AQUA AMSR-E MODIS POES AVHRR TRMM TMI ENVISAT AATSR GOES Imager Multi-sensor Improved SST (MISST) for GODAE Part I: Chelle Gentemann, Gary Wick Part II: Jim Cummings, Eric Bayler

Outline Project motivation Data –Enhanced data sets designed for use in analyses –Observation errors –Diurnal warming –Cool skin SST analyses Impact Studies

Gulf Stream SSTs Access to more SST observations should lead to: increased resolution, accuracy, stability Should lead to better NWP, hurricane prediction, ocean modeling, air-sea interaction studies, research

In principle, the merging and analysis of complementary satellite and in situ measurements can deliver SST products with enhanced accuracy, spatial and temporal coverage. Emphasis on synergy benefits Move incomplete and inhomogeneous data Blended NRT SSTs The GHRSST-PP Concept

A proposal in 2 parts Part 1 : Data Production & Analysis –Significant R&D component –Produce an improved Global 10 km NRT SST through the combination of observations from complementary IR and MW sensors Part 2 : Impact Assessment –Demonstrate the impact of these improved SST products on operational ocean models, NWP, and tropical cyclone intensity forecasting

MISST SST Data (completed) –NAVOCEANO NOAA-18, NOAA-17 –RSSTMI, AMSR-E orbital TMI, AMSR-E gridded –NOAA GOES-East&West –JPL GDACMODIS Successful production of 6 (8) SST L2P datasets with time of observation, location, bias, standard deviation, ….

MODIS L2P SST Bias Flags STD

GOES L2P SSTAOD Wind SSI

Diurnal Warming 2)Parameterization of IR and MW retrieval differences, with consideration of diurnal warming and cool-skin effects required for multi-sensor blending. TRMM DMSP POES K

Skin effect 2)Parameterization of IR and MW retrieval differences, with consideration of diurnal warming and cool-skin effects required for multi-sensor blending wind speed (m/s) SST Skin –Bulk (K)  T= e (-u/3.7) Figure from Donlon, C. J., P. Minnett, C. Gentemann, T. J. Nightingale, I. J. Barton, B. Ward and, J. Murray, “Towards Improved Validation of Satellite Sea Surface Skin Temperature Measurements for Climate Research”, J. Climate, 15(4), , 2002.

SST analyses US GODAE server Navy 9km OI SST : NOAA server AVHRR 17/18 + GOES E/W 11km OI SST: RSS server: 25km MW OI SST: 9km IR+MW OI SST: All data, documentation, and software are freely available via ftp & http

Global 10 km NRT SST

MODIS+AMSRE+TMI

Impact Studies 5)Targeted impact assessment of the SST analyses on hurricane intensity forecasting, numerical data assimilation by ocean models (both national and within GODAE), numerical weather prediction, and operational ocean forecast models. NWP – NRL Monterey Do merged SSTs improve assimilation of AMSU radiances? Are L2P errors useful? Do multiple satellite SSTs improve NOGAPS forecasts?

NOGAPS TC track Although there was no significant difference in the track forecast errors overall, there were areas where the use of the MISST SST analyses resulted in significantly improved NOGAPS TC track forecasts

Impact Studies 5)Targeted impact assessment of the SST analyses on hurricane intensity forecasting, numerical data assimilation by ocean models (both national and within GODAE), numerical weather prediction, and operational ocean forecast models. Hurricane Intensity Forecasting Do higher spatial/temporal SSTs improve accuracy of intensity forecasting? DeMaria(NOAA) : evaluate the utility of these new merged SSTs in the Statistical Hurricane Intensity Prediction Scheme (SHIPS) Cione (HRD): evaluate the merged SSTs in his developmental inner-core SST algorithm

SHIPS

Research into CO2 fluxes

Impact at operational centers NAVOCEANO has benefited from MISST by: –Providing access to multiple SST data sets from national and international providers –Access to more SST data has provided the opportunity to assimilate more data into K10, MODAS, and NCODA analyses –K10 now operationally uses AMSR-E data –NCODA has been designed to assimilate multiple data sets, currently it assimilates AVHRR, GOES, AMSR-E, AATSR, MSG

Impact at operational centers NOAA NCEP has benefited from MISST by: –MISST development of ‘best-practice’ methodologies to account for diurnal warming and cool skin effects –Development of sensor errors –AMSR-E assimilation and skin temperature determination extensions to the JCSDA CRTM (Community Radiative Transfer Model)

MISST Website

Links to all data & Reports

Thank you! AMSRE MODIS10km OI SST