SST Data Used at NOAA Climate Prediction Center (CPC) OI SST of Reynolds

1. Use of Satellite Retrieved SSTat NOAA Climate Prediction CenterPingping Xie and Wanqiu WangClimate Prediction CenterNCEP/NWS/NOAA

2. SST Data Provide Information Critical to the Assessment, Diagnostics and Prediction of Climate and Its Variability • SST Data Used at NOAA Climate Prediction Center (CPC) • OI SST of Reynolds • Reconstructed SST of Smith & Reynolds • Real-Time Global SST of EMC • Multi Platform Merged (MPM) SST (in development)

3. The Multi Platform Merged (MPM)Sea Surface Temperature Analysis • To construct a high resolution SST analysis over the western hemisphere through combining information from all available sources • Domain: 180o – 30oW; 45oS – 60oN • Resolution: 0.25olat/lon; 3-hourly • Target Period: 2002 – Present • Goals: Resolving diurnal cycle of SST

4. Algorithm Strategy • Input data: All available in-situ and advanced satellite observations • Quality control:Cross check to ensure data quality • Bias correction: Removal of large-scale/low-frequency bias in satellite observations • OI analysis: Combining SST data from all observations through the Optimal Interpolation (OI)

5. Input Data • In-situ observations Buoys and ships • Satellite Observations GOES: 3-hourly / clear sky TMI: twice daily / all sky AMSR: twice daily / all sky NOAA16: twice daily / clear sky NOAA17: twice daily / clear sky MODIS: twice daily / clear sky

6. Quality Control • Raw input data by cross-check to remove outliers that are too cold or warm compared to the median value of all input observations • This process is repeated after bias correction with stricter thresholds • In the OI computation, increment amplitude of each input observation is required not to exceed 1.5K.

7. PDF Bias Correction • PDF functions are defined for each 1ox1o box and each day using at least 500 matching pairs of satellite and in-situ observations within the last 46-day and within a box co-centric with the target 1ox1o box • A table is then established to give correspondence between the satellite-estimated and the in-situ observed SSTs with the same percentiles in the PDF functions • Bias correction is performed through this correspondence table

8. Bias Correction for TMI [2004] • Double peaks and over-estimation of high SSTs in the raw TMI retrieval histogram; • Corrected TMI SSTs closer to the in-situ observations;

9. Optimal Interpolation • First guess is taken as the analysis of the previous step plus climatoligical seasonal and diurnal increment. • Spatial first-guess error correlation follows Gaussian distribution with an e-folding scale of 50 km.

10. Current Status • Developed prototype algorithm to define the analysis • Produced the test version analysis for 2002 – 2008 • Will re-generate the analysis with revised error statistics and parameters derived from the test version analysis

11. The MPM 3-hourly SST Sample analysis for 00Z, July 3, 2004

12. Time Series of 3-Hourly SSTat [109.875oW; 26.125oN] MPM analysis

13. Mean Diurnal SST (2003-2008) MPM analysis

14. Comparison with OI and RTG SST SST SST Gradient From OI to RTG to MPM OI • Analyses become finer • Local gradient amplitude becomes larger RTG Chelton and Wentz (2005) showed that SST gradient in OI and RTG is too week. MPM

15. Wish List • Improved QC for the SST retrievals (removal of cloud contaminated pixels) • Improved calibration / bias correction • Calibration against skin temperature

16. Thank youfor providing us the SST data !