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A Methodological Study of Big Data and Atmospheric Science

大数据与大气科学的方法论研究

基本信息

批准号：
1754740
负责人：
Benjamin Kravitz
金额：
$ 50.07万
依托单位：
Indiana University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1754740&HistoricalAwards=false
关键词：
Methodological Study Big Data Atmospheric

项目摘要

This award supports a two-year project that investigates fundamental methodological aspects of Big Data in atmospheric science. The PI, in collaboration with scientists at the National Center for Atmospheric Research and several research assistants (a post-doctoral fellow and some graduate students), plans to establish some basic foundations, understandings and analysis of the relative merits of a variety of methods in the analysis and application of Big Data within atmospheric science and modeling. The main questions to be addressed by the researcher and her collaborators include the following. How do practices and technologies for data collection, dissemination and use affect the production of scientific knowledge? What is the role of theories and hypotheses within research practices and data analysis? If data-driven research constitutes a distinctive mode of knowledge production, how is that knowledge best delivered? The researcher intends to establish a philosophy of Big Data in atmospheric science; she plans to disseminate the results of her research to different audiences by producing several papers for diverse professional journals. The researcher also intends to formulate and communicate any knowledge for policy that might result from her Big Data research. She also plans to train a post-doc and some grad students so that they may serve as resources for policy makers to facilitate effective application of atmospheric science to public policy.The PI and her collaborators will examine what is involved in moving from the Big Data context of the outputs of multiple atmospheric models involving terabytes of data, to the applications and reduction of that data to a particular city's request for specific temperature forecasts, and how this analysis might become more automated through analysis of Big Data in a way not being done at present. This fundamental problem facing the regional modelers in modeling groups around the world is that there are tens of thousands of city and regional planners who need information from the regional weather models, but the information these users and impact-personnel need is not available to read off of the model without the help of the scientists who created it. They need translators between the models and the impact-personnel and users. One modeling group the PI would be working with at the National Center for Atmospheric Research is attempting to develop automation of various kinds to answer a range of questions from users and impact-personnel, automation that reduces Big Data into small and specific answers to specific questions that avoids various pitfalls and peculiarities of the models. That is, they are trying to build Big Data software systems that could act as translators. These problems are significantly exacerbated by the data being Big in one way or another, and they find that the available Big Data analytics are not helping them in the way they ideally could. The PI and collaborators propose to highlight, clarify, and define more precisely what exactly this group and others could use in their applications to social contexts.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.

该奖项支持一个为期两年的项目，研究大气科学中大数据的基本方法方面。该项目与国家大气研究中心的科学家和几位研究助理（一名博士后和一些研究生）合作，计划建立一些基本的基础，理解和分析各种方法在大气科学和建模中分析和应用大数据的相对优点。研究人员和她的合作者要解决的主要问题包括以下几点。数据收集、传播和使用的做法和技术如何影响科学知识的产生？理论和假设在研究实践和数据分析中的作用是什么？如果数据驱动的研究构成了一种独特的知识生产模式，那么如何才能最好地传递这些知识？研究人员打算在大气科学中建立一种大数据哲学；她计划在不同的专业期刊上发表几篇论文，将她的研究成果传播给不同的受众。研究人员还打算制定和沟通任何知识的政策，可能会从她的大数据研究。她还计划培养一名博士后和一些研究生，使他们成为政策制定者的资源，促进大气科学在公共政策中的有效应用。PI和她的合作者将研究从涉及tb数据的多个大气模型输出的大数据上下文，到特定城市特定温度预测请求的数据应用和减少所涉及的内容，以及如何通过大数据分析使这种分析变得更加自动化，这是目前尚未完成的方式。世界各地建模小组中的区域建模者面临的一个基本问题是，有成千上万的城市和区域规划者需要从区域天气模型中获取信息，但如果没有创建该模型的科学家的帮助，这些用户和影响人员需要的信息无法从模型中读取。他们需要在模型和影响人员以及用户之间进行翻译。PI将与国家大气研究中心（National Center for Atmospheric Research）的一个建模小组合作，该小组正试图开发各种类型的自动化，以回答用户和影响人员提出的一系列问题，将大数据简化为针对特定问题的小而具体的答案，从而避免模型的各种陷阱和特点。也就是说，他们正在尝试构建可以充当翻译的大数据软件系统。这些问题由于数据以这样或那样的方式变大而明显加剧，他们发现现有的大数据分析并没有以理想的方式帮助他们。PI和合作者建议更精确地强调、澄清和定义这个群体和其他人在他们的社会环境应用中可以使用的东西。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Varieties of Data-Centric Science: Regional Climate Modeling and Model Organism Research

以数据为中心的科学种类：区域气候建模和模式生物研究

DOI：
10.1017/psa.2021.50
发表时间：
2022
期刊：
Philosophy of Science
影响因子：
1.7
作者：
Lloyd, Elisabeth;Lusk, Greg;Gluck, Stuart;McGinnis, Seth
通讯作者：
McGinnis, Seth

Opinion: The scientific and community-building roles of the Geoengineering Model Intercomparison Project (GeoMIP) – past, present, and future

DOI：
10.5194/acp-23-5149-2023
发表时间：
2023-05
期刊：
Atmospheric Chemistry and Physics
影响因子：
6.3
作者：
D. Visioni;B. Kravitz;A. Robock;S. Tilmes;J. Haywood;O. Boucher;M. Lawrence;P. Irvine;U. Niemeier;L. Xia;G. Chiodo;C. Lennard;S. Watanabe;J. Moore;H. Muri
通讯作者：
D. Visioni;B. Kravitz;A. Robock;S. Tilmes;J. Haywood;O. Boucher;M. Lawrence;P. Irvine;U. Niemeier;L. Xia;G. Chiodo;C. Lennard;S. Watanabe;J. Moore;H. Muri

Quantifying the Efficiency of Stratospheric Aerosol Geoengineering at Different Altitudes

量化不同海拔平流层气溶胶地球工程的效率

DOI：
10.1029/2023gl104417
发表时间：
2023
期刊：
Geophysical Research Letters
影响因子：
5.2
作者：
Lee, Walker R.;Visioni, Daniele;Bednarz, Ewa M.;MacMartin, Douglas G.;Kravitz, Ben;Tilmes, Simone
通讯作者：
Tilmes, Simone

Conducting more inclusive solar geoengineering research: A feminist science framework