Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2018.02.014 |
Impact of the hybrid gain ensemble data assimilation on meso-scale numerical weather prediction over east China | |
Wang, Yuanbing; Min, Jinzhong; Chen, Yaodeng | |
2018-07-01 | |
发表期刊 | ATMOSPHERIC RESEARCH
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ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2018 |
卷号 | 206页码:30-45 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
英文摘要 | Besides the traditional hybrid covariance data assimilation (referred to as "HCDA" in this paper) method, the hybrid gain data assimilation (referred to as "HGDA") has been proposed recently to combine the ensemble Kalman filter and variational methods, showing potential advantages in global models. To evaluate the impact of HGDA on regional and meso-scale numerical weather prediction using WRF model over east China, both single observation tests and full cycling experiments for 3-weeks in July 2013 were conducted using the 3DVar, EnKF, HCDA and HGDA methods. The results of single observation tests showed that the analysis increments of HGDA retained more characteristics of the EnKF than HCDA because of utilizing the EnKF analysis ensemble mean in the re-center step. Both the hybrid data assimilation methods showed superiority over the pure EnKF and 3DVar in full cycling experiments. The average RMSE of HGDA was slightly smaller than the HCDA. It was also found that the HGDA method showed its advantage over HCDA at shorter leading time and yielded the highest precipitation score. For rainfall field, the HGDA had the best results in terms of intensity and coverage. Furthermore, the HGDA showed better results for supplying sufficient moisture conditions over rainfall area, such as precipitable water and water vapor flux. The uplift vertical velocity that contributed to the improvement of precipitation simulation was also strengthened. In general, both of the hybrid data assimilation methods showed better results than EnKF and 3DVar. Especially, the HGDA method showed advantage benefiting from the utilization of optimal EnKF analysis mean and 3DVar analysis which equals to the linearly combination of the gain matrix, considering the total error variance. |
英文关键词 | Numerical weather prediction Data assimilation Hybrid gain |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000430765000003 |
WOS关键词 | BULK PARAMETERIZATION ; KALMAN FILTER ; SYSTEM ; PRECIPITATION ; IMPLEMENTATION ; MICROPHYSICS ; CONVECTION ; FORECASTS ; EQUATION ; SCHEME |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/37952 |
专题 | 地球科学 |
作者单位 | Nanjing Univ Informat Sci & Technol, CIC FEMD, Joint Int Res Lab Climate & Environm Change ILCEC, Key Lab Meteorol Disaster,Minist Educ KLME, Nanjing 210044, Jiangsu, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Yuanbing,Min, Jinzhong,Chen, Yaodeng. Impact of the hybrid gain ensemble data assimilation on meso-scale numerical weather prediction over east China[J]. ATMOSPHERIC RESEARCH,2018,206:30-45. |
APA | Wang, Yuanbing,Min, Jinzhong,&Chen, Yaodeng.(2018).Impact of the hybrid gain ensemble data assimilation on meso-scale numerical weather prediction over east China.ATMOSPHERIC RESEARCH,206,30-45. |
MLA | Wang, Yuanbing,et al."Impact of the hybrid gain ensemble data assimilation on meso-scale numerical weather prediction over east China".ATMOSPHERIC RESEARCH 206(2018):30-45. |
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