May 28-29, 2009GHRSST 2009 International Users Symposium National Oceanic and Atmospheric Administration Group for High Resolution Sea Surface Temperature Sea Surface Temperature Products NOAA GHRSST data: GOES L2P and blended GOES/POES L4P Eileen Maturi (1), John Sapper (1), Jonathan Mittaz (2) and Andy Harris (2) (1)NOAA/NESDIS, (2) University of Maryland, Cooperative Institute for Climate Studies (CICS) GHRSST

May 28-29, 2009GHRSST 2009 International Users Symposium What is an SST L2P Product International effort focused on producing the best sea surface temperature products –Led by Global Ocean data Assimilation Experiment (GODAE) High-Resolution Sea Surface Temperature Pilot Project (GHRSST-PP) (now Group for High Resolution Sea Surface Temperature) –contents of the L2P product are described extensively in the GHRSST-PP documentation, specifically the GDS v1 rev1.6, which can be found on the GHRSST web site ( SST-Level-2 preprocessed – SST retrievals are put into a standard format and ancillary information is added, including environmental conditions and retrieval errors

May 28-29, 2009GHRSST 2009 International Users Symposium WHY NOAA L2P SST Products Known accuracy for each SST pixel is important for user applications –Marine applications –National weather prediction –Monitoring Global Climate L2P Format provides accuracy information at each pixel –Appends *Single Sensor Errors Statistics (SSES) to a standard SST value at each pixel –Plus a number of *ancillary data records * Described in later slides

May 28-29, 2009GHRSST 2009 International Users Symposium BENEFITS GEO-SST L2P Products Provides: GOES L2P Products for the International Group for High Resolution Sea Surface Temperature. GOES-SST data with retrieval bias information to NWS/NCEP/Marine Modeling and Assimilation Branch for Ocean Forecast Modeling. Good temporal resolution for accurate location of critical temperature fronts for fisheries. Ability to remove sources of retrieval bias in all Geostationary SST products. Information on the diurnal SST cycle to determine the heat stress on the coral reefs.

May 28-29, 2009GHRSST 2009 International Users Symposium GOES L2P SST Product  A GOES L2P * netCDF product file has 22 parameters for each pixel: –SST –Time, Latitude, Longitude –Satellite Zenith Angle –Aerosol Optical Depth –Surface Solar Irradiance –Wind Speed –Uncertainty estimates (bias and S.D.) –Proximity Confidence Value –QC flags (including cloud and land) –Ice concentration –Deviation from reference SST analysis (RTG) –Temporal coincidences of ancillary data c.f. SST observation –Source codes for ancillary data –Probability of clear-sky (optional field) * Generated every ½ hour for GOES-E & W, N & S sectors

May 28-29, 2009GHRSST 2009 International Users Symposium NOAA GOES L2P SST Product Operational The GOES L2P SST product (above) includes ancillary fields of aerosol optical depth, surface solar irradiance, wind speed, uncertainty estimates, proximity confidence value, and probability of clear-sky. L2P OUTPUT 1.SST 2.Aerosol Optical Depth 3.Surface Solar Irradiance 4.Wind Speed 5.Uncertainty estimates 6.Proximity Confidence Value 7.Probability of clear-sky

May 28-29, 2009GHRSST 2009 International Users Symposium MTSAT L2P SST Product  A MTSAT L2P * netCDF product file has 22 parameters for each pixel: –SST –Time, Latitude, Longitude –Satellite Zenith Angle –Aerosol Optical Depth –Surface Solar Irradiance –Wind Speed –Uncertainty estimates (bias and S.D.) –Proximity Confidence Value –QC flags (including cloud and land) –Ice concentration –Deviation from reference SST analysis (RTG) –Temporal coincidences of ancillary data c.f. SST observation –Source codes for ancillary data –Probability of clear-sky (optional field) * Generated every hour for the Full Disk

May 28-29, 2009GHRSST 2009 International Users Symposium NOAA MTSAT-1R L2P SST Product Operational The MTSAT-1R L2P SST product (left) includes ancillary fields –aerosol optical depth –surface solar irradiance –wind speed –uncertainty estimates –proximity confidence value –probability of clear-sky

May 28-29, 2009GHRSST 2009 International Users Symposium MSG L2P SST Product  A MSG L2P* netCDF product file has 22 parameters for each pixel: –SST –Time, Latitude, Longitude –Satellite Zenith Angle –Aerosol Optical Depth –Surface Solar Irradiance –Wind Speed –Uncertainty estimates (bias and S.D.) –Proximity Confidence Value –QC flags (including cloud and land) –Ice concentration –Deviation from reference SST analysis (RTG) –Temporal coincidences of ancillary data c.f. SST observation –Source codes for ancillary data –Probability of clear-sky (optional field) * Generated every 15 minutes for the Full Disk

May 28-29, 2009GHRSST 2009 International Users Symposium NOAA MSG L2P SST Product Operational The MSG L2P SST product (left) includes ancillary fields –aerosol optical depth –surface solar irradiance –wind speed –uncertainty estimates –proximity confidence value –probability of clear-sky

May 28-29, 2009GHRSST 2009 International Users Symposium Description of GOES SST L2P Product  Derived from GOES SST area files (satellite projection) therefore ½ hourly.  GOES-E & W, N & S sectors  NCEP wind speed and 3-hr average solar irradiance values –Spatially interpolated to each GOES SST pixel –Wind speed is also time-interpolated  AVHRR aerosol optical depth from NESDIS 100-km daily analysis is n-n sampled to each SST pixel, along with age of observation  Proximity confidence is derived from Bayesian probability of clear sky  Sea Ice fraction is spatially interpolated from NCEP analysis  Sunglint flag is calculated as part of Bayesian cloud detection  Also uses NCEP data and CRTM calculations to estimate SSES for each SST value (currently use NCEP profile and SST – 2 m air temp)

May 28-29, 2009GHRSST 2009 International Users Symposium Description of MTSAT1R SST L2P  Derived from MTSAT1R SST area file (satellite projection) every hour.  MTSAT1R Full Disk  NCEP wind speed and 3-hr average solar irradiance values –Spatially interpolated to each MTSAT1R SST pixel –Wind speed is also time-interpolated  AVHRR aerosol optical depth from NESDIS 100-km daily analysis is n-n sampled to each SST pixel, along with age of observation  Proximity confidence is derived from Bayesian probability of clear sky  Sea Ice fraction is spatially interpolated from NCEP analysis  Sunglint flag is calculated as part of Bayesian cloud detection  Also uses NCEP data and CRTM calculations to estimate SSES for each SST value (currently use NCEP profile and SST – 2 m air temp)

May 28-29, 2009GHRSST 2009 International Users Symposium Description of MSG SST L2P  Derived from MSG SST area file (satellite projection) every 15 minutes.  MSG Full Disk  NCEP wind speed and 3-hr average solar irradiance values –Spatially interpolated to each MSG SST pixel –Wind speed is also time-interpolated  AVHRR aerosol optical depth from NESDIS 100-km daily analysis is n-n sampled to each SST pixel, along with age of observation  Proximity confidence is derived from Bayesian probability of clear sky  Sea Ice fraction is spatially interpolated from NCEP analysis  Sunglint flag is calculated as part of Bayesian cloud detection  Also uses NCEP data and CRTM calculations to estimate SSES for each SST value (currently use NCEP profile and SST – 2 m air temp)

May 28-29, 2009GHRSST 2009 International Users Symposium GOES SST L2P SSES –Assumption is that retrieval bias depends on Clear-sky transmittance (calculated from NCEP → pCRTM) Air-sea temperature difference (currently NCEP only) –Assume that sensitivity to Air-Sea Temperature Difference (ASTD) increases with decreasing transmittance –Derive bias = offset + gradient  ASTD for different  11 derived from buoy matchup data selected with a single clear sky probability range of greater than 95%, –Post-corrected σ is estimated as a function of transmittance only –Different for sensor/day/night MTSAT-1R and MSG L2P SSES –derived in 4 separate clear sky probability bands of , , and greater than –SSES for each probability band is then derived in a similar manner to that for GOES, i.e. is a function of clear-sky transmittance and ASTD –The division of the SSES into separate clear sky probability bands allows for a more accurate parameterization of the SSES taking into account the possible presence of clouds within any given pixel. NOAA’s Single Sensor Error Statistics Methodology

May 28-29, 2009GHRSST 2009 International Users Symposium WHY GOES POES Blended SST Analysis Products SST has a strong influence on many air- sea interaction exchange processes SST is a key parameter in many environmental and process models Each Sensor has its strengths and weaknesses for generating SSTs A blended SST analysis is the best way to capture the best of each sensor

May 28-29, 2009GHRSST 2009 International Users Symposium NOAA’s POES GOES Blended SST Analysis Product Daily Operational Global SST Analysis –POES & MetOp Operational SST retrievals from ACSPO –GOES Operational SST retrievals –RTG Method employs a recursive estimation algorithm which emulates the Kalman filter, with a very fast multiscale OI algorithm used for the update step (Fieguth et al., 1998, 2002). This approach preserves fine-scale structure in SST estimates, and allows geophysical realistic treatment of land-sea boundaries ATBD for this algorithm is available upon request

May 28-29, 2009GHRSST 2009 International Users Symposium December RTG_HR

May 28-29, 2009GHRSST 2009 International Users Symposium Improvement over RTG_HR 1/12° analysis is immediate where data are available December POES-GOES

May 28-29, 2009GHRSST 2009 International Users Symposium Point-for-point comparison with RTG_HR shows S.D. of 0.45 K –Note: Bias gradually adjusting to zero Comparison with Reynolds ¼° daily OI has S.D. of 0.65 K December RTG_HR SST

May 28-29, 2009GHRSST 2009 International Users Symposium Point-for-point comparison with RTG_HR shows S.D. of 0.45 K –Note: Bias gradually adjusting to zero Comparison with Reynolds ¼° daily OI has S.D. of 0.65 K December Daily OI SST

May 28-29, 2009GHRSST 2009 International Users Symposium Point-for-point comparison with RTG_HR shows S.D. of 0.45 K –Note: Bias gradually adjusting to zero Comparison with Reynolds ¼° daily OI has S.D. of 0.65 K December POES_GOES

May 28-29, 2009GHRSST 2009 International Users Symposium ACRONYM TABLE ACSPO-AVHRR Clear-Sky Processor for Oceans Analysis-is the process of breaking a complex topic or substance into smaller parts to gain a better understanding of it. ATBD-Algorithm Theoretical Basis Document AVHRR-Advanced Very High Resolution Radiometer Bayesian Probability-interprets the concept of probability as 'a measure of a state of knowledge' and not as a frequency as in orthodox statistics. GDS-GHRSST Data processing Specifications GOES-Geostationary Operational Environmental Satellites GHRSST- Group for High Resolution Sea Surface Temperatures Imager-A picture of the earth taken from an earth orbital satellite. L2P- Level-2 preprocessed MTSAT-Multi-Functional Transport Satellite MSG-Meteosat Second Generation NCEP-National Centers for Environmental Prediction NESDIS- National Environment Satellite Data Information Service NOAA-National Oceanic Administration NWS-National Weather Service OI-Optimum Interpolation POES-Polar-Orbiting Environmental Satellites SST- Sea Surface Temperature SST L2P- SST retrievals are put into a standard format and ancillary information is added, including environmental conditions and retrieval errors SST Analysis-Merging of the different SST products to generate one SST product POES GOES SST Analysis-Merging of the POES and GOES SST products to generate SST Analysis RTG-A daily, high-resolution, real-time, global, sea surface temperature (RTG_SST) analysis SSES- Single Sensor Error Statistics