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A Deep Learning Approach to Short-Term Quantitative Precipitation Forecasting

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Auroop R. GangulyAuthors Info & Claims

CI2020: Proceedings of the 10th International Conference on Climate Informatics

Pages 8 - 14

https://doi.org/10.1145/3429309.3429311

Published: 11 January 2021 Publication History

Abstract

Short-term, spatially distributed quantitative precipitation forecasting (SD-QPF), or ‘precipitation nowcasting’, is important for hydrological and water resources applications, such as flash flood warning systems and operations of dams and reservoirs. The state-of-the-art methods in SD-QPF include radar extrapolations, numerical weather prediction (NWP) models, and hybrid methods that combine the two. Despite the diversity of methods that have been used, SD-QPF remains difficult: even sophisticated methods may not be able to consistently outperform relatively simple baselines such as Persistence. Methods in Deep Learning (DL) have demonstrated significant and often unexpected improvements across a wide variety of domains ranging from image and video processing to machine translation and speech recognition. Emerging research has suggested that that DL may improve point predictions in the context of very short-term - 0-2 hours - distributed QPF (VSD-QPF) by taking advantage of growing data from in-situ weather sensors as well as remote sensors such as radar and satellites, along with advances in computing. Here we examine the hypothesis that DL can improve VSD-QPF, specifically point predictions, based on observed hourly precipitation data over the contiguous United States, by leveraging a Convolutional Long Short-Term Memory (LSTM) recurrent neural network for 1-hour precipitation nowcasting. We find the DL approach performs better than the baseline method of Persistence and a state-of-the-art method using Optical Flow.

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Das PPosch ABarber NHicks MDuffy KVandal TSingh DWerkhoven KGanguly A(2024)Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcastingnpj Climate and Atmospheric Science10.1038/s41612-024-00834-87:1Online publication date: 18-Nov-2024
https://doi.org/10.1038/s41612-024-00834-8
Thu NTri DHoa BNguyen-Thi HHung NTuyet QVan Nhat NPham H(2024)Application of optical flow technique to short-term rainfall forecast for some synoptic patterns in VietnamTheoretical and Applied Climatology10.1007/s00704-024-05277-y156:1Online publication date: 4-Dec-2024
https://doi.org/10.1007/s00704-024-05277-y
Kwon TYoon SShin HYoon S(2023)Evaluation of High-Intensity Precipitation Prediction Using Convolutional Long Short-Term Memory with U-Net Structure Based on ClusteringWater10.3390/w1601009716:1(97)Online publication date: 26-Dec-2023
https://doi.org/10.3390/w16010097
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A Deep Learning Approach to Short-Term Quantitative Precipitation Forecasting
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CI2020: Proceedings of the 10th International Conference on Climate Informatics

September 2020

138 pages

ISBN:9781450388481

DOI:10.1145/3429309

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Published: 11 January 2021

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CI2020: 10th International Conference on Climate Informatics

September 22 - 25, 2020

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Cited By

Das PPosch ABarber NHicks MDuffy KVandal TSingh DWerkhoven KGanguly A(2024)Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcastingnpj Climate and Atmospheric Science10.1038/s41612-024-00834-87:1Online publication date: 18-Nov-2024
https://doi.org/10.1038/s41612-024-00834-8
Thu NTri DHoa BNguyen-Thi HHung NTuyet QVan Nhat NPham H(2024)Application of optical flow technique to short-term rainfall forecast for some synoptic patterns in VietnamTheoretical and Applied Climatology10.1007/s00704-024-05277-y156:1Online publication date: 4-Dec-2024
https://doi.org/10.1007/s00704-024-05277-y
Kwon TYoon SShin HYoon S(2023)Evaluation of High-Intensity Precipitation Prediction Using Convolutional Long Short-Term Memory with U-Net Structure Based on ClusteringWater10.3390/w1601009716:1(97)Online publication date: 26-Dec-2023
https://doi.org/10.3390/w16010097
Wu YYang XTang YZhang CZhang GZhang W(2022)Inductive Spatiotemporal Graph Convolutional Networks for Short-Term Quantitative Precipitation ForecastingIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2022.315953060(1-18)Online publication date: 2022
https://doi.org/10.1109/TGRS.2022.3159530
Ren SLuo F(2021)Research on Hourly Precipitation Preprocessing Method Based on Deep Learning2021 International Conference on Digital Society and Intelligent Systems (DSInS)10.1109/DSInS54396.2021.9670603(308-311)Online publication date: 3-Dec-2021
https://doi.org/10.1109/DSInS54396.2021.9670603

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