A Deep Learning Approach to Radar‐Based QPE
نویسندگان
چکیده
Abstract In this study, we propose a volume‐to‐point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With volume consisting of time series gridded radar reflectivities over Taiwan area, used machine learning algorithms to establish statistical model QPE in weather stations. The extracts spatial temporal features from input then associates these with location‐specific precipitations. contrast methods Z–R relation, leverage automatically detect evolution movement systems associate patterns location specific topographic attributes. Specifically, evaluated hourly 45 stations Taipei during 2013–2016. comparison operational scheme by Central Weather Bureau, performed comparably well general cases excelled detecting heavy‐rainfall events. By using current results as reference benchmark, proposed method can integrate heterogeneous sources potentially improve forecast extreme scenarios.
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ژورنال
عنوان ژورنال: Earth and Space Science
سال: 2021
ISSN: ['2333-5084']
DOI: https://doi.org/10.1029/2020ea001340