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Generation of a MODIS Sea Surface Temperature Composite Methodology and Validation

Generation of a MODIS Sea Surface Temperature Composite Methodology and Validation Stephanie Haines November 21, 2005. Motivation to generate a high-resolution SST composite Methodology used to create several versions of MODIS SST composites Case Study analysis of May 2004 Comparison to RTG

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Generation of a MODIS Sea Surface Temperature Composite Methodology and Validation

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  1. Generation of a MODIS Sea Surface Temperature Composite Methodology and Validation Stephanie Haines November 21, 2005

  2. Motivation to generate a high-resolution SST composite Methodology used to create several versions of MODIS SST composites Case Study analysis of May 2004 Comparison to RTG Latency effects Validation against buoy data Conclusions and future work Outline

  3. Coastal NWS offices wanted high resolution SST product Single pass of MODIS provides limited coverage Simple composite generated from multiple passes of MODIS data Main assumption is that SST does not change significantly from one day to the next High resolution (1 km), full coverage Small scale features in SST gradients retained Alternative to the Real-Time Global (RTG) SST analysis for model assimilation Lower boundary layer forcing important in mesoscale models RTG is a 0.5 degree daily interpolation of in situ and satellite data Motivation

  4. Use the 1km SST EOS MODIS product to produce a detailed and spatially continuous SST field while minimizing cloud contamination and latency effects Retain 5 clear (as determined by a cloud mask) SST values for each pixel separately for day and night Average some of clear values to obtain a moreconsistent field Version 2 discards the coldest 2 of the 5 and averages the remaining 3 (current operational method) Version 3 has reduced latency by only considering the 3 most recent, to reduce cloud contamination the coldest is discarded (experimental method) Methodology

  5. Case Study Period • May 2004 • relatively clear and dry period • General warming trend of SSTs • Version 2 method used with EOS SST product from the DAAC • EOS cloud mask applied • Loop of daytime composites shows • Spatially continuous pattern • 1 km resolution • Realistic spatial trends, varying with time

  6. RTG-SST MODIS Daytime MODIS Nighttime 29th May 2004 290 292 294 296 298 300 302 304 306 Comparison of MODIS to RTG • Large scale patterns of MODIS match well to RTG • Strong SST gradient off east coast not in RTG product • RTG is a daily product, therefore no diurnal information • The MODIS composites show diurnal differences • Operational applications of version 2 revealed some latency issues as a result of persistent cloud cover – reran May 2004 case with version 3

  7. 290 292 294 296 298 300 302 304 306 0 2 4 6 8 10 12 14 16 18 20 Latency Effect on SST 8th May 2004 • Version 3 developed because of latency issues with version 2 • Only small differences seen between the two MODIS versions, although there are differences in the latency • Even with several days latency, the composites capture both large scale and small scale SST gradients MODIS SST V2 RTG-SST MODIS SST V3 MODIS LATENCY V2 MODIS LATENCY V3

  8. Verification Against Buoy Data • SST composites capture majority of trends in the buoy data with high correlation values both day and night Daytime • Reduced latency improves the composite (average latency improvement between versions 2 and 3 is 1-2 days) • RTG compares very well to buoy data during the day. Both MODIS composites have a warm bias Nighttime • RTG too warm at night, with the largest bias of 0.53

  9. Version 2 worked very well for May 2004, but persist cloud cover and cooling SSTs revealed issues with current compositing method Version 3 has reduced latency compared to version 2 and Will be creating a Version 4 algorithm that further improves the product Add AMSR-E SST data Aqua only Plan to make AMSR-E SST available in real-time 2006 Provides SST values in cloudy conditions Conclusions and Future Work AQUA MODIS AMSR-E MODIS+AMSR-E

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