Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data
Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. To address this lim...
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MDPI AG
2025-01-01
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| author | Hao Wang Rong Yang Jianxin He Qiangyu Zeng Taisong Xiong Zhihao Liu Hongfei Jin |
| author_facet | Hao Wang Rong Yang Jianxin He Qiangyu Zeng Taisong Xiong Zhihao Liu Hongfei Jin |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. To address this limitation, we introduce the Dual-Attention Recurrent Neural Network (DA-RNN), a model that combines satellite infrared (IR) data with radar-derived vertically integrated liquid (VIL) content. This model leverages the fundamental physical relationship between temperature and precipitation in a predictive framework that captures thermal and water vapor dynamics, thereby enhancing prediction accuracy. The results of experimental evaluations on the SEVIR dataset demonstrate that the DA-RNN model surpasses traditional methods on the test set. Notably, the DA-TrajGRU model achieves reductions in mean squared error (MSE) and mean absolute error (MAE) of 30 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>) and 89 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.4</mn><mo>%</mo></mrow></semantics></math></inline-formula>), respectively, compared with those of the conventional TrajGRU model. Furthermore, our DA-RNN exhibits robust false alarm rates (FAR) for various thresholds, with only slight decreases in the critical success index (CSI) and Heidke skill score (HSS) when increasing the threshold. Additionally, we present a visualization of precipitation nowcasting, illustrating that the integration of multiple data sources effectively avoids overestimation of VIL values, further increasing the precision of precipitation forecasts. |
| format | Article |
| id | doaj-art-41daed3cad764222925d61c98a7b3cdd |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-41daed3cad764222925d61c98a7b3cdd2025-01-24T13:47:50ZengMDPI AGRemote Sensing2072-42922025-01-0117223810.3390/rs17020238Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL DataHao Wang0Rong Yang1Jianxin He2Qiangyu Zeng3Taisong Xiong4Zhihao Liu5Hongfei Jin6College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, ChinaTraditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. To address this limitation, we introduce the Dual-Attention Recurrent Neural Network (DA-RNN), a model that combines satellite infrared (IR) data with radar-derived vertically integrated liquid (VIL) content. This model leverages the fundamental physical relationship between temperature and precipitation in a predictive framework that captures thermal and water vapor dynamics, thereby enhancing prediction accuracy. The results of experimental evaluations on the SEVIR dataset demonstrate that the DA-RNN model surpasses traditional methods on the test set. Notably, the DA-TrajGRU model achieves reductions in mean squared error (MSE) and mean absolute error (MAE) of 30 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>) and 89 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.4</mn><mo>%</mo></mrow></semantics></math></inline-formula>), respectively, compared with those of the conventional TrajGRU model. Furthermore, our DA-RNN exhibits robust false alarm rates (FAR) for various thresholds, with only slight decreases in the critical success index (CSI) and Heidke skill score (HSS) when increasing the threshold. Additionally, we present a visualization of precipitation nowcasting, illustrating that the integration of multiple data sources effectively avoids overestimation of VIL values, further increasing the precision of precipitation forecasts.https://www.mdpi.com/2072-4292/17/2/238relationshiptemperaturenowcastingmultiple data sources |
| spellingShingle | Hao Wang Rong Yang Jianxin He Qiangyu Zeng Taisong Xiong Zhihao Liu Hongfei Jin Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data Remote Sensing relationship temperature nowcasting multiple data sources |
| title | Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data |
| title_full | Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data |
| title_fullStr | Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data |
| title_full_unstemmed | Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data |
| title_short | Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data |
| title_sort | enhancing precipitation nowcasting through dual attention rnn integrating satellite infrared and radar vil data |
| topic | relationship temperature nowcasting multiple data sources |
| url | https://www.mdpi.com/2072-4292/17/2/238 |
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