# Presentation: 667806

## AN ENHANCED SST COMPOSITE FOR WEATHER FORECASTING AND REGIONAL CLIMATE STUDIES

**Gary Jedlovec****1****, Jorge Vazquez****2****, and Ed Armstrong****2**
**NASA/MSFC Earth Science Office, Huntsville, Alabama**
**NASA/JPL, Pasadena, California **
** **

## 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

## 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)

## 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
** **

## 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**

## 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

- AMSR-E

- MODIS + AMSR-E

## 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)**

## 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**