对有无参数化对流的集合输出进行后处理，以创建准确、混合、高保真的降雨预报
Post-processing output from ensembles with and without parametrised convection, to create accurate, blended, high-fidelity rainfall forecasts
摘要：
山洪暴发是一个重大的社会问题，但相关的降水预报往往很差。为了解决这个问题，可以尝试使用对流参数化（全局）集合的输出，后处理以在点尺度上进行预测，或对流解析有限区域集合。在这项研究中，我们将两者结合起来。首先，我们应用“ecPoint降雨”后处理ECMWF全球集合。然后，我们使用2.2km COSMO LAM集合输出（以意大利为中心），并使用尺度选择邻域方法对其进行后处理，以补偿成员不足。然后，这两个组件进行提前时间加权混合，以创建最终的概率6小时降雨预报。为预报员创建的产品构成了欧盟资助的MISTRAL项目中的“意大利山洪使用案例”，它将是一个实时开放获取的产品。一年的验证表明，ecPoint是最熟练的集成产品。后处理的COSMO集合增加了最大的价值，夏季对流事件在晚上，当全球模式有一个低估的偏见。在两个强降雨的案例研究中，我们观察到低估的最大点总数的原始ECMWF合奏，高估的原始COSMO合奏。然而，ecPoint增加了值，并突出了受影响最严重的区域，而COSMO的后处理通过消除不可靠的细节来减少极端。从用户的角度来看，最终合并的产品看起来最好，似乎是所有产品中最有技巧的。虽然我们的LAM后处理并没有隐含地包括偏差校正（进一步工作的主题），但我们的研究仍然提供了一个独特的蓝图，成功地结合全球和LAM系统在世界各地的集合降雨预报。随着全球集合越来越接近于拥有对流允许的分辨率，它对预报产品也有重要的影响。
英文摘要：
Flash flooding is a significant societal problem, but related precipitation forecasts are often poor. To address this, one can try to use output from convection-parametrising (global) ensembles, post-processed to forecast at point-scale, or convection-resolving limited area ensembles. In this study, we combine both. First, we apply the "ecPoint-rainfall" post-processing to the ECMWF global ensemble. Then, we use 2.2km COSMO LAM ensemble output (centred on Italy), and also post-process it using a scale-selective neighbourhood approach to compensate for insufficient members. The two components then undergo lead-time-weighted blending, to create the final probabilistic 6h rainfall forecasts. Product creation for forecasters constituted the "Italy Flash Flood use case" within the EU-funded MISTRAL project and it will be a real-time open-access product. One year of verification shows that ecPoint is the most skilful ensemble product. The post-processed COSMO ensemble adds most value to summer convective events in the evening, when the global model has an underprediction bias. In two heavy rainfall case studies we observed underestimation of the largest point totals in the raw ECMWF ensemble, and overestimation in the raw COSMO ensemble. However, ecPoint increase the value and highlighted best the most affected areas, whilst post-processing of COSMO diminished extremes by eradicating unreliable detail. The final merged products looked best from a user perspective and seemed to be the most skilful of all. Although our LAM post-processing does not implicitly include bias correction (a topic for further work) our study nonetheless provides a unique blueprint for successfully combining ensemble rainfall forecasts from global and LAM systems around the world. It also has important implications for forecast products as global ensembles move ever closer to having convection-permitting resolution.
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。
