Climate Change Data Portal
DOI | 10.1016/j.atmosres.2020.104980 |
The impact of optimal selected historical forecasting samples on hybrid ensemble-variational data assimilation | |
Chen Y.; Guo S.; Meng D.; Wang H.; Xu D.; Wang Y.; Wang J. | |
发表日期 | 2020 |
ISSN | 0169-8095 |
卷号 | 242 |
英文摘要 | A resampling method that selects historical forecasting samples as supplementary samples is proposed for the hybrid ensemble-variational data assimilation system to alleviate the computational burden of ensemble forecasting samples. To select reasonable samples from all historical forecasting samples, the first modes of absolute vorticity are abstracted by the empirical orthogonal function (EOF) as indicators of atmospheric dynamic features from the background and each of historical forecasting sample, then they are matched at the analysis time. A series of single observation tests and 19-day cycling data assimilation and forecasting experiments for a Mei-yu period are carried out to evaluate the impact of the selected historical forecasting samples. The single observation tests indicate that the use of selected historical forecasting samples is able to provide reasonable flow-dependent background error covariance for the data assimilation system. The cycling data assimilation and forecasting experiments demonstrate that the analyses and forecasts as well as precipitation forecast skills are improved by using the combination of selected historical forecasting samples and ensemble forecasting samples. The sample-combined experiment performs close to the experiment with full-size ensemble forecasting samples, but it spends fewer computational resources. The diagnosis of a heavy rainfall case is presented to further illustrate the role of the selected historical forecasting samples. It is found that the simulation of vertical velocity and relative humidity are improved for the case in the experiment of the combined samples, leading to better intensity and position forecasts of the precipitation. © 2020 Elsevier B.V. |
英文关键词 | Computational cost; Data assimilation; Hybrid; Numerical weather prediction |
语种 | 英语 |
scopus关键词 | Atmospheric humidity; Orthogonal functions; Background-error covariances; Computational costs; Data assimilation; Empirical Orthogonal Function; Hybrid; Numerical weather prediction; Variational data assimilation; Variational data assimilation system; Weather forecasting; climate prediction; cost analysis; data assimilation; empirical orthogonal function analysis; ensemble forecasting; forecasting method; weather forecasting; Citrus maxima |
来源期刊 | Atmospheric Research
![]() |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/141910 |
作者单位 | Key Laboratory of Meteorological Disaster of Ministry of Education (KLME) / Joint International Research Laboratory of Climate and Environment Change (ILCEC) / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, 210044, China; Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, United States; NOAA/OAR/Earth System Research Laboratory/Global Systems Division, Boulder, , CO, United States |
推荐引用方式 GB/T 7714 | Chen Y.,Guo S.,Meng D.,et al. The impact of optimal selected historical forecasting samples on hybrid ensemble-variational data assimilation[J],2020,242. |
APA | Chen Y..,Guo S..,Meng D..,Wang H..,Xu D..,...&Wang J..(2020).The impact of optimal selected historical forecasting samples on hybrid ensemble-variational data assimilation.Atmospheric Research,242. |
MLA | Chen Y.,et al."The impact of optimal selected historical forecasting samples on hybrid ensemble-variational data assimilation".Atmospheric Research 242(2020). |
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 |
导出为Endnote文件 |
谷歌学术 |
谷歌学术中相似的文章 |
[Chen Y.]的文章 |
[Guo S.]的文章 |
[Meng D.]的文章 |
百度学术 |
百度学术中相似的文章 |
[Chen Y.]的文章 |
[Guo S.]的文章 |
[Meng D.]的文章 |
必应学术 |
必应学术中相似的文章 |
[Chen Y.]的文章 |
[Guo S.]的文章 |
[Meng D.]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
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。