期刊論文
| 學年 | 114 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2026-02-26 |
| 作品名稱 | Quantifying situation-dependent uncertainty in tropical cyclone track forecasts with a recurrent neural network approach |
| 作品名稱（其他語言） | |
| 著者 | Hsiao-Chung Tsai; Fang-Yi Lin; Yung-Lan Lin; Nai-Ning Hsu; Treng-Shi Huang; Russell L. Elsberry |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Natural Hazards 122 |
| 摘要 | Reliable forecasts of tropical cyclone tracks are essential for anticipating the environmental and societal impacts of these extreme weather events. In this study, we apply the long short-term memory (LSTM) model to better represent the spatiotemporal correlations of typhoon track forecast errors and to provide situation-dependent Cone of Uncertainty (CoU). The datasets used include the Central Weather Administration’s (CWA) official typhoon track forecasts, as well as deterministic and ensemble forecasts from the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Prediction models. In most situations, the LSTM-calibrated track forecasts generally outperform the CWA official forecasts. More importantly, the estimated CoU can reasonably cover the observed tracks. Relationships between CoUs and factors such as typhoon translation speed and direction are also examined. Incorporating global numerical model information further reduces the radius of forecast uncertainty while maintaining reliable coverage. Overall, this study demonstrates that the LSTM-based approach offers a reliable representation of typhoon track forecast uncertainty, which provides valuable insights into situation-dependent uncertainty quantification for extreme weather prediction. |
| 關鍵字 | |
| 語言 | en |
| ISSN | 1573-0840 |
| 期刊性質 | 國外 |
| 收錄於 | SCI Scopus |
| 產學合作 | 國內 |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | DEU |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128716 ) |