Efficiently Improving Ensemble Forecasts of Warm-Sector Heavy Rainfall over Coastal Southern China: Targeted Assimilation to Reduce the Critical Initial Field Errors
被引:1
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作者:
Bao, Xinghua
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机构:
China Meteorol Adm CMA, Chinese Acad Meteorol Sci CAMS, State Key Lab Severe Weather, Beijing 100081, Peoples R ChinaChina Meteorol Adm CMA, Chinese Acad Meteorol Sci CAMS, State Key Lab Severe Weather, Beijing 100081, Peoples R China
Bao, Xinghua
[1
]
Xia, Rudi
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机构:
China Meteorol Adm CMA, Chinese Acad Meteorol Sci CAMS, State Key Lab Severe Weather, Beijing 100081, Peoples R China
Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, Nanjing 210044, Peoples R ChinaChina Meteorol Adm CMA, Chinese Acad Meteorol Sci CAMS, State Key Lab Severe Weather, Beijing 100081, Peoples R China
Xia, Rudi
[1
,2
]
Luo, Yali
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机构:
China Meteorol Adm CMA, Chinese Acad Meteorol Sci CAMS, State Key Lab Severe Weather, Beijing 100081, Peoples R China
Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, Nanjing 210044, Peoples R ChinaChina Meteorol Adm CMA, Chinese Acad Meteorol Sci CAMS, State Key Lab Severe Weather, Beijing 100081, Peoples R China
Luo, Yali
[1
,2
]
Yue, Jian
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机构:
China Meteorol Adm CMA, Chinese Acad Meteorol Sci CAMS, State Key Lab Severe Weather, Beijing 100081, Peoples R China
China Meteorol Adm, CMA Earth Syst Modeling & Predict Ctr, Beijing 100081, Peoples R ChinaChina Meteorol Adm CMA, Chinese Acad Meteorol Sci CAMS, State Key Lab Severe Weather, Beijing 100081, Peoples R China
Yue, Jian
[1
,3
]
机构:
[1] China Meteorol Adm CMA, Chinese Acad Meteorol Sci CAMS, State Key Lab Severe Weather, Beijing 100081, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, Nanjing 210044, Peoples R China
[3] China Meteorol Adm, CMA Earth Syst Modeling & Predict Ctr, Beijing 100081, Peoples R China
ensemble forecast;
targeted assimilation;
warm-sector heavy rainfall;
SCALE DATA ASSIMILATION;
RELATIVE OPERATING CHARACTERISTICS;
DOPPLER RADAR OBSERVATIONS;
LOW-LEVEL JETS;
KALMAN FILTER;
PART II;
HIGH-RESOLUTION;
MESOSCALE;
MODEL;
PREDICTION;
D O I:
10.1007/s13351-023-2140-8
中图分类号:
P4 [大气科学(气象学)];
学科分类号:
0706 ;
070601 ;
摘要:
Warm-sector heavy rainfall events over southern China are difficult to accurately forecast, due in part to inaccurate initial fields in numerical weather prediction models. In order to determine an efficient way of reducing the critical initial field errors, this study conducts and compares two sets of 60-member ensemble forecast experiments of a warm-sector heavy rainfall event over coastal southern China without data assimilation (NODA) and with radar radial velocity data assimilation (RadarDA). Yangjiang radar data, which can provide offshore high-resolution wind field information, were assimilated by using a Weather Research and Forecasting (WRF)-based ensemble Kalman filter (EnKF) system. The results show that the speed and direction errors of the southeasterly airflow in the marine boundary layer over the northern South China Sea may primarily be responsible for the forecast errors in rainfall and convection evolution. Targeted assimilation of radial velocity data from the Yangjiang radar can reduce the critical initial field errors of most members, resulting in improvements to the ensemble forecast. Specifically, RadarDA simulations indicate that radial-velocity data assimilation (VrDA) can directly reduce the initial field errors in wind speed and direction, and indirectly and slightly adjust the initial moisture fields in most members, thereby improving the evolution features of moisture transport during the subsequent forecast period. Therefore, these RadarDA members can better capture the initiation and development of convection and have higher forecast skill for the convection evolution and rainfall. The improvement in the deterministic forecasts of most members results in an improved overall ensemble forecast performance. However, VrDA sometimes results in inappropriate adjustment of the initial wind field, so the forecast skill of a few members decreases rather than increases after VrDA. This suggests that a degree of uncertainty remains about the effect of the WRF-based EnKF system. Moreover, the results further indicate that accurate forecasts of the convection evolution and rainfall of warm-sector heavy rainfall events over southern China are challenging.
机构:
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences(CAMS),China Meteorological Administration(CMA)State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences(CAMS),China Meteorological Administration(CMA)
Xinghua BAO
Rudi XIA
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机构:
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences(CAMS),China Meteorological Administration(CMA)
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science & TechnologyState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences(CAMS),China Meteorological Administration(CMA)
Rudi XIA
Yali LUO
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机构:
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences(CAMS),China Meteorological Administration(CMA)
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science & TechnologyState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences(CAMS),China Meteorological Administration(CMA)
Yali LUO
Jian YUE
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机构:
CMA Earth System Modeling and Prediction Centre,China Meteorological Administration
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences(CAMS),China Meteorological Administration(CMA)State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences(CAMS),China Meteorological Administration(CMA)
机构:
School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), ZhuhaiSchool of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai
Zeng Z.
Wang D.
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机构:
School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai
National Observation and Research Station of Coastal Ecological Environments in Macao, Macao Environmental Research Institute, Macau University of Science and TechnologySchool of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai
机构:
Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R ChinaChinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
Bao, Xinghua
Luo, Yali
论文数: 0引用数: 0
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机构:
Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing, Peoples R ChinaChinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
Luo, Yali
Gao, Xiaoyu
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机构:
Tsinghua Univ, Dept Earth Syst Sci, Beijing, Peoples R ChinaChinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China