S116 A Potential Diagnostic to Characterize Different Weather-Regime Forcings of Convective Precipitation Events

Sunday, 6 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Malcolm T. Wilson, Cornell University, Ithaca, NY; and M. Wong

The ability of numerical weather prediction models to forecast convective precipitation varies under different convective forcing regimes. Past research indicates that models have better performance forecasting convective precipitation under synoptically-forced scenarios than under a mesoscale-forcing regime. Evaluations of models are necessary to see where improvements are needed in model development. To efficiently test models under different weather-regime forcings, a diagnostic that differentiates the regimes is needed. This research focused on analyzing the convective-adjustment timescale, a diagnostic that can potentially differentiate convective precipitation regimes using Convective Available Potential Energy (CAPE) and precipitation rate. Previous research has demonstrated that this diagnostic timescale is effective in differentiating the regimes using model output and primarily looking over events in Europe. In this paper, we instead use observations from radar and sounding data over the United States during the spring of 2017 to calculate the timescale, and then verify its effectiveness using surface pressure analyses from the Weather Prediction Center. This research also analyzed the sensitivity of the timescale to the parameters used in calculating the diagnostic. The parameters include the CAPE definition and the precipitation rate threshold. Results indicated that the timescale magnitude strongly depends on the precipitation threshold, but its behavior in differentiating the regimes is similar despite using different precipitation thresholds and CAPEs. This gives crucial insight that the precipitation threshold is critical in calculating the timescale and that the convective-adjustment timescale could potentially serve as a useful diagnostic in the field of model development.
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