Data Assimilation, Reanalysis and the Construction of Weather and Climate

大气科学使用数据同化，这是一个过程，通过这个过程，来自不同来源的信息被组合在一起，产生特定时间的大气状态的表示。这些来源包括传统的气象仪器和计算机模拟模型，它们使用在指定时间附近收集的观测数据。这些数据以复杂的方式合成，并对接近那个时间的条件进行了一个或多个模拟。由此产生的表示称为分析。分析每天都是以这种方式进行的，它们作为天气预报模型的初始条件。数据同化也是使用观测数据档案进行回溯性的，以产生跨越几十年的再分析数据集。这些再分析数据集已经成为天气和气候研究的主要内容；它们被用来调查大气动力学，评估气候模型，并对最近的气候变化做出定量估计。由于数据同化的产品经常用于传统观测数据的作用，一些关注似乎是相关的。其中一些用途有问题吗？或者是传统观测数据和数据同化产品之间的差异不是很深？数据同化在当代科学方法哲学研究中应该占有什么位置？这些都是激励这个项目的首要问题。该项目的第一部分将探讨数据同化及其产品在多大程度上能够与科学哲学中的核心概念相一致。该项目要解决的具体问题包括以下几个方面。像一些科学家建议的那样，数据同化系统(或许是它们包含的模拟模型)本身就可以被视为观测仪器吗？分析和再分析真的与其他数据模型有如此大的不同吗？其他数据模型在不同方面都充满了理论。如果能够产生高度准确和可靠的数据同化系统，它们会在没有收集传统观测数据的地区提供大气条件的测量结果吗？该项目的第二部分将从方法论的角度评价数据同化产品的当前使用情况(在传统观测数据中的作用)。需要解决的关键问题如下。考虑到气候模型与重新分析中使用的模拟模型有许多相同的假设，使用重新分析来评估气候模型是否涉及不可接受的循环性？更广泛地说，鉴于数据同化产品是在缺乏传统观测数据的情况下产生的，那么从数据同化产品得出关于真实大气的结论(如与大气现象的原因或大气变暖的速率有关的结论)的依据是什么？智力价值该项目旨在为科学哲学和大气科学做出贡献。在科学哲学中，它将促进对计算机模拟正在改变科学实践的方式的理解，并使与之相关的知识论复杂化，这是目前感兴趣的一个主要领域。在这样做的同时，它还将重新审查该学科的一些核心概念，如测量。在大气科学方面，该项目将解决有关数据同化的基本方法学问题，这些问题没有得到应有的重视，尽管它们与实践直接相关。广泛的影响该项目的结果将在期刊文章和演讲中广泛传播，对象既有哲学家，也有大气科学家。还将为气象学和哲学专业的本科生开发更容易理解的演示文稿。此外，一些研究有可能通过改善对气候模型的评估而造福社会，这些模型的气候变化预测是当前环境政策讨论的重要输入。特别注意：该项目由STS和CLD(气候和大型动力学计划)联合资助。

IntroductionAtmospheric science uses data assimilation, a process by which information from disparate sources is combined to produce a representation of the state of the atmosphere at a particular time. These sources include traditional meteorological instruments and computer simulation models, which use observational data collected near the specified time. The data are synthesized in complex ways with one or more simulations of conditions near that time. The resulting representation is known as an analysis. Analyses are produced in this way on a daily basis, and they serve as initial conditions for weather forecasting models. Data assimilation is also performed retrospectively using archives of observational data to produce reanalysis datasets that span several decades. These reanalysis datasets have become a staple of weather and climate research; they are used to investigate atmospheric dynamics, to evaluate climate models, and to make quantitative estimates of recent climate change. Since the products of data assimilation are often used in the roles of traditional observational data, some concerns seem pertinent. Are some of these uses problematic? Or are the differences between traditional observational data and the products of data assimilation not very deep? What place should data assimilation have within contemporary philosophical research on scientific method? These are the overarching questions that motivate this project. The first part of the project will explore the extent to which data assimilation and its products can be reconciled with core concepts in the philosophy of science. Specific questions that are to be addressed in the project include the following. Can data assimilation systems (or perhaps the simulation models they incorporate) be considered observing instruments in their own right, as some scientists have suggested? Are analyses and reanalyses really so different from other data models, which are theory-laden in various ways? If highly accurate and reliable data assimilation systems could be produced, would they deliver measurements of atmospheric conditions in regions where no traditional observational data were collected? The second part of the project will evaluate current uses of data assimilation products (in the roles of traditional observational data) from a methodological point of view. Key questions to be addressed are the following. Does the use of reanalyses to evaluate climate models involve an unacceptable circularity, given that climate models share many assumptions with the simulation models used in reanalysis? More generally, on what grounds can inferences from data assimilation products to conclusions about the real atmosphere (such as those having to do with the causes of atmospheric phenomena or the rates of atmospheric warming) be justified, given that data assimilation products are produced in response to a lack of traditional observational data?Intellectual meritThe project aims to contribute to both philosophy of science and atmospheric science. In philosophy of science, it will advance understanding of the ways in which computer simulation is changing scientific practice and complicating the theory of knowledge associated with it, currently a major area of interest. In doing so, it will also re-examine some core concepts of the discipline, such as measurement. For atmospheric science, the project will address fundamental methodological questions about data assimilation that have not received the attention they deserve, despite their direct relevance for practice.Broader impactsResults of the project will be disseminated widely in journal articles and in talks, targeting both philosophers and atmospheric scientists. More accessible presentations will also be developed for undergraduate students in meteorology and philosophy. In addition, some of the research has the potential to benefit society, by improving evaluation of the climate models whose climate change projections are an important input to current environmental policy discussions.Special Note: This project is jointly funded by STS and CLD (the Climate and Large-Scale Dynamics Program).