Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulation
Abstract S-band radar beams are easily obstructed by terrain during propagation. After the beam is blocked, the radar cannot receive the echo signal of the target area, forming a data blind spot. Traditional methods cannot obtain a complete precipitation` dataset, which increases the difficulty of p...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07160-5 |
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| author | Wang Bo Liu Xiaolin Hua Shenbing Jin Shuanglong |
| author_facet | Wang Bo Liu Xiaolin Hua Shenbing Jin Shuanglong |
| author_sort | Wang Bo |
| collection | DOAJ |
| description | Abstract S-band radar beams are easily obstructed by terrain during propagation. After the beam is blocked, the radar cannot receive the echo signal of the target area, forming a data blind spot. Traditional methods cannot obtain a complete precipitation` dataset, which increases the difficulty of precipitation estimation and leads to errors in precipitation estimation results, resulting in lower scores. This study conducted quantitative precipitation estimation using S-band dual-polarization radar under a convective scale ensemble simulation. Firstly, a certain region in Hebei Province was selected as the research object to conduct convective scale ensemble simulation to obtain more precipitation datasets. Then, the precipitation data was smoothed and used to invert the radar precipitation intensity every 6 min. The estimated hourly rainfall of the radar was matched with the hourly rainfall measurement of a single-point rainfall station. Finally, based on deep learning theory, a quantitative precipitation estimation model for S-band dual-polarization radar was constructed. The experimental results show that using the proposed method, the root mean square error (RMSE) value is less than 0.372, the mean absolute error (MAE) is less than 0.247, the correlation coefficient (CC) value is higher than 94.7%, the TS score is higher than 95.1%, and the quantitative precipitation estimation effect is good. |
| format | Article |
| id | doaj-art-5c7e614e9cbf492897fd64fd1ed3bacd |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-5c7e614e9cbf492897fd64fd1ed3bacd2025-08-20T02:30:43ZengSpringerDiscover Applied Sciences3004-92612025-06-017611410.1007/s42452-025-07160-5Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulationWang Bo0Liu Xiaolin1Hua Shenbing2Jin Shuanglong3State Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research InstituteState Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research InstituteState Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research InstituteState Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research InstituteAbstract S-band radar beams are easily obstructed by terrain during propagation. After the beam is blocked, the radar cannot receive the echo signal of the target area, forming a data blind spot. Traditional methods cannot obtain a complete precipitation` dataset, which increases the difficulty of precipitation estimation and leads to errors in precipitation estimation results, resulting in lower scores. This study conducted quantitative precipitation estimation using S-band dual-polarization radar under a convective scale ensemble simulation. Firstly, a certain region in Hebei Province was selected as the research object to conduct convective scale ensemble simulation to obtain more precipitation datasets. Then, the precipitation data was smoothed and used to invert the radar precipitation intensity every 6 min. The estimated hourly rainfall of the radar was matched with the hourly rainfall measurement of a single-point rainfall station. Finally, based on deep learning theory, a quantitative precipitation estimation model for S-band dual-polarization radar was constructed. The experimental results show that using the proposed method, the root mean square error (RMSE) value is less than 0.372, the mean absolute error (MAE) is less than 0.247, the correlation coefficient (CC) value is higher than 94.7%, the TS score is higher than 95.1%, and the quantitative precipitation estimation effect is good.https://doi.org/10.1007/s42452-025-07160-5Convective scaleS-band dual-polarization radarDeep learningQuantitative precipitationIntelligent estimation |
| spellingShingle | Wang Bo Liu Xiaolin Hua Shenbing Jin Shuanglong Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulation Discover Applied Sciences Convective scale S-band dual-polarization radar Deep learning Quantitative precipitation Intelligent estimation |
| title | Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulation |
| title_full | Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulation |
| title_fullStr | Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulation |
| title_full_unstemmed | Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulation |
| title_short | Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulation |
| title_sort | quantitative precipitation estimation method using s band dual polarization radar under convective scale ensemble simulation |
| topic | Convective scale S-band dual-polarization radar Deep learning Quantitative precipitation Intelligent estimation |
| url | https://doi.org/10.1007/s42452-025-07160-5 |
| work_keys_str_mv | AT wangbo quantitativeprecipitationestimationmethodusingsbanddualpolarizationradarunderconvectivescaleensemblesimulation AT liuxiaolin quantitativeprecipitationestimationmethodusingsbanddualpolarizationradarunderconvectivescaleensemblesimulation AT huashenbing quantitativeprecipitationestimationmethodusingsbanddualpolarizationradarunderconvectivescaleensemblesimulation AT jinshuanglong quantitativeprecipitationestimationmethodusingsbanddualpolarizationradarunderconvectivescaleensemblesimulation |