Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China
In this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/10/1652 |
| _version_ | 1797475388918595584 |
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| author | Yang Lyu Xiefei Zhi Hong Wu Hongmei Zhou Dexuan Kong Shoupeng Zhu Yingxin Zhang Cui Hao |
| author_facet | Yang Lyu Xiefei Zhi Hong Wu Hongmei Zhou Dexuan Kong Shoupeng Zhu Yingxin Zhang Cui Hao |
| author_sort | Yang Lyu |
| collection | DOAJ |
| description | In this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at the 10 m and multiple isobaric surfaces (500 hPa, 700 hPa, 850 hPa and 925 hPa) over North China for 2020. The straightforward multimodel ensemble mean (MME) method is utilized to improve forecasting abilities. In addition, the forecast errors are decomposed to further diagnose the error sources of wind forecasts. Results indicated that there is little difference in the performances of the four models in terms of wind direction forecasts (DIR), but obvious differences occur in the meridional wind (U), zonal wind (V) and wind speed (WS) forecasts. Among them, the ECMWF and NCEP showed the highest and lowest abilities, respectively. The MME effectively improved wind forecast abilities, and showed more evident superiorities at higher levels for longer lead times. Meanwhile, all of the models and the MME manifested consistent trends of increasing (decreasing) errors for U, V and WS (DIR) with rising height. On the other hand, the main source of errors for wind forecasts at both 10 m and isobaric surfaces was the sequence component (SEQU), which rose rapidly with increasing lead times. The deficiency of the less proficient NCEP model at the 10 m and isobaric surfaces could mainly be attributed to the bias component (BIAS) and SEQU, respectively. Furthermore, the MME tended to produce lower SEQU than the models at all layers, which was more obvious at longer lead times. However, the MME showed a slight deficiency in reducing BIAS and the distribution component of forecast errors. The results not only recognized the model forecast performances in detail, but also provided important references for the use of wind forecasts in business departments and associated scientific researches. |
| first_indexed | 2024-03-09T20:43:29Z |
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| id | doaj.art-2ca3fa5898a1444f9250ee632ff09c24 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T20:43:29Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-2ca3fa5898a1444f9250ee632ff09c242023-11-23T22:51:37ZengMDPI AGAtmosphere2073-44332022-10-011310165210.3390/atmos13101652Analyses on the Multimodel Wind Forecasts and Error Decompositions over North ChinaYang Lyu0Xiefei Zhi1Hong Wu2Hongmei Zhou3Dexuan Kong4Shoupeng Zhu5Yingxin Zhang6Cui Hao7Key Laboratory of Meteorology Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Meteorology Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210000, ChinaDongtai Meteorological Bureau, Yancheng 224200, ChinaMeteorological Bureau of Qian Xinan Buyei and Miao Autonomous Prefecture, Xingyi 562400, ChinaKey Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210000, ChinaBeijing Meteorological Observatory, Beijing 100016, ChinaBeijing Meteorological Observatory, Beijing 100016, ChinaIn this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at the 10 m and multiple isobaric surfaces (500 hPa, 700 hPa, 850 hPa and 925 hPa) over North China for 2020. The straightforward multimodel ensemble mean (MME) method is utilized to improve forecasting abilities. In addition, the forecast errors are decomposed to further diagnose the error sources of wind forecasts. Results indicated that there is little difference in the performances of the four models in terms of wind direction forecasts (DIR), but obvious differences occur in the meridional wind (U), zonal wind (V) and wind speed (WS) forecasts. Among them, the ECMWF and NCEP showed the highest and lowest abilities, respectively. The MME effectively improved wind forecast abilities, and showed more evident superiorities at higher levels for longer lead times. Meanwhile, all of the models and the MME manifested consistent trends of increasing (decreasing) errors for U, V and WS (DIR) with rising height. On the other hand, the main source of errors for wind forecasts at both 10 m and isobaric surfaces was the sequence component (SEQU), which rose rapidly with increasing lead times. The deficiency of the less proficient NCEP model at the 10 m and isobaric surfaces could mainly be attributed to the bias component (BIAS) and SEQU, respectively. Furthermore, the MME tended to produce lower SEQU than the models at all layers, which was more obvious at longer lead times. However, the MME showed a slight deficiency in reducing BIAS and the distribution component of forecast errors. The results not only recognized the model forecast performances in detail, but also provided important references for the use of wind forecasts in business departments and associated scientific researches.https://www.mdpi.com/2073-4433/13/10/1652wind forecasterror decompositionbiasdistributionsequence |
| spellingShingle | Yang Lyu Xiefei Zhi Hong Wu Hongmei Zhou Dexuan Kong Shoupeng Zhu Yingxin Zhang Cui Hao Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China Atmosphere wind forecast error decomposition bias distribution sequence |
| title | Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China |
| title_full | Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China |
| title_fullStr | Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China |
| title_full_unstemmed | Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China |
| title_short | Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China |
| title_sort | analyses on the multimodel wind forecasts and error decompositions over north china |
| topic | wind forecast error decomposition bias distribution sequence |
| url | https://www.mdpi.com/2073-4433/13/10/1652 |
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