1. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 POES-GOES Blended SST Analysis Presented by Eileen Maturi Presented by Eileen Maturi

2. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 2 Requirement, Science, and Benefit Requirement/Objective Mission Goal: 1) Ecosystems Coral Reef Community CoastWatch/OceanWatch National Marine Fisheries Service 2) Climate Accurate Mesoscale Forecasting Long Term Climate Studies Reprocessing Improve accuracy 0.3 ° K  0.2 ° K 3) Weather and Water Increase lead time and accuracy for weather and water warnings and forecasts Reduce uncertainty associated with weather and water forecasts, assessments, and decision tools (e.g. SST gradient inputs into models) Improve Ocean Forecasting capabilities using multi-analysis products as input into the ocean forecasts (e.g. Ocean Prediction Center- high seas forecasts) Science Can an assimilation system be developed to produce the best SST analysis, maximizing the use of all available assets while being applicable to all users? Benefit –Improved operational analysis Bias-corrected 0.1° spatial resolution sufficient to resolve mesoscale ocean features Fast & rigorous (multi-scale OI) Data-adaptive correlation length scale preserves small-scale oceanic features in input data Improved coverage of high-resolution features due to inclusion of geostationary data Rigorous error analysis –Ability to generate SST analysis with high density data –Improve the monitoring dynamic ocean features in the coastal coral reef environment

3. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 3 Challenges and Path Forward Science challenges –Microwave SST measurements closer to the coast –Diurnal variability –Ultra-high resolution (~1 km) –Dynamic first guess Next steps –Generate a 5-km Global Analysis –Incorporate microwave SST (AMSR-E SST) into analysis –Apply the same retrieval algorithm and cloud mask to generate all the IR SST inputs to the analysis –Continue to include new IR and Microwave SST retrievals into the analysis when they are available Transition Path –Incorporate the enhancements to this product into NOAA operations through the SPSRB process –Also disseminate via AWIPS, Science on a Sphere, GHRSST, WMO GTS, NOAA CoastWatch/OceanWatch

4. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 4 POES IR has POES IR has high spatial resolution GOES IR has GOES IR has high temporal resolution Microwave has Microwave has all-weather capability Combine to obtain the optimal SST analysis Maximize strengths – minimize weaknesses

5. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 5 SST INPUT (NOAA and non-NOAA Satellites) Polar Orbiting Infrared SST Retrievals –NOAA-19 –MetOp-A Geostationary Infrared SST Retrievals –GOES-11/12 –MTSAT –MSG-2

6. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 6 Benefit of Geostationary SST A data-rich environment!POES-SST coverage for 1 dayGeo-SST coverage for 1 dayGeo-SST dominates low-to-mid latitudes

7. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 7 NEDSIS’ Operational Multi-SST Analysis Operational since 2007 Rigorous multi-scale OI procedure emulates Kalman filter Data-adaptive correlation length scale strikes balance between noise reduction and feature preservation Disseminated to NWS field offices via AWIPS Daily 11-km (~2× sampling of mid- latitude Rossby Radius) global SST Analysis in real time (SST in  C) POES-GOES SST Analysis OSTIA SST Analysis (1/20°) S America

8. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 8 Key Aspects of Methodology A simple prediction is used: x(t|t-1) = x(t-1|t-1) Multi-pass approach with range of correlation lengths –Estimates and errors obtained by interpolation based on data density –In effect, we use a mixture of stationary models to accurately mimic the effect of a non- stationary model Separate basins avoid “overspill” Quad-tree → fast & rigorous OI Method scales as Nlog e (N) Biases updated on a daily basis –Derived from (O – A) –“Lightly” damped ([0.6, 0.4])

9. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 9 Product Examples December 31 2007 RTG_HR SST December 31 2007 Daily OI SST December 31 2007 POES_GOES Point-for-point comparison with RTG_HR shows S.D. of 0.45 K Comparison with Reynolds ¼° daily OI has S.D. of 0.65 K

10. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 10 MW Data for December 31 2007 Only non-MW SST analysis to show this feature

11. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 11 Challenges and Path Forward Science challenges –Microwave SST measurements closer to the coast –Diurnal variability –Ultra-high resolution (~1 km) –Dynamic first guess Next steps –Generate a 5-km Global Analysis –Incorporate microwave SST (AMSR-E SST) into analysis –Apply the same retrieval algorithm and cloud mask to generate all the IR SST inputs to the analysis –Continue to include new IR and Microwave SST retrievals into the analysis when they are available Transition Path –Incorporate the enhancements to this product into NOAA operations through the SPSRB process –Also disseminate via AWIPS, Science on a Sphere, GHRSST, WMO GTS, NOAA CoastWatch/OceanWatch