A Deep Learning Approach to Radar-based QPE
<|reference_start|>A Deep Learning Approach to Radar-based QPE: 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 a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location-specific precipitations. In contrast to QPE methods based on the Z-R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013-2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume-to-point framework performed comparably well in general cases and excelled in detecting heavy-rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios.<|reference_end|>
@article{yo2024a,
    title={A Deep Learning Approach to Radar-based QPE},
    author={Ting-Shuo Yo, Shih-Hao Su, Jung-Lien Chu, Chiao-Wei Chang, and
  Hung-Chi Kuo},
    journal={Earth Space Sci. 2021, 8, e2020EA001340},
    year={2024},
    doi={10.1029/2020EA001340},
    archivePrefix={arXiv},
    eprint={2402.09846},
    primaryClass={physics.ao-ph cs.LG eess.SP}
}