A Deep Learning Approach to Radar-based QPE
Ting-Shuo Yo Shih-Hao Su Jung-Lien Chu Chiao-Wei Chang Hung-Chi Kuo
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.