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Spatial variability of regional model simulated June–September mean precipitation over West Africa
Author(s) -
Druyan Leonard M.,
Fulakeza Matthew,
Lonergan Patrick
Publication year - 2007
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2007gl031270
Subject(s) - precipitation , climatology , environmental science , spatial ecology , scale (ratio) , climate model , common spatial pattern , spatial variability , meteorology , climate change , geology , geography , statistics , ecology , oceanography , cartography , mathematics , biology
The study examines the spatial variability of June–September 2003 mean precipitation rates (Pr03) simulated by a regional climate model on a horizontal grid with 0.5° spacing. In particular, it evaluates the relative impact of different initial conditions versus the influence of the lateral boundary conditions (LBC), and it compares small spatial scale distributions of modeled Pr03 to data from the Tropical Rainfall Measuring Mission (TRMM) and the NOAA Climate Prediction Center data for the African Famine Early Warning System (FEWS). Simulations over West Africa were made with the CCSR/GISS RM3, driven by synchronous data from NCEP reanalysis. A five‐member ensemble for a single season was generated by staggering the initial conditions of each member by 36 hr within the period May 9–15, 2003. Results showed that the LBC influence dominated over that of differing initial conditions, implying that the precipitation simulations suffered little contamination of random noise. In a second evaluation, small spatial scale distributions of Pr03 were computed as the difference between Pr03 and spatially smoothed fields. Spatial correlations between the RM3 product versus the TRMM and FEWS small‐scale components of Pr03 were highest using TRMM data provided at 1° elements. Results suggest that the model may be challenged to simulate realistic small‐scale features of the seasonal mean precipitation field, and/or that observational data sets do not adequately capture these fine spatial features.