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DOI | 10.1002/2017JD027340 |
Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF-RTFDDA | |
Wang, Haoliang1,2; Liu, Yubao2; Cheng, William Y. Y.2; Zhao, Tianliang1; Xu, Mei2; Liu, Yuewei2; Shen, Si2; Calhoun, Kristin M.3; Fierro, Alexandre O.3 | |
2017-11-27 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
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ISSN | 2169-897X |
EISSN | 2169-8996 |
出版年 | 2017 |
卷号 | 122期号:22 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | In this study, a lightning data assimilation (LDA) scheme was developed and implemented in the National Center for Atmospheric Research Weather Research and Forecasting-Real-Time Four-Dimensional Data Assimilation system. In this LDA method, graupel mixing ratio (q(g)) is retrieved from observed total lightning. To retrieve q(g) on model grid boxes, column-integrated graupel mass is first calculated using an observation-based linear formula between graupel mass and total lightning rate. Then the graupel mass is distributed vertically according to the empirical q(g) vertical profiles constructed from model simulations. Finally, a horizontal spread method is utilized to consider the existence of graupel in the adjacent regions of the lightning initiation locations. Based on the retrieved q(g) fields, latent heat is adjusted to account for the latent heat releases associated with the formation of the retrieved graupel and to promote convection at the observed lightning locations, which is conceptually similar to the method developed by Fierro et al. Three severe convection cases were studied to evaluate the LDA scheme for short-term (0-6h) lightning and precipitation forecasts. The simulation results demonstrated that the LDA was effective in improving the short-term lightning and precipitation forecasts by improving the model simulation of the q(g) fields, updrafts, cold pool, and front locations. The improvements were most notable in the first 2h, indicating a highly desired benefit of the LDA in lightning and convective precipitation nowcasting (0-2h) applications. |
英文关键词 | numerical weather forecast lightning data assimilation cloud microphysics |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000418084500004 |
WOS关键词 | MESOGAMMA-SCALE ANALYSIS ; US-ARMY TEST ; ENSEMBLE KALMAN FILTER ; SHORT-TERM FORECAST ; FLASH-EXTENT DATA ; SIMULATED ELECTRIFICATION ; EVALUATION COMMAND ; PART I ; BULK MICROPHYSICS ; TORNADO OUTBREAK |
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/32716 |
专题 | 气候变化 |
作者单位 | 1.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing, Jiangsu, Peoples R China; 2.Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA; 3.Univ Oklahoma OU, Cooperat Inst Mesoscale Meteorol Studies CIMMS, NOAA Natl Severe Storms Lab, Norman, OK USA |
推荐引用方式 GB/T 7714 | Wang, Haoliang,Liu, Yubao,Cheng, William Y. Y.,et al. Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF-RTFDDA[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2017,122(22). |
APA | Wang, Haoliang.,Liu, Yubao.,Cheng, William Y. Y..,Zhao, Tianliang.,Xu, Mei.,...&Fierro, Alexandre O..(2017).Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF-RTFDDA.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,122(22). |
MLA | Wang, Haoliang,et al."Improving Lightning and Precipitation Prediction of Severe Convection Using Lightning Data Assimilation With NCAR WRF-RTFDDA".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 122.22(2017). |
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