On the Use of the Ensemble Kalman Filter for Torrential Rainfall Forecasts

Abstract

Torrential rainfall is a threat to modern human society. To prevent severe disasters by the torrential rains, it is an essential to accurate the numerical weather prediction. This article reports an effort to improve torrential rainfall forecasts by the Ensemble Kalman Filter based on the recent studies. Two series of numerical experiments are reported in this chapter. One is a dense surface observation data assimilation for a disastrous rainfall event caused by active rainband maintained for a long time. Although an experiment with a conventional observation data set represents the rainband, the significant dislocation and the underestimated precipitation amount are found. By contrast, dense surface data assimilation contributes to improve both the location and surface precipitation amount of the rainband. The other is the rapid-update high-resolution experiment with every 30-second Phased Array Weather Radar (PAWR) data for an isolated convective system associated with a local torrential rain. The representation of this event is completely missed without the PAWR data, whereas the active convection is well represented including fine three-dimensional structure by PAWR data assimilation. Throughout these studies, the data assimilation by Ensemble Kalman Filter has a large positive impact on the forecasts for torrential rainfall events.