Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan
This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a spec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1421 |
| _version_ | 1797396128642105344 |
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| author | Chih-Chiang Wei Chen-Chia Hsu |
| author_facet | Chih-Chiang Wei Chen-Chia Hsu |
| author_sort | Chih-Chiang Wei |
| collection | DOAJ |
| description | This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence. |
| first_indexed | 2024-03-09T00:45:53Z |
| format | Article |
| id | doaj.art-a071d72c991a4cdca9c7fb2c199cd86f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:45:53Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a071d72c991a4cdca9c7fb2c199cd86f2023-12-11T17:30:06ZengMDPI AGSensors1424-82202021-02-01214142110.3390/s21041421Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern TaiwanChih-Chiang Wei0Chen-Chia Hsu1Department of Marine Environmental Informatics & Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung 20224, TaiwanDepartment of Marine Environmental Informatics & Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung 20224, TaiwanThis study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.https://www.mdpi.com/1424-8220/21/4/1421typhoonrainfallpredictionradarmachine learning |
| spellingShingle | Chih-Chiang Wei Chen-Chia Hsu Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan Sensors typhoon rainfall prediction radar machine learning |
| title | Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan |
| title_full | Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan |
| title_fullStr | Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan |
| title_full_unstemmed | Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan |
| title_short | Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan |
| title_sort | real time rainfall forecasts based on radar reflectivity during typhoons case study in southeastern taiwan |
| topic | typhoon rainfall prediction radar machine learning |
| url | https://www.mdpi.com/1424-8220/21/4/1421 |
| work_keys_str_mv | AT chihchiangwei realtimerainfallforecastsbasedonradarreflectivityduringtyphoonscasestudyinsoutheasterntaiwan AT chenchiahsu realtimerainfallforecastsbasedonradarreflectivityduringtyphoonscasestudyinsoutheasterntaiwan |