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Observational Data Used for Assimilation in the NCEP North American Regional Reanalysis

GAPP. Observational Data Used for Assimilation in the NCEP North American Regional Reanalysis Perry Shafran 1 , Jack Woollen 1 , Wesley Ebisuzaki 2 , Wei Shi 3 , Yun Fan 3 , Robert Grumbine 4 , Michael Fennessy 5

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Observational Data Used for Assimilation in the NCEP North American Regional Reanalysis

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  1. GAPP Observational Data Used for Assimilation in the NCEP North American Regional Reanalysis Perry Shafran1, Jack Woollen1, Wesley Ebisuzaki2, Wei Shi3, Yun Fan3, Robert Grumbine4, Michael Fennessy5 1SAIC/GSO and NCEP/EMC, 2NCEP/CPC, 3RSIS and NCEP/CPC, 4NCEP/EMC, 5Center for Land-Ocean-Atmosphere Studies North American Regional Reanalysis Workshop, 11 January 2005, 85th AMS Annual Meeting, San Diego, CA

  2. Introduction • North American Regional Reanalysis (NARR) assimilated great deal of data • Data usage • Assimilated in analysis • Boundary conditions • Used during execution of Eta model • Most data from NCAR/NCEP Global Reanalysis; some data from other sources

  3. Data Used in Global Reanalysis and Regional Reanalysis

  4. Radiosonde Data

  5. Precipitation Data Sources • CMAP used for Oceanic Data • CPC Merged Analysis of Precipitation • Global 2.5 deg dataset, pentads • Disaggregated using R2 precipitation weighting factors • Reliable up to about 50 deg N • Blending of CMAP influence in 15-degree zone over oceans to eliminate discontinuities • Not reliable in areas of very heavy precipitation (<100 mm/day) or near centers of tropical storms

  6. Precipitation Data Sources • CONUS precipitation • From 1/8-degree grid • Several sources • NCDC daily cooperative stations (~8000 reports/day) • River Forecast Center from CPC (~7000/day) • Hourly Precipitation Data (HPD) (~2500/day) • Analyzed using orographic Mountain Mapper also known as PRISM • Least-squares distance weighting schme • Daily precipitation datasets disaggregated using HPD weighting factors

  7. Precipitation Data Sources • Canada and Mexico • Daily gage-based 1-degree grids • Disaggregated using R2 hourly precipitation weighting factors • Data over Canada is very sparse; possibility of not ingesting all available data due to timeliness • The four data sources then remapped to Eta grid • Blended together to minimize the boundaries from different datasets

  8. Sample Distribution of Canadian Precipitation Data

  9. Data Added or Improved Upon for Regional Reanalysis

  10. Surface Data

  11. Notes About Data • Surface data: merge done between 2 sources of surface data for consistency and to eliminate duplicates • Lake ice data and SSTs over lake ice are consistent with each other • Tropical cyclones not actually assimilated but used to determine locations for CMAP blocking

  12. Climatologies

  13. Input Differences Between NARR and R-CDAS

  14. Summary • NARR assimilated a lot of data from different resources • Data assimilated in NARR system with updated 3DVAR techniques helped to create accurate high-resolution climate data set • Data to be made available to NARR users

  15. More NARR Information • NARR website: http://wwwt.ncep.noaa.gov/mmb/rreanl

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