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Dynamical downscaling prediction of persistent heavy rainfall in Henan province in July 2021 based on CMA_CPSv3 and CWRF climate models
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Tianyu HAO1, 2, Lili DONG2, Qingquan LI1, 2, Bing XIE2, Chongbo ZHAO2, Li GUO2, Xin-zhong LIANG3
Acta Meteorologica Sinica | 2025, 83(5) : 1286 - 1300
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Acta Meteorologica Sinica | 2025, 83(5): 1286-1300
Articles
Dynamical downscaling prediction of persistent heavy rainfall in Henan province in July 2021 based on CMA_CPSv3 and CWRF climate models
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Tianyu HAO1, 2, Lili DONG2, Qingquan LI1, 2, Bing XIE2, Chongbo ZHAO2, Li GUO2, Xin-zhong LIANG3
Affiliations
  • 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education,Nanjing University of Information Science and Technology,Nanjing 210044,China
  • 2.China Meteorological Administration Key Laboratory for Climate Prediction Studies,National Climate Centre,Beijing 100081,China
  • 3.Earth System Science Interdisciplinary Center,University of Maryland,MD 20742,USA
Published: 2025-10-10 doi: 10.11676/qxxb2025.20240119
Outline
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An unprecedented persistent heavy precipitation occurred in Henan province during 17—22 July 2021, causing huge economic losses. Currently, extreme precipitation forecasting is still a hotspot and a difficult issue in sub-seasonal climate prediction research. Regional climate models provide a new way to further improve sub-seasonal precipitation forecasting in China with finer spatial resolution and better parameterization of physical processes compared to that of the global models. This study uses the regional Climate-Weather Research and Forecasting model (CWRF) nested with the China Meteorological Administration Climate Prediction System version 3 (CMA_CPSv3) to improve prediction capabilities for this persistent heavy precipitation event. It is shown that the spatial distribution, magnitude, and forecast accuracy of precipitation predicted by CWRF are improved compared to that predicted by CMA_CPSv3. Although both models underestimate the amount of precipitation, the CWRF forecasts larger accumulated precipitation and spatial distribution of precipitation is more consistent with observation. CWRF forecasts initialized on 26 June and 29 June are better than that of CMA_CPSv3 on the same initial dates. The CWRF significantly improves the forecast of low-level wind fields and low-level jets in East Asia compared with the CMA_CPSv3. The CWRF is particularly effective in improving the simulation of directions of low-level jets and water vapor fluxes, allowing water vapor to converge on the windward slopes of mountain ranges and providing favorable water vapor conditions for precipitation. The CWRF better forecasts the water vapor flux convergence and ascending motions over Zhengzhou, and all these improvements lead to higher precipitation forecasting skill of CWRF.

CWRF  /  CMA_ CPSv3  /  Dynamical downscaling  /  Persistent heavy rainfall  /  Subseasonal prediction
Tianyu HAO, Lili DONG, Qingquan LI, Bing XIE, Chongbo ZHAO, Li GUO, Xin-zhong LIANG. Dynamical downscaling prediction of persistent heavy rainfall in Henan province in July 2021 based on CMA_CPSv3 and CWRF climate models[J]. Acta Meteorologica Sinica, 2025 , 83 (5) : 1286 -1300 . DOI: 10.11676/qxxb2025.20240119
Year 2025 volume 83 Issue 5
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Article Info
doi: 10.11676/qxxb2025.20240119
  • Receive Date:2024-08-28
  • Online Date:2026-03-27
  • Published:2025-10-10
Article Data
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History
  • Received:2024-08-28
  • Revised:2024-11-20
Funding
Affiliations
    1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education,Nanjing University of Information Science and Technology,Nanjing 210044,China
    2.China Meteorological Administration Key Laboratory for Climate Prediction Studies,National Climate Centre,Beijing 100081,China
    3.Earth System Science Interdisciplinary Center,University of Maryland,MD 20742,USA
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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