ON THE IMPROVEMENT OF PRECIPITATION FORECAST SKILL FROM PHYSICAL INITIALIZATION

This study explores the impact of physical initialization on the numer ical weather prediction of tropical rainfall. The goal is to improve t he definition of the initial state by assimilation of proposed or curr ently available surface and satellite-based observations during a pre- integration phase using a global spectral model. Physical initializati on refers to the use of reverse algorithms consistent with the physics of the numerical model which can provide a modification of the initia l state via incorporation of an analysis of tropical rainrates. This m odification of the initial state variables is accomplished in an assim ilation phase of the model forecast. The physical initialization proce ss produces, in a diagnostic sense, a thermodynamic consistency betwee n the humidity variable, the surface fluxes, rainfall distributions, d iabatic heating and the clouds. A diabatic initialization is achieved by a Newtonian relaxation of the above diagnosed humidity variable whe re the divergent wind is permitted to evolve in response to the impose d surface fluxes and the condensation heating in a consistent manner. An important finding of this study is related to the absolute correlat ion of the observation only based rainfall and the model-based rainfal l at the initial time and at the end of a one day forecast which are s ignificantly improved with the use of physical initialization. The ''o bserved'' rainrates are obtained from algorithms that translate satell ite-based measurements of outgoing longwave radiation and radiances fo r an array of microwave frequencies. In addition, the available rainga uge records over the land area are incorporated to define the ''observ ed'' rainfall over the gaussian transform grid squares of a global spe ctral model at a high resolution (T106). Thus the rainfall measures ar e averages over roughly 100 x 100 km(2) and 7.5 min which is the time step of the spectral model. It is for this averaged representation tha t we are able to demonstrate a very marked improvement in nowcasting a nd one day forecasts of tropical rainfall. The monthly mean rainfall c limatology, thus obtained, nearly replicates the rainfall analyses pro vided to the physical initialization.