1. Status of SSES at the Bureau of Meteorology Leon Majewski, Justin Freeman, Helen Beggs Bureau of Meteorology Melbourne, Australia

2. Bureau of Meteorology Helen Beggs Oceanographic remote sensing Ocean analysis and prediction Leon Majewski Oceanographic remote sensing Product development and generation Operational data processing Justin Freeman Product development and generation Data analysis and research

3. AVHRR processing system AVHRR Stitching Calibration Navigation CAPS NLSST L2P MDB SSES Cloud Cloud Prox AODAAC RDAC (BOM) GDAC L3P NWP AVHRR International agencies and researchers Agencies and universities Ocean prediction, researchers McIDAS Forecasters and www Experimental

4. AO-DAAC Australian Ocean Distributed Active Archive Centre  Portal for satellite ocean data Sea Surface Temperature Ocean Colour  OPeNDAP interface to data netCDF (GHRSST) and HDF4 (MODIS/SeaDAS)  Contributors CSIRO, University of Tasmania, BOM, Geoscience Australia, Western Australian Satellite Technology and Applications Consortium

5. MTSAT-1R processing system MTSAT-1R Calibration Navigation McIDAS NOAA GeoSST L2P MDB SSES Bayesian Cloud Cloud Prox L3P NWP AODAAC RDAC (BOM) GDAC International agencies and researchers Agencies and universities Ocean prediction, researchers McIDAS Forecasters and www Experimental

6. AVHRR: CLAVR-3  Implemented in McIDAS and CAPS Cloud screening

7. Bayesian cloud screening  High rate of clearing  Need to generate a test data set to determine efficiency Impact of parameter variations and any changes in sensor characteristics

8. Cloud screening MTSAT-1R: Bayesian cloud screening  Merchant et al; Mittaz implementation

9. Matchup database generation Following Medspiration guidelines  netCDF format  Added sensor brightness temperatures Enables tuning or constraining of algorithms Parallelised using MPI  Parameters specified via XML Box size Time constraint

10. N S N S AVHRR MDB: AVHRR Observations from ADAM  Oracle database  Accessed using python Parallelised using MPI ADAM (observation DB) No Buoy data in this section of imagery

11. N S N S MTSAT-1R MDB: MTSAT-1R ADAM (observation DB) Observations from ADAM  Oracle database  Accessed using python Same code as AVHRR

12. Impact of parallelization on MDB Problem size Number of satellite pixels: 2750 x 2750 pixels Number of in situ observations: 7667 3 seconds 110 seconds 7 seconds Example for MTSAT-1R data

13. MDB: MTSAT-1R Distribution of matchups  3 months (Jan-Mar 2007)  90000 matchups (1 hr, 12x12 km box)

14. MDB: MTSAT-1R Insert stats

15. N S AVHRR MTSAT-1R N S MDB: AVHRR & MTSAT-1R Observations from MTSAT Same code as AVHRR  Constrained by: Time, distance, viewing geometry

16. SSES calculation Following GDS 1.7 (~2.0) Generalised, simplified process  Easy to comply with  User constraints influence statistics 1 hour window vs 6 hour window  Assumptions: Cloud proximity is major factor SST algo. performance is equal over image

17. SSES calculation AVHRR  25 km x 25 km box centred on nearest satellite pixel to in situ observation  +/- 6 hours  CPx = 5 MTSAT-1R  12 km x 12 km box centred on nearest satellite pixel to in situ observation  +/- 1 hour  CPx = 5

18. Future plans AVHRR  Implement Bayesian approach to cloud screening  Skin SST  Improve automation and data accessibility RDAC, GDAC, AO-DAAC, kml  Reprocess historical archive of HRPT data 1990 to present  Increase cross-platform comparisons Ongoing and historical platforms

19. Future plans MTSAT-1R  Reprocess data holdings  Generate a complete MDB Include more BT information  Investigate calibration Cross calibrate with AVHRR and other available sensors Cross calibrate FY-2D?

20. AVHRR, MTSAT images

21. AVHRR, MTSAT zoom

22. AVHRR ~5km pseudo MTSAT