Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic Forecasting
One of the major issues in developing convective-scale ensemble forecasts is what is widely known as under-dispersion. This can be addressed through the consideration of spatial uncertainties via post-processing, motivating the development of various techniques to represent the spatial uncertainties...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/10/9/490 |
| _version_ | 1818336587612684288 |
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| author | Xiaoran Zhuang Haonan Zhu Jinzhong Min Liu Zhang Naigen Wu Zhipeng Wu Shiqi Wang |
| author_facet | Xiaoran Zhuang Haonan Zhu Jinzhong Min Liu Zhang Naigen Wu Zhipeng Wu Shiqi Wang |
| author_sort | Xiaoran Zhuang |
| collection | DOAJ |
| description | One of the major issues in developing convective-scale ensemble forecasts is what is widely known as under-dispersion. This can be addressed through the consideration of spatial uncertainties via post-processing, motivating the development of various techniques to represent the spatial uncertainties of ensembles. In this study, a recently developed fraction-based approach (the ensemble agreement scale, EAS) is applied to characterize the spatial predictability and spread−skill performances of precipitation forecasts using a WRF-EnKF convective-scale ensemble forecast system over the Yangtze and Huai river valleys, China. Fourteen heavy rainfall events during the Meiyu season of 2013 and 2014 were classified into two categories—strong forcing (SF) and weak forcing (WF)—using the convective adjustment timescale. The results show that the spatial predictability and spread−skill relationship are highly regime-dependent and that both exhibit lower values under WF. Furthermore, a new object-based probabilistic approach (OBJ_NEP) is proposed as a supplement to traditional neighborhood ensemble probability (NEP) and a recently proposed fraction-based approach (EAS_NEP). The results of the application of OBJ_NEP are evaluated, and a comparison is made between NEP and EAS_NEP for the analysis of experiments involving both idealized and ‘real’ events by using objective verification methods. The results imply that OBJ_NEP can be effectively employed under different large-scale forcings. Consequently, these results can aid the understanding of spatial-based approaches to probabilistic forecasting, which has been widely applied to post-processing processes of convective-scale ensemble forecast systems (CSEFs) in recent years. |
| first_indexed | 2024-12-13T14:41:42Z |
| format | Article |
| id | doaj.art-e62dc4c65bfd4a12b2e96c613505a088 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-12-13T14:41:42Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-e62dc4c65bfd4a12b2e96c613505a0882022-12-21T23:41:36ZengMDPI AGAtmosphere2073-44332019-08-0110949010.3390/atmos10090490atmos10090490Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic ForecastingXiaoran Zhuang0Haonan Zhu1Jinzhong Min2Liu Zhang3Naigen Wu4Zhipeng Wu5Shiqi Wang6Key Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaChongqing Climate Center, Chongqing Meteorological Bureau, Chongqing 401147, ChinaKey Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaChongqing Weather Station, Chongqing Meteorological Bureau, Chongqing 401147, ChinaKey Laboratory of Meteorological Disaster of Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaOne of the major issues in developing convective-scale ensemble forecasts is what is widely known as under-dispersion. This can be addressed through the consideration of spatial uncertainties via post-processing, motivating the development of various techniques to represent the spatial uncertainties of ensembles. In this study, a recently developed fraction-based approach (the ensemble agreement scale, EAS) is applied to characterize the spatial predictability and spread−skill performances of precipitation forecasts using a WRF-EnKF convective-scale ensemble forecast system over the Yangtze and Huai river valleys, China. Fourteen heavy rainfall events during the Meiyu season of 2013 and 2014 were classified into two categories—strong forcing (SF) and weak forcing (WF)—using the convective adjustment timescale. The results show that the spatial predictability and spread−skill relationship are highly regime-dependent and that both exhibit lower values under WF. Furthermore, a new object-based probabilistic approach (OBJ_NEP) is proposed as a supplement to traditional neighborhood ensemble probability (NEP) and a recently proposed fraction-based approach (EAS_NEP). The results of the application of OBJ_NEP are evaluated, and a comparison is made between NEP and EAS_NEP for the analysis of experiments involving both idealized and ‘real’ events by using objective verification methods. The results imply that OBJ_NEP can be effectively employed under different large-scale forcings. Consequently, these results can aid the understanding of spatial-based approaches to probabilistic forecasting, which has been widely applied to post-processing processes of convective-scale ensemble forecast systems (CSEFs) in recent years.https://www.mdpi.com/2073-4433/10/9/490convective-scale ensemble forecastspatial predictabilityprobabilistic forecast |
| spellingShingle | Xiaoran Zhuang Haonan Zhu Jinzhong Min Liu Zhang Naigen Wu Zhipeng Wu Shiqi Wang Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic Forecasting Atmosphere convective-scale ensemble forecast spatial predictability probabilistic forecast |
| title | Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic Forecasting |
| title_full | Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic Forecasting |
| title_fullStr | Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic Forecasting |
| title_full_unstemmed | Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic Forecasting |
| title_short | Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic Forecasting |
| title_sort | spatial predictability of heavy rainfall events in east china and the application of spatial based methods of probabilistic forecasting |
| topic | convective-scale ensemble forecast spatial predictability probabilistic forecast |
| url | https://www.mdpi.com/2073-4433/10/9/490 |
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