Relative Importance of Radar Variables for Nowcasting Heavy Rainfall: A Machine Learning Approach

• Published in: IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
• Main Authors: Wang, Yi Victor, Kim, Seung Hee, Lyu, Geunsu, Lee, Choeng-Lyong, Lee, Gyuwon, Min, Ki-Hong, Kafatos, Menas C.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural network; Cell culture; Cells; Classification; convolutional neural network; Data models; deep learning; dual-polarimetric weather radar; early warning; flash flood; Fuzzy logic; hydrometeorological hazard; Input variables; Lasso; Learning algorithms; Machine learning; mesoscale convective system; Mesoscale phenomena; Meteorological radar; Meteorological satellites; Modelling; Nowcasting; Numerical prediction; Numerical weather forecasting; permutation importance; Permutations; Precipitation; Predictive models; Radar; Radar data; Rain; Rainfall; random forest; Regression analysis; Regression models; Remote sensing; Squalls; storm; Storms; support vector regression; Tracking; Weather; Weather forecasting
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3231125

Highly short-term forecasting, or nowcasting, of heavy rainfall due to rapidly evolving mesoscale convective systems (MCSs) is particularly challenging for traditional numerical weather prediction models. To overcome such a challenge, a growing number of studies have shown significant advantages of using machine learning (ML) modeling techniques with remote sensing data, especially weather radar data, for high-resolution rainfall nowcasting. To improve ML model performance, it is essential first and foremost to quantify the importance of radar variables and identify pertinent predictors of rainfall that can also be associated with domain knowledge. In this study, a set of MCS types consisting of convective cell, mesoscale convective cell, diagonal squall line, and parallel squall line, was adopted to categorize MCS storm cells, following the fuzzy logic algorithm for storm tracking, over the Korean Peninsula. The relationships between rain rates and over 15 variables derived from data products of dual-polarimetric weather radar were investigated and quantified via 5 ML regression methods and a permutation importance algorithm. As an applicational example, ML classification models were also developed to predict locations of storm cells. Recalibrated ML regression models with identified pertinent predictors were coupled with the ML classification models to provide early warnings of heavy rainfall. Results imply that future work needs to consider MCS type information to improve ML modeling for nowcasting and early warning of heavy rainfall.