Accurate Rainfall Prediction Using GNSS PWV Based on Pre-Trained Transformer Model
With an increase in the intensity and frequency of extreme rainfall events, there is a pressing need for accurate rainfall nowcasting applications. In recent years, precipitable water vapor (PWV) data obtained from GNSS observations have been widely used in rainfall prediction. Unlike previous studi...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/12/2023 |
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| author | Wenjie Yin Chen Zhou Yuan Tian Hui Qiu Wei Zhang Hua Chen Pan Liu Qile Zhao Jian Kong Yibin Yao |
| author_facet | Wenjie Yin Chen Zhou Yuan Tian Hui Qiu Wei Zhang Hua Chen Pan Liu Qile Zhao Jian Kong Yibin Yao |
| author_sort | Wenjie Yin |
| collection | DOAJ |
| description | With an increase in the intensity and frequency of extreme rainfall events, there is a pressing need for accurate rainfall nowcasting applications. In recent years, precipitable water vapor (PWV) data obtained from GNSS observations have been widely used in rainfall prediction. Unlike previous studies mainly focusing on rainfall occurrences, this study proposes a transformer-based model for hourly rainfall prediction, integrating the GNSS PWV and ERA5 meteorological data. The proposed model employs the ProbSparse self-attention to efficiently capture long-range dependencies in time series data, crucial for correlating historical PWV variations with rainfall events. Additionally, the adoption of the DILATE loss function better captures the structural and timing aspects of rainfall prediction. Furthermore, traditional rainfall prediction models are typically trained on datasets specific to one region, which limits their generalization ability due to regional meteorological differences and the scarcity of data in certain areas. Therefore, we adopt a pre-training and fine-tuning strategy using global datasets to mitigate data scarcity in newly deployed GNSS stations, enhancing model adaptability to local conditions. The evaluation results demonstrate satisfactory performance over other methods, with the fine-tuned model achieving an MSE = 3.954, DTW = 0.232, and TDI = 0.101. This approach shows great potential for real-time rainfall nowcasting in a local area, especially with limited data. |
| format | Article |
| id | doaj-art-2134bfafea3244bfad7b4ff1c87e8943 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-2134bfafea3244bfad7b4ff1c87e89432025-08-20T03:16:39ZengMDPI AGRemote Sensing2072-42922025-06-011712202310.3390/rs17122023Accurate Rainfall Prediction Using GNSS PWV Based on Pre-Trained Transformer ModelWenjie Yin0Chen Zhou1Yuan Tian2Hui Qiu3Wei Zhang4Hua Chen5Pan Liu6Qile Zhao7Jian Kong8Yibin Yao9School of Earth and Space Science Technology, Wuhan University, Wuhan 430072, ChinaSchool of Earth and Space Science Technology, Wuhan University, Wuhan 430072, ChinaSchool of Earth and Space Science Technology, Wuhan University, Wuhan 430072, ChinaSchool of Earth and Space Science Technology, Wuhan University, Wuhan 430072, ChinaDepartment of Forecasting and Networking, China Meteorological Administration, Beijing 100081, ChinaSchool of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430062, ChinaSchool of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430062, ChinaGNSS Research Center, Wuhan University, Wuhan 430062, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430062, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430062, ChinaWith an increase in the intensity and frequency of extreme rainfall events, there is a pressing need for accurate rainfall nowcasting applications. In recent years, precipitable water vapor (PWV) data obtained from GNSS observations have been widely used in rainfall prediction. Unlike previous studies mainly focusing on rainfall occurrences, this study proposes a transformer-based model for hourly rainfall prediction, integrating the GNSS PWV and ERA5 meteorological data. The proposed model employs the ProbSparse self-attention to efficiently capture long-range dependencies in time series data, crucial for correlating historical PWV variations with rainfall events. Additionally, the adoption of the DILATE loss function better captures the structural and timing aspects of rainfall prediction. Furthermore, traditional rainfall prediction models are typically trained on datasets specific to one region, which limits their generalization ability due to regional meteorological differences and the scarcity of data in certain areas. Therefore, we adopt a pre-training and fine-tuning strategy using global datasets to mitigate data scarcity in newly deployed GNSS stations, enhancing model adaptability to local conditions. The evaluation results demonstrate satisfactory performance over other methods, with the fine-tuned model achieving an MSE = 3.954, DTW = 0.232, and TDI = 0.101. This approach shows great potential for real-time rainfall nowcasting in a local area, especially with limited data.https://www.mdpi.com/2072-4292/17/12/2023rainfall predictiondeep learningtransfer learningGlobal Navigation Satellite System (GNSS)precipitable water vapor (PWV) |
| spellingShingle | Wenjie Yin Chen Zhou Yuan Tian Hui Qiu Wei Zhang Hua Chen Pan Liu Qile Zhao Jian Kong Yibin Yao Accurate Rainfall Prediction Using GNSS PWV Based on Pre-Trained Transformer Model Remote Sensing rainfall prediction deep learning transfer learning Global Navigation Satellite System (GNSS) precipitable water vapor (PWV) |
| title | Accurate Rainfall Prediction Using GNSS PWV Based on Pre-Trained Transformer Model |
| title_full | Accurate Rainfall Prediction Using GNSS PWV Based on Pre-Trained Transformer Model |
| title_fullStr | Accurate Rainfall Prediction Using GNSS PWV Based on Pre-Trained Transformer Model |
| title_full_unstemmed | Accurate Rainfall Prediction Using GNSS PWV Based on Pre-Trained Transformer Model |
| title_short | Accurate Rainfall Prediction Using GNSS PWV Based on Pre-Trained Transformer Model |
| title_sort | accurate rainfall prediction using gnss pwv based on pre trained transformer model |
| topic | rainfall prediction deep learning transfer learning Global Navigation Satellite System (GNSS) precipitable water vapor (PWV) |
| url | https://www.mdpi.com/2072-4292/17/12/2023 |
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