Diabatic Forcing and Initialization with Assimilation of Cloud Water and Rainwater in a Forecast Model

Abstract

In this study, diabatic initialization, diabatic forcing, and liquid water assimilation techniques are tested in a semi-implicit hydrostatic regional forecast model containing explicit representations of grid-scale cloud water and rainwater. Diabatic forcing, in conjunction with diabatic contributions in the initialization is found to help the forecast retain the diabatic signal found in the liquid water or heating rate data, consequently reducing the spinup time associated with grid-scale precipitation processes. Both observational Special Sensor Microwave/Imager (SSM/I) and model-generated data are used. A physical retrieval method incorporating SSM/I radiance data is utilized to estimate the 3D distribution of precipitation in storms. In the retrieval method the relationship between precipitation distributions and upwelling microwave radiances is parameterized, based upon cloud ensemble-radiative model simulations. Regression formulae relating vertically integrated liquid and ice-phase precipitation amounts to latent beating rates are also derived from the cloud ensemble simulations. Thus, retrieved SSM/I precipitation structures can be used in conjunction with the regression formulas to infer the 3D distribution of latent heating rates. These heating rates are used directly in the forecast model to help initiate Tropical Storm Emily (21 September 1987). The 14-h forecast of Emily's development yields atmospheric precipitation water contents that compare favorably with coincident SSM/I estimates. In additional model experiments, explicit cloud water and rainwater fields are retained through the analysis-initialization phase of the forecast cycle, (i.e., from the end of one forecast to the beginning of the next sequential forecast). This allows the continuing forecast to resume the preforecast precipitation production where it left off, provided the mixing ratio field is modified to prevent cloud evaporation. This procedure is shown to be straightforward when grid-scale cloud water and rainwater variables are explicitly computed and retained for use in prognostic calculations.