Hilliker, J.L., G.S. Young, and J.M. Fritch, 2007

An objective, statistical forecast system for short-term probabilistic forecasts of thunderstorms

National Weather Digest, 31, 9-23

Abstract

An objective statistical system that generates short-term probabilistic forecasts of convection (radar reflectivity [greater than or equal to] 40 dBZ) solely from observational input is presented. This prototype is tested for Oklahoma City (OKC) by using several high-resolution regional datasets including 4-km resolution WSR-88D radar data, 404 MHz profiler data, and surface data from the Oklahoma mesonet. Data from the traditional, 12-h radiosonde network are also included. Antecedent observations (predictors) are correlated to future convection observations at OKC (the predictand). This procedure is repeated for 11 lead times between 6 and 360 min, inclusive, with each forecast equation containing 4-10 of the most powerful predictors.

Radar data provide the greatest contribution to skill, particularly for lead times [less than or equal to] 60 min. Specifically, the upstream percent areal coverage of reflectivities above a given threshold is the most powerful predictor of convection for all lead times. As lead times increase, an increasing contribution comes from the surface mesonet and then upper-air data. The absolute value of convergence and climatological departure of relative humidity are the most powerful predictors from the mesonet data. By 360 min, the final equations include a synergistic combination of predictors from radar, surface, and upper-air data.

The overall performance of the prototype system is encouraging. When applied to independent data, the system has a skill score of 0.39 relative to persistence climatology (alternatively, a 39% improvement in mean squared error) at 12-min lead times. Skill gradually decreases to 0.09 by the 360-min lead time, although significance testing reveals that forecast performance remains superior to persistence climatology at the 99.95% level.