1. AN ENHANCED SST COMPOSITE FOR WEATHER FORECASTING AND REGIONAL CLIMATE STUDIES Gary Jedlovec 1, Jorge Vazquez 2, and Ed Armstrong 2 1NASA/MSFC Earth Science Office, Huntsville, Alabama 2NASA/JPL, Pasadena, California

2. Motivation Need for high-resolution SST datasets for coastal applications and modeling fluxes of heat and moisture from ocean to atmosphere is closely coupled to SST coupling poorly represented in models used in coastal weather studies (Chelton et al. 2007; Lacasse et al. 2008) due to failure to resolve areas of large SST gradients Current datasets insufficient in coastal or regions of large gradients in SST Presentation describes the collaborative research of scientists with the GHRSST-PP and the SPoRT program to: produce an enhanced high-resolution (1km) SST product based on a proven current composite approach. Enhancement will include the incorporation of Single Sensor Error characteristics contained in the GHRSST-PP products. provide a near real time product from Level 2P data for distribution to user community

3. GHRSST-PP Global Data Assimilation Experiment (GODAE) High Resolution Sea Surface Temperature Pilot Project (GHRSST-PP) operationally produces improved high and medium resolution global SST products from a number of different satellite sensors the MODIS and the AATSR derived SSTs both have 1km spatial resolutions Microwave derived SSTs from AMSR-E and TMI produce SSTs at lower resolutions (25km) - not impacted by clouds. Background Short-term Prediction Research and Transition (SPoRT) project – NASA / MSFC activity to transition unique NASA observations and research capabilities to the operational weather community focus on improvements in regional, short-term weather forecasts primary end users are NWS Forecast offices in Southern Region MODIS, AMSE-E, AIRS data and associated products nowcasting products such as total lightning, convective initiation indices, GOES aviation products unique regional weather model predictions (driven by NASA data)

4. Current technique EOS science team algorithm used to process MODIS direct broadcast data at Univ. of South Florida (or archived data from DAAC) – Terra and Aqua, day and night Assume day-to-day changes in SST are relatively small and preceding days values can be used to fill in cloudy regions Remap SST data to 1km grid at each time Apply cloud mask to filter cloud-free data (Jedlovec et al. 2008) Consider three most recent cloud-free SST values for each pixel (from the past week of data) – call this a collection For each collection, exclude coldest value (bad data and extra cloud filter) and average other two to produce a composite SST value for each pixel 4x daily SST composite for Gulf of Mexico and near Atlantic region 12 17 22 27 MODIS Single Pass MODIS Composite

5. Research approach: Enhancement to the current MODIS 1km SST composite product (Haines et al. 2007). Three primary aspects goal of the work. add 1km AATSR data and integrate AMSR-E (microwave) data to reduce the latency of the composite extend coverage of composite SST product for both the West and East Coasts of the United States, including the Gulf of Mexico incorporate error GHRSST-PP data / source error characteristics to the composite maps Validate approach with in-situ data from the World Ocean Database (WOD) and other sources and determine improvements of the enhanced composites (i.e., accuracy, reduced latency. etc.) Transition products to SPoRT program to support operational activities which include numerical weather forecasting and fisheries, and regional climate studies

6. MODIS 1km SST composite using 3 days of data (similar to Haines and Jedlovec approach). data from relatively clear days, some cloudy regions (black) large amplitude small spatial scale gradients in SST field are important and need to be preserved AMSR-E data AMSR-E SST 25km resolution at same composite length as MODIS. Only large scale events captured and missing data along the coast. MODIS and AMSR-E intercalibration accuracy of data allows for a combination of data from different instruments AMSR-E provides accurate information in cloudy regions new composite can utilize error estimates and latency to form a weighted composite product →maintains accuracy and resolution →reduces MODIS latency MODIS AMSR-E MODIS + AMSR-E

7. Advanced composing technique 4x daily composites – MODIS (Terra & Aqua), AATSR (morning), and AMSR-E (Aqua) Procedure: remap SST data to common 1km grid at each time apply cloud mask to filter cloud-free data implement bias removal between instrument SSTs with PDFs combine AATSR and MODIS data at common time (morning) where MODIS data missing, use microwave SST to fill in (nearest neighbor or bilinear interpolation) consider three most recent cloud-free MODIS/AASTR/AMSR-E SST values for each pixel (from the past week of data) – call this a collection process each collection using error weights SST wgt = (SST1xrmsr1 + SST2xrmsr2 + SST3xrmsr3) / (rmsr1 + rmsr2 + rmsr3)

8. Summary: Results have shown that low resolution SST data sets are insufficient to resolve air-sea coupling dynamics in areas of high SST gradients such as the Gulf Stream and along upwelling areas associated with the Eastern Boundaries of ocean basins. In a collaboration between SPoRT and the NASA/PODAAC/GDAC a new enhanced MODIS 1km coastal, that covers the US coasts, will be produced/ o simple methodologies will be implemented to use the error characteristics contained within the GHRSST data sets If successful the potential exists that these higher resolution data sets and composites will significantly impact weather prediction forecasting and fisheries management