SI2-SSE: MetPy - A Python GEMPAK Replacement for Meteorological Data Analysis

MetPy项目旨在通过提供一套经过充分测试的现代软件工具，使大气科学研究和教学变得更容易和更具可重复性。气象学家需要许多专门的计算和地图才能了解天气并做出可靠的预测。他们使用的工具必须提供正确的结果，因为生命和财产依赖于准确的预测和研究。这个项目将把大部分功能从一个被广泛使用和信任的--但老化和最不受支持的--软件程序GEMPAK(通用气象包)移植到MetPy中，该程序使用Python编程语言开发，具有精心设计的新软件架构。由于在许多科学界中非常流行，所以选择了Python作为首选语言。MetPy将成为气象界进入这一不断增长的科学软件生态系统的入口。除了在MetPy中提供GEMPAK的功能之外，这个项目还将实现更好的用户界面，这将帮助学生和研究人员更容易地开始。软件团队将在其MetPy的开发中使用软件开发的最佳做法，并确保它可以与所有常见的气象数据源一起工作。MetPy的每个相关方面都将以一种易于消化的方式记录在MetPy项目网页上。开发团队将与大学教师合作，帮助修改他们的课程材料，以整合MetPy。此外，该团队将每年教授MetPy和Python培训研讨会，让大学教授、学生和专业人员获得有关如何以更快、更稳健的方式进行研究的动手培训。该项目寻求通过将基础软件程序GEMPAK的关键功能元素引入创新丰富的巨蟒生态系统来满足大气科学界的需求。通过投入软件开发资源来增加MetPy可以使用的数据类型和文件格式的数量，改进底层数据模型，并达到与GEMPAK相同的功能，MetPy可以被定位为社区支持的旧包的替代品。这项工作利用了整个Python生态系统，并支持大气科学界向由Python驱动的可复制工作流的转变(已经在很好地进行中)。这一过渡将为社区带来许多好处。通过将GEMPAK所需的功能引入Python生态系统，该项目将使大气科学家能够：简化探索性分析的过程，拥有可从教室带到工作人员的跨平台工具链，简化研究工作流程，使科学更容易和更具重现性，提供具有文献参考的经过测试的特定领域计算库，并创建出版质量的数据可视化。教育工作者和研究人员将能够用现代工具包取代他们对不再开发且越来越难以维护的遗留软件的使用，从而提高大气科学研究的灵活性和可重复性。纳入现代自动化软件构建和测试工具、社区支持的可靠文件和学习材料以及快速纳入新的环境数据来源的能力，将加强大气科学软件工作流程的可持续性。最后，大气科学工具链的现代化为基于网络的工具(例如Jupyter笔记本)的使用打开了大门，这些创新在使用遗留软件时将很难或不可能得到利用。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The MetPy project aims to make atmospheric science research and teaching easier and more reproducible by providing a set of well-tested and modern software tools. Meteorologists require many specialized calculations and maps in order to understand the weather and make reliable predictions. The tools they use must provide correct results, since lives and property depend on accurate forecasts and research. This project will port the bulk of the functionality from a widely used and trusted -- but aging and minimally supported -- software program called GEMPAK (the GEneral Meteorological PAcKage) into MetPy, developed using the Python programming language, and with a well-designed, new software architecture. Python has been selected as the language of choice because it has become very popular in many scientific communities. MetPy will be the meteorological community's entry into this growing scientific software ecosystem. In addition to making GEMPAK's functionality available in MetPy, this project will implement a better user-interface, which will help students and researchers get started more easily. The software team will use software development best practices in its development of MetPy, and ensure that it can work with all common meteorological data sources. Every relevant aspect of MetPy will be documented in an easy to digest way on the MetPy project webpage. The development team will work with university instructors to help revise their course materials to integrate MetPy. In addition, the team will teach MetPy and Python training workshops each year, allowing university professors, students, and professionals to get hands-on training on how to do their research in a faster and more robust way. This project seeks to fill a need within the atmospheric science community by bringing key functional elements of a foundational software program, GEMPAK, to the innovation-rich Python ecosystem. By devoting software development resources to increasing the number of data types and file formats MetPy can work with, improving the underlying data model, and reaching feature parity with GEMPAK, MetPy can be positioned as a community-supported replacement for the older package. This effort leverages the entire Python ecosystem, and supports the movement (already well under way) of the atmospheric science community to Python-driven reproducible workflows. This transition will provide a number of community benefits. By bringing needed functionality from GEMPAK to the Python ecosystem, this project will allow atmospheric scientists to: simplify the process of exploratory analysis, have a cross-platform toolchain that can be carried from the classroom to the workforce, simplify the research workflow to make science easier and more reproducible, provide a tested library of domain-specific calculations with literature references, and create publication-quality data visualizations. Educators and researchers will be able to replace their use of legacy software, which is no longer being developed and is increasingly hard to maintain, with a modern toolkit that allows increased flexibility and reproducibility within atmospheric science research. Sustainability of the atmospheric science software workflow will be enhanced by the inclusion of modern automated software build-and-test tools, robust community-supported documentation and learning materials, and the ability to quickly incorporate new sources of environmental data. Finally, modernizing the atmospheric science toolchain opens the door to the use of innovations like web-based tools (Jupyter notebooks, for example) that would be difficult or impossible to take advantage of when using legacy software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.