Worth of radar data in the real-time prediction of mean areal rainfall by nonadvective physically based models (original) (raw)

1991, Water Resources Research

Covariance analysis was used to determine the reduction in rainfall forecast and estimation variances offered by radar reflectivity data. Covariance analysis of a particular nonadvective linear physically based model indicated that the utility of the radar reflectivity data of various elevation angles is limited in mean areal rainfall predictions, even when a very small density of rain gauges exists over the region of interest and good quality radar data are used. This applies to both raw reflectivity and radar rainfall data converted through a Z-R relationship. The ratio of mean areal rainfall prediction variances, defined as variance with radar data divided by variance without radar data, was found to be greater than 0.8 in most cases. On the other hand, the radar data reduced the estimated variance of the vertically integrated liquid water content considerably, even when high-density rain gauge data were present. !. INTRODUCTION Forecasting mean areal rainfall accurately and reliably on a basin scale and in real time has been one of the pressing needs of hydrology. Recently, it has been addressed in review papers of Georgakakos and Hudlow [1984] and Georgakakos and Kavvas [1987]. They discuss the suitability of spatially lumped quantitative rainfall prediction models developed by Georgakakos and Bras [1984a, b] and Georgakakos [1984, 1986c] for use in real-time flood forecasting. The models were developed in an effort to improve shortterm precipitation predictions on the scale of small-and medium-sized hydrologic basins (100-1000 km2). They have already been coupled to hydrologic models [Georgakakos, 1986a, b] to form integrated hydrometeorological forecast systems for the real-time prediction of floods and flash floods [Georgakakos, 1987]. The models, as originally formulated, use point data of readily available variables as input, such as surface air temperature, dew point temperature, and pressure. Real-time observations of rain gauge rainfall are used in model state updating. It seems reasonable to expect improvements in model predictions if additional information on the relevant processes could be included. In particular, further improvements are expected if real-time observations of radar reflectivity were used. The rainfall prediction models are based on the principle of the conservation of liquid water mass and utilize adiabatic and pseudo-adiabatic air parcel ascent processes for the determination of the condensation and deposition source term. Computations of cloud precipitation rate are based on parameterizations of cloud microphysics. Evaporation of cloud drops in the subcloud layer of unsaturated air is also modeled. Those models predict hourly precipitation rates, given input in the form of surface air temperature, pressure, and dew point temperature. The models have been formulated in state space form, and state estimators have been designed to process mean areal rainfall observations in real time for (1) state updating and (2) determination of forecast uncertainty. The models, when tested with field data,