Python Indian Weather Radar Toolkit (pyiwr):用于分析和可视化天气雷达数据的开源 Python 库

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2024-06-13 DOI:10.1016/j.jocs.2024.102363
Nitig Singh , Vaibhav Tyagi , Saurabh Das , Udaya Kumar Sahoo , Shyam Sundar Kundu
{"title":"Python Indian Weather Radar Toolkit (pyiwr):用于分析和可视化天气雷达数据的开源 Python 库","authors":"Nitig Singh ,&nbsp;Vaibhav Tyagi ,&nbsp;Saurabh Das ,&nbsp;Udaya Kumar Sahoo ,&nbsp;Shyam Sundar Kundu","doi":"10.1016/j.jocs.2024.102363","DOIUrl":null,"url":null,"abstract":"<div><p>The Python Indian Weather Radar Toolkit, abbreviated as \"pyiwr\", is an open-source Python library tailored for the purpose of handling data from the Indian Doppler Weather Radar (DWR). This paper provides a comprehensive overview of the pyiwr, which serves as a toolkit to read, analyze, process, and visualize weather radar data. Apart from this, the toolkit offers a range of robust functions implementing various algorithms covering several aspects of the radar data processing and quality control that facilitate the manipulation and analysis of weather radar data. To demonstrate the practical applicability of pyiwr, various case studies are presented, focusing on processing raw reflectivity data (clutter correction), Quantitative Precipitation Estimation (QPE) using Z-R relationship and time-series analysis of reflectivity and rain intensity, both spatially as well as at a specific location, during various meteorological events. This module enhances the accessibility and compatibility of radar data, enabling researchers, weather forecasters, and hydrologists to efficiently work with DWR data (particularly Indian DWR) that fosters advancements in weather radar research and applications. The open availability of pyiwr's source code on GitHub ensures that researchers and practitioners can not only access the toolkit but also contribute to its ongoing development.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Python Indian Weather Radar Toolkit (pyiwr): An open-source Python library for processing, analyzing and visualizing weather radar data\",\"authors\":\"Nitig Singh ,&nbsp;Vaibhav Tyagi ,&nbsp;Saurabh Das ,&nbsp;Udaya Kumar Sahoo ,&nbsp;Shyam Sundar Kundu\",\"doi\":\"10.1016/j.jocs.2024.102363\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The Python Indian Weather Radar Toolkit, abbreviated as \\\"pyiwr\\\", is an open-source Python library tailored for the purpose of handling data from the Indian Doppler Weather Radar (DWR). This paper provides a comprehensive overview of the pyiwr, which serves as a toolkit to read, analyze, process, and visualize weather radar data. Apart from this, the toolkit offers a range of robust functions implementing various algorithms covering several aspects of the radar data processing and quality control that facilitate the manipulation and analysis of weather radar data. To demonstrate the practical applicability of pyiwr, various case studies are presented, focusing on processing raw reflectivity data (clutter correction), Quantitative Precipitation Estimation (QPE) using Z-R relationship and time-series analysis of reflectivity and rain intensity, both spatially as well as at a specific location, during various meteorological events. This module enhances the accessibility and compatibility of radar data, enabling researchers, weather forecasters, and hydrologists to efficiently work with DWR data (particularly Indian DWR) that fosters advancements in weather radar research and applications. The open availability of pyiwr's source code on GitHub ensures that researchers and practitioners can not only access the toolkit but also contribute to its ongoing development.</p></div>\",\"PeriodicalId\":48907,\"journal\":{\"name\":\"Journal of Computational Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Science\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S187775032400156X\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Science","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S187775032400156X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

Python 印度天气雷达工具包缩写为 "piwr",是一个开源 Python 库,专门用于处理印度多普勒天气雷达(DWR)的数据。本文全面介绍了 pyiwr,它是一个读取、分析、处理和可视化天气雷达数据的工具包。除此以外,该工具包还提供了一系列强大的函数,可实现各种算法,涵盖雷达数据处理和质量控制的多个方面,便于操作和分析天气雷达数据。为了展示 pyiwr 的实际应用性,介绍了各种案例研究,重点是处理原始反射率数据(杂波校正)、使用 Z-R 关系进行定量降水估算 (QPE),以及在各种气象事件期间对反射率和雨强进行空间和特定位置的时间序列分析。该模块增强了雷达数据的可访问性和兼容性,使研究人员、天气预报员和水文学家能够高效地使用 DWR 数据(特别是印度 DWR),从而促进天气雷达研究和应用的发展。pyiwr 的源代码在 GitHub 上开放,这确保了研究人员和从业人员不仅能访问该工具包,还能为其持续开发做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Python Indian Weather Radar Toolkit (pyiwr): An open-source Python library for processing, analyzing and visualizing weather radar data

The Python Indian Weather Radar Toolkit, abbreviated as "pyiwr", is an open-source Python library tailored for the purpose of handling data from the Indian Doppler Weather Radar (DWR). This paper provides a comprehensive overview of the pyiwr, which serves as a toolkit to read, analyze, process, and visualize weather radar data. Apart from this, the toolkit offers a range of robust functions implementing various algorithms covering several aspects of the radar data processing and quality control that facilitate the manipulation and analysis of weather radar data. To demonstrate the practical applicability of pyiwr, various case studies are presented, focusing on processing raw reflectivity data (clutter correction), Quantitative Precipitation Estimation (QPE) using Z-R relationship and time-series analysis of reflectivity and rain intensity, both spatially as well as at a specific location, during various meteorological events. This module enhances the accessibility and compatibility of radar data, enabling researchers, weather forecasters, and hydrologists to efficiently work with DWR data (particularly Indian DWR) that fosters advancements in weather radar research and applications. The open availability of pyiwr's source code on GitHub ensures that researchers and practitioners can not only access the toolkit but also contribute to its ongoing development.

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 Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
期刊最新文献
AFF-BPL: An adaptive feature fusion technique for the diagnosis of autism spectrum disorder using Bat-PSO-LSTM based framework Data-driven robust optimization in the face of large-scale datasets: An incremental learning approach VEGF-ERCNN: A deep learning-based model for prediction of vascular endothelial growth factor using ensemble residual CNN A new space–time localized meshless method based on coupling radial and polynomial basis functions for solving singularly perturbed nonlinear Burgers’ equation Implementation of the emulator-based component analysis
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1