WORTH OF RADAR DATA IN THE REAL-TIME PREDICTION OF MEAN AREAL RAINFALL BY NONADVECTIVE PHYSICALLY BASED MODELS

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.