Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018JD028375 |
Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural NetworksN | |
Asanjan, Ata Akbari1; Yang, Tiantian1,2; Hsu, Kuolin1,3; Sorooshian, Soroosh1; Lin, Junqiang4; Peng, Qidong4 | |
2018-12-13 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES |
ISSN | 2169-897X |
EISSN | 2169-8996 |
出版年 | 2018 |
卷号 | 123期号:22页码:12543-12563 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Taiwan; Peoples R China |
英文摘要 | Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood 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 Quantitative Precipitation Forecasting (0-6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. Our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000452994100004 |
WOS关键词 | PREDICTION ; RAINFALL ; TIME ; CLIMATE ; INFORMATION ; SIMULATION ; FREQUENCY ; PRODUCTS ; MODELS ; RADAR |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/31992 |
专题 | 气候变化 |
作者单位 | 1.Univ Calif Irvine, Ctr Hydrometeorol & Remote Sensing, Dept Civil & Environm Engn, Irvine, CA 92697 USA; 2.Univ Oklahoma, Sch Civil Engn & Environm Sci, Norman, OK 73019 USA; 3.Natl Taiwan Ocean Univ, Ctr Excellence Ocean Engn, Keelung, Taiwan; 4.China Inst Water Resources & Hydropower Res, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Asanjan, Ata Akbari,Yang, Tiantian,Hsu, Kuolin,et al. Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural NetworksN[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2018,123(22):12543-12563. |
APA | Asanjan, Ata Akbari,Yang, Tiantian,Hsu, Kuolin,Sorooshian, Soroosh,Lin, Junqiang,&Peng, Qidong.(2018).Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural NetworksN.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,123(22),12543-12563. |
MLA | Asanjan, Ata Akbari,et al."Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural NetworksN".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 123.22(2018):12543-12563. |
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