GSTDTAP  > 气候变化
DOI10.1002/2017JD027340
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
ISSN2169-897X
EISSN2169-8996
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
被引频次:30[WOS]   [WOS记录]     [WOS相关记录]
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).
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Wang, Haoliang]的文章
[Liu, Yubao]的文章
[Cheng, William Y. Y.]的文章
百度学术
百度学术中相似的文章
[Wang, Haoliang]的文章
[Liu, Yubao]的文章
[Cheng, William Y. Y.]的文章
必应学术
必应学术中相似的文章
[Wang, Haoliang]的文章
[Liu, Yubao]的文章
[Cheng, William Y. Y.]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。