Short‐Term Precipitation Forecast Based on the PERSIANN System
Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE Short-Term Precipitation Forecast Based on the PERSIANN 10.1029/2018JD028375 System and LSTM Recurrent Neural Networks Key Points: Ata Akbari Asanjan1 , Tiantian Yang1,2 , Kuolin Hsu1,3, Soroosh Sorooshian1 , • fi Arti cial intelligence techniques are 4 4 useful tools in support of forecasting Junqiang Lin , and Qidong Peng complex precipitation in short range 1 (0–6 hr) Department of Civil and Environmental Engineering, Center for Hydrometeorology and Remote Sensing, University of 2 • Long Short-Term Memory structure is California, Irvine, CA, USA, School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, capable of learning spatial and USA, 3Center for Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung, Taiwan, 4China Institute of fi temporal correlations, ef ciently Water Resources and Hydropower Research, Beijing, China • The framework provides accurate precipitation forecasts, especially for the convective systems that have fl fl complex evolving dynamics Abstract Short-term Quantitative Precipitation Forecasting is important for ood forecasting, early ood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Correspondence to: T. Yang, Quantitative Precipitation Forecasting (0–6 hr).
[Show full text]