A Study on Local Heavy Rainfall Forecasting Method Using Convolutional LSTM

2021 ◽  
Author(s):  
Haruki Takehana ◽  
Yoshiki Yanagawa ◽  
Astuo Ozaki ◽  
Kazuyoshi Oshima
Keyword(s):  
Heavy Rainfall ◽  
Rainfall Forecasting ◽  
Forecasting Method ◽  
Convolutional Lstm

scholarly journals Numerical Simulation of 2010 Pakistan Flood in the Kabul River Basin by Using Lagged Ensemble Rainfall Forecasting

2014 ◽  
Vol 15 (1) ◽  
pp. 193-211 ◽  
Author(s):  
Tomoki Ushiyama ◽  
Takahiro Sayama ◽  
Yuya Tatebe ◽  
Susumu Fujioka ◽  
Kazuhiko Fukami
Keyword(s):  
River Basin ◽  
Heavy Rainfall ◽  
Dynamical Downscaling ◽  
Rain Gauge ◽  
Rainfall Forecasting ◽  
Ensemble Forecasts ◽  
Inundation Area ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Environmental Prediction ◽  
Kabul River

Abstract Lagged ensemble forecasting of rainfall and rainfall–runoff–inundation (RRI) forecasting were applied to the devastating flood in the Kabul River basin, the first strike of the 2010 Pakistan flood. The forecasts were performed using the Global Forecast System of the National Centers for Environmental Prediction (NCEP-GFS) and were provided four times daily. Dynamical downscaling was also applied to the forecasts by the Weather Research and Forecasting Model (WRF), a regional model. The forecasts of the rainfall and inundation area were verified by comparing rain gauge–corrected Global Satellite Mapping of Precipitation (GSMaP) data and the observed indicator of an inundation map based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The GFS predicted a sign of heavy rainfall in northern Pakistan 4 days ahead of the onset. However, most of the forecasts predicted it in wrong places, and only those performed after the rainfall onset predicted it in the accurate location. Downscaling corrected the locations of the misplaced GFS forecasts and also underestimated or overestimated rainfall amount derived from GFS. Finally, downscaled forecasts predicted a reliable amount of rainfall in the Kabul River basin 1 day ahead of the rainfall onset and predicted a high probability of heavy rainfall 3 days ahead. Lagged ensemble forecasts of discharge and inundation distribution based on GFS rainfall predicted the probability of the actual discharge and inundation distribution, but in low reliability. The reliability substantially improved when downscaled rainfall was used. The reliability of the flood alert system combining NCEP-GFS, dynamical downscaling by WRF, and the RRI model was at an acceptable level in this study.

A radar rainfall forecasting method designed for hydrological purposes

1990 ◽  
Vol 114 (3-4) ◽  
pp. 229-244 ◽  
Author(s):  
Thomas Einfalt ◽  
Thierry Denoeux ◽  
Guy Jacquet
Keyword(s):  
Rainfall Forecasting ◽  
Radar Rainfall ◽  
Forecasting Method
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