17.6 A Statistical Approach to the Kain-Fritsch Convective parameterization

Friday, 10 November 2006: 11:45 AM
St. Louis AB (Adam's Mark Hotel)
Yong Song, Univ. of Missouri, Columbia, MO; and C. K. Wikle and C. J. Anderson

Convective parameterization is one of the most important aspects of numerical modeling of the atmosphere, especially for the numerical weather forecasting. We have implemented a stochastic trigger function for convective initiation in the Kain-Fritsch (KF) scheme within the Penn State/NCAR Mesoscale Model version 5 (MM5). In our approach, convective initiation within MM5 is modeled by Bernoulli random variables. The probability of initiation is then modeled through a probit transformation in terms of the standard KF trigger variables, but with random parameters. The distribution of these random parameters is obtained through a Bayesian importance sampling Monte Carlo procedure informed by radar reflectivities. Kernel density estimates of these distributions are then incorporated into the KF trigger function, giving a meaningful stochastic (distributional) parameterization. We demonstrate this approach with several case studies.

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