By Dr. Jean-Fran~ois CayulaPrincipal ScientistPlanning Systems Inc.

Doug MayData Division DirectorBruce McKenzie

OceanographerDan Olszewski

OceanographerandKeith WillisComputer ScientistNaval Oceanographic OfficeStennis Space Cente1; Mississippi

T he Naval Oceanographic Office(NAVOCEANO) is responsible

for analyzing and processing satellitedata to produce high-quality multi-channel sea surface temperature(MCSST) data.1 NAVOCEANO dis-tributes the MCSST data locally forthermal analyses, oceanographic mod-els, and to various external oceano-

(Top) Sate/1ite-derived SST minusbuoy RMS versus the differencebetween the SST values obtainedfrom two day-time equations (top).Associated sample quantity his-togram (bomom).

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~(/)-J tz

(/) r~a: r

(Left) Satel1ite-derived SST minusbuoy RMS versus satel1ite-derivedSST minus SST analyzed field for day-time data (top). Associated samplequantity histogram versus SST ana-lyzed field minus satel1ite-derivedSST (bottom).14000

"' 120000)~ 10000

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graphic and weather centers. Recently,there has been heightened interest inproviding quantitative frror estimatesfor satellite SST data. Indeed, suchinformation is crucial to applicationssuch as optimal interpolation analysisor model assimilation. This article

FEBRUARY 2004 / SEA TECHNOLOGY / 67

50 2 3

abs(NLSST .FLO)(OC)

vice (NOAA/NESDIS). The reliabilityestimation scheme has also beenadded to local area coverage and geo-stationary operational environmentalsatellite (GOES) data processing. Agoal ofMCSST processing at NAVO-CEANO is to generate only high-qual-ity MCSST retrievals and, thus, mini-mize the inclusion of cloud-contami-nated data or otherwise invalid data inthe processing stream.3 First, AVHRRI b records go through various qualitycontrol steps to check parameters suchas scan time, calibration coefficients,frame synchronization, localizationand data quality errors, and to limitother parameters such 'as the satellitezenith angle. Cloud detection usestests on the visible, near infrared andinfrared channels, spatial coherenceand inter-comparison of differentMCSST equations. SST processing isbased on II by II targets offour-kilo-meter pixels. Only pixels that pass alltests are used to produce SSTretrievals from the averaging of twoby two pixel unit arrays. Reliabilityestimates are only computed for thosepoints where SST retrievals are suc-cessfully generated. Finally, oneshould note that although night-timeand day-time data are handled in asimilar way, the equations and testsare not the same. For example, night-time processing does ,not use visiblechannel data. Reliability estimates areseparately determined for day-timeand night-time data. Also, equationsare regularly updated and differ fromsatellite to satellite. Reliability para-meters may be adapted accordingly.

describes the scheme developed atNAVOCEANO to add such reliabilityinformation, in the fonn of root-mean-square (RMS) error, to the operationaldata stream. First, the data aredescribed, this is followed by anexplanation of the method used toclassify each MCSST sample in cate-gories and how error estimates areassigned to each category. Results oftracking actual, v~rsus estimated,RMS error for over a year of opera-tional processing, as well as the stabil-ity of classification parameters, are

presented, and possibilities for poten-tial expansion of this work are out-lined in the conclusion.

Satellite DataThis study deals with Global Area

Coverage Advanced Very-High-Res-olution Radiometer (AVHRR) lb andHigh-Resolution Infrared RadiationSounder (HIRS) digital data receivedby NAVOCEANO from the NationalOceanic and Atmospheric Admini-stration, National EnvironmentalSatellite, Data and Information Ser-

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(heavy duty casters and nylon pull strap) available. Match-Up BuoysThe initial reliability estimate para-

meters were determined from threemonths of buoys matched to SST datafor the period of September toDecember 2001. Continuous monitor-ing of reliability estimates and updat-ing of the parameters also depend onthe last month of buoys matched withSST data. Both drifting and mooredbuoy data, which are received daily atNAVOCEANO, are used in the match-up database. Although the distanceand time-matching parameters aremodifiable, buoys are matched tosatellite data if they are within adefault 25-kilometer distance and fourhours of the satellite retrieval.3.

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Design. A simple reliability estimatemethod was chosen for ease of main-tenance in an operational environ-

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68 / SEA TECHNOLOGY / FEBRUARY 2004 CIRCLE NO.71 ON INQUIRY CARD

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ment. As a consequence, it was decid-ed to classify samples in three cate-gories: clear, probably clear and ques-tionable. It was also decided to assignRMS error values to each category,because it encompasses both the stan-dard deviation and the bias in onenumber.

Determination of Variables to beUsed for Classification. The effect ofseveral variables on the temperaturedifference (RMS error) between satel-lite and buoy values was investigatedon three months worth of satellite datamatched to buoys. Several variableswere surprisingly found not to signifi-cantly affect RMS errors. Amongthese, the satellite zenith angle, whichis already cut off at less than 53°, didnot show much effect.

Similarly, uniformity tests onAVHRR channels 2 (day-time), 3(night-time) or 4 demonstrated rela-tively little impact, probably becausecloud detection already places strongconstraints on these variables. Fornight-time data, a test using' HlRSchannels 8 and 7 was not conclusive,nor was a ratio of AVHRR channels Iand 2 for day-time retrievals.

Three derived variables, SST field,equation inter-comparison and sun-

duced SST (opsst), the weighted aver-age of climatology ( clim) and the ana-lyzed 3100-kilometer SST field(KIOO). Weights are 1/3 for climatol-ogy and 2/3 for the lOO-kilometer SSTfield. The analyzed lOO-kilometerfield is generated daily from the past

glint (this last one being for day-timeonly) had a definite relationship to theRMS error and were selected to builddecision rules for classifying data intothe three categories.

The SST field test is based on theabsolute difference between the pro-

"...itwas decided to classify samples in three categories: clear,probably clear and questionable."

sunglint=exp(- ( ( satzen+so lzen )la )~-(azmuth/b)~)

where a and b are two constants select...ed such that with angles in degrees,a=50 and b=80.

Classification Rules. The SST fieldtest is the first test to be applied to thedata. The following decision rule is

implemented:field test~tfl => category 1tfl <field test~tf2 => potential cate-gory 2field test>tf2 => potential category 3where tfl and tf2 are thresholds. Forboth day-time and night-time, tfl is setto lo C and tf2 to 2° C. These thresh-olds have remained unchanged sinceinception. One property of the fieldtest is that it works very well inregions with little variability. How-ever, the test is much less successful indynamic regions, often failing to clas-sify clear pixels as category I. Al-though most pixels are in areas of lowvariability, dynamic ocean regions

36 hours of SST data and current fieldvalues. Each cell in the grid is lO oflatitude by lo of longitude, hence thelOO-kilometer field name. The equa-tion for the field test value follows:field test=opsst-(( clim+(2*Kl 00))/3)

The equation for the inter-compari-son test is the absolute differencebetween two different equations forcomputing MCSST. For day-timedata, the two equations are the non-linear equation4 that uses channels 4and 5 (nl45d) and the multi-channelequations that uses channels 4 and 5(mc45d):intercomp day=abs(nl45d-mc45d)

For night-time data, the equationsare the non-linear equation usingchannels 3, 4 and 5 (nl345n) and themulti-channel equation, which useschannels 3 and 4 (mc34n):

intercomp night=abs(nl345n -mc34n)Sun-glint depends on the position of

the sun relative to the target and thesatellite. It also depends on the rough-6ness of the water surface. Becausewater surface roughness is not imme-diately available, the suQ-glint pseudo-probability formula is a function ofonly the solar zenith (solzen) andazimuth (azmuth) angles and the satel-lite zenith (satzen) angle:

with pronounced SST fronts areregions ofhigh interest. To remedy theproblem, pixels that fail the category 1field test are not directly classified incategory 2 and 3, but go through addi-tional testing to determine their final

category.For night-time pixels, the additional

testing is accomplished with the inter-comparison test. The test relies on theassumption that, under clear condi-tions, the difference in temperaturevalues between the two equationsshould be small. In effect, this is anelaborate way to detect the differingresponses of channels 3, 4 and 5 in thepresence of clouds. The followingdecision rule was devised:intercomp night<tn => category 1Else category nwhere n is either category 2 or 3,depending on the field test results, andtn is a threshold that was determinedfrom the data. The current value for tn,0,3° C, has remained unchanged since

Observed RMS

2.5Observed RMS error: buoy minusMCSST, for each category as afunction of time for day-time data(top). Number of matches in eachcategory used to compute RMSerrors (bottom).

16~ 15,

inception. Day-time pixels in potentialcategory 2 and 3 go through the sametesting as night-time pixels, but sun-glint is added as a supplementary test.The decision rule becomes:intercomp day<td and sunglint<ts =>category 1Else category nwhere n is a.gain category 2 or 3,depending orf the field test results,while td and ts were determined fromexamining three months of data. Thevalu~ of td is currently set to 0.3° C,while ts is set to 0.1° C.

Determination of RMS Errors,Monitoring and Updating. The initialvalues for the RMS errors associatedwith each category and type of datawere derived from the initial threemonths of matched-up buoy data andthe previously selected decision rulethresholds. For day-time, the RMSerrors assigned to the categories 1, 2and 3 were 0.45° C, 0.65° C and 1.5°C, respectively, Values for night-timedata were 0.4° C, 0.85° C and 1.5° C.Initially, about 90 percent of the day-time samples were classified as clear,while less than 10 percent were classi-fied as probably clear and the remain-ing were classified as questionable.About 98 percent and two percent of

May 22 Aug 30 Dec 8 Mar 18 Jun 26 Oct 4 Jan 122002 2002 2002 2003 2003 2003 2004

Countnight-time samples were simi-larly classified as clear andprobably clear, respectively, andthe remaining were classified as ..questionable. I ~, E

The actual RMS errors for =

each category and type of data, I ~as well as the number of sam- I'Spies falling in each category, I 5have been monitored daily usingthe last 30 days of buoysmatched to SST data. Results forthe combined day-time andnight-time data indicate that the repar-tition of samples among the three cat-egories has remained stable. Likewise,the RMS errors by category havestayed within expected bounds. Forcategory I, with about 10,000 buoymeasurements matched to SST, vari-ability on the order of one hundredth

May 22 Aug 30 Dec 8 Mar 18 Jun 26 Oct 4 Jan 122002 2002 2002 2003 2003 2003 2004

of a degree can be expected. For cate-gory 2, with less than 1,000 buoy mea-surements matched to SST, variabilityon, the order of a tenth of a degree isexpected. For category 3, consideringthe small number of matched samples,the RMS error for 30 days of data canvary by as much 10 C. For this reason,

Recently, we have examined modelwinds and aerosol forecast data to con-tribute to the reliability scheme. It mayalso be possible to improve the qualityof the reliability estimates by thor-oughly investigating the correlationbetween several satellite-derived mea-surements and temporal and spatialinformation.

major requirements in an opemtionalenvironment. Future work may includeexpanding the assignment of reliabilityestimates to samples that are currentlydiscarded. This would allow end-usersto select the data according to their actu-al needs.

the assigned RMS error for category 3is not updated and has been kept at1.5° C, which on average appearsacceptable. On the other hand, theassigned RMS errors for categories Iand 2 have been periodically updatedto reflect observed trends.

ReferencesFor a full list of reference, please

contact author Dr. Jean-Fran~ois Cay-ula at j.cayula@ieee.org. /st/

ConclusionsIn this paper, we have presented a

method to add quantitative estimates ofreliability to every MCSST sampleoperationally generated at NAVO-CEANO. Since the reliability estimatecapability was added to the processingstream, thresholds for the various deci-sion rules have not needed updating,while the assigned RMS error values(reliability estimates) have only re-quired minor adjustments. Thus, thecurrent scheme appears to be robust andlow-maintenance, both attributes being

Dr. Jean-Fran~ois Cayula started to workwith satellite-derived oceanographic dataat the University of Rhode Island, where hereceived both his M.S. and Ph.D. in elec-trical engineering. Since 1994, he has beenwith Planning Systems 1nc. supporting sev-eral projects for the Naval ResearchLaboratory and the Naval Oceanographic

Office.

Doug May is the Data Division director ofthe Oceanography Department at theNavalOceanographic Office. He has over20 years' experience in the receipt, pro-cessing and dissemination of near-real-time oceanographic data from satellitesand automated in-situ sensors.

Bruce McKenzie is an oceanographer withthe Naval Oceanographic Office and hasworked on operational SST productionsince 1991. He received his B.S. degree inoceanography from the University of

Washington.

Dan Olszewski has been an oceanograph-er with the Naval Oceanographic Officeworking with the collection and processingofremotely sensed data since 1986. He hasspecializesin the production of satellite-derived multi-channel sea surface temper-ature retrievals.

Keith Willis is a computer scientist with theNaval Oceanographic Office. He has pro-vided software support for Navy satelliteprocessingfor more than 10 years.

FEBRUARY 2004 I SEA TECHNOLOGY I 73CIRCLE NO.80 ON INQUIRY CARD