AN ENHANCED SST COMPOSITE FOR WEATHER FORECASTING AND REGIONAL CLIMATE STUDIES

1. AN ENHANCED SST COMPOSITE FOR WEATHER FORECASTING AND REGIONAL CLIMATE STUDIES • Gary Jedlovec1, Jorge Vazquez2, and Ed Armstrong2 • NASA/MSFC Earth Science Office, Huntsville, Alabama • NASA/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. MODIS Single Pass MODIS Composite 12 17 22 27 • 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

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 • SSTwgt = (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/ • 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