Progress, challenges, and future steps in data assimilation for convection-permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021

IF 2 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Guannan Hu, Sarah L. Dance, Ross N. Bannister, Hristo G. Chipilski, Oliver Guillet, Bruce Macpherson, Martin Weissmann, Nusrat Yussouf
{"title":"Progress, challenges, and future steps in data assimilation for convection-permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021","authors":"Guannan Hu,&nbsp;Sarah L. Dance,&nbsp;Ross N. Bannister,&nbsp;Hristo G. Chipilski,&nbsp;Oliver Guillet,&nbsp;Bruce Macpherson,&nbsp;Martin Weissmann,&nbsp;Nusrat Yussouf","doi":"10.1002/asl.1130","DOIUrl":null,"url":null,"abstract":"<p>In November 2021, the Royal Meteorological Society Data Assimilation (DA) Special Interest Group and the University of Reading hosted a virtual meeting on the topic of DA for convection-permitting numerical weather prediction. The goal of the meeting was to discuss recent developments and review the challenges including methodological developments and progress in making the best use of observations. The meeting took place over two half days on the 10 and 12 November, and consisted of six talks and a panel discussion. The scientific presentations highlighted some recent work from Europe and the USA on convection-permitting DA including novel developments in the assimilation of observations such as cloud-affected satellite radiances in visible channels, ground-based profiling networks, aircraft data, and radar reflectivity data, as well as methodological advancements in background and observation error covariance modelling and progress in operational systems. The panel discussion focused on key future challenges including the handling of multiscales (synoptic-, meso-, and convective-scales), ensemble design, the specification of background and observation error covariances, and better use of observations. These will be critical issues to address in order to improve short-range forecasts and nowcasts of hazardous weather.</p>","PeriodicalId":50734,"journal":{"name":"Atmospheric Science Letters","volume":"24 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asl.1130","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Science Letters","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/asl.1130","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 10

Abstract

In November 2021, the Royal Meteorological Society Data Assimilation (DA) Special Interest Group and the University of Reading hosted a virtual meeting on the topic of DA for convection-permitting numerical weather prediction. The goal of the meeting was to discuss recent developments and review the challenges including methodological developments and progress in making the best use of observations. The meeting took place over two half days on the 10 and 12 November, and consisted of six talks and a panel discussion. The scientific presentations highlighted some recent work from Europe and the USA on convection-permitting DA including novel developments in the assimilation of observations such as cloud-affected satellite radiances in visible channels, ground-based profiling networks, aircraft data, and radar reflectivity data, as well as methodological advancements in background and observation error covariance modelling and progress in operational systems. The panel discussion focused on key future challenges including the handling of multiscales (synoptic-, meso-, and convective-scales), ensemble design, the specification of background and observation error covariances, and better use of observations. These will be critical issues to address in order to improve short-range forecasts and nowcasts of hazardous weather.

Abstract Image

允许对流的数值天气预报数据同化的进展、挑战和未来步骤:2021年11月10日和12日举行的虚拟会议报告
2021年11月,英国皇家气象学会数据同化(DA)特别兴趣小组和雷丁大学主办了一场关于数据同化用于允许对流的数值天气预报的虚拟会议。会议的目的是讨论最近的事态发展和审查各种挑战,包括方法的发展和在最佳利用观察方面取得的进展。会议于11月10日和12日举行了两个半天,包括六次会谈和一次小组讨论。科学报告重点介绍了欧洲和美国最近在对流允许数据分析方面的一些工作,包括同化观测的新进展,如可见通道中受云影响的卫星辐射、地面剖面网络、飞机数据和雷达反射率数据,以及背景和观测误差协方差建模方法的进步和操作系统的进展。小组讨论的重点是未来的主要挑战,包括处理多尺度(天气尺度、中尺度和对流尺度)、集成设计、背景和观测误差协方差的规范,以及更好地利用观测结果。为了提高对危险天气的短期预报和临近预报,这些都是需要解决的关键问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Atmospheric Science Letters
Atmospheric Science Letters METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
4.90
自引率
3.30%
发文量
73
审稿时长
>12 weeks
期刊介绍: Atmospheric Science Letters (ASL) is a wholly Open Access electronic journal. Its aim is to provide a fully peer reviewed publication route for new shorter contributions in the field of atmospheric and closely related sciences. Through its ability to publish shorter contributions more rapidly than conventional journals, ASL offers a framework that promotes new understanding and creates scientific debate - providing a platform for discussing scientific issues and techniques. We encourage the presentation of multi-disciplinary work and contributions that utilise ideas and techniques from parallel areas. We particularly welcome contributions that maximise the visualisation capabilities offered by a purely on-line journal. ASL welcomes papers in the fields of: Dynamical meteorology; Ocean-atmosphere systems; Climate change, variability and impacts; New or improved observations from instrumentation; Hydrometeorology; Numerical weather prediction; Data assimilation and ensemble forecasting; Physical processes of the atmosphere; Land surface-atmosphere systems.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信