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This paper documents the characteristics of extreme precipitation events (EPEs) in the southeastern United States (SEUS) during 2002-11. The EPEs are identified by applying an object-based method to 24-h precipitation analyses from the NCEP stage-IV dataset. It is found that EPEs affected the SEUS in all months and occurred most frequently in the western portion of the SEUS during the cool season and in the eastern portion during the warm season. The EPEs associated with tropical cyclones, although less common, tended to be larger in size, more intense, and longer lived than 'nontropical' EPEs. Nontropical EPEs in the warm season, relative to those in the cool season, tended to be smaller in size and typically involved more moist, conditionally unstable conditions but weaker dynamical influences. Synoptic-scale composites are constructed for nontropical EPEs stratified by the magnitude of vertically integrated water vapor transport (IVT) to examine distinct scenarios for the occurrence of EPEs. The composite results indicate that 'strong IVT' EPEs occur within high-amplitude flow patterns involving strong transport of moist, conditionally unstable air within the warm sector of a cyclone, whereas 'weak IVT' EPEs occur within low-amplitude flow patterns featuring weak transport but very moist and conditionally unstable conditions. Finally, verification of deterministic precipitation forecasts from a reforecast dataset based on the NCEP Global Ensemble Forecast System reveals that weak-IVT EPEs were characteristically associated with lower forecast skill than strong-IVT EPEs. Based on these results, it is suggested that further research should be conducted to investigate the forecast challenges associated with EPEs in the SEUS. [ABSTRACT FROM AUTHOR]

The objective of this paper is to characterize the precipitation amounts originating from tropical cyclones (TCs) in the southeastern United States during the tropical storm season from June to November. Using 12 years of precipitation data from the Tropical Rainfall Measurement Mission (TRMM), the authors estimate the TC contribution on the seasonal, interannual, and monthly precipitation budget using TC information derived from the International Best Track Archive for Climate Stewardship (IBTrACS). Results derived from the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 showed that TCs accounted for about 7% of the seasonal precipitation total from 1998 to 2009. Rainfall attributable to TCs was found to contribute as much as 8%-12% for inland areas located between 150 and 300 km from the coast and up to 15%-20% for coastal areas from Louisiana to the Florida Panhandle, southern Florida, and coastal Carolinas. The interannual contribution varied from 1.3% to 13.8% for the period 1998-2009 and depended on the TC seasonal activity, TC intensity, and TC paths as they traveled inland. For TCs making landfall, the rainfall contribution could be locally above 40% and, on a monthly basis, TCs contributed as much as 20% of September rainfall. The probability density functions of rainfall attributable to tropical cyclones showed that the percentage of rainfall associated with TC over land increased with increasing rain intensity and represent about 20% of heavy rainfall (>20 mm h−1), while TCs account for less than 5% of all seasonal precipitation events. [ABSTRACT FROM AUTHOR]

This study compared two types of approaches to downscale seasonal precipitation ( P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two multimodel ensemble (MME) schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each model's ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique [spatial disaggregation with bias correction (SDBC)]. The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño-3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all lead times for all NMME models. The SDBC downscaled T2M were skillful only for the Climate Forecast System, version 2 (CFSv2), model even at far lead times, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model. [ABSTRACT FROM AUTHOR]

This study describes an EOF-based technique for statistical downscaling of high-spatial-resolution monthly-mean precipitation from large-scale reanalysis circulation fields. The method is demonstrated and evaluated for four widely separated locations: the southeastern United States, the upper Colorado River basin, China's Jiangxi Province, and central Europe. For each location, the EOF-based downscaling models successfully reproduce the observed annual cycle while eliminating the biases seen in NCEP-NCAR reanalysis precipitation. They also frequently reproduce the monthly precipitation anomalies with greater fidelity than is seen in the precipitation field derived directly from reanalysis, and they outperform a suite of regional climate models over the two U.S. locations. With the relatively high skill achieved over a range of climate regimes, this technique may be a viable alternative to numerical downscaling of monthly-mean precipitation for many locations. [ABSTRACT FROM AUTHOR]