Tuesday, November 14, 2006
141-5
Understanding Rainfall Spatial Variability in the Southeast U.S.A. at Different Time Scales.
Guillermo Baigorria1, James W. Jones1, and James J. O'Brien2. (1) Univ. of Florida, Frazier Rogers Hall, Agricultural and Biological Engineering Dept., Gainesville, FL 32601, (2) Center for Ocean-Atmospheric Prediction Studies, The Florida State Univ., Tallahassee, FL 32306
Farmers from rainfed regions around the world face rainfall variability as a major risk factor. Numerical climate models and crop models are tools that can support decision making related to this variability. However, temporal and spatial variability in rainfall and differences in the scales at which these tools represent processes make it difficult to combine them for use in climate risk analysis. The objective of this study is to understand the rainfall spatial variability in the Southeast US at daily and monthly time scales as a basis for developing bridges between these tools. We first determined the historical record length that is stationary followed by an analysis of the monthly spatial characteristics of rainfall variables. Rainfall data from 523 weather stations (NCDC–NOAA) were obtained for the period 1915 to 2004 and divided into 15-year subsets for comparisons. Differences in rainfall were found between the most recent 15-year period and all others occurring during 90 years period of record. Thus only data from 1990 to 2004 (208 weather stations) were used to avoid the detected changes in climate in the region. Correlation, covariance and variance matrices of daily and monthly rainfall amounts were calculated at monthly steps. The same statistics were also computed for frequency of rainfall events and monthly number of rainy days. Results show different spatial patterns at different temporal scales; two spatial patterns were well established. A widely spread correlation in a northeast – southwest direction was found around weather stations during the frontal rainy season, and a concentric short distance-decay in correlations existed around weather stations during the summer convective season. Spatial correlations among daily rainfall amounts are needed for spatial weather generators in a storm-by-storm basis while monthly spatial statistics are needed to ensure the validity of downscaled data from numerical seasonal rainfall forecasts.