PyDDA:一个用于风力反演的Python直接数据同化框架

Q1 Social Sciences
R. Jackson, S. Collis, T. Lang, C. Potvin, T. Munson
{"title":"PyDDA:一个用于风力反演的Python直接数据同化框架","authors":"R. Jackson, S. Collis, T. Lang, C. Potvin, T. Munson","doi":"10.5334/jors.264","DOIUrl":null,"url":null,"abstract":"This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/ openradar/PyDDA. PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals\",\"authors\":\"R. Jackson, S. Collis, T. Lang, C. Potvin, T. Munson\",\"doi\":\"10.5334/jors.264\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/ openradar/PyDDA. PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence.\",\"PeriodicalId\":37323,\"journal\":{\"name\":\"Journal of Open Research Software\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Open Research Software\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5334/jors.264\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Open Research Software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/jors.264","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 8

摘要

该软件吸收来自任意数量的天气雷达的数据以及其他空间风场(例如数值天气预报模型数据),以检索高分辨率的三维风场。PyDDA使用NumPy和SciPy的优化技术,结合Python大气辐射测量(ARM)雷达工具包(Py ART),使用3D变分技术(3DVAR)创建风场。PyDDA在GitHub上托管和分发,位于https://github.com/openradar/PyDDA。PyDDA有潜力被大气科学界用于开发雷达网络的高分辨率风反演。这些反演结果可用于数值天气预报模型和羽流建模的评估。本文展示了如何使用PyDDA将2台NEXt代RADar(NEXRAD)WSR-88D雷达和高分辨率快速刷新雷达的风场同化在一起,以在飓风佛罗伦萨内部创建高分辨率风场。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals
This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/ openradar/PyDDA. PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Open Research Software
Journal of Open Research Software Social Sciences-Library and Information Sciences
CiteScore
6.50
自引率
0.00%
发文量
7
审稿时长
21 weeks
×
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