RECONSTRUCTION OF ENSEMBLE RAINFALL FORECAST BY THE APPLICATION OF SEQUENTIAL DATA ASSIMILATION TECHNIQUE

Abstract

 Accurate rainfall forecast is indispensable to realize non-structural counter measures for floods. On the other hand, there are still some difficulties in deterministic numerical weather forecasting, and the Japan Meteorological Agency makes test operations of ensemble weather forecast by using a meso-scale regional model in recent years. In this study, preliminary experimetns were conducted to develop a technique for reconstructing ensemble weather forecasting information by application of the particle filter which is one of sequential data assimilation techniques. The results of reconstruction experiments with different variance-covariance matrices in particle fileter showed that appropriate variance-covariance matrix is important to avoid degeneration of ensemble forecast. Observation at multiple times could improve results of reconstruction by partile fileter. Simultanueous filtering and reconstruction of ensemble forecast at multiple observation sites showed significant improvement of thread scores in target sites.