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Climate Dynamics

, Volume 37, Issue 5–6, pp 1061–1080 | Cite as

High-resolution subtropical summer precipitation derived from dynamical downscaling of the NCEP/DOE reanalysis: how much small-scale information is added by a regional model?

  • Young-Kwon Lim
  • Lydia B. Stefanova
  • Steven C. Chan
  • Siegfried D. Schubert
  • James J. O’Brien
Article

Abstract

This study assesses the regional-scale summer precipitation produced by the dynamical downscaling of analyzed large-scale fields. The main goal of this study is to investigate how much the regional model adds smaller scale precipitation information that the large-scale fields do not resolve. The modeling region for this study covers the southeastern United States (Florida, Georgia, Alabama, South Carolina, and North Carolina) where the summer climate is subtropical in nature, with a heavy influence of regional-scale convection. The coarse resolution (2.5° latitude/longitude) large-scale atmospheric variables from the National Center for Environmental Prediction (NCEP)/DOE reanalysis (R2) are downscaled using the NCEP/Environmental Climate Prediction Center regional spectral model (RSM) to produce precipitation at 20 km resolution for 16 summer seasons (1990–2005). The RSM produces realistic details in the regional summer precipitation at 20 km resolution. Compared to R2, the RSM-produced monthly precipitation shows better agreement with observations. There is a reduced wet bias and a more realistic spatial pattern of the precipitation climatology compared with the interpolated R2 values. The root mean square errors of the monthly R2 precipitation are reduced over 93% (1,697) of all the grid points in the five states (1,821). The temporal correlation also improves over 92% (1,675) of all grid points such that the domain-averaged correlation increases from 0.38 (R2) to 0.55 (RSM). The RSM accurately reproduces the first two observed eigenmodes, compared with the R2 product for which the second mode is not properly reproduced. The spatial patterns for wet versus dry summer years are also successfully simulated in RSM. For shorter time scales, the RSM resolves heavy rainfall events and their frequency better than R2. Correlation and categorical classification (above/near/below average) for the monthly frequency of heavy precipitation days is also significantly improved by the RSM.

Keywords

Root Mean Square Error Grid Point Root Mean Square Heavy Rainfall Summer Precipitation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Young-Kwon Lim
    • 1
    • 2
  • Lydia B. Stefanova
    • 1
  • Steven C. Chan
    • 1
  • Siegfried D. Schubert
    • 2
  • James J. O’Brien
    • 3
  1. 1.Center for Ocean-Atmospheric Prediction StudiesFlorida State UniversityTallahasseeUSA
  2. 2.Global Modeling and Assimilation OfficeNASA / Goddard Space Flight CenterGreenbeltUSA
  3. 3.Center for Ocean-Atmospheric Prediction StudiesFlorida State UniversityTallahasseeUSA

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