Scikit-downscale: an open source Python package for scalable climate downscaling
Scikit-downscale: an open source Python package for scalable climate downscaling
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Scikit-downscale：用于可扩展气候降尺度的开源 Python 包
DOI：
10.1002/essoar.10507604.1
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Kent, Julia
中科院分区：
文献类型：
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作者：
Hamman, Joseph;Kent, Julia
来自地球系统模型的气候数据越来越多地被用于研究气候变化对一系列广泛的地球物理（森林火灾、渔业等）的影响。和人类系统（水库运营、城市热浪等）。在这些数据可以用于研究这些系统之前，必须执行通常称为偏差校正和统计降尺度的后处理步骤。“偏差校正”用于校正气候模型输出中的持续偏差，“统计降尺度”用于提高模型输出的时空分辨率（即1度至1/16度网格框）。出于我们的目的，我们将这两个部分称为“降尺度”。在过去的几十年里，应用程序社区开发了大量的降尺度方法。这些方法中的许多是后处理例程的临时集合，而其他方法则针对非常特定的应用程序。降尺度方法的扩散给气候应用界留下了大量的研究需要整理，而没有太多的综合指导方法选择或适用性。受气候变化带来的紧迫社会环境挑战的激励，并考虑到以前的降尺度工作的经验教训，我们已经开始致力于以社区为中心的气候降尺度开放框架：scikit-downscale。我们相信，社区将受益于一个精心设计的开源降尺度工具箱的存在，该工具箱具有标准接口以及基准数据库，以测试和评估新的和现有的降尺度方法。在这本笔记本中，我们提供了scikit-downscale项目的概述，详细介绍了如何使用它来缩小一系列表面气候变量，如气温和降水量。我们还强调了如何使用scikit-downscale框架来比较现有的方法，以及如何扩展它来支持新的降尺度方法的开发。
Climate data from Earth System Models are increasingly being used to study the impacts of climate change on a broad range of biogeophysical (forest fires, fisheries, etc.) and human systems (reservoir operations, urban heat waves, etc.). Before this data can be used to study many of these systems, post-processing steps commonly referred to as bias correction and statistical downscaling must be performed. “Bias correction” is used to correct persistent biases in climate model output and “statistical downscaling” is used to increase the spatiotemporal resolution of the model output (i.e. 1 deg to 1/16th deg grid boxes). For our purposes, we’ll refer to both parts as “downscaling”. In the past few decades, the applications community has developed a plethora of downscaling methods. Many of these methods are ad-hoc collections of post processing routines while others target very specific applications. The proliferation of downscaling methods has left the climate applications community with an overwhelming body of research to sort through without much in the form of synthesis guiding method selection or applicability. Motivated by the pressing socio-environmental challenges of climate change – and with the learnings from previous downscaling efforts in mind – we have begun working on a community-centered open framework for climate downscaling: scikit-downscale. We believe that the community will benefit from the presence of a well-designed open source downscaling toolbox with standard interfaces alongside a repository of benchmark data to test and evaluate new and existing downscaling methods. In this notebook, we provide an overview of the scikit-downscale project, detailing how it can be used to downscale a range of surface climate variables such as air temperature and precipitation. We also highlight how scikit-downscale framework is being used to compare existing methods and how it can be extended to support the development of new downscaling methods.