OPERATIONAL RAINFALL PREDICTION ON MESO-GAMMA SCALES FOR HYDROLOGIC APPLICATIONS

Presented is a rainfall prediction methodology for application in oper ational hydrologic forecasting with forecast lead times of 1-6 hours a nd spatial-resolution scales of 10-30 km. The essential elements of th e prediction methodology are a mathematical model for precipitation pr ediction from surface and upper air meteorological variables; operatio nal forecasts of temperature, pressure, humidity, and wind fields by l arge-scale numerical weather prediction models; surface and upper air meteorological observations; remote and on-site rainfall observations; and a state estimator for real-time updating from local frequent rain fall observations and for probabilistic predictions. This paper formul ates a class of rainfall models suitable for this prediction methodolo gy. The models are based on the differential equation of conservation of cloud and rainwater equivalent mass and on a newly introduced advec tion equation for a parameter that determines updraft strength. The la tter advection equation is a prognostic equation for the strength of c onvection in space and time. The innovative features of the model form ulated and tested are the inclusion of the prognostic equation for the advection of regions of active convection, the formulation of the sta te estimator component for state updating and probabilistic forecasts, and the utilization of a numerical solution scheme which reduces arti ficial numerical diffusion and can be used with the state estimator be cause of its explicit form. Utilization of the prediction model formul ated was exemplified in several case studies of summer convection in O klahoma using data available during routine forecast operations. The c ase studies show that when verified with radar rainfall data, the mode l's hourly precipitation predictions over a 20,000 km(2) area with a 1 00-900 km(2) resolution are better than simple persistence and explain more than 60% of the observed hourly rainfall variance. Sensitivity s tudies quantify dependence of rainfall predictions to microphysical an d state-estimator parameters.