Using the Gradient Boosting Decision Tree to Improve the Delineation of Hourly Rain Areas during the Summer from Advanced Himawari Imager Data

AbstractA new classification scheme based on the gradient boosting decision tree (GBDT) algorithm is developed to improve the accuracy of rain area delineation for daytime, twilight, and nighttime modules using Advanced Himawari Imager on board Himawari-8 (AHI-8) geostationary satellite data and the U.S. Geological Survey digital elevation model data. The GBDT algorithm is able to efficiently manage the nonlinear relationships among high-dimensional data without being affected by overfitting problems. The new delineation module utilizes several features related to the physical variables, including cloud-top heights, cloud-top temperatures, cloud water paths, cloud phases, water vapor, temporal changes, and orographic variations. The scheme procedure is as follows. First, we perform extensive experiments to optimize the module parameters such that the equitable threat score (ETS) reaches its maximum value. Then, the GBDT-based modules are trained and classified with the optimum parameters. Finally, validat...