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Background – GLDAS  Land Information System (LIS)

Analysis of multiple precipitation products as part of the Global Land Data Assimilation System (GLDAS) project Jon Gottschalck University of Maryland, Baltimore County (UMBC) Goddard Earth Science and Technology Center (GEST) Hydrological Sciences Branch NASA / Goddard Space Flight Center

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Background – GLDAS  Land Information System (LIS)

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  1. Analysis of multiple precipitation products as part of the Global Land Data Assimilation System (GLDAS) project Jon Gottschalck University of Maryland, Baltimore County (UMBC) Goddard Earth Science and Technology Center (GEST) Hydrological Sciences Branch NASA / Goddard Space Flight Center July 13, 2004

  2. Background – GLDAS  Land Information System (LIS) • Merging of GSFC NLDAS and GLDAS codes • Offline global high resolution terrestrial modeling system • Multiple resolutions (2.0° x 2.5°, 1.0°, 1/2°, 1/4°, 1/8°, 5 km, 1 km) • Capability of running over regional domains (e.g., CONUS) • Runs 4 LSMs: Mosaic, Noah, CLM2, and VIC • Baseline atmospheric forcing from GDAS, GEOS, ECMWF

  3. Background – Land Information System (LIS) – cont. • UMD vegetation classification (AVHRR, MODIS), “tiling approach” • High resolution soil data (Reynolds et al. 2000) • Lookup table and satellite based LAI (AVHRR, MODIS) • Meteorological forcing corrected for elevation (P, T, LW, and q) • Satellite based observations update critical forcing fields (SW/LW radiation and precipitation)

  4. Methodology – General Procedure • Purpose: Obtain an understanding of the accuracy and usefulness of a number of precipitation estimates in order to determine the best way to proceed for LIS precipitation forcing • Initial analysis period: March 2002 – February 2003 (currently extending through February 2004) • Regions ofAnalysis: CONUS, Australia • Types ofDatasets: • Global modeling system estimates: GEOS, GDAS, ECMWF • Satellite only derived estimates: Persiann, Huffman, CMORPH • Merged satellite and gauge estimates: CMAP, AGRMET • Ground radar estimates: Stage II NEXRAD • Gauge only estimates: Higgins, Ebert

  5. Methodology – Dataset Specifications

  6. Methodology – Assessment • Approach focuses on “end user” concept • Methods of Assessment: • Seasonal accumulation • Seasonal correlation of daily precipitation • Evaluation of warm season diurnal cycle  accumulation and frequency • Distribution of warm season precipitation rate

  7. CONUS - Seasonal Total Precipitation – March-May 2002

  8. CONUS - Correlation of Daily Precipitation – March-May 2002

  9. CONUS - Seasonal Total Precipitation – June-August 2002

  10. CONUS - Correlation of Daily Precipitation – June-August 2002

  11. CONUS - Seasonal Total Precipitation – Sept.-Nov. 2002

  12. CONUS - Correlation of Daily Precipitation – Sept.-Nov. 2002

  13. CONUS - Seasonal Total Precipitation – Dec. 2002-Feb. 2003

  14. CONUS - Correlation of Daily Precipitation – Dec.-Feb. 2003

  15. CONUS Summary

  16. Evaluation of diurnal cycle • Hourly composites of accumulation and frequency of precipitation • Calculated precipitation rate distribution • Eight locations: • Miami, Florida • New Orleans, Louisiana • Oklahoma City, Oklahoma • Minneapolis, Minnesota • Phoenix, Arizona • Seattle, Washington • Richmond, Virginia • Boston, Massachusetts

  17. JJA 2002 Diurnal Precipitation – Total Precipitation

  18. JJA 2002 Diurnal Precipitation – Frequency

  19. JJA 2002 Rate Distribution – Frequency

  20. JJA 2002 Diurnal Precipitation – Total Precipitation

  21. JJA 2002 Diurnal Precipitation – Frequency

  22. JJA 2002 Diurnal Precipitation – Total Precipitation

  23. JJA 2002 Diurnal Precipitation – Frequency

  24. Assessment Summary • Seasonal total precipitation: • CMAP has lowest error in spring, summer, and fall • ECMWF performs the best of the model estimates • Correlation of daily precipitation: • CMAP and AGRMET show the greatest correlation overall • GDAS and ECMWF perform the best of the model products • Persiann and Huffman show good correlation during summer especially over the central US

  25. Assessment Summary – cont. • Evaluation of diurnal cycle: • Currently, inconclusive results foraccumulation • Persiann performs well in Miami, FL • CMAP / AGRMET perform well in Minneapolis, MN • Satellite products overestimate in Phoenix, AZ • Persiann, Huffman, and AGRMET are best for frequency

  26. Upcoming Plans • Upcoming plansfor LIS • Based on seasonal totals and correlation plan to use CMAP • Alter CMAP temporal disaggregation; Investigate using Persiann, AGRMET, or Huffman to interpolate CMAP • Extend analysis period into 2004 and evaluate Australia

  27. 5-6 May 2003 Case study

  28. Australia - Seasonal Total Precipitation – Dec.-Feb. 2003

  29. Australia - Seasonal Total Precipitation – June-August 2002

  30. Australia Summary

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