Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, road viability, ski resort management and tourism attractiveness. Meteo-France operates the PEARP-S2M probabilistic forecasting system, including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool refines the elevation resolution and the Crocus snowpack model represents the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on non-homogeneous regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24 h HN in the French Alps and Pyrenees. The calibration is tested at the station scale and the massif scale (i.e. aggregating different stations over areas of 1000 km 2 ). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

中文翻译：

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新积雪高度集合预报的统计后处理

摘要。预测新雪 (HN) 的高度对于雪崩灾害预测、道路可行性、滑雪胜地管理和旅游吸引力至关重要。Meteo-France 运营着 PEARP-S2M 概率预报系统，包括 PEARP 数值天气预报系统的 35 个成员，其中 SAFRAN 降尺度工具改进了高程分辨率，Crocus 积雪模型代表积雪中的主要物理过程。它提供了比直接 NWP 诊断更好的 HN 预测，但表现出显着的偏差和分散不足。我们对这些集合预测应用了统计后处理，基于非齐次回归和删失偏移 Gamma 分布。观察结果来自对法国阿尔卑斯山和比利牛斯山脉 24 小时 HN 的手动测量。校准在站点规模和地块规模（即在 1000 km 2 区域内聚合不同站点）进行测试。与原始预测相比，两个空间尺度都获得了类似的改进。因此，后处理可以应用于地块的任何点。测试了两个训练数据集：(1) 一个 22 年的同类再预测，其 NWP 模型分辨率和物理选项与操作系统相同，但没有相同的初始扰动；(2) 具有异构模型配置但相同扰动方法的3年实时预测。训练数据集的影响取决于提前期和评估标准。长期重新预测提高了强降雪的可靠性，但由于实时扰动的差异导致过度分散。因此，