高影响天气事件的无缝预测：可操作预报的比较研究
Seamless prediction of high-impact weather events: a comparison of actionable forecasts
AI总结：
本文提出一种基于对角评分的新高影响天气指数，并与EFI等集合统计量比较，证明其在较长预见期具有更强判别能力，为高影响天气无缝预测奠定基础。
摘要：
本文引入了一种新的高影响天气预报指数，并与已广泛应用的极端预报指数（EFI）进行比较评估。该比较研究还纳入了另外两种集合汇总统计量：尾部偏移（shift-of-tail）和标准化集合平均异常。所有这些预报都基于相同的要素：欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts）运行的集合预报，以及由一组再预报（reforecasts）推导出的相应模式气候态。新指数源于极端事件预报验证方面的最新进展：它是作为对角评分（diagonal score）的一致预报而推导出来的，对角评分是连续排名概率评分（continuous ranked probability score）的一种加权版本，专门针对高影响事件。在本研究中，我们强调了预报离散化对于信息沟通和决策制定的重要性。当用户可以在预报超过阈值时决定采取行动时，该预报即为可操作预报。我们进行了预报验证，以评估不同指数的潜在技巧以及它们作为可操作预报的具体技巧。在所研究的可操作预报中，新提出的指数表现出最强的判别能力，尤其是在较长预见期方面，这为跨时间尺度的高影响天气无缝预测铺平了道路。
英文摘要：
A new index for high-impact weather forecasting is introduced and assessed in comparison with the well-established extreme forecast index (EFI). Two other ensemble summary statistics are also included in this comparison study: the shift-of-tail and a standardised ensemble mean anomaly. All these forecasts are based on the same ingredients: the ensemble forecast run at the European Centre for Medium-Range Weather Forecasts and the corresponding model climatology derived from a set of reforecasts. The new index emerges from recent developments in forecast verification of extreme events: it is derived as a consistent forecast with the diagonal score, a weighted version of the continuous ranked probability score targetting high-impact events. In this study, we emphasise the importance of forecast discretisation for communication purposes and decision-making. A forecast is actionable in the situation where a user can decide to take action when a threshold is exceeded by the forecast. Forecast verification is performed to assess both the potential skill of the different indices as well as their specific skill as actionable forecasts. Among the investigated actionable forecasts, the new proposed index demonstrates the strongest discrimination power, in particular at longer lead times, paving the way for seamless predictions of high-impact weather across time ranges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
