使用R中的每日气候模型输出数据和未来热浪包进行工作。

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
R Journal Pub Date : 2017-01-01 Epub Date: 2017-06-08 DOI:10.32614/rj-2017-032
G Brooke Anderson, Colin Eason, Elizabeth A Barnes
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引用次数: 2

摘要

气候变化影响的研究可能需要对气候模式的输出进行大量处理,特别是在使用集合技术将多个气候模式的输出和每个模式的多次模拟结合起来时。当识别和描述多日极端事件(如热浪和霜冻天气)时,这种处理可以特别广泛,因为这些必须根据每天的时间步长从模型输出中处理。此外,气候模型输出的格式和遵循的标准对于大多数R用户来说可能不熟悉。在这里,我们概述了在r中处理每日气候模型输出数据的情况。然后介绍了我们开发的未来热浪包,该包旨在简化气候模型输出中识别、表征和探索多日极端事件的过程。这个包可以输入气候模型输出文件目录,使用可定制的事件定义识别所有极端事件,并使用用户指定的函数总结输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Working with Daily Climate Model Output Data in R and the futureheatwaves Package.

Working with Daily Climate Model Output Data in R and the futureheatwaves Package.

Working with Daily Climate Model Output Data in R and the futureheatwaves Package.

Working with Daily Climate Model Output Data in R and the futureheatwaves Package.

Research on climate change impacts can require extensive processing of climate model output, especially when using ensemble techniques to incorporate output from multiple climate models and multiple simulations of each model. This processing can be particularly extensive when identifying and characterizing multi-day extreme events like heat waves and frost day spells, as these must be processed from model output with daily time steps. Further, climate model output is in a format and follows standards that may be unfamiliar to most R users. Here, we provide an overview of working with daily climate model output data in R. We then present the futureheatwaves package, which we developed to ease the process of identifying, characterizing, and exploring multi-day extreme events in climate model output. This package can input a directory of climate model output files, identify all extreme events using customizable event definitions, and summarize the output using user-specified functions.

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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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