Evidence-based studies in the environmental sciences frequently rely on the presence of spatially dense climatological data. However, such data are often available only at a fixed set of locations that may be regularly or irregularly arranged across a region. Spatial interpolation enables the approximation of variables of interest at locations between those sites. When conducting interpolation in collaboration with an end user or in interdisciplinary research, mutual knowledge exchange allows for greater insight on what is required of an interpolation method since each may have different pros and cons. We outline and discuss several key considerations one should make in an interpolation study, such as the purpose of the variable and the goals of the end user, including how the variable is used to inform decisions. This process is then illustrated via case study within a wildland fire weather context. For the province of Ontario, Canada, we contrast several methods for interpolating the Fire Weather Index (FWI), comparing them quantitatively via metrics and qualitatively using a proposed categorical gradients visualization scheme. Conditional simulations and a spatial ensemble are also investigated. This work is in collaboration with the Ontario Ministry of Natural Resources and Forestry.

中文翻译：

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关于应用于加拿大安大略省火灾天气指数的插值方法的选择

环境科学中的循证研究经常依赖于空间密集的气候数据的存在。然而，此类数据通常只能在一组固定的位置可用，这些位置可能在一个区域中有规律或不规则地排列。空间插值可以在这些站点之间的位置逼近感兴趣的变量。在与最终用户合作或在跨学科研究中进行插值时，相互知识交流可以更深入地了解插值方法的要求，因为每种方法都可能有不同的优缺点。我们概述并讨论了在插值研究中应该考虑的几个关键因素，例如变量的目的和最终用户的目标，包括如何使用变量来为决策提供信息。然后通过荒地火灾天气背景下的案例研究来说明这个过程。对于加拿大安大略省，我们对比了几种插值火灾天气指数 (FWI) 的方法，通过指标进行定量比较，并使用提出的分类梯度可视化方案进行定性比较。还研究了条件模拟和空间集合。这项工作是与安大略省自然资源和林业部合作开展的。