AI 中文总结
本文采用多变量对象方法，利用Met Office Unified Model的预报数据，识别出导致2018-2020年喀拉拉邦洪水的共同大尺度天气驱动因子，证明中期可预测性并提出相关概念模式。
AI 中文摘要
喀拉拉邦部分地区连续三年遭受毁灭性洪水袭击，这一序列在历史上并无先例。本文采用基于多变量对象的方法，研究英国气象局统一模式（Met Office Unified Model）针对2018、2019和2020年季风季节的全球预报，以识别和理解导致洪水的大尺度天气驱动因子。研究的关键目标之一是确定这些洪水事件背后天气驱动因子的相似性，另一个目标是探究是否可以通过使用多变量方法对预报输出进行后处理来提高可预测性，该方法使用除降水之外的变量，而降水本身往往内在可预测性较低。为此，事件识别首先聚焦于分析结果，之后聚焦于第5天的预报。研究发现，基于对象的诊断评估方法的多变量版本（MvMODE）能够成功识别出所有三个季节中对应事件日期的连续数日，这些日期组合在一起具有产生洪水的潜力。这一结果在分析结果和5天预报中均得到实现，证明了两点：a）这些事件具有共同的天气驱动因子；b）存在可被挖掘的内在可预测性。5天预报在每次情况下都与分析得到的对象相匹配，证明潜在的大尺度驱动因子在中期范围内是可预测的。基于这些结果，本文提出了一种概念性天气模式演变，可用于未来识别此类事件，该天气模式与中纬度地区的大气河流具有一定相似性。
英文摘要
Parts of Kerala were hit by devastating floods three years in a row. This sequence was historically without precedent. This paper uses a multivariate object-based approach to examine the global forecasts of the Met Office Unified Model for the 2018, 2019 and 2020 monsoon seasons to identify and understand the large-scale synoptic drivers that led to the floods. Identifying similarities between synoptic drivers behind these flooding events was a key objective, as was understanding whether one could enhance predictability by using a multivariate approach for post-processing forecast output, using variables other than just precipitation, which is often inherently less predictable on its own. To this end event identification focused on the analyses first, and then on a day 5 forecast. The study found that the multivariate version of the Method for Object-based Diagnostic Evaluation (MvMODE) was able to successfully identify sequences of days in all three seasons corresponding to the event dates, which in combination, had flood-producing potential. This was achieved in both the analyses and in the 5-day forecasts, proving that a) the events share common synoptic drivers and b) there is inherent predictability which can be tapped into. The 5-day forecasts matched the analysed objects on each occasion, proving that the underlying large-scale drivers may be predictable into the medium-range. Based on the results, the paper proposes a conceptual synoptic pattern evolution which can help identify such events in future. The synoptic pattern has some similarities to atmospheric rivers in the mid-latitudes.
CommentsMet Office deliverable D4.1.2 for the Met Office Weather and Climate Science and Services Partnership India project (2025)
