Statistical Properties of Global Precipitation in the NCEP GFS Model and TMPA Observations for Data Assimilation

Details

• Journal Title:
  Monthly Weather Review
• Personal Author:
  Lien, Guo-Yuan ; Kalnay, Eugenia ; Miyoshi, Takemasa ; Huffman, George J.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research)
• Description:
  Assimilation of satellite precipitation data into numerical models presents several difficulties, with two of the most important being the non-Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, improving the model forecast beyond a few hours by assimilating precipitation has been found to be difficult. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low-resolution NCEP Global Forecast System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA). The samples are constructed using the same model with the same forecast period, observation variables, and resolution as in the follow-on GFS/TMPA precipitation assimilation experiments presented in the companion paper.
• Source:
  Monthly Weather Review, 144(2), 663-679
• DOI:
  https://doi.org/10.1175/mwr-d-15-0150.1
• ISSN:
  0027-0644 ; 1520-0493
• Format:
  PDF
• Publisher:
  American Meteorological Society
• Document Type:
  Journal Article
• Funding:
  Grant no. NA100OAR4310248 ; Grant no. NA10OAR4310248
• Rights Information:
  Other
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:167765b4c05383976f88d80c726575695020f33eeeac4ec762bb11020aaf3a03999c747726640c087702a04069ab64d7a064e0a3fe6d2be15ce67dc8d7910c95
• Download URL:
  https://repository.library.noaa.gov/view/noaa/65045/noaa_65045_DS1.pdf
• File Type:
  [PDF - 3.54 MB ]