1.3 Rainfall distributions for varying environmental conditions along the Balcones Escarpment, Texas

Monday, 11 January 2016: 4:15 PM
Room 242 ( New Orleans Ernest N. Morial Convention Center)
Larry J. Hopper Jr., NOAA/NWS, New Braunfels, TX; and N. L. Hampshire

Heavy rainfall frequently produces flash flooding near the Balcones Escarpment in South Central Texas, an area with numerous small basins where elevated topography rapidly descends into the coastal plains. This region's complex terrain, soil characteristics, and increased urbanization of the Austin-San Antonio corridor results in much faster river rises and shorter lag times between heavy rainfall and peak discharge than in the coastal plains and most parts of the United States. Accurately predicting the magnitudes of basin-average and locally heavy rainfall totals remains challenging despite modeling advances, due to the region's vulnerability to simultaneous deep Gulf and Pacific moisture taps, efficient warm rain processes, and numerous mesoscale factors that may enhance orographic lifting (e.g., low-level jets, slow-moving fronts, outflows, MCVs). Therefore, the main objective of this study is to quantify statistical distributions of rainfall totals along the Balcones Escarpment for different storm types and environmental conditions.

Six years (September 2009-August 2015) of CoCoRAHS and COOP daily rainfall observations along and adjacent to the Balcones Escarpment in the Austin-San Antonio corridor are matched with radar images and meteorological archives to identify and classify individual storm events. Storms are excluded if radar and hourly rainfall data indicate that a distinct rain event cannot be identified due to the observation time or not satisfying the minimum rainfall threshold. Statistical rainfall distributions are determined for each storm event and groups of storms broken down by season, primary forcing mechanism, and storm structure. In addition, maximum, mean, and selected percentiles of rain totals for each event will be correlated with SPC mesoanalysis parameters like precipitable water, upwind propagation vectors, and warm cloud layer depth (i.e., LCL to freezing level) to determine which exhibit the greatest predictability. If time and results permit, an attempt will be made to develop and evaluate a local operational index for forecasting average and locally heavy rainfall totals in the region that may be applied to SPC mesoanalysis and high-resolution model data in real-time and global models prior to an event to improve decision support for rainfall and flash flooding expectations.

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