Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China
The mesoscale Weather Research and Forecasting (WRF) model has been widely employed to forecast day-ahead rainfalls. However, the deterministic predictions from the WRF model incorporate relatively large errors due to numerical discretization, inaccuracies in initial/boundary conditions and paramete...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/5/838 |
| _version_ | 1827670383713583104 |
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| author | Yu Du Ting Xu Yuzhang Che Bifeng Yang Shaojie Chen Zhikun Su Lianxia Su Yangruixue Chen Jiafeng Zheng |
| author_facet | Yu Du Ting Xu Yuzhang Che Bifeng Yang Shaojie Chen Zhikun Su Lianxia Su Yangruixue Chen Jiafeng Zheng |
| author_sort | Yu Du |
| collection | DOAJ |
| description | The mesoscale Weather Research and Forecasting (WRF) model has been widely employed to forecast day-ahead rainfalls. However, the deterministic predictions from the WRF model incorporate relatively large errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations, etc. Among them, the uncertainties in parameterization schemes have a huge impact on the forecasting skill of rainfalls, especially over the Sichuan Basin which is located east of the Tibetan Plateau in southwestern China. To figure out the impact of various parameterization schemes and their interactions on rainfall predictions over the Sichuan Basin, the Global Forecast System data are chosen as the initial/boundary conditions for the WRF model and 48 ensemble tests have been conducted based on different combinations of four microphysical (MP) schemes, four planetary boundary layer (PBL) schemes, and three cumulus (CU) schemes, for four rainfall cases in summer. Compared to the observations obtained from the Chinese ground-based and encrypted stations, it is found that the Goddard MP scheme together with the asymmetric convective model version 2 PBL scheme outperforms other combinations. Next, as the first step to explore further improvement of the WRF physical schemes, the polynomial chaos expansion (PCE) approach is then adopted to quantify the impacts of several empirical parameters with uncertainties in the WRF Single Moment 6-class (WSM6) MP scheme, the Yonsei University (YSU) PBL scheme and the Kain-Fritsch CU scheme on WRF rainfall predictions. The PCE statistics show that the uncertainty of the scaling factor applied to ice fall velocity in the WSM6 scheme and the profile shape exponent in the YSU scheme affects more dominantly the rainfall predictions in comparison with other parameters, which sheds a light on the importance of these schemes for the rainfall predictions over the Sichuan Basin and suggests the next step to further improve the physical schemes. |
| first_indexed | 2024-03-10T03:20:16Z |
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| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T03:20:16Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-3f0a5b05e3b7431186a4f86f3eeebf0b2023-11-23T10:03:39ZengMDPI AGAtmosphere2073-44332022-05-0113583810.3390/atmos13050838Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, ChinaYu Du0Ting Xu1Yuzhang Che2Bifeng Yang3Shaojie Chen4Zhikun Su5Lianxia Su6Yangruixue Chen7Jiafeng Zheng8College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaEngineering Practice Center, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaQuanzhou Jinjiang International Airport, Quanzhou 362299, ChinaQuanzhou Jinjiang International Airport, Quanzhou 362299, ChinaCollege of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaThe mesoscale Weather Research and Forecasting (WRF) model has been widely employed to forecast day-ahead rainfalls. However, the deterministic predictions from the WRF model incorporate relatively large errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations, etc. Among them, the uncertainties in parameterization schemes have a huge impact on the forecasting skill of rainfalls, especially over the Sichuan Basin which is located east of the Tibetan Plateau in southwestern China. To figure out the impact of various parameterization schemes and their interactions on rainfall predictions over the Sichuan Basin, the Global Forecast System data are chosen as the initial/boundary conditions for the WRF model and 48 ensemble tests have been conducted based on different combinations of four microphysical (MP) schemes, four planetary boundary layer (PBL) schemes, and three cumulus (CU) schemes, for four rainfall cases in summer. Compared to the observations obtained from the Chinese ground-based and encrypted stations, it is found that the Goddard MP scheme together with the asymmetric convective model version 2 PBL scheme outperforms other combinations. Next, as the first step to explore further improvement of the WRF physical schemes, the polynomial chaos expansion (PCE) approach is then adopted to quantify the impacts of several empirical parameters with uncertainties in the WRF Single Moment 6-class (WSM6) MP scheme, the Yonsei University (YSU) PBL scheme and the Kain-Fritsch CU scheme on WRF rainfall predictions. The PCE statistics show that the uncertainty of the scaling factor applied to ice fall velocity in the WSM6 scheme and the profile shape exponent in the YSU scheme affects more dominantly the rainfall predictions in comparison with other parameters, which sheds a light on the importance of these schemes for the rainfall predictions over the Sichuan Basin and suggests the next step to further improve the physical schemes.https://www.mdpi.com/2073-4433/13/5/838uncertainty quantificationWRF modelSichuan Basinrainfall predictionparameterization schemes |
| spellingShingle | Yu Du Ting Xu Yuzhang Che Bifeng Yang Shaojie Chen Zhikun Su Lianxia Su Yangruixue Chen Jiafeng Zheng Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China Atmosphere uncertainty quantification WRF model Sichuan Basin rainfall prediction parameterization schemes |
| title | Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China |
| title_full | Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China |
| title_fullStr | Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China |
| title_full_unstemmed | Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China |
| title_short | Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China |
| title_sort | uncertainty quantification of wrf model for rainfall prediction over the sichuan basin china |
| topic | uncertainty quantification WRF model Sichuan Basin rainfall prediction parameterization schemes |
| url | https://www.mdpi.com/2073-4433/13/5/838 |
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