Use of four-dimensional data assimilation by Newtonian relaxation and latent-heat forcing to improve a mesoscale-model precipitation forecast - A case study

The Penn State/NCAR mesoscale model was used to study special static-initialization (SI) and dynamic-initialization (DI) techniques designed to improve short-range quantitative precipitation forecasts (QPFs), as applied to the heavy convective rainfall that occurred in Texas, Oklahoma, and Kansas during the May 9-10, 1979 SESAMY IV study period. In the DI procedure, two types of four-dimensional data assimilation (FDDA) procedures were used to incorporate data during a 12-h preforecast period, one using the Newtonian relaxation, the other using latent-heat forcing. It was found that combined use of either the preforecast or in-forecast latent-heat forcing with the Newtonian relaxation produced an improved forecast (relative to a conventional forecast procedure) of rainfall intensity compared to the use of the Newtonian relaxation alone. The use of the experimental SI with prescribed latent heating during the first forecast hour produced greatly improved rainfall rates.