1 / 32

MODIS Sea-Surface Temperatures for GHRSST-PP

US GHRSST Meeting. November 28, 2005. MODIS Sea-Surface Temperatures for GHRSST-PP. Robert H. Evans & Peter J. Minnett Otis Brown, Erica Key, Goshka Szczodrak, Kay Kilpatrick, Warner Baringer, Sue Walsh Rosenstiel School of Marine and Atmospheric Science University of Miami. Outline.

greg
Download Presentation

MODIS Sea-Surface Temperatures for GHRSST-PP

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. US GHRSST Meeting. November 28, 2005 MODIS Sea-Surface Temperatures for GHRSST-PP Robert H. Evans & Peter J. Minnett Otis Brown, Erica Key, Goshka Szczodrak, Kay Kilpatrick, Warner Baringer, Sue Walsh Rosenstiel School of Marine and Atmospheric Science University of Miami

  2. Outline GHRSST MODIS division of effort Status of MODIS SST MODIS approach to SSES Initial observations Space and Time resolution of sst analysis fields has important implications for sst retrieval coverage and quality High latitude summer bias and standard deviation are likely too large. Available in situ data are sparse Conclusions

  3. Real Time MODIS for GHRSST July, 2005 formation of MODIS SST processing team (JPL, OBPG - GSFC, Miami)Division of effort:Miami - algorithm development, cal/val, base code developmentOBPG (Bryan Franz) integrate code into OBPG processing, process MODIS Terra, Aqua; day, night; global 1km; SST, SST4; transfer files to JPLJPL PO.DAAC (J. Vazquez, E. Armstrong) - convert OBPG files into L2P, add remaining fields, ice mask, distance to clouds…, transfer files to Monterrey

  4. MODIS Collection 5 changes • Time dependant SST and SST4 algorithm coefficients • Time dependant Mirror side corrections (Terra only) • Improved cloud flagging • - use of a more stringent Reynolds test • - day 865nm reflectance for clouds & aerosols • - night sst, sst4 comparison for clouds & aerosols • Change in map file resolution from SMI power of 2 projections to a true 4km, 36 km and 1 degree and maps to better assist incorporation of MODIS SST data into models.

  5. Aqua Collection 4 & 5 SST & SST4 residuals

  6. Aqua Collection 5 validation Statistics

  7. Terra Collection 4 & 5 Mirror Side 1 & 2 residuals

  8. TERRA MODIS Collection 5 validation Statistics

  9. MODIS Single Sensor Error Statistics Approach Bias and Standard Deviation Hypercube Hypercube dimensions (partitioning of Match-up database): - Time- quarter of year (4) - Latitude band (5): "60S to 40S" "40S to 20S" "20S to 20N" "20N to 40N" "40N to 60N" - Sat Zenith angle intervals (4): "0 to 30 deg" "30+ to 40 deg" "40+ to 50 deg" "50+ deg" - Surface temperature intervals (8): 5 degree intervals - Channel difference intervals:SST(3), SST4(4) ch31-32 (SST): 0.7<, 0.7->2.0, >2.0 ch22-23 (SST4) 0.5 degree intervals: -0.5<, -0.5->0, >0 ->0.5, >0.5 - Quality level (2) cube created only for ql=0 and 1 Note for ql2 and 3 the bias and standard deviation are each fixed to a single value No interpolation between adjacent cells in Hypercube

  10. 11-12 μm nighttime Terra SST 1 day per calendar quarter Every other orbit shown to eliminate orbit overlap 2005 February 1 May 1 August 1 October 31

  11. 11-12 μm SST Ql=0 bias 1 day per calendar quarter No ice mask February 1 May 1 Hypercube residuals relative to in situ obs August 1 October 31

  12. 11-12 μm SST DT analysis 1 day per calendar quarter February 1 May 1 DT analysis relative to Reynolds OI August 1 October 31 Modis Terra-Reynolds

  13. Quality 0 & 1 Terra SST Global Bias from Hypercube and DT analysis DT Quality 0 Quality 0 Predicted bias Sat - buoy Oct 31, 2005 night Sat - Reynolds OI Oct 31, 2005 night Median -0.1 Quality 1 DT Predicted bias Quality 1 Median -0.4

  14. Challenge of using SST analysis field as reference SST4 night Terra Oct 31, 2005 -Top Left Hypercube bias -Bottom Left DT analysis bias -Top Right Areal coverage using OI-Sat<3K -Bottom Right Areal coverage using all pixels High gradient, mesoscale variability not represented by OI Contemporaneous higher resolution analysis (better than 25km desired) Ql=0 all

  15. 11-12 μm SST Standard deviation 1 day per calendar quarter February 1 May 1 August 1 October 31

  16. Conclusions New monthly coefficients removed seasonal bias trends, Terra mirror side trends coefficients delivered for Terra, now available for Aqua -SST4 rms order 0.35C, SST order 0.45 -SST4 less affected by dust aerosols, water vapor Improved quality filtering removed cold clouds and significant dust aerosol concentrations -Introduction of SSES hypercube provides insight into bias and standard deviation trends as a function of time, latitude, temperature, satellite zenith angle, brightness temperature difference as a proxy for water vapor and retrieval quality level Hypercube developed and tested for Terra, in progress for Aqua -Base code for SST and SST4 delivered to OBPG -Delivery of Hypercube code in progress

  17. END

  18. August 1 2005 Laptev Sea

  19. August 1 2005 Laptev Sea MODIS SST - Reynolds

  20. 4um SST bias 1 day per calendar quarter February 1 May1 August 1 October 31

  21. 4 um SST standard deviation 1 day per calendar quarter February 1 May1 August 1 October 31

  22. 4 um SST DT analysis 1 day per calendar quarter February 1 May1 August 1 October 31 Modis Terra-Reynolds

  23. 4um SST 1 day per calendar quarter February 1 May1 August 1 October 31

  24. Atmospheric correction algorithms The form of the daytime and night-time algorithm is: SST = c1 + c2 * T11 + c3 * (T11-T12) *Tsfc + c4 * (sec (θ)-1 )* (T11-T12) where Tn are brightness temperatures measured in the channels at nm wavelength, Tsfc is a ‘climatological’ estimate of the SST in the area, and θ is the satellite zenith angle. This is based on the Non-Linear SST algorithm. (See Walton, C. C., W. G. Pichel, J. F. Sapper and D. A. May,1998, “The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites.” Journal of Geophysical Research,103, 27,999-28,012.) The night-time algorithm, using two bands in the 4m atmospheric window is: SST4 = c1 + c2 * T3.9 + c3 * (T3.9-T4.0) + c4 * (sec (θ)-1) Note: the coefficients in each expression are different.

  25. October 31 2005 MODIS TERRA Night SST 11-12μm Best Quality QL=0 Bias 11-12 μm

  26. October 31 2005 MODIS TERRA Night Stdev Best Quality QL=0 Bias 11-12um

  27. October 31 2005 MODIS TERRA Night DT 11-12 um Modis-Reynolds Best Quality QL=0 Bias 11-12 um

  28. October 31 2005 MODIS TERRA Night SST4um Bias 4 u Best Quality QL=0 Bias 4 um

  29. October 31 2005 MODIS TERRA Night Stdev4 um Best Quality QL=0 Bias 4 um

  30. October 31 2005 MODIS TERRA Night DT 4um DT 4 um Best Quality QL=0 Modis-Reynolds Bias 4 um

  31. Histogram QL=0 Predicted Bias SST October 1 2005 Histogram QL=0 DT analysis Terra SST -Reynolds October 1 2005

  32. Histogram QL=1 Predicted Bias SST October 1 2005 Histogram QL=1 DT analysis Terra SST -Reynolds October 1 2005

More Related