A Volume-to-Point Approach of QPE with Radar Data
Year: 2020
Type: preprint
Abstract: In this study, we proposed a volume-to-point framework for quantitative precipitation estimation (QPE) based on the QPESUMS (Quantitative Precipitation Estimation and Segregation Using Multiple Sensor) Mosaic Radar dataset. 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 (CWB), 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 QPF in extreme precipitation scenarios.
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Awards: Summer Extreme Convective Rainfall over Taipei Metropolitan– the Study and Application to Disaster Reduction( Iv ), Developing an Artificial Intelligence Based Forecasting Model for the Extreme Rainfall Events of the Greater Taipei Area during the Mei-Yu Season., The Long-Term Variation of Frontal System Is Taiwan and the Frontal Rainfall Estimation under Climate Change Scenarios
OpenAlex ID: W4205221277