Understanding rainfall spatial variability in southeast USA at different timescales

Abstract

Abstract:This study seeks to understand the spatial variability of monthly and daily rainfall in Alabama, Georgia, and Florida, USA. Monthly spatial statistics are needed to improve downscaling from climate models producing seasonal rainfall forecasts, and spatial correlation of daily rainfall is needed to inform spatial weather generators used in climate risk analysis. 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 (National Climate Data Center) were obtained for the period 1915-2004 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 the 90-year 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.

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

“…The correlation distances (slope) during the NEM are found to be larger than in the SWM, indicating a higher spatial correlation of rainfall during the NEM. The observation of weaker correlation during the SWM than in the NEM is consistent and analogous to earlier reports that show smaller correlation distances during summer than in winter (Baigorria et al, 2007;Dzotsi et al, 2014;Li et al, 2014). Weak correlation in summer is attributed to the large spatial variability of rainfall due to highly localized and short-lived convective systems (Krajewski et al, 2003;Dzotsi et al, 2014;Li et al, 2014).…”

Spatial coherency of extreme weather events in Germany and Switzerland

“…winter half-year (here, the percentiles for the definition of the extremes also have been separately calculated). Similar to the spatial correlation of precipitation (Baigorria et al, 2007), f usually is smaller in summer than in winter, owing to the greater importance of local convective (heavy) precipitation in mid-latitude summer compared to winter, when (heavy) precipitation is more often triggered by synoptic-scale cyclones and their attendant frontal systems. An interesting difference between extreme precipitation and windstorm events is obvious from Figures 2 and 3.…”

Improvements in WRF simulation skills of southeastern United States summer rainfall: physical parameterization and horizontal resolution

“…In addition, the passage of frontal systems also contributes to SE US summer rainfall (Kunkel et al 2012). Differences in the factors that control rainfall in different subregions of the SE US make the summer rainfall highly heterogeneous (e.g., Stooksbury and Michaels 1991;Baigorria et al 2007;Li et al 2013a). These spatially heterogeneous features cannot be well represented by GCMs due to their coarse resolution and oversimplified physics (Taylor et al 2012).…”