Eileen Maturi 1, Jo Murray 2, Andy Harris 3, Paul Fieguth 4, John Sapper 1 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

28 November 2006MISST Meeting Washington, D.C. 2 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

28 November 2006MISST Meeting Washington, D.C. 3 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 fronts 5.Extendable to other SST datasets + scalable For NWP, climate research, fisheries, coastal services…

28 November 2006MISST Meeting Washington, D.C. 4 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

28 November 2006MISST Meeting Washington, D.C. 5 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

28 November 2006MISST Meeting Washington, D.C. 6 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)

28 November 2006MISST Meeting Washington, D.C. 7 Multi-scale Optimal 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)

28 November 2006MISST Meeting Washington, D.C. 8 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

28 November 2006MISST Meeting Washington, D.C. 9 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

28 November 2006MISST Meeting Washington, D.C. 10 Boundaries of Ocean Basins

28 November 2006MISST Meeting Washington, D.C. 11 Data error characterization

28 November 2006MISST Meeting Washington, D.C. 12 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 noise SST = GOES (night)+ atmospheric term+ obs noise SST = 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

28 November 2006MISST Meeting Washington, D.C. 13 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

28 November 2006MISST Meeting Washington, D.C. 14 One day of bias-corrected observations Estimated daily absolute deviationDerived correlation length scales

28 November 2006MISST Meeting Washington, D.C. 15 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

28 November 2006MISST Meeting Washington, D.C. 16 POES/GOES SST Regional Analysis-East Coast Click on image to play

28 November 2006MISST Meeting Washington, D.C. 17 Tropical Instability Waves Depicted by SST Analysis Click on image to play

28 November 2006MISST Meeting Washington, D.C. 18 Validation vs. RTG Analysis & TMI

28 November 2006MISST Meeting Washington, D.C. 19 More RTG & TMI comparisons

28 November 2006MISST Meeting Washington, D.C. 20 Comparisons in Baja, CA

28 November 2006MISST Meeting Washington, D.C. 21 FUTURE WORK SST retrievals from non-NOAA satellites Microwave e.g. AMSR-E, WindSat –Most usefulness is when can make measurements along the coasts NESDIS will analyze all satellite SST datasets using this methodology to produce a single “best estimate” global analysis

28 November 2006MISST Meeting Washington, D.C. 22 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

28 November 2006MISST Meeting Washington, D.C. 23 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:

28 November 2006MISST Meeting Washington, D.C. 25 REFERENCES References –Fieguth, P.W. et al., “Mapping Mediterranean altimeter data with a multiresolution optimal interpolation algorithm'', J. Atmos. Ocean Tech, 15 (2): , –Fieguth, P., Multiply-Rooted Multiscale Models for Large-Scale Estimation, IEEE Image Processing, 10 (11), , 2001 –Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, “Statistical Processing of Large Image Sequences'', IEEE Transactions on geoscience and remote sensing, 14 (1), 80-93, 2005

28 November 2006MISST Meeting Washington, D.C. 26 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