Seasonal tropical cyclone precipitation in Texas: A statistical modeling approach based on a 60 year climatology

Abstract

Sixty years of tropical cyclone precipitation (TCP) in Texas has been analyzed because of its importance in extreme hydrologic events and the hydrologic budget. We developed multiple linear regression models to provide seasonal forecasts for annual TCP, TCP's contribution (percentage) to total precipitation, and the number of TCP days in Texas. The regression models are based on three or fewer predictors with model fits ranging from 0.18 to 0.43 (R2) and cross‐validation accuracy of 0.05–0.36 (R2). La Niña exhibits the most important control on TCP in Texas. It is the major driver in our models and acts to reduce the vertical shear in the Caribbean and the tropical Atlantic, thereby generating more precipitating storms in Texas. Lower maximum potential velocity, the theoretical maximum wind speed that storms can attain, in the Gulf of Mexico, and low‐level vorticity in the Atlantic hurricane main development region increased the modeled R2 by 20% or more. Both variables have negative coefficients in the TCP models. Lower maximum potential velocity and vorticity are associated with tropical cyclones with lower maximum wind speed and slower translation speed. Such weak TCs produce the majority of TCP and extreme TCP events in Texas. The quartiles of the TCs with strongest maximum wind speed and fastest translation speed are not associated with the largest mean daily precipitation based on observations in Texas. We have also shown that sea level pressure in the Gulf of Mexico, sea surface temperature in the Caribbean, and the North Atlantic Oscillation are potentially important predictors of seasonal TCP in Texas.