Eileen Maturi1, Jo Murray2, Andy Harris3, Paul Fieguth4, John Sapper1

1 NOAA/NESDIS, U.S.A., 2 Rutherford Appleton Laboratory, U.K., 3 University of Maryland, U.S.A., 4 University of Waterloo, Canada

MISST 2006 Meeting Washington, D.C.

28 November, 2006

MULTI-SEA SURFACE TEMPERATURE ANALYSIS

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OBJECTIVE

• Develop estimation scheme for combining multi-satellite retrievals of sea surface temperature into a single analysis

• Apply complementary SST datasets available from polar orbiters, geostationary IR and microwave sensors

• Use the computing power available to implement this estimation scheme

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NOAA Requirements

1. Operational SST Analysis using POES and GOES SSTs

2. Daily Global 0.1°0.1° 6 hourly at 5 km at selected regions 3. Accurate global error characterization 4. Preservation of information about fronts5. Extendable to other SST datasets + scalable

For NWP, climate research, fisheries, coastal services…

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SELECTED SST DATASETS

• AVHRR: – 1-km resolution– global coverage, good-quality retrieval

• GOES:– 4-km resolution– good temporal coverage– resolve diurnal cycle

• FUTURE: Microwave SSTs (TMI, AMSR, WINDSAT)– Low-resolution– SST retrievals not made closer than ~100km of coast– Insensitive to clouds and aerosol– Some sensitivity to wind speed

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Data Quality Control

• The observational SST data are quality-controlled using a spatial temporally varying consistency check with the previous day's SST analysis– the thresholds vary by the error estimate in the analysis and

the estimate of SST variability

• Data are then averaged into the 0.1° and 5-km resolutions used in the analyses

• Inter-sensor bias estimation with the NOAA-17 daytime SSTs (with CLAVR-x cloud mask value=0) are chosen as the reference dataset

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METHODOLOGY

• Initial guess of SST background field• Initial guess of SST variability• Observations with well-characterized errors• Definition of relationship between

observational datasets (i.e. assume one or more bias terms which are spatially correlated)

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Multi-scaleOptimal Interpolation SST

• Optimal, rigorous way of combining SST observations from AVHRR and GOES-Imager

• Individual datasets are bias-corrected• Error estimates are dynamically updated from

one day to the next• “Multi-scale” means that high resolution is

achieved at relatively little computational cost (global analysis runs in 40 minutes on a single Dell cpu)

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DAILY SST UPDATE

• System dynamics predict – the new SST estimate – the associated error information.

• Assume that the ocean dynamics are very slow– apply a very simple dynamic model– each pixel independently evolving randomly

• This model implies a simple estimate prediction: T(t|t-1) = T(t-1|t-1)

• The initial SST is modified implicitly by introducing new measurements– the measurement at any time t consists of two independent

components – the SST observations T and the predicted error estimates

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Daily SST Update (Contd)

The new SST estimate is obtained by adding the estimated anomaly field to the previous SST estimate…

• Propagation of error statistics – achieved by appropriately down weighting the impact of

the previous SST estimate• by increasing the associated error variance • calculating error estimate based on both this error and

the observational error associated with the new observations

• Data preprocessing observational noise is empirically determined from each dataset– Vital to ensure the appropriate SST and error estimates

are used in the analysis

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Boundaries of Ocean Basins

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Treatment of individual SST datasets

Select AVHRR daytime as baseline SST:

SST = AVHRR (day) + observation (obs) noise

Other datasets are considered relative to this baseline:

SST = AVHRR (night) + diurnal warming + obs noiseSST = GOES (night) + atmospheric term + obs noiseSST = GOES (day)+ atmospheric term + obs noise

• Where obs noise is assumed uncorrelated with Gaussian distribution• Other bias terms are assumed to be spatially correlated with length

scales >> length scales associated with SST variability

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Correlation Map

• Variability of the SST field – Gulf of Mexico-short length scale (distance from data interpolation for sst

determination)– Southern Pacific-long length scale

• Spatial Variability of measurements DATA DRIVEN (daily)

– low variability-appropriate to use long length scales• lots of data than do not want to use a long length scale (cause smoothing)

(this allows us to preserve fine scales features)– lot of variability, good data coverage, use short length scales

• Three fixed correlation length scales( fixed in software covers range)– Generate 3 stationary maps of SST anomaly– interpolate between these to get our SST anomaly map that matches the

correlation map– Add the new SST anomaly to the previous day SST analysis

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One day of bias-corrected observationsEstimated daily absolute deviationDerived correlation length scales

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VALIDATION

• Evaluated against the RTG_SST ½°×½° resolution (also planning validation against 1/12°×1/12°) operational NCEP product

• Traditional in situ data e.g. buoys

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POES/GOES SSTRegional Analysis-East Coast

Click on image toplay

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Tropical Instability WavesDepicted by SST Analysis

Click on image toplay

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Validation vs. RTG Analysis & TMI

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More RTG & TMI comparisons

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Comparisons in Baja, CA

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METEOSAT-8 SSTs

• Meteosat-8 SST planned to be generated by NOAA/NESDIS February 2007

• Will be included in Multi-SST Analysis after errors are characterized

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MTSAT-1R

• SST product covering the Western Pacific and Eastern Indian Ocean to be developed from MTSAT-1R data by June 2007

• Will be included in Multi-SST Analysis after viable SST product is available and errors have been characterized

MULTI-SST ANALYSIS

WEB SITE: http://www.orbit.nesdis.noaa.gov/sod/sst/blended_sst

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SUMMARY

• NESDIS’ new SST analysis is:– Due to become an operational product (the ‘way

forward’, e.g. CoastWatch→OceanWatch)– Will benefit from improved pre-processing based

on MISST-derived knowledge– Is an ideal tool/test-bed for ascertaining the

benefits of the products and techniques being developed within MISST