JP1J.19
Using Radar Reflectivities to Inform a Stochastic Trigger Function for Convectivie Initiation in a Mesoscale Model
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. A number of convective parameterization schemes have been developed in recent years. Among these schemes, the Kain-Fritsch (KF) scheme has been widely used for the MM5, Eta , WRF and various other models. We have implemented a stochastic trigger function for convective initiation in the KF shcme 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.
Joint Poster Session 1J, Assimilation of Radar Data in NWP Models (Joint with 32Radar and 11Mesoscale)
Monday, 24 October 2005, 1:15 PM-3:00 PM, Alvarado F and Atria
Previous paper