DADA：天气和气候相关事件探测和归因的数据同化
DADA: Data Assimilation for the Detection and Attribution of Weather- and Climate-related Events
摘要：
我们描述了一种新的方法，允许系统的天气和气候相关事件的因果关系，在近实时。该方法是专门设计的，以促进其在气象中心的实施，依靠数据处理时，例行进行数值预报的天气。也就是说，我们表明，因果归因可以作为所谓的数据同化程序的副产品，每天运行更新气象模型与新的大气观测，因此，所提出的方法可以利用强大的计算和观测能力的天气预报中心。我们解释了这种方法的理论基础，并勾勒出“基于数据同化的检测和归因”（DADA）程序的最突出的特点。该建议说明的背景下，经典的三变量洛伦兹模型与额外的强迫。最后提出了几个理论和实践研究问题，需要解决，使该建议易于操作的天气预报中心。
英文摘要：
We describe a new approach allowing for systematic causal attribution of weather and climate-related events, in near-real time. The method is purposely designed to facilitate its implementation at meteorological centers by relying on data treatments that are routinely performed when numerically forecasting the weather. Namely, we show that causal attribution can be obtained as a by-product of so-called data assimilation procedures that are run on a daily basis to update the meteorological model with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a "data assimilation-based detection and attribution" (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. Several theoretical and practical research questions that need to be addressed to make the proposal readily operational within weather forecasting centers are finally laid out.
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