Forecasting Precipitation from Radar Wind Profiler Mesonet and Reanalysis Using the Random Forest Algorithm

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 6; p. 1635
• Main Authors: Wu, Yizhi, Guo, Jianping, Chen, Tianmeng, Chen, Aijun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Algorithms; Analysis; Atmospheric models; Convection; Datasets; ERA-5 reanalysis; Gauges; Humidity; Learning algorithms; Machine learning; Mathematical models; Meteorological satellites; Precipitation; Predictions; Radar; radar wind profiler; Rain; Rain and rainfall; Rainfall; random forest; Severe weather; Spatial discrimination; Spatial resolution; Velocity; Vorticity; Weather forecasting; Wind; Wind profiles; China; Beijing China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15061635

Data-driven machine learning technology can learn and extract features, a factor which is well recognized to be powerful in the warning and prediction of severe weather. With the large-scale deployment of the radar wind profile (RWP) observational network in China, dynamical variables with higher temporal and spatial resolution in the vertical become strong supports for machine-learning-based severe convection prediction. Based on the RWP mesonet that has been deployed in Beijing, this study uses the measurements from four triangles composed of six RWP stations to determine the profiles of divergence, vorticity, and vertical velocity before rainfall onsets. These dynamic feature variables, combined with cloud properties from Himawari-8 and ERA-5 reanalysis, serve as key input parameters for two rainfall forecast models based on the random forest (RF) classification algorithm. One is for the rainfall/non-rainfall forecast and another for the rainfall grade forecast. The roles of dynamic features such as divergence, vorticity, and vertical velocity are examined from ERA-5 reanalysis data and RWP measurements. The contribution of each feature variable to the performance of the RF model in independent tests is also discussed here. The results show that the usage of RWP observational data as the RF model input tends to result in better performance in rainfall/non-rainfall forecast 30 min in advance of rainfall onset than using the ERA-5 data as inputs. For the rainfall grade forecast, the divergence and vorticity that were estimated from the RWP measurements at 800 hPa show importance in improving the model performance in heavy and moderate rain forecasts. This indicates that the atmospheric dynamic variable measurements from RWP have great potential to improve the prediction skill of convection with the aid of a machine learning model.