| dc.description.abstract | During Taiwan′s weak synoptic summer, there are often unpredictable and disastrous strong convective events occurring in the afternoon, making it important to rapidly and accurately predict the initiation of convection (CI). Due to the non-linear characteristics of small-scale strong convection, and the ability of Deep Neural Networks (DNN) to fit non-linear functions, this study focuses on weak synoptic strong convection events using the Weather Research and Forecasting (WRF) system and adopting an Observing System Simulation Experiment (OSSE) framework to obtain various variables. After detailed case analysis, prediction target definition, and data processing, DNN is used to forecast strong convection or CI for one hour, and various analyses like SHAP (SHapley Additive exPlanations) are conducted on DNN to determine the importance of each variable, leading to exploration in many different angle.
After extensive discussions, this study provides guidelines for the establishment of DNN system parameters, data selection and processing, and uses various validation methods, including objective scores, graph overlay analysis, performance diagram, SHAP analysis, persisFail experiments, coordinate system testing experiments, and sensitivity testing experiments. A series of discussions were conducted on the model performance, prediction tendencies, and variable importance. The analysis of variable importance is consistent with previous statistical results for the CI, and methods to enhance DNN forecasting ability, such as adding spatially-related variables and solving problems with mixed training, were also considered. Although the prediction performance of the DNN cannot be compared with fuzzy logic, the results show a significant improvement over extrapolation methods, and can be quickly applied to actual data using only pure station variables. When using central weather bureau standards and considering the smallest forecast units as townships, the ETS (Equitable Threat Score) forecast scores for strong convection and the CI are 0.338 and 0.333, respectively, and the ETS and event occurrence frequency only weak positive correlation. | en_US |