological 

example of a more general truth. Even ship measurements have a token biss of 

the order of a few tenths of a degree caused by heating of the thermistor by 

the hot engine room environment. This ship bias can be neglected if one dealt 

in SST anomaly (departure from climatology) since ships with a similar bias 

are the principal source of the data used to generate the climatology. The 

ships. however. are very poorly distribute~ globally, and individually 

represent very noisy measurements. Each remote sensor bas its own distribution of data as well. The infrared measurements are very sparse in cloudy 

areas; the SMMR has coverage limitations due to the interferenco of land are •• 

and only half-time operation. If one simply averdged the data from these 

various sources. the combination of the biases and varying sampling would 

introduce artifacts into the analyzed product which would reflect the sampliOR 

and which could be quite misleading. What is needed is an analysis scheme 

which uses the strength of each type of measurement to compensate for the 

weakness of the other. In particular. the remote sensors provide reasonable esti~tes of gradients of SST over length scales grea~er than the sensor 

resolution but smaller than hemispheric. Ships can be considered unbiased but 

are poorly distributed on small scales except in the densest shipping lanes. 

An analysis scheme based on a suggestion by Holl (1981) has been developed 

which exploits just this approximately complementarity to provide an improved 

SST analysis. The resulting accuracy appears to be in the required -a.S·C 

range for the particular case of using SMMR and ship data to produce two 

degree latitude by two degrees longitude monthly analyses. Proper use of 

other data sources could only improve this product. Moreover. it could easily 

be argued that the mathematics used in this analysis technique are not particularly sophisticated or even rigorous and a more p~ecise treatment may be of 

benefit. However the technique is very precisely targeted to the problems 

actually observed in the measurements. and extreme care must be taken. in any 

attempt to improve the mathematical foundation of the technique. not to lose 

sight of the measurement realities with which the technique is designed to 

desl. It could also be argued that biases of the sort observed here are 

inherent t~ all currently available remote sensing approaches to the measurement of any geophysical variable. and that the SST case studied here is only 

one of the Bi~pler examples of a more general problem. 

6-1 

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B. DES~RIPTION 0,' THE TECHNIQUE 

Consider an analysis algorithM as depicted in Figure 6-1. This analysis 

algorithm accepts measurement values with associated uncertainties at the grid 

points of the field. It also accepts values for the first nnd second 

derivatives of the field in finite difforence form, ~gsin with associated 

uncertainties. If there are enough values of the field and its derivative 

specified, then the field is overdetermined and the analysis algorithm derives 

a minimum weighted square error solution as the resultant field. An 

associated uncertainty field is a by-product of this computation. An analysis 

techniqce of this form is the basic building block of the Bias Removal 

Analysis Technique (BRAT). The specific finite differences used are: 

Differences of adjacent cells row-wise: 

R(i,j) • F(i+l,j) - f(i,j) 

Differences of adjacent cells column-wise: 

C(i,j) • F(i,j+l) - F(i,j) 

Differences of adjacent cells diagonally (both ways): 

Dl(i,j) • F(i+l,j+l) - F(i,j) 

D2(i,j) • F(i+l,j-l) - F(i,j) 

Two-space differences both column and row-wise: 

R2(i,j) a F(i+2,j) - F(i,j) 

C2(i,j) • F(i,j~2) - F(I,j) 

Finally, the Lambertian (a second difference operator): 

L(i,j) D 4F(i,j) - F(i+l,j) - F(i-1,j) - F(i,j+l) - F(i,j-1) 

The weight applied to any observation is the inverse square of its 

presumed uncertainty. For present purposes one degree celcius is used 

GRADIENT ~ ~ ABSOLUTE INFORMATION BLACK MEASUREMENTS 

(ASSOCIATED BOX (ASSOCIATED 

UNCERTAINTY) '--_--:-__ -' UNCERTAINTIES) 

l 

RESULTANT FIELD 

(ASSOCIATED 

UNCERTAINTY) 

Figure 6-1. Generalized Analysis Algorithm \~'hich in the Basic Building 

Block of the Bias Removal Analysis Technique. 

6-2 

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as the uncertainty for individual SMMR and ship measurements. The woight of 

any of the differences ia similarly the inverse square of the uncertainty 

which is derived directly from tho uncertainties of the associated points in 

the field from which the finite differences arc derived. The resulting 

problem is to solve ior a aet of values for F'(i,j) which minimizes the 

function: 

I 21 ••••• + [L(i,j) - L (i,j)} 

WL (i,j) 

where the unprimed quantities are given and the primed are solved for. 

denotes summation over all grid points, and the W(i,j) represent data 

weights. Traditionally, this equation ia reduced to a matr{x form which 

results in a formal solution: 

theY t,j 

If F is not particularly large the equation can actually be solved this way. 

However, in a practical case the field could be defined on 3 50 x SO arraYi 

this would result in the F vector in the above equations having a length of 

2,500 and the matrix A would be 2.500 x 2,500. The direct inversion of such a 

matrix would hardly be a practical matter. One alternative is to solve for F 

by minimizing the error summation with respect to each eiement of F individually and to iterate through all the locations until satisfactory convergence 

is obtained. It can readily be shown that such a procedure is convergent (in 

a mathematical sense). It was however found that the convergence in the 

present application was unreasonably slow. Fortunately. a compromise between 

the two extremes of solving for all elements of F or a single element at a ti~ is possible. It was found that solving for S elements at a time provided 

reasonable convergence and the inversion of a 5 x S ~trix is a simple matter 

on any modern computer. 

Figure 6-2 illustrates the use of this basic building block in a typical 

application of BRAT. In this case we have treated the SMMR day and night data 

as though they come from different sensors since the bias and drift problems arc 

independent on the day and night portions of the orbit. In principle. other 

sensor types could be included at this level; each sensor should be selfconsistent os for os bias and drift are concerned so thin saree day/night 

separation would seem appropriate for any current space-borne remote sel.sor. 

At this level. no derivative information is available so climatology is used 

with an extremely small weight (10-6) simply to prevent numerical probl~ms. 

6-3 

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'-----r------J----- OTHER RELATIVE DATA 

BB 

RESULTANT 

FIELD 

SHIPS 

BUOYS 

(OTHER ABSOLUTE DATA) 

20 120 I "MOtlTH 

SST ANOMALY 

RMS lho (MAYBE) 

Figure 6-2. 'Application of the Basic Building Block in the Bina 

Removal Analysis Technique 

Each application of the buildin~ block of this level results in a ;'ea 

surface temperature anomaly field with an associated uncertainty. &achof 

these resultant SST fields is differentiated and the weights of the 

derivatives Are determined from the weights of the elements of the SST field. 

The derivative fields are averaged with their associated weights. The 

weighted average of the resulting set of derivative fielda is used as input to 

the final application of the building block along \:ith the ship d/.ta. In this 

manner any constant bias in the ~MMR or other re~tely censed da~a is 

automatically re~ved, and any slowly changing bias is overpowe~ed in the 

weighted least square error solution as long as there are enough ship data. 

Conversely, random noise in the ship data is smoothed out by the derivative 

constraints provided by the SMMR data. However, any bias in the ship data i. 

passed through the analysis unmodified. 

In order to test this bias removal analysis technique (BRAT), we have 

chosen an area in the Pacific Ocean from SooS to SOON and from 13°E to SooW. 

We have produced monthly analyses of SST anomaly (with respect to the 

Robinson-Bauer climatology (Reynolds 1983) with a spatial grid Gize of 2 x 2 

degrees. We have used the day and night observations of the SMMR as different 

relative Densors an~ the file of ship observations 3vailable from NOAA as the 

absolute sensors. We have used all beam positions in the SMMR night data but 

6-4 

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a restricted cet of day data to reduce the impact of ionospheric Faraday 

rotation on the resulting allslysiR. Thifj will provide an extremely demanding 

test' of the ability of the analysis to reduce large scale binoes since the 

varioWi SST workshops showed that in this raapect the SMHR was the most 

severoly afflicted of the re .....ote sensing techniques examined. 

Figure C-IOc (Appendix C) ahowe the resulta of application of BRAT to the 

data from July 1980. One can easily sec patterns of anomalies which look 

reasonable. Thoy rarely exceed 2·C in magnitude and are spatially coherent. 

J~ exception one might be inclined to questiora ie the cold anomaly extending 

across the South Pacific which exceeds 2.S·C. Because of the interference of 

large land areas with the Sl~ relrievalo, there are large data gaps around 

the continents. The data gap in the Micronesia area eugsests excessive 

conservatism in protecting SMMR retrievals against this interference. 

Figures C-IOa and C-IOb show analyses for the same period based on 

ship-only and SMMR-only data respectively. Note that the ship data have large 

gaps particularly in the Southern Hemisphere. Note also that even where the 

ship data are den~e the SSTs have a great deal of uncorrelated structure 

Quggeoting a noioy product. It is particularly worthy of note thLlt the 

suspicious feature in the South Pacific discussed in the previous paragraph 

is, in fact, supported in the Ghip data although there is not nearly enough 

data to delineate its form. 

On the other h3nd, the S~~-only product is quite smooth and delineates 

the anomaly pattern of figure C-10a quite well. However there is a noticeable 

drift in the absolute value which is particularly noticeable in the North 

Pacific •. This io precisely the aort of error that the biBS removal analysis 

technique is designed to reduce. Quantitative estimates of the quality of 

these analysis products are difficult. However. estimates have been attempted 

and suggests an rms error of the order of O.S·C. 

6-5 

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NBS-IS8Sl 

SECTION VII 

INTERCOi-IPARISON OF GLOBAL SST FIELDS DERIVED Io"ROM 

SATELLITE SENSORS ~~ SHI? OBSERVATIONS 

A. INTRODUCTION 

S. E. Pazan 

Scrippa Institution of Oceanography 

La Jolla, California 

Near global comparisons have been made between binned forms of SST 

obtained from different satellite instruments, ship meteorological observations, and the output of a meteorological model. Binning hereinafter means 

gridding by simple summation of data values in a bin, which in this study was 

a 2- x 2· box or a 10· x 10· box surrounding a grid node. In the binned comparisons, the question is asked: are the binned data from two instruments 

drawn from the pame population? The first two measures of statistical moments 

of the populations are used to test the hypothesis that they are. 

Comparisons were also attempted between raw forms of the various SST 

retrievals, using a structure function analysis. The results of this analysis 

·were relatively uninteresting and are therefore omitted for the Bake of 

brevity. 

B. DATA SOURCES 

Five differert types of SST field estimates were available for intercomparison. They were: :. Multi-channel sea surface temperatures (MCSST) from 

the Advanced Very High Resolution Radiometer (AVHRR); 2. the Scanning Multichannel Micr~wave Radiometer (SMMR); 3. the High-Resolution Infrared Sounder 

(HIRS); 4. ship engine intake temperatures collected for marine weather analyses; 5. Fleet Numerical Oceanography Center (FNOC) sea surface temperature 

monthly mean analyses. The instruments, data r.ollection procedures, and error 

budgets of the respective SST analyses have been discussed eloewhere in the 

NASA/JPL SST workshop reports (JPL 1983 and 19Ht.), and will not be discussed 

further here. 

C. ANALYSIS TECHNIQUES 

The "pool-permutation procedure" (PPP) described by Preisendorfer and 

~arnett (1983) has been used to determine whether the first and second moments 

of SST anomaly fields prod~ced by the different instruments are statistically identical. The PPP measure of the difference between first moments 

is called "SITES", and the measure of the difference between second moments is 

called "SPRED." The PPP method allows a rigorous, nonparamctric test of the 

contention that the satellite-derived SST fields are similar to each other 

7-1 

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and/or a SST field derived from convGntional ship data. The power of the 

method ,(detection level) is estimable. Further, the technique h robust for 

relatively limited data sets (only 4 realizations in our case). 

The PPP teat atatlstics values ("SITES" and "SPRED") fall into eight 

categories; the hypothesis of statistical identity is considered to be shown 

to be false only if the statistic falls in the last category, the category 

with the largest SITES or SPRED statistic value. If the PPP test were applied 

to a completely random se~ ~f data fields, any randomly selected permutation 

would fall into the last ca:",egory lout of every 8 times; in this sense, the 

test has a confidence level I,f 7 out of 8, or S8t. 

For farther discussion of this method,'the reader is referred to 

Preisendorfer and Barnett (1983). 

D. RESULTS 

The PPP tests were applied to the data sets discussed in Section B. As 

diacussed above, the PPP test statistic values fall into 8 unique values, 

called "categories." 

1. Sensitivity Test 

Before drawing any conclusions from the results of PPP intercomparisons it is useful to have some idea of the power of the test, in particular testing the validity of applying this statistical test to a time series 

only four months long. Tests of the PPP were done using modified versions of 

the ship data sets, i.e., four month 'test data' sets were conRtructed using a 

transformation. T' • F(~,a) + I, where F represents a random population with a 

mean of ~ and a standard deviation of a. Several realizations of the PPP test 

were made for each pair of p and a values Table 7-1. 

For p not equal to zero and large a, the SITES test poorly discriminates 

between the T and T' fields; that is, the ability of this test to discriminate 

between fields actually degrades with increasing a. For instance, given a 

p • 0.05, the SITES test was able to discriminate between T and T' for a a of 

0.06, but it was not able to discriminate for a a of 0.35. On the other hand, 

the SPRED test discriminates well between data sets differing by a random 

noise element of the mean bias is smnll. As the mean bias increases, this 

teat is les8 capable of discriminating. Thus, when p ~ 0 statistical identity 

io rejected for a ) 0.35 both in the northeln and southern regions. However, 

if p is increased to 0.2, a must be 0.46 before statistical identity is uniforaly rejected. For a plausible bias, P, and random noise, a, the SPRED test 

also seems quite sensitive and complements the SITES test well. 

a. Comparisons of Global Satellite Data Fields. Results of the 

global SST comparisons are shown in Tables 7-2, 7-3, nnd 7-4. Since other 

results have indicated that AVHRR may have problems with water vapor contami7-2 

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With an Artifica1 Data Set T', mlcre Each Temperature T'(.) • F(~.a) + T(.). 0', 

, , F is a Random Function With a Mean cf ~ and a Standard Deviation of o. These 

" Intercomparisons Were Run 20 Times for 20 Independent Random Realizations and 

the Number of Times a PPP Test RejecLcd the Statistical Identity of T' and T 

" is Tabulated vs. the Number of Times a PPP Test Accepted the Statistical IdenI: tity of T' and T. , I 

;.' 

... : · , 

· , SITES Tests: 

• Northern Re~ion (30 0 N - SOON) 

Number of Rejections/Number of Acceptances ~ 

" ~ o '" .06 0.12 0.17 0.23 0.35 0.46 

0.00 6/14 1/19 1/19 6/14 1/19 4/16 

-, 0.05 20/ 0 20/ 0 20/ 0 19/ 1 13/ 7 9/11 

~l 0.10 20/ 0 20/ 0 20/ 0 20/ 0 20/ 0 19/ 1 

ij Southern Region (500 S - 30°5) 

r: 

Number of Rejections/Number of Acceptances 

f 

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.. 

. 0.00 3/17 5/15 3/17 3/17 5/15 3/17 

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0.10 20/ 0 20/ 0 20/ 0 20/ 0 17/ 3 14/ 6 

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, Number of Rejections/Number of Acceptances I 

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0.00 12/ 8 19/ 1 20/ 0 20/ 0 20/ 0 20/ 0 

0.05 0/20 2/18 15/ 5 19/ 1 20/ 0 20/ 0 

, I I 0.10 0/20 . 0/20 0/20 13/ 7 20/ 0 20/ 0 , 0.20 0/20 0/20 0/20 0/20 17/ 3 20/ 0 

0.40 0/20 0/20 0/20 0/20 1/19 18/ 2 

0.60 0/20 0/20 0/20 0/20 0/20 7/13 

Southern Region (50 0 S - 30 0 S) 

Number of Rejections/Number of Acceptances 

~ o = .06 0.12 0.17 0.23 0.35 0.46 

~ ...... i 0.00 3/17 7/13 15/ 5 18/ 2 20/ 0 20/ 0 ',..J 

.' . 0.05 0/20 2/18 . 12/ 8 17/ 3 20/ 0 20/ 0 

-' 0.10 0/20 0/20 2/18 13/ 7 20/ 0 20/ 0 

r' 0.20 0/20 0/20 0/20 1/19 14/ 6 20/ 0 C 0.40 0/20 0/20 0/20 0/20 5/15 19/ 1 

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TABLE 7-2. Number of Space Grid Points. Below and Left of the Data ~~trix 

Diagonal, the Number of Space Points in the Data Swarms Used in 

the PPP Tests ere Printed. Rowand Column Numbers Indicate Thp.~e 

Instruments: 1) AVHRR, 2) St-~, 3) HIRS, 4} Ship, and 5) [NOC. 

1 

AVHRR 1 

SMMR 2 223 

HIRS 3 1140 

Ship 4 984 

FNOC 5 517 

b) Global Tropics (30·S-30·N, 

1 

AVHRR 1 

SMMR 2 1250 

HIRS 3 3210 

Ship 4 1110 

FNOC 5 936 

c) South Temperate (61l0S-300S, 

1 

AVHRR 1 

SMMR 2 470 

HIRS 3 2080 

Ship 4 192 

FNOC 5 

,.' .. / .. 

2 

234 

233 

235 

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2 

1430 

289 

824 

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2 

470 

5 

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1040 

535 525 

3 4 

1360 

1150 470 

3 4 

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TABLE 7-3. Pool Permutation Procedure, SITES Statistics on 2· Binned Data. 

Test the Hypotbesia That TVo Datasets are Drawn From the Same Population. 

Below and Left of the First Data Matrix Diagonal, the Relative Category of the 

SITES Comparison Between the Obacrved Data Swarms is Printed; the PPP Test 

Statistics Fall Into Eight Unique Values, Called "Categorics". Above the 

Right of the Data Matrix Diagonal. T(rue)/F(alse) is Printed, Indicating 

Whether or Not the Bypotheais ia Supported or Rejected, Respectively. Below 

and Left of the Second Data Hatr.i~ Diagonal, the Percentage of the Artificially Constructed Data Swarm Test Values Which Fall Below the Obcerved 

Swarms' Test Value is Printed; Above and to the Right of the Second Data 

Matrix DiaGonal, the SITES Test Value for the Observed Data Swarms i8 Printed. 

~SITES Statistics** 

AVHRR 

SMMR 

HIRS 

Ship 

mac 

AVHRR 

SMMR 

BIRS 

Ship 

moc 

Category/Hypothesis 

1 234 5 

1 

2 

3 

4 

5 

8 

8 

6 

8 

F F 

F 

8 

8 8 

8 8 

T F 

F F 

F F 

F 

8 

Category/Hypothesis 

1 234 

1 

2 8 

3 8 

4 6 

5 8 

F F T 

F F 

8 T 

8 7 

888 

5 

F 

F 

F 

F 

Percent/Test Value 

1 2' 3 

1 

2 

3 

88 

88 

59 

88 

1.5 

88 

88 

88 

1.0 

0.82 

.4 

5 

88 

88 

Percent/Test Value 

1 2 3 

1 0.86 0.8~ 

2 82 1.3 

3 82 82 

4 67 82 72 

5 82 82 32 

c) South Temperate (60·S-30·S, 0·-360 0 E) 

Category/Hypothesis 

1 234 

AVHRR 1 

SMMR 2 8 

BIRS 3 8 

Ship 4 8 

F F F 

F T 

8 F 

5 8 

Percent/Test Value 

1 2 3 

1 1.1 1.7 

2 83 1.3 

3 83 83 

4 83 46 83 

7-5 

4 

0.32 

1.2 

1.0 

8S 

4 

0.63 

0.98 

1.0 

82 

4 

0.60 

1.1 

1.2 

5 

11 

6.3 

9.5 

12 

5 

3.7 

3.3 

2.7 

3.4 

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Table 7-4. SITES Statistics on 10· Bi~1ed Data. (See Table 7-3 for r 

Explanation of the Organization of This Table.) I 

I 

I 

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**SITES Stetistica** 

a) North Temperate (30·N-60·N, OO-360·E) 

Category/Hypothesis Test Percent/Test Value 

1 2 3 4 5 1 2 3 4 5 

AVHRR 1 F F T F 1 2.4 1.0 0.17 13 

St1MR 2 8 F F F 2 88 1.1 1.8 6.8 

HIRS 3 8 8 T F 3 88 88 1.2 15 

Ship 4 2 8 7 F 4 10 88 73 17 

moc 5 8 8 8 8 1 5 88 88 88 88 i I ,-I 

I 

b) Global Tropics (30·S-30·N, 0·-360·E) , I 

I, 

Category/Hypothesia Test Percent/Test Val~e I' 

1 2 3 4 5 1 2 3 4 5 

AVHRR 1 F F T F 1 82 1.1 1.2 0.57 4.0 

SMMR Z 8 F F F 2 82 1.8 1.2 5.1 

BIRS 3 8 8 T F 3 82 82 1.1 4.0 

Ship 4 7 7 7 F 4 67 82 72 4.6 

moc 5 8 8 8 8 1 5 82 82 82 82 

c) South Temperate (60·5-30·5, OO-360·E) 

Category/Hypothesis Test Percent/Teat Value 

1 2 3 4 1 2 3 4 

AVBRR 1 F F T 1 1.3 2.7 0.59 

SMMR 2 8 F T 2 83 2.7 1.2 

HIRS 3 8 8 T 3 83 83 1.5 

Ship 4 7 5 7 :. 73 46 63 

7-6 

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nation in the Tropica. the testa were taken on data swarma for three areaa: 

the first north of 3C·N; the second between 30·S and 30·N; and the third south 

of 30·S. These teats are displayed in a compact m5trix form. in which rowa 

and columns each represent data svarms for particular instruments. The column 

and row order are alwaya the SaQe: AVHRR. SMMR (nighttime), HIRS. ship. and 

FNOC. For exampl~. the entry for the first row and second column (and viceveraa) is an AVHRR-SMMR teMt result. Different results are shown above and 

below the matrix diagonal. in order to show result. in a compact form. 

Figure 7-1 shows the spatial pattern of grid points used in each PPP comparison. Sincd the time and space ~i8tribution for each data swarm differs. 

the s~atial pattern of coincident grid pointa differs for each sensor combination. The maximum number of space points is fOWld in the AVHRR vs. RIRS COGlparison. B~cause of the coastline mask on SMMR data, comparisons with SMMR 

have fewer apace pointa. The numbor of observations in the tropics 

(Table 7-2(b» tends to be somewhat larger than in the northern regiona; SMMR 

comparisons no longer have the fevest spatial points, reflecting the fact that 

relatively less ocean lies adjacent to coastland in the tropics; therefore the 

SMMR land m5sk is not as important. The southern region (Table 7-2(c» generally bas fewer data points in the comparisons; its FNOC analysis i8 absent 

£o·.1th of the equator, while ship data is much sparser. Sl'1MR comparisons are 

sparse in the southern hemisphere al.o. 

b. 2· SITl.:S Tests. Results of the 2· bir.ned SITEf. test for the 

northern region (Table 7-)(a» indicate that the data swarma are not drawn 

from the same populdtion for any comparisons except the AVHRR - ship com!'CIrison. The test category for the AVHRR - ship SITES comparison is 6, indicating 

an imperfect agreement. The SITES test values are all greater than 0.8 except 

for the AVHRR - ship comparison. 

Results of the 2· binned ~ITES test for the tropics (Table 7-3(b» indicate that ship and AVHRR SST and ship and HIRS are statistically identicnl. 

Interestingly enough, the hypothesis of statistical identity was rejected for 

the AVHRR vs. BIRS. Since the El Chichon volcanic eruption contaminated AVHRR 

dat for July, 1982. this result de~nds a closer examination. Figure 7-1(c) 

shows the data distribution of ships VB. AVHRR and indicates very few spatial 

points were present in the tropics. This is probably due to the paucity of 

ship data. On the other hand, n color plate (Figure C-7) of A~IRR SST - ship 

SST indicates that some of the areas of greatest difference in this month are 

.lOt present in the joint distribution map, apPArently because cne of the other 

months was deficient in either ship or AVHRR data in those regions. Also, 

<'"ath of the equator, the thematic IMp indicates that the SST differences are 

ve.y small. The consequence of this is that over the four months the PPP 

tests are unable to di~criminate effectively between tropical ship SST and 

AVHRR SST. in spite of widespt'ead E1 Chichon contamination. 

c. 10· SITES Tests. SITES statistics for 10· binned data are 

sho ...... 1. in Table 7-4. In the ~~herll Hnd tropical regions the results are 

identical to 2° binned data SITES results. The southern region diffars for l"· bins in that statistical identity ·is accepted between AVHRR and ships and 

BIRS and ships. 

7-7 
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Figure 7-1. ;patial distributions of ' ) AVIIRR vs S~r-!R grid poin~s d d) AV HRR •• .; b) AVIIRR ~ use in PPP vs. FNOC' ) ~ vs. HIRS; c) AV' comparisons: 

g} S~!:'tR vs. n:oc.' e S,t:1R vs. IIIRS' f) ,~,.1RR vs. ship; 

vs. FNOC' k) 'I" h) HIRS vs. ship",} S.!:'.R vs. ship' , .Slip vs • sh' 1 P • ,1 IIIRS ~ vs • FN DC " ' J') ship 

7-8 

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Figure 7-1 • (Continued) 

7-9 

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d. L· SPRED Tests. Results of the SPRED test, i.e., the equality of 'the second '!'.oments, for thp. northern region (Table 7-5(a», indicate 

that the data sets are not drawn from the same statistical population for all 

SMMR comparisons except S~ vs. AVIIRR, and reject the statistical identity of 

ships and AVHRR. On the other hand. this test indicates statistical identity 

between any pair drawn from the AVHRR, ship. or HIRS data swarms. In the 

tropics, the SPREt test (Table 7-5(b» rejects statistical identity only for 

HIRS vs. ship. The test otherwise accepts statistical identity for every comparison test. In the southern regions, the test (Table 7-5(c» rejects statistical identity in AVHRR vs. SMMR and HIRS vs. SMMR. Ship vs. SMMR indicates statistical identity, but with very few spatial points as mentioned 

before. . 

However, as has been concluded previously, where SITES comparisons indicate a possible bias, 3PRED test results indicating identity should be suspect. This would invalidate many of the positive SPRED comparisons. Conversely, where SITES comparisons are good, very small SPRED values indicate no 

identity by the SPRED test. For this reason, ship vs. AVHRR comparisons, 

indicating statistical identity, are suspect. 

e. 10· SPRED Tests. Table 7-6 shows SPRED results for 10· 

binned data; the results are essentially the same as the 2· bin SPRED test. 

E. SUMMARY 

The SI1ES test accepts statistical Identity between AVHRR and ship data 

swarms. The SPRED test is consistent with these results, in the following 

sense. The presence or absence of a large SITES test value can bias the SPRED 

test towards acceptance of statistical identity. or rejection of statistical 

identity, respectively. Thereforu, t.:1': rejection of statistical identity in 

the SPRED tests between AVHRR and ships and the North temperate regions is 

probably due to the enhanced sensitivity of the SPRED test when SITES values 

are small. Contrariwise, acceptance of statistical identity in the SPRED test 

between SMMR and FNOC in the tropics, for instance, is made suspect by the 

clear rejection of statistical identity in the SITES tests between that particular pair. 

RIRS and ships shaN good agreement in both SPRED and SITES at times. but 

this is more inconsistent than the PPP test results of AVHRR and ship 

comparisons. 

The general rejection of statistical identity in comparisons between 

Datellite sensors themselves is noteworthy. Not even AVHRR and UIRS were 

statistically alike by the PPP tests. This seems to i.nply that the level of 

noise in AVHRR and HIRS was larger than that in the binned ship data. It 

implies that although at least two G~tellite sensors approach ship SST by the 

statistical measures used in this study. all of the binned satellite SSTs were 

noisier than the binned in situ ship SST. SMMR differs by particularly large 

PPP statistic values from other satellite sensors, and the FNOC SST fields 

show the least agreement with other sens6rs. 

7-10 

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Tabla 7-5. Pool Pennutation Procedure, SPRED Statistics on 2· Binned Data. 

(a) 

AVBRR 

SMMR 

HIRS 

Ship 

FNOC 

Toat the Hypothesis That Two Datasets are Drawn From the Same 

Population. Below and Left of the First Data Matrix Diagonal, 

the Relative Category of the SPRED Comparison Bet~een the Observed Data Swar.::s is Printed; Above and Right of the Data Matrixdiagonal, T(rue)/F(alBe) is Printed, Indicating Whether or Not 

tho Hypothesis i8 Supported or, Rejected, Respectively. Below 

ane! Left of the Second Data Matrix Diagonal, the Percentage of 

the ArtificiallyCcnstructed Data Swarm Teot Values Which Fall 

Below the Observed Swarms' Test Value is Printed; Above and to 

the Right of the Second Data Matrix Diagonal, the SPRED Test 

Value for the Observed ~~ta Swarms is Printed. 

**dPRED Statistics •• 

North Temperate (30·'~-60"ll, 0·-360·E) 

Category/ 

Hypothesis Percent/Tet; Value 

1 2 345 1 2 3 4 5 

1 T T F T 1 0.14 0.66 x 10-5 0.066 0.22 x 10-3 

2 6 F F F 2 63 0.13 0.45 0.47 

3 1 8 TT 3 1.0 88 0.055 0.36 x 10-5 

4 887 T 4 88 88 73 0.037 

5 1 8 1 5 5 1.0 88 1.0 49 

(b) Global Tropics (J0·S-30·N, 0·-360 0 E) 

Category/ 

HypothetJis Percent/Test Value 

1 234 5 1 2 3 I. 5 

AVHRR I T T T T 1 0.0087 0.57 x 10-4 0.078 0.12 

SMffil 2 3 T T T 2 32 0.0018 0.017 0.047 

HIRS 3 , 1 F T 3 1.0 1.0 0.15 0.048 

Ship 4 468 T 4 26 59 82 0.0057 

FNOC 5 4 7 5 2 5 37 75 54 11 

(c) South Temperate (60·S-30·S, 0·-3600 E) 

Categoryl 

Hypothesis Percent/Test Value 

1 234 1 2 3 4 

AVHRR 1 FTT 1 0.035 0.0057 0.00098 

SMMR 2 8 F T 2 83 0.11 0.0033 

HIRS 3 4 8 T 3 41 83 0.018 

Ship 4 327 4 21 21 63 

7-11 

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AVHRR 

SMMR 

HIRS 

Ship 

FNOC 

- .. . ~ 

Table 7-6. SPRED Statistico on 10· Binned Data. (S.,e Table 

Explanation of the Organization of This Table.) 

"SPRED Statistics" 

North Temperate (30·N-60·N, 0·-360·£) 

Category/ 

Hypothesis 

Test Percent/Test-Value 

I 2 3 4 S I 2 3 4 

1 TTFT 1 0.12 0.055 0.11 

2 6 F F F 2 63 0.47 0.58 

3 7 8 T F 3 71 88 0.053 

4 8 8 7 F 4 88 88 73 

S 5 8 8 8 1 5 49 88 88 88 

(b) Global Tropics (30·S-30·N, 0·-360·£) 

Category/ 

Hypothesis 

Test Percent/Test Value 

I 234 5 I 2 3 4 

AVHRR 1 TTTT 1 0.051 0.012 0.19 

SMMR 2 5 TTT 2 57 0.015 0.0063 

HIRS 3 I 6 F T 3 1.0 64 0.20 

Ship 4 718 T 4 67 1.0 82 

FNOC 5 5 3 1 5 1 S S3 22 1.0 44 

(c) South Temperate (60·5-30·5, 0·-360 0E) 

Category/ 

Hypothesis 

Test Percent/Test Value 

1 234 1 2 3 ' 4 

AVHRR 1 T T F 1 0 .. 012 0.17 0.076 

SMMR 2 6 F T 2 66 0.46 0.032 

BIRS 3 4 8 T 3 41 83 0.016 

Ship 4 815 4 83 1.0 50 

7-12 

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The similarity of 10- binned results and 2- binned results indicates that 

the sensitivity of the PPP test is not affected by changing bin size on these 

scales.. This implies that the noise problems cannot be overcome easily by simply gathering more data • 

7-13 

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SECTION VIII 

N86-16862 

REQUIREMENTS FOR SEA SURFACE TEMPERATURE CROUND TRUTH 

IN TIlE INDONESIAN RECION 

J. Penrose 

Western Australia Institute of Technology 

The comparatively low density of ship and XBT observations in large are&8 

of the Southern Hemisphere and the tropics limits the extent to which satellite SST estimates can be validated on a global baDis. The additional difficulties associated with previous SST estimates in the tropics havo been 

recognized for some time (Barnett et al., 1979) and are still residually 

apparent in the region centered on the Indonesian archipelago (JPL 1983, p. 

3-4) where both extensive cloud cover and high atmospheric water VB.por content 

occur, notably in the October to April periods of each year. These factors 

affect AVERR retrievals particularly and substantially degrade SST returns 

which, in drier areas appear to give useful agreement with ship derived 

values. SMMR ~-eturns and to a lesser extent HIRS returns are affected by the 

distributed nature of the land mass in the Indonesian region. AVH~~ has the 

advantages in this region of high sampling density and small footprin~. 

Present methods of SST retrieval applied in this region provide ~pparcnt high 

positive temperature anomalies where cloud cover allows retrievals to 

proceed. While this is pr~8umably a coneequenco of over correction for 

atmospheric water vapor effects, adequate analysis is difficult to achi~ve 

because of limited gr.ound truth availability in the region. 

Two related problems thus arise i .. evaluating AVHRR SST retrievals in the 

Indonesian region. On~ concerns the regior.~l nature of the processing apparently required for AVHRa data and the second concerns the requirements and 

availability of around truth information. AVHRR regression coefficients 

derived in one set of regional environmental conditions can result in large 

SST errors wi.en applied to data in other regions (JPL 1983, p.-S-3). Pending 

a fuller understanding of the global atmosphere it may be necessary to develop 

regional processing algcrithffia for multichannel water vapor corrections, a 

process particularly called for in the Indonesian region. This in tu~n calls 

for improved ground truth data sets both of SST and at~spheric water vapor. 

Tbe omall AVERR footprint 1s better suited to compari~on with ship point 

measurements than the larger BIRS and SMMR footprints, SST retrievals from 

which are better conpared with binned ship data. The nature of shipping in 

the Indonesian region suggests that point, rather than binned ship data are 

more likely to arise. This raises a difficulty already experienced in working 

with differently conditioned ground truth data sets. Reynolds (1982) nas 

indicated that the wiclespread availability of ship near-surface temperature 

estimates; notwithstanding the substantial noise in such data, makes it more 

valuable than the more precise, but tess dense XBT data. Further, single 

point observations of SST are usually assumed to be representative of an 

ensemble mean value over the spatial dimensions characterlzing one or more 

pixels (JPL 1983, p. 5-7}.This assumption may require examination in parts 

of the Indonesian region where comparatively large horizontal temperature 

gradients may be expected. 

8-1 

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Recommendations: 

(1) Attention ahould be given to developing regional multichannel processing techniques applicable to the tropical region centered on 

Indonesia. This will call for improved ground truth data sets in 

tbe area. 

(2) Becauae the spatial density of 81.'ound truth data sets in the 

Indonesian region can be expected to remain low, attention should 

be given both to the development of ~igh accuracy in ground truth 

wherever possible and to the statistical significance of co~pariBans made with the data sets that do become available. 

(3) Wherever possible, S5! ground truth data in the tndonesian region 

should include measurements illustrative of the Gpatial character 

of the temperature field, notably over length scales comparable 

with the AVBRa footprint. 

(4) Cooperation in items (1)-(3) above should be sought from scientiats 

and institutions active in the Indonesian region in order to maximize the amount of gro~,d truth data made available. 

8-2. 

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SECTION IX 

REFERENCES 

Barnett, T. P •• W. C. Patzert, S. C. Webb, and B. R. Bean, 1979: Climatological usefulness of satellite determined sea surface temperatures in the 

tropical Pacific. Bull. Am. H~t. Soc., 60, 197-205. 

Chaline, H. T., N. L. Evans, V. Gilbert, and R. D. Haskins, 1984: 

for a passive Ii advanced moisture and temperature sounder. 

23, 979-989 • 

Requirements 

Appl. Opt., 

COSP.\R, (1985): International workshop on satellite derived sea surface temper3ture for global climate applications. Report of workshop held 

Hay 29-31, 1985, WashingtJn, D.C. (in preparation). 

Deschamps, P. Y. and R. Frouin, 1984: Large diurnal heat ina of the sea surface observed by the BCMR experiment. J. Phys. Oceanos., 14, 177-184. 

Garret, J., 1981: The performance of the FGGE drifting buoy system. Adv. 

Space Res., !, 87~94, Pergamon PresG, Oxford. 

Holl, H. M., 1981: Alternating parallel analylJis. In: n.e Scientificl 

Technical Report for the Expanded Ocecn Thermal Struclure Analysis System 

(Contract iiN0028-80-c-y75l, U. S. Navy Fleet llumerical Oceanography 

Center), Meteorology International Inc •• Monterey, CA. 

JPL, 1983: Satellite-derived aea surface temperature: Workshop-I. ~PL Publication 83-34, Jet Propulsion Laboratory, California Inst. of Technology, 

Pasadena, CA, 147 pp. 

JPL, 1984: Satellite-derived sea surface temperature: Workshop-II. JPL Publication 84-5, Jet Propulsion Laboratory, California Inst. of Technology, 

Pasadena, CA, 145 pp. 

Legeckis, R. and W. Pichel, 1984: Monitoring of long waves in the eastenl 

equatorial Pacific 1981-83 using satellite multi-channel sea surface temperature charts. NOAA Tech. Report, NESDIS 8, NOAA/NESDIS, Washington, 

D.C. 

McClain, E. P., 1981: Multiple atmosphe4ic window techniques for satel!itederived sea ourfnce temperatures. Oceanography from Space, Plenum Press, 

New York, pp. 73-85. 

McClain, E. P., W. G. Pichel, C. C. Walton, : .• Ahmad, and J. Sulton, 1983: 

Multi-channel improvements to satellite-derived global sea surface temperatures. Adv. Space Res., ~, 43-47. Pergamon Press, ~~ford. 

Njoku, E. G., 1985: Satellite derived sea surface temperature: Workshop comparisons. Bull. Am. Met. Soc., 66. 274-281. 

Preisendorfer, R. and T. P.Barnett. 1983: Numerical model-reality intercomparisnn tests using small-sample statistics. J. Atm. Sci., 40, 1884-1896. 

9-1 

.. ..... .. ' i ... 
/. 

J 

.... ". 

,. 

I " 

/ 

/. 

Reynolds, R., 1982: A monthly avernged cli=atology of sea surface temperalure. 

NOAA Tech. Report NWS 31, NOAA/~~S, Silver Spring, MD, 35 pp. 

Reynolds, R. W., 1983: A comparison of sea surface temperature climntologies. 

J. Clim. Appl. Meteoral., ~, 447-459. 

Robinson, I. S., N. C. Wells, and H. Chamock, 1984: The sea surface thermal 

boundary layer and its relevance to the meaaurement of sea surface temperature by airborne and opaceborne radiometer£. Int. J. Rem. Sens., ~, 

19-45. 

Saur, J. F. T., 1963: A study of the quality of sea water temperatures 

reported in logs of ships' weather observations.· J. Appl. Heteorol., ~, 

417-425. 

Schwalb, A., 1978: The TIROS N/NOAA A-G satellite series. NOAA Tech. Memo. 

NESS 95, NOAA/NESDIS, Washington, D.C., 75 pp. 

Smith, W. L., 1968: An improved method for calculating tropospheric temperature and moisture from satellite radiometer measurements. Mon. Wea. 

~, 96, 38:-396. 

Smith, W. L. t 1980: Operational sounding algorithms. VAS Demonstration Sounding Workshop. NASA Con!. Publ. 2157, NASA/Goddard Space Flight Center, 

Greenbelt, MD, pp. 1-10. 

Smith, U. L. and B. M. Woolf, 1992: Algorithcs used to retrieve surface skin 

temperature and vertical moisture and temperature profiles from VISSR 

Atmospheric Sounder (VAS) radiance observations. Froc. AMS 4th Conf. 

Atmos. Rad., Torontc. Canada, American Heterological Soc., Bocton, MA. 

Stowe, L. L., 1984: Personal communication (address: Climate and Earth 

Sciences Laboratory, NOAA/NESDIS. E/RAU, Washington, D.C. 20233). 

Strong, A. E., L~ L. Stowe, and C. C. Walton, 1983: Using the·NO~~-7 AVHRR 

eata to monitor El Chichon aeroGol evaluation and subsequent sea surface 

temperature anomalies. Proc. 17th Int. Symp. Rem. Sensa Env., Univ. of 

Michigan, Ann Arbor, MI, pp. 107-122. 

Strong, A. E. and E. P. McClain, 1984: Improved ocean surface temperatures 

from space - comparisons with drifting buoys. Bull. Am. Met. Soc., 65, 

138-142. 

Strong. A. E., 1984: Use of drifting buoys to improve accuracy of satellite 

sea surface temperature measurements. Trop. Ocean-Atm. Newsl., 25, 16-18. 

Susskind, J., J. Rosenfield, D. Reuter, and M. T. Chahine, 1984: Remote 

sensing of weather and climate parameters from HIRS2/MSU on TIROS-N. ~ 

Geophys. Res., 89, 4677-4697. 

Tabata, S., 1982: An evaluation of the quality of sea surface temperatures and 

salinities measured at Station P and Line P in the northeast Pacific 

Ocean. J. Geophys. Res., 17. pp. 374-385. 

9-2 

! ., -- .. _----- .. ..- . . ------_._-- - ---~~ -~-- - -_ .. - ~il..:..J!L.-!..,!_~ .. ~_.~_. ..... ."."". _-r;:' •. ~ ~.('f,.~~ .. II#' 

.\ 

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/ 

/ 

/ 

/ 

. '" 

> .. 

/ 

--! 

I 

I 

I 

.. 1 

! 

, i 

I / 

/ 

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~ . 

Walton, C. C., 1980: Deriving sea surface temperatures from TIROS-N data. 

Remote Sensing of Atmospheres and Oceans, Academic Press, New York, 

pp. 547-578. 

Walton, C. C., 1985: Satellite measurements of sea surface temperatures in 

the presence of volcanic aeroGol. J. Clim. Appl. Meteoral., 24, 501-507. 

WCP, 1984: Report of the TOGA workshop on sea surface temperature and net 

surface radiation, La Jolla, CA, March 1984. World Climate Program 

Report WCP-92, WHO, C.P. 5, 1211 Geneva 20, Switzerland. 

Wilheit, T. T., J. Gatlin, D. Han, B. M. Krupp, A. S. Milman, and E. Chang, 

1983: Retrieval of ocean surface parameters from the scanning multichannel microwave radiometer (SMMR)'on the Nimbus-7 satellite. ~ 

Trans. Geosci. Rem. Sens., IS, 225-224. 

9-3 

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APPENDIX A 

CORRELATION TABLES 

A-l 

. . ~ -------- --- _-;:_·· .. ·~~T·r;.k~,,--.~ __ ---------____ ____ ..... ; 

pv III 

.;.,_ ...... /___ __ _ .-_ ..... l1li •••. _""",: .. __ :.-.,,,..,..'03 ", '~~,~ ..' , •• ~:.·~· -a: TO .. ~ __ ~~ ______ ~~ __ .. ______ .. uHa~=1~ •. ~~ ____ . ____ .. ~ 
'," 

,,~, 

;\l 

~~i t1 

I J 

1 

'., 

\ 

-

> I 

IV 

(' 

~ 

Table A-l. Statistical Comparisons 8etveen 2' Lntitude-Longitude Binned SST Ano~ly Fields. All Data ~sked up to 600 ka from Land: <.> Novecb~r 1979. elohal. 

Tw. Dr",. ~.rr'9' ,'f An,ft.l, c, ••• C.".I"I$ft~ 

'Iil.ft. ~ •• 4., N f. Sb' 0., Sf •• G.; l t. ,. •• ,., C 

Af I ••• f ••• ,., ••••• ftl.' •• ,.~.ft ,'1' ,.Iftf. 

CC--Cr ••• CI".I.f.ln, a--•••• , S--~ •• "~.'4 ~"I"I.ft 

abe.t b •••• • -~~.~b.r ., c.~ .. .• 1" I ••• r •• t,e"e' ., •• Ie ••• r., ••.• r c'l.~ t.~.~.'.r •• -'A •• re. t~~.'.r •• 

NOM 

Avilla 

W:>UIND WllI<t IT, "'lAM 'AZAM ,,;u UMSlac CI.I"I'fCl., HU8 '"L''''''' "",'S ~" IPS WOTi Itt tlOY ~"I\' "Ie,lif ",/tHl IZ$/ttLL. 

C(LL 214 MOM e • III C • .1 C • a. C J .. C I \7 C .71 t ~YtII. I. ·1 U I, -8 ., I -, .. I ., ill I -I :4 a -I II I .• · .. t' MOIl I .. I " I .. I \I • , \11 I · \. • I .. • , ,. 8 IU 

N .. 4". II lilt II 7lJ .. .13 .. 41. .. 4\7 It ."4 

'USSUIQ C •• 1 e • '1 C ,tI C . :~ c • '7 e , .. HUI I -, :z , I .. • • 17 • I 4\ • • u • -g ~ .. I. I :~ IJ I .. G III • I It • • •• .. lln II 7lli II II Ii II 417 .. ~71 

I 

II · 4!.n ,~ 

IIILHUfi c 114 C I 41 C 1\8 C • Zt C IU "lll'YWf I -, U • -I " • I .. I III I. I" tN1. "1~1' I 1,27 • • '1 • I •• I I JJ I I II CLLL 214 " In .. a\ II III " 4'1 •.. In. 

'AZI~~ c I II C ... C • u c ..... fHl" I ... • III • I II • , .. ''i/c[lL • I .. , .11 I In I 

• It II II~ II , .. 13: II 7n 

'A1M c • \C: 

C I .. ~r'. • .u • -I 2Z )21/Clll. • , 7,. • IU .. \4 II liS 

rteE C ·c u c. , It JUors a ., .. • -. ,. I I U I au II • 4 .. 41 • 

T'AW'JAC C UT • -. 

· .. It '. IU 

" 47J 

CLJIUITCl 
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(b) November 1979. North Pacific 

Tw. ~.,r._ A.ere, •• 3T ~'~'" er.,. C.rr.I.' •••• 

I.,a.ft, ~., •• , M ,. 2. I G" ". I." d., E ,. It. I •• , E 

A' I.... • •••• r •• 't.ftC., ., ,.ftft •• ,rat , ••• ,. CC--Cr ••• C,rr.I.".ft, I--t ••• , I--S,.ft',,~ O •• I.,a •• ,b'., "'" "--H.~.r " [.~ ••• ,Iad .~ ••••• ,a ••• , .a •• I • ••• r.,_ •• er c.l.ftft ,.~.r.'.r •• Rift •• r.w ,~pr.'.r.~ 

IfOM IUSSIII>ID IIIJLliC I" ,,.Z,," UNCS'AC Cl.1"""Q. 

AIMlII "11iS HILIWI f,HIrI or 

HOY ,~ •• CHT IS/tILL 

CUl 21' 

NOM C ..• " C .. ~] C . 174 C. liS C 

AVliU I -. .. I. .It I. -I 21 I -I II I. · I ... 

MOIl I. ." I ..• 17 II ..• " • ." • • 7. II .• sn II, U. " 397 " xa .. ~~ 

£USSlIII~ C. .. " C • 12 C. I 21 C I II 

HI a!! I. • "I I ., .. I -I U I .,. 

• t 'l I .. , .8 I • 17 S . ,n II .. ~24 .. Jf7 .. XI II ~'S 

> I 

"'" III lit[ IT I C .. , 19 C •• ,. C 

HILHMI · .. • -I •• I. -, •• I -. ~ 

$MII .IICHT IS • r.. 5 .. t 31 • I 2. 

'" aLL Zl4 ·11 JU II 117 .. U. 

'AZ"" C • U C · .. \ l>H1" I. I 12 I. I ZI 

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II .. 12l II. l" 

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1'"' zt.' cr.q .. U 21. dIr", lit' d., [U 291 I CI., [ 

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C ... 17 c .. 1 " C .. ., l2 C Il':o e .. , II 

It. -0 n D. o Z, I. tI 'li7 I. I 27 I. -I 4, •. I 14 S .• 7 • • , ... , I ..• " t ..• " It .. 1142 " .. 4;1 " 73 " 21S II .. IIU 

C .. 1 :n e • 1'5 e • 21 C .. I .. D. • ,S D • ft, I. I 74 II -. II 8I'IM "I(;jfT .... " B .. I II •.. 1 '2 • . , 12 C£LL 234 It 41 .... 72 II .. 214 .... lIU 

'ioZAH C •• 79 c .... SH:'I I . . " I. -. ,7 'S/tELL I , '.38 I ... U 

II . , " .. 43 

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Tw. ~'9'el A.~.ql SST An.A.l, Cr ••• C"'II""n, 

119""' 2 •. ' deq • t. '.I •. ' deq 5j I".' dl, 't. 21 •• '" [ 

At ll,st I .~.'r,.tj.nC.) .t "~'n .',. ,tin'. CC--C" •• CI,rel""n, '--81", S--S"nd.,. ~"""'n .~,., bl", N--H.~e, It c.~.ft •• lld ID,r'."',A,' 

I' •• I •••• r.VI "er CII,Aft '~per.t.". A'n., ,.w '~er.t"e. 

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TWI Peg~ .. Av~a9' ssr Anlftal, C~I •• Clr~'I.ti.n. 

ae9iln, 54.' de; "I. 2 •. ' dl9 HI 10 •. ' de9 E t, 290.' d'9 E 

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(C--Crt •• Cerrela'ltn, 8--8i~., S--S'ondard D"l,'lln 

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h'JHl1R 

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AIIHRII GUSSKIH~ IIILHEITI PAINt TRAH~AC CLlKA'OL 

PAY HICHT HIRS PIll""'" SHIPS xtT 

DEC ~ III'H' )S/CllL CELL 2J. C 

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'.95 C .••. " C .. '.41 C .. 1.la C •• '.75 C .. '.73 C ...... •• '.13 •• 1.7. •• l.ll •• • ••• •• ' .• B BoO '.25 B I. '.57 .. '.U SoO '.BII S .. 1.2B H •• SOl H II .• '.5 • •.. • 72 8 ... 82 •• 581 H .. 581 H .. 571 H .. 3". N .. 119 H .. 581 

C .• '.9a C .. '.43 C .. '.18 C .. '.77 C .. '.75 C ...... •. -'.13 D. '."a II. 1.25 B . .... S •• ' .• 7 I. • 51 

N 

.. l.i6 S .. '.8& S .. 1.27 S ..•. 5' S .. I." S ..•. IID •• StS H .. S~5 H .. 572 N .. 31' II .. liB H .. !i9S 

CoO '.43 C .• 1.'9 C .• '.77 C •. '.75 C ...... I. '.71 I. 1.28 •• • ••• D. ' .• 6 I. '.53 •.• '.B6 S .. 1.26 S .. '.51 S .. '.71 S ... sa Ie .. S9S H .. 'i72 H .. 376 M .. IBS ... S9S 

C .. 0.06 C .. '.:!9 C .• '.27 C ...... 8. '.57 •. -O.ll •. -1.39 I. -0.10 S .. 1.20 S ..•. a9 8 .. 1.211 M II .. '.00 .. 572 N •• l76 II .. 1110 N .. S9S 

C .. '.22 C .• 0.25 C .. '.U •. -1. eo t. -1.J9 I. -1.7. SoO l.U B .. 1 29 II .. 1.14 H .. Ul II .. HIS ... 572 

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(h) December 1981, Hid Pacific 

Tw. D.;~ .. A.~.,. SST An,"aJ, C~ ••• C.~r.l.tl.n. 

leql.n. 2 •. ' d., II ,. 2'.' d •• 8, 1 ••.• de, E ,. 29 •.• d., [ 

At l ••• t & ,b.er •• tl.nC.) .t c,"",n O~l' "Int. 

CC--Cr ••• Clrr.l.tlon, 1--1' •• , S--St.nd.rd D •••• tl.n 

.bllt bl •• , N--Hunb.r .t e,"",n •• l.d ,b •• r •• tlln.) 

Dl •• 1 • ••• r.q • •• er e.l."" ""p.r.t.r •• "In •• r.w ',"per.t.r •• 

NOAA 

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DAY 

DEC 

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AVHRIt 

DAY 

DEC 

NOAA NOM 1OIISllI(I ND WILHEITI AVHU 'A ZAN TlAHSPIIC CLIMTDL AVHRII IURa /lILIINt SHIPS lilT NICHT 6"'" "ICHT )S/CElL 

CELL 214 

C .• '.58 C •• 1.'2 C .• '.&7 C .. D .J3 C ...... S C .• '.2S C ...... •. -'.ll I. -1.07 II. -'.39 I. - •. JS It. a.l2 I. -1.21 e. - •. 12 8 .. '.62 5 .• '.33 S •• 0.87 S .• '.95 B ...... I .. '.79 B .. '.79 H .. '" H •• ... , H •• na9 N •• '83 N .. 41 N .. tl H .. , .. 9 

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,.~ r,; t~·~1;::;:,;r~;'7:',~·'·;i ~::;~:·'Cj';~;>;:~·~~'~::"7~~;"i:(~r:;'.;' .~:~:;~ ;"i;::ii~.~'L:'~: :~: !:' ':" :~~,::_.~: ~_ :~_~;.:-:: _.~~.~~~ 0 ~~f 

(i) December 1981 , South Pacific 

Tw. D.qr •• ~ •• r.qe SST An.".l, Cr ••• C.rrel.,l." • • e;lln. 21.1 deq S to 5~.0 deq 8, l ••.• d., E f. 29 •• ' ,., E 

~'l ••• , 1 .b •• r •• ,i.nC., ., tl~ln q~I' pllnt. 

CC--Cr ••• Clrrel.'I.n, .--P'e., S--St.nd.rd D.".,'.n 

.bl.' bl •• , N--Huftb.r .f CO""'" ,.lld .b •• r •• 'l.n.' 

.Ia. l. a,erage •• er c.lw"" 'e"per.ture. "Inu. ~.w teftptr.,.r •• 

NOAA NOA~ HOM SUSSKIND WILHEJTI PAIAN CLJMlOL ~VHU ~I/HU ~I/HRR HIRS "I LtlAi. SlUrs 

DIIY Nlc;Hl ~~ HIGHT )5/CELL DEC CELL 234 

C .. '.51 C .• '.e9 C •• '.!il C .. C.25 C .. '.l7 C ...... •. -'.9. D. -'.38 B. -'.B6 •. -'.11 •• -0.5' •• -'.l7 

S .. '.75 S •• '.36 S •.•. B3 II .. LID e .. I.u I .• '.8l 

H •• 104' N .• U53 H •• IOS2 H .. 714 H .. l2 H •• US3 

C .. 1.11 C .. '.37 C ..•. U C .• '.49 C ...... I. a.5'!. B. '.a, •• 1.43 •• '.99 •• '.BO S .. 0.55 S •• l.tI S .. LU B .• '.56 e .. D.72 

H .• lns H •• ta94 H •• 732 H •• 12 If .. &095 

C •• '.49 C .. '.l4 C .• ' •• 7 C ...... Il. -D.47 t. -1.23 B. -'.'2 D. 0.24 s ..•. B2 B .. LIS 8 .. '.60 B .• 0.73 

H .• U91! N .. 736 If .. 12 H •• "" 

C ..•. U C .• -'.U C ...... 

•• '.12 •• '.74 •• '.71 

S .. l.l2 S •• '.K B •• '.17 

If •• 73S H •• t2 N .• U9a 

C •. ' .• 4 C ...... 

•• '.14 •• t.lt SMa NICHl 11 .• '.77 B .• D.9J CELL 234 H .. U H .. 736 

PIlIAN C ...... SHIPS •. -0.14 )S/CtLL S .• '.5,," If .. 12 

CLltlA10L 
) 

[: ii:'. ~~;~. ~~ ~::.. ,:,"'.' ::~ty~ ~'~ i~~;;~'::',~~;f:~j!i~~;;,,;' );/ i.!i;;:"·~i;70(;J~.;·;I<~Fll;';;.~~{::~E-"·~ .. ~~~~~:i· ~~:: 'j~:'.,~~: ;(;~-\~,~;~~.~~§3~:·~.:~~·~>,>· ~'::;:i~\.:.,\": .. ;;,,_ .. ; ~. ' ,:~ '<i~;J 

(j) December 1981, North Atlantic 

!WI DIVrl1 AVlr.VI SST An.n.l, Criss Clrrll.ttlna 

.IV1In. 54.' d~V N tl '.a d,W SI 29 •. ' dlV E te 34 •. ' dlV E 

At 1,.,t I .b"r •• ti,nC,) .t (I"",n vrtd pIln,. 

CC--Cr ••• C,rrol.'lon, B--81 •• , B--S'.nd.rd Dovllttln 

.b,., bl." H--Nvnblr ., 'Iftnen •• Ild ob •• r •• 'I.ns' 

Bl •• I, •• lrIVo IVlr e.l.ftn ,.nper.tor •• ninv. r.w ,enpor.,.ros 

NOAA NOM NOAA SUSSKIHD IIJLHElTl PAZAH CLIKATOI. 

A\lHAII A\lHIIA A\lHRR HIAS HILHAH SHIPS 

DAY NI!;HT SNN. HICHT )5/CELL 

DEC CELL 234 

HOAI. C .• '.51 C .••. ,. C .••. 21 C .• D.ll C .• 1.49 C ...... A\lHIIA I. '.OJ I. '.01 D. '.14 •• t .2l I. '.22 •• -O.lD 

DAY B .• 0.39 B .. '.29 S .• 0.79 B .. 1.29 B .. '.44 B .. '.'4 

DEC N •• 344 N •• 344 N .. 344 ". .. 333 N •• 233 H .. 344 

NOAA C .• '.97 C .. O.lB C .• '.32 C .. '.15 C ...... A\lHRR B. -I. C2 !!I. 0.14 •• •. oa B. '.11 B. -D.ll 

HICHT B .. 0.13 9 .. 0,74 B .. l.lB B .. ',42 B .• 1.57 

H .. 420 N .• 420 H .. 31J H •• 255 N .. 420 

> I NOAA C .. '.19 C .. 0.24 C .. '.74 C .. '.CO .... "\lHRII B. 0, II'> D. 0.10 B. 0.15 B. -D.lD .... B .• 0.73 9 .. l. 21 B ..•. 41 S .• '.51> ;0 .::0 N .. 420 N •• 313 M .. 255 N .. 4ZD 

~~ BUSSKIND C .. 0." C .. '.13 C .••. eo 0 .... .. HlRS D. ;oF! -0.12 8. -0.10 8. -0.27 

B .. 1.35 P .. '.17 S .. O. 5B 

H .0"0 .. J73 If .. 255 H .. 4:!~ C:):D >I:l 

UILIIEITI C .. '.33 C .. '.08 r ITI 

HILHA" B. -G.42 B. -1.22 

SHI1R HICHT B .. l.l~ 5 .. I.Z3 ~in CELL 234 H .. 227 H •• 373 

PAZAH C .. 0.10 

(;HIPS D. -1.t4 

)S/CELL S .. 1.55 

H .. 2SS 

CLl"ATOL 
/ 

) 

) 

p;]~:~i,~~2':~~:i~i.~;.~::J1 ~:';:?!1P?r:'P::"f:tf't.:;h}'tn:']·,lli},jt:,~u,~~t.1f'!tr!M;~!;;r?HtfX~::~t;i;'~~Nf~rt:ti~i:,,··~·:\:~·:~t~~1F?FI'Z{;::f~:~;;f::q~;;;?:;i.':~;;::~''i~>~fi·;:''!'t.~;r~~i{;;~~::r '·~~i·\:~~':~';::;i~f ~ • •.•.• " •• , "I ' " I." , i . . '- -,--.. ,.. ... "~ .. '.'. "."'~!j" ~ .... - .. :. '-~ •• ;; ........ .... ................ , "#- ........ - ~. - .. 

(k) March 1982, Global 

, ... t.t,. •••• .,. ••• a,1 "" .... 1, tr ••• ' .. ·,..l.u .... _.,I.n. st. .•• ., .. to st..' .0, '0 •.• d., 'to _I ... .,.: At 1 ••• , I ••• .,. ..... "r.' .t , ...... " ,r.d ,"ft'. cc--cr ••• e.rr ...... ." .--1"1, '~"'t.,.ct.,.cI D.'IaU, .. ,:t ......... N--H ...... It c.""." •• 11' .b.~,. .. U.".J 11.0 'e .,.,..,. , • .,. ell .... '''''M'I'.,. •• "UI •• riM , ... .,..t,,." 

100M NOM _II WSIlIIID lOlL""'" .. IL .. I" MTn "\/HR_ -- _. HIIO ...- 10 I (;liT ..... VAl MY "1(;111 C[LL 2:W _/SHIP .... W.OIITE 110M _. C ...... C ...... C •• '.<1 C .. '.49 C •• '.sa C •• '.'4 

I. -'.n I. - •. t8 I. -'.41 I. -. ,r? t. -'.11 .. '.21 MY I .. '.43 S, ••. ,. •.. '.tS I .. I U e, .~ I .. ',17 

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~ C ••• ,. C .. '.JS C ... 54 C .. '.U C ..• 24 II ..... I . .... I. -1,.2 I. '.U I t," I. .~ 

HIIOHT •..•. n I .. l.tI I .. "tS I ... 52 I .. ',U .... 4Sn .... 4'179 .... Jill .... 1474 .... 4n 

MOM _. C .• I,n C •• '.'14 C .• '.111 C ... 1II D. -I. ZI 8. - .... I. · ... t. '.41 I ... tS I, .• 97 t,. 1\. I ..• II II .... 12 .... J841 II • 251'1 .... 417 

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C .. '.111 C .•• , .. 

I. '.n I. '.'4 

I ..•. " I ..• n .... 441 II •. U79 

C .• '.n C ...... .. . ... I. - •. I. I .. ',71 t, .• n II .• 447 N .• UII 

C .• '.11 C ...... I. - •. ,. I. .... •.. 1 •• I ..• U " .. 4\1 1I .• 4U4 

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C .. -' " C ...... 

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(1) March 1982, North Pacific 

Tu. ~r •• _ •• "'9_ SST An .... l' Cr, •• C.rr.l,tt,,, • •• ,10", 56.' de, II to 21.' d., II, t ... de, E te 211.' ,., E 

At 1 ••• , I .b •• rv,ttlnC" .t C'''''.n ;,..1. pI'." .. 

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I .... 11 .,.r'9_ .... ,. ell,,,,, ""P.r"wr •• ftl" •• riM ' ... FI.,..t.,.. •• 

HOM 

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I11III 

110"" llYN .. NOM 

AYI1U SUSSKIND "ILHAN WILHEITI 'IIZIIN HUS SHIIR HICHT ,_ SHI'S 

NleHT CELL 234 5,,,,1I/5HI' IS/CELL 

C .. '.7' C .. '.1. C .. '.11 C .. '.3t t. -D.3t I. -'.Z' I. • .• , I. 0 tt 

B .. '.52 I .. ' .• D B .. t .3 9 . t.t2 N ..• B3 N .. 483 N .. 4B3 N .. 429 

C .. '.910 C ... t8 C ..• 39 II. '.It I. t u I. '.49 I .. '.17 B ... ,~3 9 .. I fl N .. 5tS N .. 595 N .• 534 

C .. '.tS C .. '.38 I. • 't I. I •• B ... 'IS B .. I OJ 

N .. 595 N .. 'iJ4 

C .. 0.'9 I. -1.5. 

B .. t.J:! N. ~l4 

COIll'O~IIE 

C .. '.4S C ..• 34 

•. '.ll I. • 22 

B .. '.73 5 ..• ~6 

N.. 434 II. 34. 

r .. '.62 C .. D.56 D. '.S5 I. '.n 8 .. ',5t B ..... Ii .. 531 II .. 434 

C .. , 61 C ... '>2 I. o 45 D 8 SI I ... S3 5 .. ' .• a N .. 538 M .. 4J4 

C ..•. 11. C .. '.ll I. - •. 5' I. -I 47 

B . '.97 B. ..,.. M .. 'i3~ N. 434 

SMR Hie/IT C .. I." C. o 3" D, •• S ft -. OS ClLL Z34 B .. 0 OJ B .. 0 .t~ 

H .• 'i3. N .. 392 

IIHHlnl 

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C .. '.57 I. -1.13 

S .. G. 410 

H .. JY4 

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C .. '.44 C ...... I. •. to .. 0.38 

I .. '.n I ..• 73 N .• "3 N .. 48J 

C .. '.5t C ...... I. • 3d I. • 74 

B .. '.Il I ... 57 N •• Zit M .. 5tS 

C .. '.'il C ...... I. '.lD • •. U I .. 1.82 I ..• 5' N .. lSI N .. 595 

C .. '.17 C ...... I. -'.U I. -1.27 

t .. '.24 B .. '.es N .. 2St N .. 595 

C .. '.2'> C .. I.n I. -I 38 .. '.27 B •. 1.260 B .• 1.1t II . 2260 N 534 

C .. 1.36 C . .... I. -D.018 I. 1.22 B .. 0 Y4 B .. 0 .. N .• l28 N .. 538 

c .. a 38 C .. I 18 •. -t 29 e. • 27 S ..• 91 Boo • 48 N .. l27 N .. 434 

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(m) March 1982, Mid Pacific 

Tw • .... r .. " • .,..,. 1St M .... l' Cr ••• Cerr.h.h" • 

.... 10". 2'.' d ... M to 2 •. ' d ... 8, In.' d., [t. 29 •. ' d., E 

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NO'" NOM NOM IUSsllND "JLftM IIILH[1T1 IATEI All"" 

_. A\/IlII HilS SMl ~1c:HT HAN WlY 

NOM 

AI/HII 

DAY 

- DoIlY NIc:Hl tiLL 214 ""'./iIIIP 

- CeNOSITE C •••.• 11 C .• '.17 C •. '.13 C ..• 45 C .. '4] C .. '.1' I. -'.U I. - •. " I. -'.27 I. -'.41 I. -•.•• I. -'.18 I .. '.41 S ..•. J. II .. '.'2 B .. '.7' •.. '.58 II .• '.tI .... '" M •• '47 M •• '47 M •• 9Ia .. .. ,;u, M., UI 

.. OM " ...... NIQ41 

C .• '.92 C .• '.ll C .. 1.57 C •. I." C .• 1.'1 I. .. " I . I .• , I. -I .•• I. •. J. I. '.22 t .. 1.18 8 .. '.51 8 .. '.U I .. '.19 B .. '.11 N •• IlZi N .• IIZI N .• "1 N •• I'" M .• 221 

110M ,,\/til. 1; •• I.J4 C .. '.51 C .. '.51 C .••. " I. -. II I. - •. " I. •. I, I . 1.11 II ..• 55 8 ..•• 5 S .•• 4. B .. '.11 N .• 1152 N .• 1121 If .. 1141 M .• 221 

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ENIII Nlc:>r. tELLl34 

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(0) March 1982, North Atlantic 

TWI D.,r .. A.~rl,e SST An'ftll, Crl •• C.rrell',.n • • ~,,"' S •. I d~ " t. 1.1 •• , II ztl' •• , E " 361.1 .e9 ( 

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(p) July 1982, Clobal 

, •• .....,. .......... It, ....... 1' CI ••• C.,r.l.tI ••• 

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> I 

N 

N 

'" 

•• ",:","f 

··~~{~~~"~?~{;';:!'!~;;;::;~'~~;~~~j'~'TTIt;ri:7Y~\Y:~'!.!!·'I:'~:;~;~~"i'j~;l; ~:')":F~"~j:. ~:S:.~~C;~~7":~' , 

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Statistical Comparisons Between 2- Latitude-Longitude Binned SST Anomaly Fields with 3 x 3 

Cell Spatial S~othing. All Data Masked up to 600 km from Land: (a) November 1979. Global. 

Tw. D~q~e. A •• ~.q. SST An'".I, C~I" CI~~.lotlln • • e9 10n • ~6.a de; H tl S&.O d.; SI I.' do; L t. 1'0.' d09 £ 

A1 I ••• t I 'b •• ~ •• 'lln(.) .t CI"ftln ;~Id pllnt. (C--C~I" C.~~.I.tlln, B--BI •• , S--St.nd.~d DpIldtlon 

ob •• t blo" H--Nu~bo~ " CI""on •• lld Ib.~~ •• tlon.) 

BI •• 10 '.'~'9 •••• ~ col'ftn te"p.~.t.r •• "in •• ~.w '."per.t.~ •• 

NOAA SUSSKIND 

"'IIHIII HilS 

WILHElTl PAZAN PAZAN FGt;t TIIAHSPAC CLJMTot. HILHAN SHIPS 

NUll 

"~ U~ .. C .. I.J4 

AIIHIIII e, -I, SJ 

NOli 5 .. '.78 ft .• 3089 

SUSSKIND 

HIRS 

WILHEJTI 

HILHAH 

SHHR HICHT 

!:ELL 234 

PAZAH 

SHIPS 

)S/CELL 

PAZAN 

SHIPS 

>~'O/CELL 

rC:;l 

ltUOYS 

TRAHSPAC XBT 

CllHATOL 

SHIPS 

SHill NIGHT )S/ClLL )ZO/(;ElL I:El.L 234 C .. '.41> 8. -I.el a .._- _ ••• 0" 

H •. lSOZ 

C .. '.31 

8. -0.3-\ 

S ... 89 

H •• tau 

C., 0.711 C .•• 87 

8. -a.Z4 II, -O.J;? :: .• :. JS 5 .. ',12 :. .. 324 H •• 6 

C .. '.4t C .. '.3& 

8. D .ZO B. 0,4'.0 

9 .. '.62 9 .. 0,56 

N .. 3Z4 H .. 6 

C •••. 54 C •• a. ,to 

e. -'.72 8. -2.tt 

S .. 1,9t 5.,0.42 

N.. tS2 H.. I> 

C .. 1.00 8. e, UD 

5 .. '.10 H.. 6 

BUOYS XIT 

C ...... C •• '.78 C ...... B. -1.16 S, -',20 8. -. 6S 

5 .. '.ID 5 .. '.JZ 9,. ',74 H •• 1 H .. JO N •. J106 

C ...... C .. -1.13 C ..• II 

D, 0.26 II. O.Z6 8. -I. IS 

5 .. O. I' S., '.69 S .. 0 64 

H .. 1 H .• Ja N .• 3997 

C .• '.32 C .. 1.00 

8. -1.26 8. 0.14 

9 .. 1.64 S .. 1.89 

H .. Z7 N .. tosS 

C . , 611 C ..• to II, o IS 8 D." 5 .. D 34 S .. I S4 H. , Z'I H. JZ4 

C .. I OQ II -I 24 

5 1 II H. 6 

C .. 1.01 8. -0 77 

S . 1.00 N .. 1 

C .. ~ to II. 0.33 

S,. '.44 

N. , JO 

I 

, 
j 

;I>-

I 

N 

!.oJ 

NOAA 

AVHAR 

NOV 

SUSSKIND 

HIRS 

WIt HUTI 

HILMH 

/ 

(b) November 1979. North Pacific 

Two Doqr •• Av~r'9. SST Ano".l, Cro •• Corr.l.t.ons •• 9 10n • Sb.O dR9 "to 20 0 drg"j 100.0 drg £'0 ?9 •. ' dog £ 

A, S ••• , •• b~er •• 'lon(.' ., co"",n grId p,In,m 

(C--Cr ••• Correl.'lln, B--Bl •• , S--St.n~.rd D.',."ln 

.bt., bl •• , N--Nv"b.r ., Con"on •• lid .b~orv.tlon.' 

81 •• S •• vwr'ge over c.l.~n tr"por.tvre. "In •• row '~.r.'.r •• 

HOAA sussy-rHD WrLHEJTI PAIAIf TRANSPAC CLI"ATOt. AIIHU HIRS IIILMH SHIPS XBT NOV S~HR NICHT IS/CELL 

CELL 234 

C .. '.4S C .. 0.1>0 C .. '.IIS C ..•. 7" C ...... ». -8.DJ D. D. lb II. -1.27 B. -D.21 B 0.04 

S ...... S .. 0 75 S .. D. JJ S .. 0.32 9 .. '.63 N .. 5.7 N .. 401 N .. l?t. N .. 29 II .. 5.? 

C .. 8.21 C .• '.40 C .. -C.28 C ..... II. '.28 B. e. at. B. 0.26 B. 0.0? 

S .. '.97 5 ..•. 1>5 S .. 0.1>9 5 .. 8.52 

II .. 400 N .. 17" II .. 29 H .. 517 

C .. '.5B C .. 0.54 C ...... B. -I 7" B. -1.34 II. -D.24 SHHIf NICHT 5 .. '.7E1 S .. C.5J ll .. 0.94 tELL 234 H .• 148 .... 2b H .. 4DO 

PAZAH C ..•. 68 C .. '.ID SHIPS B. II. 1S B. D.19 )5/CElL S .. '.34 S .. D.U H •• 29 II .. 17" 

TRAHSPAC C ... D.to XBT &. ..~? 

S D.40 H .. 29 

ClJHATOL 

! ' 

. --._~. -_"·_·.· __ ~-_r _ '_. 

00 

"11:0 

0- " is O~ :Or0" c:> >0 

~~ 
! / 

E:'~ "'::, ~: ~,~,,,,:,:-~~cL ~~? T;:"~'t'·, ,~.} '1, ~"':'* ,<'i£;~t .1':'""i~~'Fm?\,~'\,;:r::<. r 1{';;:~r':~,;;.;r?~l·'.;,T~~\, .. •• :. t' :,FT;'.: j?i y{:::?~~;r?~~~:r~c;;~~; ~~,·~7.;:;;:o:';i' ,.' 'J __ :·{:-·t·:;:·~i-'~~-.i-~,.~ ~J; 

. , :.:;: . ...;.,; .. ",:". . • ,.' .>.~','i :~' 

"~.'-------.'------.------. (~- -; 

> , 

N 

.tHOM 

AVHRR 

HOV 

SUSSKIND 

HIRS 

II IlHE ITI 

"ll"AH 

(c) November 1979, Mid Pacific 

Two De9~.e Au.raV. SST An,"al, Cr,a. C,rrel,ttln. 

a,qt,n. zc.o d.~ H tl ZO .• deq SI 101.' de9 E t. 29 •. ' deq E 

At le,.t I Ib.e~v'ti'n(.) .t co"",n grid p'int. (C--Cr ••• C,rrel'ti,n, 8--Bl •• , S--St.nderd D •• i.tlln 

ab.'t bl~., H--Nuftbar .f ('"".n o.lid Ib •• ro.tt,n.) 

8i •• I. I.er.ge ••• r cllu"n t.r.F.r.tu~ •• Ainu. ~.w t~8r.t.r' • 

• 'CAA 

AI/HRR 

HOY 

SU55K IND IIILHEIT I PAIAII 

HIRS tlllHAH 51Hl'S 

Coo •. " 

e, -1.42 

S .. '.30 

N •• 611 

5H"8 NICHT )S/CELL CELL 2J4 

C .. '.34 

8. -I. DO 

S .. 1.67 

N.. 50S 

C .. I.n 

B. - •. as 

So, '.14 

Noo II 

C ..•. u C .. '.76 

B. -0.39 B. D.~5 

B .. '.77 9 ..•. 00 

Noo 929 No, 4 

FCCE 

BUOYS TaAHSPAC CLJ"ATDI. XU 

C ...... c ...... B. I .• 6 B. -0.92 

S .• 0.10 S .• 0,61 N .. 1 N •• 6t4 

C •••. U C .• '.10 .. '.27 D. -e.43 

9 .. '.10 S ..•. 4? N •• 1 N .. 963 

C .. D." C ...... 

~R IHCHT 

C .. -D. J4 

8. .." 

S .. '.21 

M.. 4 

8. 1.66 8. - •. ." 

9 .••. DD CELL 234 Noo 

PIIZAN 

SHIPS 

)S/CELL 

FCC" 

8tJOY~ 

TRf.HSI'/IC 

X8T 

CLI""TOL 

1 

S .. D .66 N •• 934 

C ...... 

B. -'.21 

S .• D.12 H •• 4 

C •• ' .• 0 B. -0.63 

S .. '.10 

H.. 1 

! , 

~ , 

~ 

I 

I 

I 
) J ) 

r;.:~~···"} ~~.~ ':'I'~'~~~ ", .'{>, ·"·S .. ' ';·':(.··.;rr:'~~'I'·f;~'::t~:i7'):)·~·f·,;f::·:"·~.'::;'.,~'.·f'p';·" .. ·~i.:'il'.:;.'.~:~~ '~~":~':-'; ;· .. I,;.~,i\;/-'; Y'''! ;~:;·,·~;.·;;l •• ".~ .~tf.:):/~ ;v;of:'; ',.. 1~:" ,.,f·wy··,,,.',, ';' ' .............. - " " '_> .. :;';j ;~"~ 

1

'" f.: "1\' ... _., .... ''*:l.'· ~. . \' ....•.• ··f·i .. l~"'~ I 

~.. '.~ " : .: \. \" i. .... - --.--.-~-... . ... -: ;'., :.-' ":'1- " 

l • 

.-. ~CO-""."''''''''-''~~'';~;-''-''::-''""'"1'Ti'':';;'T-f"'!?''~~~T..:C;:'''I"'''',.",:~;-;-",,:-.'l'''O;~ .':'lI""'€-"~"'~'-~'~::: (? ~,'"l::r:r:·~~:":'"'77""'~~~::;~5~;~.n;--~.~~~.' .. ~::---:~~~,:~ .. ~~~, .. 

> I 

N 

VI 

(d) November 1979. South Pacific 

Tv. D.q ••• A.tr,qp SST An,n,l, Cr05. C'rr.I"t'n~ 

R.~t.n, 20.0 d.q 5" S6.0 d.q 51 10D.' dpg E,o 290.' d.q E 

At I.,., I ob.tr."t,nC.) .f connon grtd p,tn,. 

CC--Cr ••• Corrt1.,tln, 8--8105, S--S'.ndord D •• I.,lon 

.b •• , bi •• , N--N""b.r 0' co"""n .~ltd Ib •• r."lon., 81 •• t •••• r'9. o •• r c.lunn ',nper.'vrv. "Inu~ ~.w t."p'r.t.r.~ 

NOM 

AV~RR 

HOV 

SUSSKIND 

HUS 

IIILHEIT/ 

IIILIIAN 

SKI'IR NI;HT 

CELL 234 

PAZAN 

SHIPS 

)5/CELL 

FCC~ 

DUOTS 

CLIIIATOL 

NOAA 

AVHRII 

HO\l 

5USSlIINII 

HIRS 

C .. '.23 

D. -1.82 

S .. '.96 H.. OJI 

IIILHE1T/ 

"ILIIAN 

S""R NIGHT 

CELL 2J4 

C .. '.19 

8. -I.Z5 

S •. 0.59 

H. 511 

C .. t.47 

8. -I.'ll 

B .. '.18 

N.. SIB 

PAZAH 

SHIPS 

)5/CUL 

FGG~ 

DUOrs 

CLIIIATOl. 

C •• O.ID 

8. -0.65 

5 .. '.90 H.. B4Z 

C •• t.OO 

8. 0.23 

S .. 0.59 

H.. " .. 

C .• 0.00 

8. 0.19 

S .. 0 66 

H.. 5Z1 

~. 

-::. ., 

i 

,:1 

,~ r-'} 

1'...1, I:' 

, 'I:' ., f:,," 

; I 

I/i 

i. 

/ ... 
r:... • .• ...'... 

~""" •. ~,~., v) '., .. '".~.__, "'.~, :. :' ~. , .,,",' ". '.,'.;" ·;'h·:'\:~:;:;?~i!:.<;!';'~ L,._ ' , :',1 _ 

J:! ...... ;iE>,'''-1'''·''i''1··,,.,A _--.- ""'~" ",,,.~,.,; ___ ••• ":"" • __ j~'.,,";.,, .__ :': ~2F;~ .r 

,t~ 

(e) November 1979. North Pacific 

Tw. D.Qree Average SST Ar.t"al, Cr •• , C.rrela,I.n, 

a·91.ft. 56.0 dev H t. 8.0 de9 5, 290 .• de9 E to 361.' dlV E 

At lea.t S .bs.r.a'l,nC,) a' co"",n 9r ld p,ln,s 

CC--cr ••• Clrrela'lln, 8--81." S--Standard Devl.tlon • b •• t bl.s, H--N'~.r ., CO"".ft "Ild ,bser.atiens) 

'la. 1 ••• orIV' •• or ell,"" '~"p.r.ture. "inus row 'eftp~.t.re. 

I!OAA SUSSXJHD 'AZAH CLIIIIITOl.. AVHRR HIRS SHIPS HOI/ )S/CELL 

HOAA 

AVHlII C .. '.15 C .. '.41 C .. e." •. -1.lD 8. -1.20 HOI/ II. -0.36 S .• '.62 S .. '.38 S .. '.36 N .. U6 H .. 144 N .. 2911 

SUSSItlND 

HIllS C .. 0.49 C .• D.OO I. '.35 8. -1.13 

S .. '.52 S .. 0.56 N .. 144 H .. 311 ;... 

I 

N 

CJ\ 

'AZAN 

SHII'S C •• '.00 

)5/CELL 8. -8.23 

9 .. '.33 

H .. 144 

CLI"ATOL 

•• _ ..... I • _, , __ ........... _.~~._. __ ~ ..... __ ..... __ ._._ 

• t 

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I 

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," .... - ..... _._--..;:, 

(f) December 1981. Global 

TWI D.9r •• A •• ,.19. liT Anl~.J, Cr ••• C,rr.l.1I,n_ 

, :~.~~Wr¥?,;~·:;~~r~;~;.:~'~:~~~.~~:";~~:'~~'::~~>J:]j~ 

~ f.j 

~ ~ A. 

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WllHUTI PIlIAN PAlM "IUIAN SHIPS SHIPS SNNa NIGHT IS/CtLL 1211CELL CELL i!l4 

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C .. '.23 C .. l.tI C .• I. .. .. O. flo .. '.ll I. • . l' B. . " S ... 28 & .. '.'7 H .. 2BJI N .. 2JS H .. 2 

C ..•. 24 C .. 1.45 C •• I." D. 1.14 •. -0 i!1 II. -1.'4 B ... 87 9 .. '.42 S .. 1.'5 N .. 286J H .. 2J5 H .. 2 

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TWI D~r •• AWl,.". 1ST An,,,,I, Cr ••• C.rr •• " •• ns .... u, 56.' dog II to •.• dog 5, 291.' dog [t. 361.' ~ •• E 

At 1 ••• t t tb •• ,. •• UlnCI' .t c,""on t;rl. p.ln,. CC--cr ••• C.rr.I.U.n, '·-8i •• , S--EH.nd.rd D •• lou"n .b •• t Itl •• , N--H ..... b.,. If c ..... ,,, •• lid ,b.e,. •• ,!,"., .1 •• S .. "."9' I.e,. ell."" ,."p.,..'I111,. •• ''In''l r'lIt , .... p.,..t." •• 

IIOAA 110M HOM SUSSKlIID SUSSKIND "llMII PATEl 

AVHIl. AVHRI AVHRI Hln HlIll SMJI 111I0Il1 VIla 

OAT 111I0Il1 IlEICt\TED C£ll 234 

lUl IIOAA C .. '.tI C .. '.'4 c .. '.84 c ..•. 17 C .. '.7' C .. '.'4 

A ........ I. -I.U I. 

- •. ,. I. -1.43 I. -1.7'i I. -1.55 .'. -'.l4 

DAY I .. '.41 I .. '.34 S .• ' . .z 9 .. '.53 •.. '.67 S ...... 

lUL II .. 'ill II .. 58 II .. 'ill II .. 58 II .. 51 II .. 4 

NOM C •. '.'7 e .. '.81 C .. ' .• 3 e ...... C .• '.14 

AIIHRR I. '.14 I. '.65 .. I.J. I. -1.42 8. 1.38 

NICHT t .. 1.15 8 .. '.34 B .. '.ll 8 .. '.45 I .... ., 

M .. 2J6 II .. 2J6 II .. 236 N .• 158 II .. 41 

NOAA e .. '.79 C ... O. C .. '.5; c ...... 

II ..... " I. '.51 I. '.2' I. -1.50 I. 1.29 

B .•• If B .. '.37 8 .. '.49 B .. '.ZI 

II .. 2U. II .. 236 M .. lSI H .. 4. 

BUSSXIIID C ... 97 C .. '.44 C .. '.78 

HilS D. -O.J2 I. -. 97 I. '.74 8 .. C .14 S. '.42 S .. '.15 

II .. 31t N .. 231 II. 41 

SUS~UND C .. '.4Y C • 87 

HilS •. -1.67 I. • a9 

IlEIC,'IED S .. 1.44 5 .. '.Ii! II .. 231 II. 4. 

":tPWf C . .... 5""_ HleHT .. i. 71 

Cnl 2J4 S .. '.l!> II .. J8 

PATES .. VAS 

,,,z,, .. SHlr3 

)SICEll 

CliKA10i 

'AZ,," CLlllA10L 

&HI,. 

)S/Clll 

c .. '.'4 c .. '.n 

•. -'.U •. -'.13 

B .. '.l9 I .. '.93 II .. 47 II .. 58 

~ 

f. 

e ... 19 C ...... I. '.65 I. I.tt 

B .. '.31 8 .• '.57 II .. 157 II .. 2U 

e ...... c ...... I. '.41 D. '.07 B .. 1.37 B ...... II .. IS7 II .. 236 

C .. '.15 C ..... 

I. -'.'4 •• '.41 B ... 36 D .• '.37 

II .. IS7 II .. 311 

e .. '.1lY Coo '.11 

00 

.... ::0 I . '.27 .. o 72 

B 

II 

.. 

.. '.31 

157 S II 

.. 

.. '.47 

311 ~~ 0> 

::or- C .• '.66 C ...... 

I . 1.17 I. I.l' oO-U S ... 51 5 '.42 c:> II .. III II .. Zll ):PC) 

r-m 

C ... 42 C ..• II .~ en II. -I S'i I -'.79 S. • 2" 5 .. 1.24 

If .. l8 .... •• 

c ...... 

I. '.41 !I. ..... N .. 157 
> , 

.l>-

N 

/ 

./ ~~ . 

_. . Ii'" "AI .:- ~.""""."" .. """1f"1't • .' ... !'" :,1.: \.,.[11 [<' ~ ~-i'.Wf9'1D"" \' .. ~, ""1"'".'"'" i"O".""r<,.' 'i:,~ Oil: ":":":;1::~JT~4 -------~. •... ---.... ._._....--,. -... ----- •• ......--~-.-., .. '. .... ' . 

l_~. . ' ..... :':...-.. , ................ ~.' ... n.~""'*"" ... ~~ ..... (., .. .,. ".'" _.. . . .. r~;J ~ 

.. ~. 

'I'able 1\-3. Same as Table A-2 Except BIRS and BIRS Version 2 Replaced by BIRS Version 2 Weighted and BIRS 

Version 2 Weighted Smoothed. (All Other Data Sets Smoothed.) (a) March 1982. Global. Two D.9" •• A".""9_ SST An .... d, c,..~. Co,."rl'1J,ons R.9Lon. S6.0 df'q H 't 56.0 d.; S, 0.' d.q t: t. 36D 0 de9 E 

11'1 1 •• ", 1 obi •• ,.",U,"'" .t ,."".n ;r1d punt. 

(C--Cr ••• C.,.,. ... ".n, '--8h~, S--Sunlh"d » ... hU,n 

.b,., ba •• , H-·Nu .. b.,. .f c,,,,",on v,Ud .b •• "",'l,n.) 

,WAA 

AVHRR 

PAY 

MAR 

HOAA 

AVHRII 

HICHT 

NOflilte 

AVHIIR 

SUSSlt'lh:) 

HIRS 

SUSSKlN~ 

HIRS 

VERSION 2 

PUU1AN 

Sr11'1R Nt GHT 

CllL ~3'" 

WllHEll1 

HAN 

S""k/SHI~ 

CO,.,POSI TE 

lt~T~S 

VAS 

PA1A" 

SHI'S 

IS/CELL 

P~ZAN 

SHIPS 

)20/C£l.L 

TNANSPAC 

XPT 

CL I!\ATOl 

~OAA 

P.VHPJl 

PAY 

MR 

NOAA 

AVtUiH 

HH,HY 

N~AA 

AVHRR 

'1 •• 11 ,v."'9" • .,.,. ell,,,," ' .... p.r.'v,. •• ,.In,,1 r ... '."p.r",,.., 

SUSSKIHZ> SUSS.INP MILMN WI L)fE ITI BATES 

HU5 HilS S""R HI~HT HAN VA~ 

VERSION 2CHL 23. S""RI~HI~ 

cO"'~O~nE 

,hZA" 

SHI'~ 

l~/CELL 

'AlAN 

SHIPS 

121/llLL 

lRAN~PAC 

XU CLIMATOL 

CO. 93 C.. D 99 C.. D. SO C. D. 62 C.. •. 6~ 

P. -0.]7 B. -D 18 I. -D.Sl 8. -0 S~ B. -~.l7 

S.. • 31 S. • 17 S. 8 11 SO.. S. 0.77 

~ .. JJ~J H .• J.c. H .• l404 N .. l404 H .• 2Jl1 

C. • .60 C 

t. -'.21 ft. 

S • S. S .. N. 168. N. 

0.17 C .. '.7. C. 

• 2. 8. '.11 • o.~ S .. D lJ 6. 

291 N. 2J7 H. 

1.21 C .. '.91 C .... to • 13 • -0.l9 t. -.' •• • 21 5.. D. 27 S.. 0 84 

~.... "N .. J'" 

;, .. 

.. 

C.. D 9a C.. D.'!> C. 0.'9 C. '.65 C. '.71 t.. '.21 C.. O. 8Z C. 0.94 C. '.75 C.. • .. 

8. D 17 8. -. Db 8. -I •• 8 • '7 •. '.27 8 0.6' II. '.57 e. 0,7 a 0.2'> a. 0" S .. I 19 S .. 0.72 5 .. 06. 9 .. 07Z 5 .. "0 5 '.59 9 .. '.23 S • Zl S • 32 9 .. 072 

H .. 1921 N .. JUI H .. 19l1 H .. 2~D7 N. ZIIJ N... 311 N.. J.a N.. 2l N. 18 N .. 3921 

C .. I.S C. 0.50 C. 065 C .. O.U C ... 21 C. '.77 C .. I." C. 8. -0.2' 8. -12' •. -1.ll I. O.U a '.51 •.•.•• t. '.60 a. s .. '.76 S .. '.69 S. '.73 9 .. ' .• 8 S . 0.S9 S ... 29 S. '211 I . N .. 1989 N .. J989 N .. 2641 II .• 21sa N.. JU ".. 368 H.. 2J N. 

• 7. C ... It '.14 t. -0." 

•. n 1 .. '.79 III H .• 3989 

C. • 9J c. o 17 C. •. IS c .. t .• ", c ..• 40 C .• 11 C .. 1.41 c ..... 8. e. DO B o D~ 8. •. \1 I. '.01 8. ·1.Z9 B. - •. JI •. -1.7& I. I 10 

5 .. D 25 5 1.00 S ~. 6H S .... S. ~ .. '.n S .•. '7 S . '.SO! 5. • .64 H. HZI H .. U67 H .. 219. .... JIl " .. 368 N .. 2J N .. 18 N •• 21 

c .. 4.17 c .. a .• 4 c .. '.49 C ..•. S~ C .. D.J' C .. '.11 C ...... 

I. O. IS .. ':1.2' D. I.S2 •. -1.29 •. -'.27 e. -. IZ .. 0.11 s .. ; .? ~ .. 0.61 S ..•• y S .. '.31 , .. '.J:! S '.51 S .• 5' H •. 2967 H .• 21,. N .. UI N .. .168 N .. 2J H. 18 H •.•• 21 

C .. 0.0" c .. -' 14 C .. t. I~ C .. -1 .. C ..•. sy C · .. i" 8. r .28 ". • .89 t. • 17 •. -1.,8 8. -I S. .. -.... $. • Jy & • Ol S . 1.7" S. t 4, 5 • S. 5 ..•. ,. 

H. 1~18 NO' 2S8 N .. lit N. 11 N. UI lit . .6t1&7 

C .. -' .• ~ C .. 0.75 C .. '.~l C ... 78 c .. '.It II. , .J9 D. -1.'7 8 - •.• S ,. -... f: -0." 'I .. 1.61 9. 825 S .•• IS i ... II I .. '.'. H .. ?J' NO' lQ7 H .. 14 N .. 18 " .. ~". 

C .. 2.79 '.11 ft. -0 91 -I 61 

S 

N 

..•

.. . Z. SI · 301 ~. 

C .. I It C .. D.7' .... .. .... a. -. 47 " Il s ... It S. • 3~ ~ 

N .. ZJ H III N. · .. " 

C .• '18 C .. n I. -. 47 II .Il 

S ..• I~ S .. 2J ... • H .. 2l 

C ...... 

a. on 3 .. I 48 

H .. 18 

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...... ~ 

i~ \: 

:.: ~~ I' , 

l' I -£ •. , •. / .h 

, =,'1 r ~ ,::1 

, . ·1 

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r' ,/ 1 • , I' . 

, 

-

~ 

, 
APPENDIX n 

ERROR-PARTITIONING TABLES 

B-1 

~ I .•• _'* ....... ,.----~ ....... --.. - .- _. . .... ~ .. .,. 

----------------~-...:..-----""------~------ - ---
) 

I~ 

Table 8-1. 

I·· 

tI:! 

~ ! I 

1 N 

'1 

, 

1 

[(;\ 

} / 

.__..&'..... F SCM_ spz·c;;.:;..;::_,,",:,,:'.::: __ :=-_' .-_.--_ .... ---~ 

Partitioned rms Error Between Triplet Combinations of the 2- - ~inned SST Anomaly Data Sets: AVHP~. SMMR. BIRS. and Climatology: (8) November 1979, AVHRR. 

F.t1S ERROR TABlE FOR DATA SET I 

FILE flAME: SSTS"HOP: ISSTlF.FlE60004. BL Y 

OESCR I PT I OH: HOM AUHRA 

LAT I TUDE BOUltOS" -5S TO SS LOfIG I TUUE ~. o TO 3W 

OVERfiLL AIJEP.RGE R:1S EAAOfl= e. S3 

2 

:; 

4 

2 

3772 

3773 

3 

0.34 

4538 

4 

0.:59 

9.67 

Lf'PER R I GIfT TR I ANGLE OF TABLE COUTA I NS RMS E.AAOO I ti TR I II) 

L!U:R LEFT TRIPJIGLE (jF TAaE CONTAHIS fll.R1BER CCi1MOH GRID POINTS IN TRIFtO 

RO~ AIlO COlUMN NUftBER OF TABLE DEFINE THE 2ND flt() 3RD DATA SETS IN TRIAO 

RQI.I/COLUl1li FILE liAME 

1 

2 

3 

4 

SSTSHO?: ISST1RAE50B04.ElY SSTSHOP.(SST1SOC60e04.E~Y 

SSTSHO?: ISSTlhcg60684.BLY 

SSTSHOP:ISSTJtgz&ee.bin 

OESCR I PT I ON 

NOAA f1UHRR 

Sl'tr.R CELLS 234 

HIRS/MSU2 

CLIMflTCl.OGY 

--.------
• 

\ ... ~ 

'J 

fA' 

., 

'::\ 

• 

\ 

G:\ 

t:!J 

I 

w 

'-~ J 

-----------_--"""'JtZ':t2'r--:'r ~ . '-:-.: ~~- :::--:.---..:, _=:'.: =-~~ ~~~ 

(b) November 1979, SMHR 

RMS ERROR TADLE FOR DATA SET 2 

FILE rif1M£: SSTSHOP: rSSTlSOCOCB(l4.El'r' 

OESeR I PT I Oli: St!f1R CEllS 234 

LAT I TUDE BOUtIOS= -55 TO 55 LONG ITUDE SOllI'IOS= e TO 3Ge 

OIJEMLl AVEAAC.£ Iit1S ERROR- e. B? 

2 

3 

4 

2 

3772 

3773 

3 .. 9.01 9.n 

9.94 

3849 

lffER RIGHT TRIANGlE OF TRBlE CONiA/NS RnS ERF.'OR IN TRIAD 

LOWER LEFT TRIAlfGLE OF TABLE COflTAINS tlllt1eER CCl1l1OtI GRID POINTS iii TRIHO 

ROW RNa COLurdi NUMBER OF TABLE DEF I fiE THE 21iO AHO 31='.0 DATA SETS IN TR I AD 

ROU/COLUMN FILE NAME 

I 

2 

3 

.. 

" 

SSTSHop:rSSTlAP~60004.6lY 

SSTS'HCP: [SST lSOC601304. 8l Y 

SSTSHOP:ISSTlhc9~ea~4.BLY 

SSTSHOP:ISSTltg=6ee.bin 

' •• -. -.--___ .. J~ ....... ~ ______ • __ •... _ • 

OESCR I PT I OIi 

tlOAA FIVHAA 

SllIlR CELLS 234 

HIRS/MSU2 

Cl I l1fHOlOvl' 
.I 

, I'!~ ;1.""""-4_, '. ""'f~ .... ~,~", 'G' ""i'-"'\""": ...... -~ •• ,':,.,. ••..• '1 .-..... ---

I I~~ ! 

I 

"'! .:.,: 

';t: 

• 

~ 

III 

I 

~ 

/ 

-.--~ ............ _,,. ~ - -~;. ---'""'"'"'!I" ··--.··.r·,·, :: i.' """.l~,.:t~r ,",'N ', ..... ,,' I. , l .• , :. , "..-- ... ".-- .' ,.' .. .:'::,! ~ 

. - -~ ... -- ~' ......... - ...... --.... ~...,.... ... "'!'...-" ....... - .... _---_ ...... _.-._ .. -.-

(e) November 1979. SIRS 

RI'1S ERROR TRaE FOR CATA SET 3 

FILE NRt'1E: SSTSHOP: [SSTJhcg6eOO4.BlV OESCRIPTIOIi: HIRS/It"'~2 

LAT I TUDE BOUtIOS= -55 TO 5!5 LONG I TUOC BOl.HlS= e TO 30e 

OVEAALL AVERAGE RNS EP.ROO- 9. 56 

2 

3 

.. 

3n2 

4538 

2 

9.66 

3849 

3 4 

9.49 

0.62 

UPPER R r OHT TR r RNOtE OF TABlE CONTA r NS Rt1S ERROR 'N TR r AD 

LOWER LEFT TR r AtIOLE OF TAKE CONTA J tiS NUt'.BER COI't1ON GR I 0 PO J NTS J N TR I FlO 

ROU ArlO COLUMlI fiUl1BER OF TABLE OEF J NE THE 211) fiNO 3m) DATA SETS J H TR I AD 

p.oo /COLUllIi F I L£ liME 

I 

2 

:3 

4 

SSTSH'-~:rSSTlfu1Eeo~e4.BLV 

SSTSHGP: rSSTJSOC66Be4.BLY 

SSTSHOp:rSSTlhc~6eOB4.BLY 

SSTSHOP:(SSrltgz~~a.bln 

DESC.RIPTlQti 

NOP.A AlJtiP'.ff 

SM:':R CELLS 234 

HIASIMSU2 

CL Il1am.OGV 
/ 

g- "'-::::1 """r-' '''''' ..... - ... ,-_. ,. ,,~.~'.. . _~.~.~ .. ' _._:~ \', i . 

I 

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'.-.\~--. ·-"--'.~-;~-~:.~~ .. _ .. .'.~;.::".::"":-2~ .. :L~~?~,'i· . .t-.. e.j~_._!·_S 5.::_ • _ok 'WSL_ -~.-= ~ 

(d) Novelllber 1979, Climatology 

RMS EBROR TABLE FOR DA rA SET 4 

FILE lii1!1E: SSTSHOP: ISSTJtgz500.bin 

OESCR I PT ION: CL I NATOLOGY 

LATITUDE BOUNDS= -55 TO 5S LONGITUDE BOUNDS: e TO 369 

(JI.lEPJ1LL AlJEAAGE RMS ERROR= 8. 03 

2 

3 

4 

3773 

4538 

2 3 

9.78 9.76 

13.45 

3849 

4 

UPPER RIGHT TR I ANGLE OF TABLE COliTA I NS RNS EP.ROR IN TR I AD 

LO~IER lEFT TR I AtIGLE CF TABLE cmlTA I US NUMBER COMMON OR I 0 PO I HTS IN TR I no 

AO~ AND COLUllIi NUI'iBER OF TABLE DEF I NE THE 2ND AND 3RD DATA SETS IN TR I AD 

P.OI-l/COLUI1N FILE N.~E 

1 

2 

3 

4 

SSTSKOP: [SST InF.E~.oee4. SL'" 

SST SHOP : [SST 1 soc(.e'304 . Bl Y 

SSTSHOP: [SSTlhcg60'004. BlY 

SSTSKOP: [SST) i:gzeea. bin 

DESCRIPTION 

NOAA AVHM 

SMMP. CELLS 234 

HI RS/MSU2 

CLIMATOlOGV 
{l~. 

. j 

I 

I 

-. 

~ 

'\ \ 

.... (;1.-;: . ,. •.. \;.'--: .~' 1 T ~ ... " 

t21 

, 

0-

.I ! 

-~.~: •. ,'''' -.-_ •• --..".. ~ ,- ':0." ~ ;-r": ~ ?tr, .~f.t. ':: :}~. ~ i Wi "fl, ..... ,. i ~- ~C - ~.s;:q,~ 

.' . .. ~--.,.. ~-... ·""'-.,·-r.! ... '"I:r V'!-, .. t', ......... __ ...,... •..• "... '''_ 

(e) December 1981. AWRR 

Rr1S EF.ROA TABLE FOR DATA SET I 

FILE NFL'1E: SSTSHOP: (SSTlAAE60003.BLY 

OESCR I PT ION: fmAR AVHP.R 

LAT I TUDE BOUtIOS= -55 TO 55 LONG I TUDE BOUrIlS= e TO 360 

OUEARLL AUEP,RGE RMS ERROR= e. 4IJ 

2 

3 

4 

2 

3849 

3849 

3 

0.3a 

4612 

4 

0.45 

e.n 

UPPER RIGHT TR I FtNOLE OF TABLE COtfTn I /'IS RMS ERROR I tf TR I AD 

LCUER LEFT TRIP~"iGLE OF TABLE CONTAINS NLmER COtnJtf GRID POltITS Iff TRIAD 

RO~ AND COlUMN tUIBER OF TABlE OEF I fiE THE 2ND AND 3RO DATA SETS Iff TR I AD 

ROI-l/COUJMti FILE liME 

:2 

3 

4 

SSTSHOP:(SSTIARE60003.BLY 

SSTSHOP: (SSTISOC6ee03.BL" 

SSTSHOP:(SSTlhcg60003.BlY 

SST SHOP : (SST Jtgz60i3. bill 

OESCR I PT I ON 

NOAA AVHPoR 

Sl1t1R CELLS 2J.f 

HIRSIMSU2 

CL I MATOLOO'l 

- ---
r...f;;l .... =~~<:~::._~_ \.<T~~ ___ ... 

"A\ 

= I 

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. ~:~~~~ .. -·~.N.:~ .. ~~~~~=~~~~.: r .. " ~f t :.; :/: r,.):I,-~~.: ~,~~~l~ ~~:~ .. #.~::~ .... _ ..... _.~....::::_'::'~i __ :_. ,. ~-::70~:~ 

(f) December 1981, SMMR 

RHS ERROR TABLE FOR DATA SET 2 

FILE t'A~E: SSTSHOP: (SSTl50coee03.BLV 

DESCRIPTION: SMMR CELLS 234 

LflT I TUDE BOUtms= -55 TO 55 LONG' TUDE BOUNOS= e TO 3GO 

Ol'ERAl.L AI.'ERAGE RMS ERROR= a. 95 

2 3 " 

13.92 0.86 

2 

3 3840 1.ee 

" 33 .. 13 3352 

UPPER R' GHT TR' ANGLE OF TABLE CGiiTA' NS f'J1S ERROR 'N TR' AD 

LO\.JER LEFT TR' ANGLE OF TABLE COOTR' NS NUMSER CO'1M"jf OR' 0 PO, NTS 'N TR I tiC 

ROW FIND COLUt1N trJr~BER OF TflBlE DEF'IiE THE 21m AND 3.'ID DATA SETS IN TR I flO 

P.oU/COLUlili F I i.E HRI1E 

2 

3 

" 

SSTSHOP:[SST1ARE~~oe3.BLY 

SSTSHOP: [ss. isoceemn. BL· ... 

SSTSHOP: (SST] hCQ6eea:; . BL Y 

SSTSHO?:{SSTltg~6ee.bin 

OESCR I PTI ON 

110M AI)HRR 

SfUlR CELLS 234 

HIP.S/IISU2 

CL 'flilTOLOGY 

" 

" 
. ~~ "." '.\. ".\ r.-=J·· .... , .... - .... -~, .... ,-... , . l... !"."\'·,;~·o .. ,,~.!:.· . .,.~ ___ ~ ...... _ .... 

t:=' 

I 

00 

. \ I } '. 

.-.\ ~"". 

"--"'-~':~"~-.~~.'.:.~=~:'_~~';~~~~·.l,...~!"l'1",,",".'I";:~:~T"'I .. "'i'."J~·r'":'1i"'~"',"_"." .... : .. :_~~~:,. .. w __ ,.t't'~~ ......... :".' ....,._ ... ,-.-~""'-';"''':''''''''''"'''''''.'''''''''--''''''''''','''':"'~~r. ... ~'lI.1'11 

(g) December 1981, BIRS 

RMS EP.RCR TABLE FOR DATA SET 3 

FILE NA~iE: SST SHOP : [SSTlhcg6eae3, BL Y 

OEseR I PT I Oli : HI RS /MS1J2 

LAT I TUDE 80UNDS= -~ TO:5:5 LONG I TUDE 60l1tlOS= e TO 300 

OVERALL AVERFtGE AttS ERROR- e. 53 

2 

3 

4 

3849 

4512 

2 

9.74 

3852 

3 4 

0.49 

9.46 

UPPER R I OHT TR I ANGLE OF TABLE CONTA I NS fiNS ERROR IN TR I AD 

LO~R lEFT TRIANGLE Of TABLE COfITRltiS NUMBER COMMOtI GRID POINTS IN TRIAD 

ROI-! fiNO COLUfm IfUI18ER OF TABLE OfF I tiE THE 2ND RtID 3RO DATA SETS I It TR I AD 

ROl-I/COLUtlN FilE IWtE 

1 

2 

3 

4 

S~.TSHO?: ISSTlAAEI!.C003. BL 'y' 

5STSHOP:(SST)~OC6eBe3.BlV 

SSTSHCtP : I SST 1 hcg60ee~ . SL II 

SSTSHOP:(SSTltgzCa9.bin 

OEseR/PTIOU 

tIORA t1Vt1FiR 

511MB CELLS 234 

HI R:; /tlSLi2 

CL I tlRTOLOOY 

cIIl. \ .- -'., •.. --.. - .'- c.·,_ ..... _ ..... ~ ............ · 

. 
.I 

:A~~ .... '~~=1~." ~·~~·~~~.:~17"""'· .,..-.. ,. 

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(h) December 1981. Climatology 

P~S ERROR TABLE FOR DATA SET 4 

FILE NAME: SSTSHOP:[SSTltgz600.bin DESCRIPTION: CLIMATOLOGY 

LAT I TUDE BOUTiOS= -55 TO 55 LONG I TUDE BOUNDS= a TO 360 

OIJEP.RLL A'JERAGE W1S EP.ROR= e.:59 

2 3 4 

9.73 9AS 

2 334G 9.30 

3 4512 3852 

4 

UPPER RIGHT TRIANGLE OF TABLE CONTAINS RroS ERROR IN TRIAD 

LQl.JER LEFT TR I ANGLE OF TABLE CONTA I tiS NUr£ER COM.'1ON OR I 0 PO I NTS 

ROW AriD COLU~m f)UMSER OF TABLE OEF I HE WoE 2ND AND 3RD DATA SETS 

ROU/COLIJ11N FILE NAME DESCRIPTION 

, SSTSHOP:(SST1AAEOOet'3.SLV tlOAl1 AVHRR 

2 SSTSHOP:[SSTISOC60e03.BLY SMMR CELLS 234 

3 SST SHOP : ISSTlhcg50ee3. BL V HI RS/tiS1I2 

4 SSTSHOP:[SSTltgz~Oe.bin CL IllfITOLOGY 

--.--~ '.-- .... -.... -.-------

:1 

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t,! 

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t~ r: 

V: 

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IN TRIAD I. 

IN TRIAD I 

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) 

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I 

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o 

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-, ~ , ~w_~ .. _.-- --~ ... ~.: ... '" ~'_ -t'~ fO~ .... ""~ ... ~~~, l,., 1. '~::::X:~!::~4:.::::':" :_ ... ~::::~t.~ ;e ••• ,,.,. ,. ",'fl". ,_ ... """" ' ',#- :;: ~1~~:1 

U) March 1982, AVBRR 

RMS ERROR TABLE FOR DATA SET 1 

FILE tlftl!E: 55 TSHOP: (SST I AAE60002 . BL Y 

DESCR I PT I Oli : liOAA AIJHRR 

LATITUDE BOUNDS: -55 TO 55 LONGITUDE BOliNDS: e TO 360 

OUEPRLL AUERAGE RMS ERROR= 0.60 

2 

3 

4 

2 

3760 

3781 

3 

a.l3o 

4612 

4 

0.62 

a.59 

LIPPER RIGHT TR I ANGLE OF TABLE CONTA illS FIllS ERROR IN TR I AD 

LOIJER LEFT TR I AliGLE OF TABLE COIiTA I NS tiUMBER COtIMO~l GR I D PO I NTS IN TR I AD 

ROt.l AND COLUMN tl.'tIBER OF TABLE DEF I NE THE 2ND AND 3RO DATA SETS Iii TR I AD 

ROt.JICOLlIMN FILE NAI1E 

1 

2 

3 

4 

SSTSHOP:ISST1ARE6ee02.BLY 

SSTSHOP: ISSl'lSDC60e02. BL 1,' 

SSTSHOP: (SST lhag66a02. BLV 

SSTSHOP: [SST I tg:6CO. bin 

DESCRIPTION 

NOAA AUHBR 

SlltlR (ELLS 234 

HIRS/tlSU2 

CLIMiliOLOGY 

- ... --:"".# 

" " 

.<,. 

.............. ~'. 

.; 

-" 

" .... 

, 

. '. 

.... 

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'. 

\., 

~ .• -- ..... ~~.---,-y-' .... '!''I'''''~"7': ... _.a.__ -x-. 

(j) Merch 1982, SMMR 

PJ1S ERROR TA8LE FOR ~TA SET 2 

FILE NAME: SSTSHOP:[SSTISDC00002.BLV 

OESCR I PT I Oli . St"J1F: CELLS 234 

LAT I TUDE BOUtIOS= -55 TO 55 LOtlO I ruD:; SOlINeS= o TO 360 

O'JERAlL AVERAGE RMS ERROR:: e. 98 

2 

3 

4 

2 

37813 

3781 

3 4 

0.9B 0.07 

0.9S 

3790 

lI'f'PER RIGHT TRIANGLE OF TAElE r.otiTAINS RMS EProR IN TRIAD 

LOWER LEFT TRIHliGLE OF TA8LE COIiTAlliS rrul18ER COl1tlOti GRID POINTS IN TRIAD 

ROW AIID COLUl1t1 tlUMSER OF TABLE OEF I tiE THE 2ND ANO 3RO DATA SETS IN TR I AD 

ROW/COLUMN FILE NAI;E 

1 

2 

J 

4 

SSTSHOP: [SST I AAW?B02 . BL'T' 

SSTSHOP:(SSTISOC60002.ELY 

SSTSHOP:ISSTlhogB0I302.Sll' 

SSTSHOP:[SSTltgz6~e.bin 

... __ .... -..-' .... "'_.' •. _-,_.- .... "'-, 'X ............ ' 

OESCR I PT I ON 

tlOAA RI.!HFiR 

SIUtP. CELLS 234 

HI RS/l1SU2 

CLIMATOLOGY 

.. --.----_-t. .~ 

.\ 

.... ~.:-:.--. 

_4> 

~ 111'\ __ • 
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N 

J -' ....;. ...... 

~-------. -'--",'.'" --.--•• ~-~':" ... #..__;r'"' i~~:-": T,·.:tt~~~2:::~:::P~:~'_~,~' .. ~~~.' roo·.. .., """"'fIt. '" ." :::tJ,~;C;~\:~:~ 

~ .-- \;' -- -.. - (k) March 1982, BIRS 

RMS ERROR TAElE FOR DATR SET 3 

FILE IlriflE: SSTSHOP: (SSTlhag6(1(102.BLY 

OESCRIPTION: HIRS/MSl:2 

LAT I TUDE BOU~lOS= -5~ TO 55 LONG I TlIDE BOllNDS= e TO 360 

OVERALL AVERAGE RMS ERROR- 0.69 

2 3780 

3 

.. 4512 

2 

0.65 

3790 

3 .. 0.72 

0.56 

UfF-ER R I CIHT TR I A!'fOLE OF TfISLE CONTA I NS RI1S ERROR I tf TR I AD 

LOWER LEFT TR I AliGLE OF TABLE CONTA I NS NUMBER C()MI1(lN ~ I 0 PO I NTS Itt TR I AD 

RO~ AriD COLUMI'I NUt1B~R OF TABLE DEF I NE THE 2ND AIm 3RD DATA SETS IN TR I AD 

RO~ iCOLutlfi FILE NAME 

1 

2 

3 

4 

SSTSHOP: [SST JAAEeel.~2. SLV 

SSTSHOP:(SST1SDC60;)02.ELV 

SSTSHOP: [SST lhaQ6Ge02. BL'l' 

SSTSHOP:[SSTltgzoOO.bin 

DESCRIPTION 

NOAR ffVHFiA 

Stu1R CELLS 234 

HI RS,1ISU2 

CL I ltATOLOG'r' 

-~-;-----.•. -_ ... _---- -.~ 

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. 

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iJ 

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w 

J / 

(1) March 19B2. Climatology 

RMS EP.ROR TABLE FOR DflTA SET 4 

FILE NruIE: SSTSHOP:ISSTltg:6B0.bin 

OESCR I PT I ON: CL I tlRTOLOGY -

LAT I TUDE 80UNDS= -55 TO 55 LONG I TUDE BOUNDS= B TO 3613 

Ol,.'ERfiLL AVERAGE R,'1S ER.~R= e. 43 

2 3 4 

9.49 9.59 

2 3781 0.38 

3 4612 37913 

4 

UPPER RIGHT TRIANGLE OF TABLE CONTAINS RMS ERROR IN TRIAD 

LOI-IER LEFT TR I ANGLE OF Tfl8LE CONTA I NS lillMSER COMMON CO:; 10 PO I NTS IN TR I AD 

ROU AND COLUtIN Nl.'l1BER OF TABLE DEFINE THE 2ND AND 3RO DATA SETS IN TRIRO 

ROI-I/COLu/1N FILE NAME 

I 

2 

3 

4 

SSTSHOP: ISST1AAEoee02.8LV 

SSTSHOP: ISSTISDC60882.8LY 

SST SHOP : [SSTlhcqE.(1092. BL'v' 

SSTSHOP:ISSTltgz600.bin 

OESCR I PT I ON 

tiOAA AlJHRR 

StiMR CELLS 234 

HIRS/I1SU2 

CLJIlATOLOGY 

/ 

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:1 

f1 

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I ., 

p. 

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01 

I 

.... 

~ 

". '~","" .. .., ......... _-. __ .. . 

(m) July 1982. AVHRR 

RNS ERROR TABLE FOR DATR SET 1 

FILE t'lAnE: SSTSHOP: [SSTlAAE60001.BL\' _ 

DESCRIPTION: NOAA AIJHRR 

LATITUDE BOUNDS= -55 TO 55 LONGITLIDE BOUNDS= e TO 360 

OVERRLL AVERAGE RIIS ERROR= (\. 87 

2 3 4 

2 13.76 0.88 

3 2138 13.98 

4 2139 4094 

UPPER RIGHT TR I AliGLE OF TA8LE COIHR I NS RI1S ERROR IN TR I AD 

LOUER LEFT TR I ANGLE OF TABLE '::OtITA I NS NlJI18ER corUl0li GR I 0 PO i NTS I tl TR I AD 

ROl~ AND COLUMN NUtlBER OF TABLE DEF I NE THE 2ND AND 3RO DATA SETS IN TR I AD 

ROWiCOLUtlti FILE NAI1E 

2 

3 

4 

SSTSHCP: (SSTIRAE60C01.BLY 

SSTSHOP: [SSTlSDCM~j(\ 1. SLY 

SSTSHOP: [SST Ihao5000 1. BLY 

SST SHOP: I SST) t9z6(lO . bin 

DESCRIPTION 

NOAA AVHRR 

~;MMR CELLS 234 

HIRS/MSU2 

CL I t-1ATOLOO'y' 

---- -........ ", 

/ 

• 

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j 

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~ ... ~ ~ ..... ~ ..................... -.... -.. .-. - ~ 

(n) July 1982, SMMR 

P.t15 E'P'p'(IR TABLE FOR CATA SET 2 

FILE liR!1E: SSTSHOP, (SST ISOCMoe 1, BL V 

OEseR I PT I Dri: S!UIR CELLS 234 

LAT I TUDE eoUHDS- -55 TO 55 LOtiO I TUDE OOUNOS- e TO . 36e 

OOEP.HlL AVERAGE P.11<; EP.P.OP.= 1.01 

2 

3 

4 

2138 

2133 

2 3 '4 

1.85 9.95 

1.93 

230' 

UPPER RIGHT TRIAlIGLE OF TABLE CONTFlINS RMS ERROR III TRIAC 

LOWER LEFT TRlftHIjLE OF TR8LE CoriTAlliS tilmE'.";R COfIt'.oH GRID POINTS IN TRIAD 

P.ou F1NO COlU,'1Ii tfUtlBER OF TABLE DEF ItE TIE 2t1O RHO 3RO 001TA SETS IN TR I AD 

oo.J/COLUl1ii FILE IlAflE 

1 

2 

3 

4 

SSiSHOP: (SST JP.RE6eOe I. BL V 

5S TSHCtP: (55 T lSOC600a 1 ,BL II 

SSTSHOP: I~ST 11"."9(000 I, BL't' 

SSTSHO?: ISSTltgze00.bin 

DESeR I PTI ON 

NOAA HI)HPll 

SMl1R CELLS 234 

H i P.S If1S.IJ2 

ClIl1HTOI..~'"'V 
.~ 

~. ' 

r:1 

" -.,-, .. --.. ~ ... - \ . 

(i) 

= I 

~ 

C' 

;,-'.';"i' . __ .,. "._.or-"N,... ...... ~""~ .... ~ ........ -. 

(0) July 1982, BIRS 

R:1S EP.ROR TABlE FOR DATA SET 3 

FILE tW1E: SSTSHOP' [SSTlhc96eOO1.BtY 

OEScn I PTI Oti : H I AS /MSU2 

LAT I TUOE BOUl1OS= -~~ TO~:S LOf1G I TUDE BOUriDS~ e TO 300 

OVEAAlL Al'EAAOE At1S ERF'.Of!- e. 75 

2 

3 

4 

2138 

4e94 

2 3 

9.74 

23a:i 

4 

9.76 

0.75 

UPPER RIGHT TR I AHGlE OF TABlE ronA I ItS PJ1S E?P.OA Iii TR I fl) 

LIM:A LEFT TRIF:HQlE ~ TABLE COHTAlliS liI...R1t'ER COOOOti ClAIO F1JIHTS IN TRIAD 

P.()ij AND COLtx1N NUMBER OF TABLE OEFIf£ THE 2ND AND 3RO CATR SETS Iii TRIFID 

ROU /COLlJI1!'f FILE NAt'.E 

t 

2 

3 

4 

." 

SSTSHOP:(SSTIAREoeoal.DLV 

SSTSHO?:(SSTlSOOOeOal.BLY 

SSTSHOP: [SSTlhog6ee91.BLY 

SSTSHOP:[SSTltgz5aO.bin 

DESCRIPTION 

NOAR AVH?.R 

St1NR CELLS ::34 

HI RS/MSU2 

Cli MATOLOGV 

/ 
/ 

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I

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I 

l 

tA, 

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, 

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t>' 

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(p) July 1982, Climatology 

p.>1S ERROR TRBlE FOR DATA SET 4 

FiLE IiP.ME: %T<;:HOP: (SST Jtgz600. bin 

OESCR I PT ION: CllMHTOI..OCiV 

LAY I TUDE BOU!IDS= -55 TO 55 LONG I TWE BO\JiDS= e TO 36e 

OVC..Rfll.L AlJEP.nGE Pl1S ERROR= e. 47 

2 3 4 

0.~ 0.4:1 

2 2139 9.39 

3 4994 23B5 

4 

;!'. 

UPPER R I GPlT TR I fVi6l..E OF TAE.tE CooTA I tiS RI1S ERROR IN TR I AD 

LO\.IER LEFT TR I ANGLE OF TABlE CeriTA I liS rUIBEA COITION OR I 0 PO I NTS IN TR I fI) 

P.O~ fiNO COLUI1li NUMBER OF TABLE DEF I HE THE 2ND Ali) 3P,o OATA SETS IN TR I AD 

P~/COLUMN FILE tW1E 

'2 

3 

4 

~ 

SSTSHOP: [SST JAAE~aaa 1 . Bl Y 

SSTSHOP: (SST1SOC600BI.BLY 

SSTSHOP: (SST lhag6eee l.BLY 

SSTSHOP:(SSTltgzeea.bin 

OESCR I PT I Olf 

NCAA AIJHP.R 

St1l1R CELLS 234 

HI FiS /MSU2 

eli tlATIA..OGV 

.. 
\~J 

I· 

i 

! 

. 

\ 

I 

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r 

<, 

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,-. :_.: ... ~~~. L.L _"'" 

Table B-2. 

!:rI 

I 

... 

(X) 

. ",. ,~:."f"'."··."'l."7":~·~·:-

Partitioned rms Error Between Triplet Combinations of the 2- - Binned SST Anomaly Data Seta 

With 3 x 3 Cell Spatial Filter Applied: AVBRR, SMMR, BIRS, and Climatology. (a) November 

1979, AVERR. 

PoNS EP.ROR TABLE FOR OOTA SET 1 

FILE NAME: SSTSHOP: (SSTJAAEsMOOI.BLV 

OESeR I PT ION: I-IOM AVHRR 

LATITUDE BOUNDS= -55 TO 55 LONGITUDE BOUNDS- e TO 369 

OIJERfilL Rl'ERAGE P.t1S ERROR= e. 82 

2 

3 

4 

. 1 2 

1496 

1592 

3 

0.70 

3aW 

4 

0.sa 

9.86 

UPPER RIGHT TR l AtKllE OF TABLE CONTA I tiS RMS ERROR IN TR I f{) 

LOUER LEFT TR I flIG..E OF TABlE CONTA I tiS tlUMBER eotmi GR I 0 PO I NTS JtI TR I AD 

RO~ AND COLl~~i t~8ER OF TABlE DEFINE Tt~ 2ND AND 3RO DATA SETS IN TRIAD 

F;O'.l /COI..Urui FilE NPJiE 

1 

2 

3 

4 

SSTSHO?:ISST)RAEs6G~OI.8lY 

SSTSHO?:iSST1SOCsooeOl.BLV 

SST SHOP : (SST 1 h·J';l~6e001 . filII SSTSHOP:(SSTlt9~seca.bjn 

OESCR I PT ION 

NOAA fiVHP.R 

St1l1R CELLS 23-4 

HIRS/MSU2 

CL I MATOLOGY 

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I 

ttl 

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(b) November 1979. SMMR 

RMS EP.P.OR TABLE FOR DATA SET 2 

FILE Nf1ME: SSTSHOP: (SST) SOCsMOO 1. Bl V 

OESCR I PTI ON : St1t'1R CELLS 234 

LATITUDE SOUNDS= -~~ TO ~5 LONGITUDE BOUNOS= B TO 3M 

OVERFIlL RIT~GE RMS ERROR: e. 87 

2 

3 

4 

1495 

lsaz 

2 3 4 

B.OS 0.82 

9.82 

1646 

UPPER RIGHT TR' ANGLE OF TABlE COI'fTA' NS RMS ERROR 'N TR' AD 

LOUER LEFT TR I ANGLE OF TABLE COtlTA \tIS NUMBER COtmON OR I 0 PO, tITS IN TR I AD 

ROW RNO COLUl1N NUMBER OF TASLE OEF' tiE THE 2ND AND 3RO DATA SETS 'N TR I AD 

ROIJ /COLUMtt FILE tIAl"IE 

2 

3 

4 

SSTSHClP: (SST JAAE~f,%() 1. Bl'" 

SSTS!-lOP: (SST ISOCsf500~ 1. Bl'l 

SSTSHO?: (SSTlhog:::60GB1.BL'I 

SSTSP~P: [SSTJtgz~6ae.bjn 

OESCR I PT I ON 

NOAA fil JHAA 

Slaw. CELLS 234 

HI RS/1ISU2 

CL 111fHOLOG'r' 

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(e) November 1979, BIRS 

RMS EPF.oo mBLE FOR DATA SET 3 

FILE ~iiiME: SSTSHOP: ISSTlhogs6eeaI.BLY 

OE~~RIPTION: HIRS/MSU2 . 

LflT1TUDE BOLIrIOS= -55 TO 55 LONGITUDE BOlKJS= 9 TO 369 

OVERALL ~JERAGE RMS ERROR- 9.37 

2 1496 

3 

4 3089 

2 

9.26 

1846 

3 4 

9.37 

0.48 

UPPER RIGHT TR I ANGLE OF TABLE CONTA I NS Rr.S ERROR IN TR I AD 

LO~ER LEFT TA I ANGLE OF TABLE CONTA I tiS NUMBER COI1l1ON GR I 0 PO I HTS IN TR I AD 

ROI-I AriD COLUfm NUMBER OF TABLE OEF I tiE THE 2ND AtlO 3fID DATA SETS IN TR I AD 

RO~/COLUNN FILE N~~ 

1 

2 

3 

4 

SST SHOP : (SST IAAEs6caa I. BL'!' 

SSTSHOP:(SSTISDCs6e001.BLV 

SSTSHOP: ISSTlhG9!50091. BL'" 

SSTSHOP:[SSTltgzseea.bin 

DESCRIPTION 

tjOf\R fiI..JHRR 

Sl1MR CELLS 234 

HIRS/MSiJ2 

CL II1ATOLOGV 

.. --_._._.'. 

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--. .. . .:-~ 

", 

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(d) November 1979. Climatology 

RtlS EP.ROR TABLE FOR DATA SET 4 

FILE f~1E: SSTSHOP:[SSTlt9z~6B0.bin 

DESCRIPTION: CLIMATOLOGY 

LATITUDE BOUNDS= -55 TO 55 LONGITUO~ BOUNDS= 9 TO 369 

OVERALL AVERAGE RMS ERROR= 0. 42 

2 3 4 

9.44 0.47 

2 1502 0.36 

3 3089 1846 

4 

LIPPER RIGHT TR I ANGLE OF TABLE CONTA I NS RMS ERROR IN TR I AD 

LO\.JER LEFT TRIANGLE OF TfIBlE COt/TAINS NUMBER COMMON GRID POINTS IN TRIAD 

RmJ AND COLUI1tI NUMBER OF TABLE DEFINE THE ::::10 AUD 300 DATA SETS IN TRIAD 

ROI-I/COLUMli FILE NAME 

1 

2 

3 

4 

SSTSHOP:[SST1AAEs6eaal.BLY 

SSTSHOP:[SSTlsocs6eoal.BLY 

SSTSIlOP: [~3Tlho9S60atJ 1. Bl Y 

SSTSHOP:[SSTltgzsOaO.bln 

OESCR I PT ION 

NOAA AVHFiA 

SI1MR CELLS 234 

HIRS/llSU2 

CLIMATOLOGV 

/ 

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.~, ~_011'''''!·'''''''~ 1':" -:..-.--"! .. ",,. ., .......... '" ... ,. ~-" ............. ~ .. " ... ' ....... _ .... _, 

(e) December'1981. AVHRR 

AMS ERROR TABlE FOR DATA SET 1 

FILE NAME: SSTSKO?: [SST lAAEsOe002. BLY 

OESCR I PTI ON: NOAA R'.IHRR 

LATITUDE BourIDS= -55 TO S5 LONGITUDE BOUNDS- e TO 369 

OUEAALL AliEP.AGE PJ1S ERROR= e. 47 

2 

3 

4 

1 2 

2827 

2931 

3 

13.48 

3970 

4 

e.~a 

e ..... 

UPPER RIGHT TR I ANGLE OF TABlE CONTA I NS RtIS ERROR I tf TR I AD 

LOWER LEFT TRIANGLE OF TABLE CONTAItiS tfU/'lBER COl1I1ON GRID POINTS Itf TRIAO 

RO\.J AND COLUMN NUI1BER OF TABLE OEF I HE lHE 2Nj) Am 3RO DATA SETS. IN TR I AD 

ROJ.J ICOLIJMlf FILE NAtiE OESCA I PT I ON 

1 SST SHOP : (SSTlA.GEsMB02 _ BL'r' NOAA AUHAA 

2 SSTSHOP;(SST1SDCs53e02,BLY SI1MR CELLS 234 3 SSTSHO?: (SS', ]t'.agsf.GOe2. BL Y HIRS/MSU2 4 SSTSH~o:(SSTlt9%~609,bin CLI MATOLCGY 

................. -... ,.. .......... _ .... ..I.'.-~: -:'." 

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(f) December 1981, SMMR 

RMS ERROR TABLE FOR DATA SET 2 

DESER~~~TON:~~sMRR~CEttSI2~~SP~~~~.~Y. 

LAT I TUDE BOUtiDS= -55 TO 55 LONG I TUDE BOUNDS= 9 TO 369 

OVERALL AVERAGE RMS ERROR= 9.77 

2 3 4 

0.7S 0.76 

2 

3 2827 e.77 

4 2831 2863 

UPPER RIGHT TR I ANGLE OF TABLE CONTA I NS RMS ERROR I tf TR I AD 

LOWER LEFT TRIAN&tE OF TABLE CONTAINS NUMBER CONNON GRIO POINTS IN TRIAD 

ROW AND COLU!1N NUMBER OF TABLE DEFINE THE 2ND AND 3RO DATA SETS IN lRIRD 

ROW/COLUMN FILE NAME 

1 

2 

3 

4 

SSTSHOP:(SST1AAEs6ee02.BLY 

SSTSHOP: (SST]SOCse00~2.BLY 

SSTSHOP:[SSTlhog~5Baa2.BLY 

SST SHOP , [SSTJtgzs6eO.bin 

OESCR I PT I ON 

NOAA flIJHF:R 

SMMR CELLS 234 

HIRS/NSU2 

CLIMATOLOGY 

" 
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l·~: . 

G>l 

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(g) December 1981, BIRS 

f'J1S ERROR TABLE FOR CATA SET 3 

FILE NAME: SSTSHOP:ISSTlhaQs5eee2.BlY 

DESCR:PTION: HIRS/MSU2 - LATI TUDE 6O'..1NOS= -~5 TO 55 LONG ITUDE BOlJt[)S:: 

OVERALl AVERAGE RMS ERROR- 9.47 

2 2827 

3 

.. 3979 

2 

9.42 

2863 

3 .. 9.55 

9.44 

e TO 36e 

UfPER R I OHT TR I RtiCLE a:' TABlE CONTA I NS FIt1S ERROR IN TR I AD 

I 

LOI-lI:n LUT Tn I nnot..c: or"' TMOI..C: conT'" I m I'YJt'U:Ia1 connon on I 0 roo inTO I" Tn I no 

RQI.l AND CClUMN NUMBER OF TABlE DEFINE THE 2ND AriD 3RO DATA SETS IN TRIAD 

P.oJ.I/COLUf1li FILE NAME 

1 SSTSHOP; [SSTlAAEs6eG02.BL'r' 

2 SSTSHOP:(SSTISnCs68302.8LY 

3 SSTSHOP: (SSTlhagsf,00\32. BL Y 

4 SSTSHO?:[SSTltgzs6eS.bin 

OESCR I PT I otf 

NOAA AVHRR 

SMt1R CELLS 234 

HIRS/t1SU2 

CL I tlATO'.OOI' 

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(h) December 1981, Climatology 

RI1S ERP,OR TABLE FOR DATA SEf 4 

FILE I"\fiNE: SSTSHOP: [SST 1 tgzs600 . bin 

OESCR I PT I OIi : CL I MRTOLOGY 

LATITUDE BOUNDS= -55 TO 55 LONGITUDE BOUNDS= 9 TO 369 

OVERALL AVERAGE RMS EMOR= e. 38 

2 

3 

4 

2931 

3979 

2 3 

9.34 9Aa 

9.32 

2863 

4 

UPPER RIGHT TRIANGLE OF TABLE CONTAINS RMS ERROR IN TRIAD 

L()t..IER LEFT TR I AtIGLE OF TABLE CONTA I NS NUMBER COMMON GR I 0 PO I NTS I N TR I flO 

ROW AND COLUMN tlUMBER OF TABLE OEF I tIE THE 2ND AND 3RD DATA SETS IN TR I AD 

ROW/COLUMli FILE NflME 

2 

3 

4 

SSTSfiOP: [SST IF:REs6etl~2. BL V 

SST::HOP: [SST lSOCs6(1002. BL V 

SSTSHOP: {SST]hog~6eOe2.SLV 

SSTSHOP:[SSTltgzsOOa.bin 

DESCRIPTION 

NORA t1~'HRR 

S!lI1R CELLS 234 

HIRS/MSU2 

CLIMATOLOGV 

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(1) March 1982, AWllR 

P.'1S ERROR TABLE FOR DATA SET 1 

FilE NAt1E: SSTSHOP: [SST 1 AAEseea03 . BL Y 

DESCRIPTION: NOAA AUHP.R 

LAT I TUDE BOUNDS- -S5 TO S5 LONG I TUDE BOUfIOS- e TO 36B 

OUEAAlL filJERRGE RMS ErnOR= a. 42 

2 

3 

4 

2 

2841 

2841 

3 

9.23 

~Sc;;l 

4 

0.38 

0.66 

UPPER R I (;HT TR I ANGLE OF TABLE COi'iTA I NS RMS EP.ROR IN TR I AD 

...... :r-.. ~~ ,-

LOWER LEFT TR I FlliGLE OF TABlE CONTA I NS NUI1BER COI1MOtt OR I D PO IIiTS I" TR I AD 

ROt.! AriD COLUMN NLlMBER OF TABLE DEF I NE THE 2tiD AND 3RO DATA SET$ IN TR I AD 

ROIUCOLUMN FILE NAME 

I SSTSHOP:ISST1RAEsee033.BLY 

2 SSTSHOP:[SSTiSOCs60e03.BLV 

3 SSTSHOP: iBSTlhcgs5Cea3.BLY 

4 SSTSHOP:ISSTltgzs603.bin 

DESCRIPTION 

NOAA fitJHAA 

SHt'IR CELLS 234 

HI RS/MSU2 

CL ItlATOLOGY 

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(j) March 1982. SMMR 

RMS ERROR TABLE FOR DATA SET 2 

FILE tIAME: SSTSHOP: [SST1SDCsMBB3.BLY DESCRIPTION: SMMR CELLS 234 

LATITUDE BOUNDS= -55 TO 55 LONGITUDE BOUNDS= 9 TO 360 

OIJERALL Ftl'ERAGE RMS ERROR= 13.75 

2 

3 2841 

.. 2841 

2 3 

13.71 

2867 

.. 13.64 

0.91 

UPPER RIGHT TRIANGLE OF TABLE CONTAINS RMS ERROr. IN TRIAD 

LO\.JER LEFT TR I AliGLE OF TABLE COtlTA I NS IIUMBER COttMON GR I 0 PO I NTS IN TR I AD 

ROW AtlD COLUMN NUMBER Or TABLE DEFINE THE 211D AIm 3RD DATA SETS Itl TRIAD 

ROW/COLUMN FILE NAME 

2 

3 

4 

SSTSHOP:ISST1AAEs60003.BLY 

SSTSHOP:lSST1SDCso8003.BLY 

SSTSHO?: [SST Ihcgs6013B3 . SLY 

SSTSHOP:[SSTltgzs5ee.bin 

DESCRIPTION 

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SI1r1R CELLS 234 

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(k) March 1982, HIRS 

RMS ERROR TABLE FOR DATA SET 3 

FILE ItAME: SSTSHOP:(SSTlhcgs68003.BLY DESCRIPTION: HIRS/MSU2 

LATITUDE BOUNDS= -55 TO 55 LONGITUDE BOUNDS= 

OVERALL AVERAGE RNS ~~ROR. 9.45 

2 

3 

4 

2841 

3989 

2 

0.67 

2857 

3 4 

0.32 

0.36 

9 TO 369 

UPPER RIGHT TR I AtfGLE OF TABLE CONTA I NS RMS ERROR IN TR I AD 

-........ , 

LOWER LEFT TR I ANGLE OF TftSLE COliTA I NS NUMBER CONtION OR I 0 PO I NTS IN TR I AD 

ROW AIiD COLUI1N NUMBER OF TABLE DEF I NE THE 2ND AIID 3RD DATA SETS IN TR I AD 

ROW/COLUMN FILE IiAME 

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SSTSHOP:[SSTJAAEsooee3.BLV 

SSTSHOP:(SST1SDCso0033.BLY 

SSTSHOP: [SSTlhc9~600B3. SLY 

SSTSHOP:[SSTltgzs5ee.bin 

DESCRIPTION 

NOAA AVHRR 

SI1MR CELLS 234 

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(1) March 1982, Climatology 

PJ1S EF'roR H :"E FOR DATA SET 4 

FILE liME: S'STSHOP: (SST Jtgzs6e0. b in 

LIESCR I PT I orf. ell MATOLOG\' 

LAT I TUDE BOIJliO;'= -55 TO 55 lO:iG I TUDE BOIJtfO<:'...= e TO 360 

(rIJEAALL fflJEP.F1GE FiMS EF:P.of<= 0." 7 

2 3 4 

9.69 9.45 

2 2841 0.26 

3 3989 2£57 

4 

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F:(II.J H:iD CO!.Ul1;f IMiSER OF TFtBlE DEF litE THE 2hu RrIO 3f.D DATA SETS IIf TR /FiD 

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FILE tlf1t1£: SSTSHr.JP: (SSTl~s60004.BlV 

OESCRI PT J Oli: liOOA AVHF.fI 

LAT I TUOE BOUNDS- -55 TO 5S LOflG I TUDE B(U()S. eTO ~ 

()IJERAlL AUERAc-E PJtS ERP.oR= e. 39 

2 

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2664 

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UPPER RIGHT TR I AUG!...:: 0;: TABlE CONTA J,.,S RMS mACA I tf TR I AD 

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RttS EF;P.OR TABlE FOR DATA SET 2 

FILE tIAME: S5TSHOP: (SST 1;;DCsWOM.et.'r' 

DESCFi I PT I Ofi: Sr.t1R CELLS 234 

LAT'TUDE BOUtIDS= -55 TO 55 LONG I TUG'E BOUt'.os= e TO 3W 

O'JERF!I...L Fi'.'Ef'IiGE Rl1S ERROR- 9. 65 

2 3 .. 9.67 0.55 

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4 2664 2893 

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F:OU ICOllJMN FILE NAME 

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SSTSH~o: [SSTltgzs6ee.bin 

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110M A!JHAA 

SMMR CELLS 7.34 

HIRSiilSU2 

CLIMHTOLOGY 

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(0) July 1982, BIRS 

R:1S ERRoR TRBlE FOO OOTA SET 3 

FILE f'flI1E: SSTSH(J>: (SSTJhcgs600a4.et.y DESCRIPTION: HIRSIMSU2 

LATlTlJO£ BOLtiOS: -~:s TO ~ LOOOITLOE BO:.If)Sa e TO 309 

OVERAlL flIJERAGE RMS ERROR. e. 46 

2 

3 

of 

-

2661 

3721 

2 

9.57 

-

2683 

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(p) July 1982. Cli~atology 

RMS ERROR TABLE FOR DATA SET 4 

FILE r~: SSTS4OP:(SSTltg2s60B.bin 

Description: Climatology 

Latitude Bounds a -55 to 55 

Longitude Bounds • 0 to 360 

Overall Average rms Error - 0.59 

2 

3 

4 

2664 

3721 

9.64 a.69 

9.46 

2883 

UPPER RIGHT TRIANGLE OF TABLE CONTAINS RMS ERROR IN iRIAD 

LOt.lER LEFT TAI.ANGLE OF TABLE COfiTAINS NUMBEA COMMON GRID POltfTS iN TRIAD 

ROt.l AND COLUMtf NUMBEA OF TABL E OEF I HE THE 2t1O AND :mO DATA SETS IN TA I AD 

ROO/COLUliti FILE NAME 

1 

2 

3 

4 

SSTSHOP: ISST1AAE~6e&a4.61Y 

S8T~·HOP: (SSTlSOCs50004. ELY SSTSHOP:(SST;hcQ~60a04.8LY 

SSTSHOP: ISSTltgzs6ea.bin 

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froM AlJHRR 

SMt1R CELLS 234 

HIRS/t1SU2 

CL I 11FITOLOGY 

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(b) 

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Table B-3. Average Partition~d rms Error for Each Sensor: (a) No 

Smoothing; (b) 3 x 3 Spatial Filter Applied to all 

2· - Binned Data. 

November December March July 

1979 1981 1982 1982 

AVllRR 0.53 0.49 0.60 0.B7 

SMMR 0.87 0.95 0.98 1.01 

HIRS 0.56 0.53 0.68 0.75 

Climatology 0.63 0.50 0.43 0.41 

AVHRR 0.82 0.47 0.42 0.39 

Sl-m 0.87 0.77 0.75 0.65 

HIRS 0.37 0.47 0.45 0.46 

Climatology 0.42 0.38 0.47 0.59 

-~-. 

\ 
~. ,j 

APPENDIX C 

COLOR IMAGES 

C-l 

I 

f' 

I 

I ... • 
" 

.'! .. , , 

", 

" 

# 

Figure C-l. 

, r '.' -r~- ! .. , 

-.,: /," , /~. 

~&'~ers 

§7 Ri~ GVALflY 

, 

In situ data distributiun for November 1979 showing the 

nUr.lber of SST measurements per month per 20 latitudelongitude bin for (a) ships. (b) XDT's. (e) FGGE buoys. 

C-2 

-::;---~~J..".<""" ... _...". ... ..,."~--,,.-------.- -

/ { 

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f 

t 

r 

l' 

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. . . "

f 

;. 

" . 

. -

( 

) 

, 'j' 

(I 

.. 

Figure C-2. 

/' ...-!I'~····/· 

SST anomalies for November 1979: 

(c) XBT's. (d) FGGE buoys • 

C-3 

.I 

(a) AVHRR. (b) ships, 

! 

~ 

.1 

j 

~ 

~ 

.. 
/ 

.' 

.' /. 

/ 

/ 

I 

/ 

.., 

.'; 

,. 

'I 

ry-_._--_ ... 

Figure C-3. SST anomalies for December 1981: 

(c) HIRS, (d) St'lM.l{. 

C-4 

(a) ships, (b) AVHRR, 
I 

;' 

,/ 

\ 

, i 

t 

. • 

, 

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~i ~ ... , '1. 

f •• .!i. 

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, 

,: 

Figure C-4. 

~--.. --

SST anomalies for March 1982: 

(c) HIRS. (d) SHMR. 

c-s 

~ . "./ 

ORIGINAL PAGE IS 

OF POOR QUALITY 

(a) ships. (b) AVHRR. 

/ , 

.. 
Figure C-S. 

~ ... ' , 

I 

I 

:' 

Fcr July, 1982: (a) AVHRR SST data distribution, 

(b) AVURR SST anomalies, (c) ship SST. anomalies. 

C-6 

.-
... ! 

j 

; 

~ I r.,.. t , I 

;: f 

t "- ~ 

" ~ 

• f 

sf' 

. ' 

I 

Figure C-6. 

..•.... -------------.--.------.--- '\,,; . -_._-.. _... I 

HIRS SST anomalies for: (a) March 1982 unweighted 

(supplied by J. Susskind). (b) March 1982 weighted 

(supplied by J. Susskind). (c) July 1982 weighted • 

C-7 
, ...• 

" 

( 

I 

J .' 

I' 

\. 

.' . . '.: 

"~. ",: 

\ 

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-~4_ 

. ? 

W1l 

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Figure C-7. 

1.<;., 

SST anomalies for July 1982: 

(c) HIRS, (d) St-lMR. 

C-8 

(a) ships, (b) AVHRR, 

- I 

I , 

. 

, , 

'. 
0·1 POOR QUALITY 

,;;_r(i~\l~;'!;:':~~;}i:;f~~':~\;'i~g:~:lC~.~;~:!:{T":· ..•. ~~';~i:~P c. ," 

.. ,.' ',·:r.:t~7;'~ ;:11t~fu'~';~~c.r:· :1~l~~lfir:{ff~~<niU:~tl~ j1\j~m~~TiVZ~i'~ {i!.r~ , . .,{ ,-.: _ . 

. , 

J", 

. 

, -" . '-; ''''~ .. ,,~: ..... ~., '~~~{~.' I ,,',", ... ~~'l.;&~': .• :-~ ;r ", .. ~\~",:,:~ .. ", .' " ".' .:~.: ·:X;.:{ ·;:ft:-t:~~~':~:.' :;'~ ;~~~:' ':1~:~~:'~'-:'7',":':~~~l :-~!~~~~~~~' : .;>; ~ ':' ,<::.~:~ ;','h ,·"~,rt, '.-, ,'. J.. ~: ·:<~t~'*'\ .', '':' ",,\ .. ~ ./· .. ,,,,a'··· )-'" ,~>.,.",::.' ,-.;~:- " ;_; ._~ ,"r:::' ":~~ ~~:: "' 1.- • ft .>i.~· ~-.:~ .. ~,;'_: ~.: .. ~,~":-:.,,,,~: .. ~~:}.~;~;.~! .. -):~: "~.:.-::~"~;:", )~:: ~. ~'-~_,~~.;'::~ . ," ..... ; .. ~"''l:~j .. '. > ~·~·~i· ... '·" --::-"J'i"'<'.':h";'''-;~:;'''~'~'j- :JJ;i..J:"":"",~,-:~,, '::". ,., 

Fi!>ure C-S. SST anom:llies for ~Llrch and July 198:! in the VAS 

observation, region. 

C-9 

. 

,. 

\ 

g:;:.:-~~~~tw;t~.$ZW~7J~§@..:i~r'~~~T~~~~!iI~~C~"~!?~ 

/ 

" 
.,. " 

Figure C-9. AV!lRR SST llnoma1y diff(;rencl'S. day millUS night: 

(a) D~ccmber 1981, (b) March 1982, (c) July 1982. 

C-IO 

., . 

"'" 

~. 

~ 

~~~7-e:tk;~::)'Em.@~'S~t;£~~Sif,f~~~~~.\\">~;~'"m~\'SZ:\~&3';"£"~YiJ 

\ , 
FIgure C-IO. 

Of' POOR QUALITY 

SST anomali~s for July 1980: (a) ship, (b) SM.'IR. 

(c) blended St-iMR/ship (supplied by T. Wilhcit). 

C-ll 

"-I 

• 
, 

", 

I , 

! 

'" . , 

. ; 

\ ' 

APPENDIX D 

LIST OF PARTICIPANTS 

(Workshop III) 

Mark R. Abbott 

Jet Propulsion Laboratory, 169-236 

4800 Oak Crove Drive 

Pasadena, CA 91109 

Larry Armi 

A-030 

Scrippo Institute of Oceanography 

La Jolla, CA 92093 

John J. Bates 

Space Science & Engineering Center 

University of Wisconsin 

Madison, WI 53706 

Robert L. Bernstein 

SeaS pace 

5360 Bothe Avenue 

San Diego, CA 92122 

Moustafa T. Chahine 

Jet Propulsion Lcboratory, 180-904 

4800 Oak Grove Drive 

Pasad~na, CA 91109 

Dudley B. Che1ton 

College of Oceanography 

Oregon State University 

Corvallis, OR 97331 

Donald Collins 

Jet Propulsion Labor.atory, 169-236 

4800 Oak Grove Drive 

Pasadena, CA 91109 

Wayne E. Esaias 

Code EE 

NASA/Headquarters 

Washington, D.C. 20546 

Daesoo Han 

Syatema & Applied Sciences Corporation 

5809 Annapolis Road 

Hyattsville, MD 20784 

Jeffrey E. Hillnnd 

Jet Propulsion Laboratory, 138-308 

4800 Oak Grove Drive 

Pasadena, CA 1J1l09 

D-1 

, 

1 • 

! \

' , , 

..-

~ \;; J ... , ........ 

,', 
II 

t 

,I 

~ . 

I 

Paul H. Hwang 

Code 910.2 

NASA/Goddard Space Flight Center 

Greenbelt, MD 20771 

w. Timothy J.iu 

Jet Propulsion Laboratory, 169-236 

4800 Oak Grove Drive 

Pasadena, CA 91109 

E. Paul McClain 

NOAAiNational Environmental Satellite, 

Data, and Information Service 

Washington, D.C. 20233 

Andrew S. Milman 

Hughes Aircraft/Space & Comma Div. 

P.O. Box 92919 

Los Angeles, CA 90009 

Eni G. Njolcu 

Jet Propulsion Laboratory, 168-314 

4800 Oak Grove Drive 

Pasadena, CA 91109 

Stephen E. Pazan 

A-030 

Scripps Institution of Oceanography 

La Jolla, CA 92093 

.John Penrose 

School of Physics and Geosciences ~cstern Australia Institute of Technology 

Kent Street 

Bentley 6102 

Western Australia 

Joel Susskind 

Code 911 

NASA/Goddard Space Flight Center 

Greenbelt, MD 20771 

Th)m49 T. Wilheit 

Code 913 

NASA/Goddard Space ~light Center 

Greenbelt, MD 20771 

Yoshizumi Yasuda 

Chika University 

1-33. Yayoi-cho 

Chiba 260. JAPAN 

D-2 

,';', ~-:-. ~ -" 

. . - " 

ft&~ ~~?i!~~~a,", ;' ... :,::elA:1S4:JCC:~l!ae 

" 

; 

I' 

, . 

>., 

. ...: 

~ .. :~ 
. ) 

I 

, \' 

I 

" , 

'. 

.... ' 

.. J '. ' • 

)' 

AMTS 

AVHRR 

FCCE 

FNOC 

CARP 

GLA 

GMT 

... 

GOES 

GOSSTCOMP 

GSFC 

BIRS 

JPL 

LST 

McIDAS 

MCSST 

HSI 

HSU 

NASA 

NCAR·' 

NDBO 

NESDIS 

NOAA 

PODS 

SIO 

SMMR 

SR 

SST 

SSU 

STD 

TIROS 

TOVS 

VAS 

". 

APPENDIX E 

GLOSSARY 

Advanced Moisture and Temperature Sounder 

Advanced Very Bigh Resolution Radiometer 

First CARP Global Experiment 

Fleet Numerical Oceanography Center 

Global Atmospheric Research Project 

Goddard Laboratory for Atmospheres 

Greenwich Mean Time 

Geostationary Operational Environmental Satellite 

Global Operational SST Computation 

Goddard Space Flight Center 

High-Resolution Infrared Sounder 

Jet Propulsion Laboratory 

tA"sl Standard Time 

Han-Computer Interactive Data Access System 

Multi-Channel SST 

Multi-Spectral Imaging 

Microwave Sounding Unit 

National Aeron~utics and Space Administration 

National C~nter for Atmospheric Research 

National Data Buoy Office 

Nation3l Environmental Satellite, Data, and Information Service 

National Oceanic and Atmospheric Administration 

Pilot Ocean Data System· 

Scripps Institution of Oceanography 

Scanning I1ultichannel Microwave Radiometer 

Scanning Radiometer 

Sea Surface Temperature 

Stratospheric Sounding Unit 

Salinity. Temperature. Density Probe 

Television-·Infrared Observational Satellite 

TIROS Operational Vertical Sounder 

VISSR Atmospheric SounJer 

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Vertical Temperature Profiling Radiometer 

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TECHNICAL REPORT STANDARD TITlE PAGE 

I. Report No. 12. Gowmmont AccessIon No. 3. Recipient's Catalog No. 

4. "Title and Subtitlo 5. Report Data 

S4tellite-Derived Sea Surface Temperature October 15--,- 1985 - Workshop III 6. Performing Organization Code 

7. Author(s) 8. Performing Organization Report No. 

JPL Publication 85-63 

9. Performing Organization Ncwno and Address 10. Work Unit No. 

JET rROPULSION LABORATORY 

California Institute of Technology It. Contract or Grant No. 

4800 Oak Grove Drive NAS7-918 

Pasadena, California 91109 13. Type of Report and Period Covered 

JPL Publication 

12. Sponsoring Agency Name and Address 

NATIONAL AERONAUTICS AN~ SPACE ADMINISTRATION 14. Spons.oring Agency Code Washington, D.C. 20546 : 

15. Supplementary Notes 

16. Abstract 

This is the third report in a series of three workshops, sponsored by the Nati~nal Aeronautics and Space Administration, to investigate the state of the 

art in global sea surface temperature measurecents from space. Three workshops 

were necessary to process and analyze sufficient data from which to draw conclusions on the accuracy and reliability of the satellite measurements. In 

this workshop (Workshor III), the final two (out of a total C C four) months of 

satellite and in situ data chosen for study were processed and evaluated. 

Results from the AVHRR, HIRS, SHHR, and VAS sensors, in comparison wi.th in situ 

data from ships, XBTs, and buoys, confirmed satellite LIDS accuracies in the 

0.5 to 1.00C range, but with variable biases. These accuracies may degrade 

under adverse conditions for specific sensors. A variety of color maps, plots, ' .. and statistical tables are provided for detailed study of the individual sensor 

SST measurements. 

17. Key Word!. (Selected by Author(s» 18. Distribution Statement 

Geosciences and Oceanography (General) 

Meteorology and Climatology Unclassified/Unlimited Physical Oceanography 

19. Security Class if. (of this report) 20. Securit,' Class if. (of this poge) 21. No. of Poge, Fi- 22. PrJ ce 

Unclassified Unclassified 

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JPL 019<4 A 91113 

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End of Document