Spatial variability of regional model simulated June–September mean precipitation over West Africa
Article first published online: 29 SEP 2007
Copyright 2007 by the American Geophysical Union.
Geophysical Research Letters
Volume 34, Issue 18, September 2007
How to Cite
2007), Spatial variability of regional model simulated June–September mean precipitation over West Africa, Geophys. Res. Lett., 34, L18709, doi:10.1029/2007GL031270., , and (
- Issue published online: 29 SEP 2007
- Article first published online: 29 SEP 2007
- Manuscript Accepted: 30 AUG 2007
- Manuscript Revised: 28 AUG 2007
- Manuscript Received: 16 JUL 2007
- regional climate model;
- West African monsoon;
- spatial variability
 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.