Abstract. Reliable estimation of precipitation fields at high resolution is a key issue for snow cover modelling in mountainous areas, where the density of precipitation networks is far too low to capture their complex variability with topography. Adequate quantification of the remaining uncertainty in precipitation estimates is also necessary for further assimilation of complementary snow observations in snow models. Radar observations provide spatialised estimates of precipitation with high spatial and temporal resolution, and are often combined with rain gauge observations to improve the accuracy of the estimate. However, radar measurements suffer from significant shortcomings in mountainous areas (in particular, unrealistic spatial patterns due to ground clutter). Precipitation fields simulated by high-resolution numerical weather prediction (NWP) models provide an alternative estimate, but suffer from systematic biases and positioning errors. Even though these uncertainties can be partially described by ensemble NWP systems and systematic errors can be reduced by statistical post-processing, NWP precipitation estimates are still not reliable enough for the requirements of high resolution snow cover modelling. In this study, better precipitation estimates are obtained through a specific analysis based on a combination of all these available products. First, a pre-processing step is proposed to mitigate the main deficiencies of radar and gauges precipitation estimation products, focusing on reducing unrealistic spatial patterns. This method also provides a spatialised estimate of the associated error in mountainous areas, based on a climatological analysis of both radar and NWP-estimated precipitation. Three ensemble daily precipitation analysis methods are then proposed, first using only the modified precipitation estimates and associated errors, then combining them with ensemble NWP simulations based on the Particle Filter and Ensemble Kalman Filter data assimilation algorithms. The performance of the different precipitation analysis methods is evaluated at a local scale using independent ski resort precipitation observations. The evaluation of the pre-processing step shows its ability to remove the main spatial artefacts coming from the radar measurements and to improve the precipitation estimates at the local scale. The local scale evaluations of the ensemble analyses do not demonstrate an additional benefit of ensemble NWP forecasts, but their contrasted spatial patterns are challenging to evaluate with the available data.

中文翻译：

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法国阿尔卑斯山基于雷达的高分辨率集合降水分析

摘要。高分辨率降水场的可靠估计是山区积雪建模的关键问题，山区降水网络的密度太低，无法捕捉其随地形的复杂变化。为了进一步同化雪模型中的补充雪观测，还需要对降水估计中剩余的不确定性进行充分量化。雷达观测提供具有高空间和时间分辨率的降水空间估计，并且通常与雨量计观测相结合以提高估计的准确性。然而，雷达测量在山区存在明显的缺点（特别是由于地面杂波导致的不切实际的空间模式）。高分辨率数值天气预报（NWP）模型模拟​​的降水场提供了替代估计，但存在系统偏差和定位误差。尽管这些不确定性可以通过集合数值天气预报系统来部分描述，并且可以通过统计后处理来减少系统误差，但数值天气预报降水估计对于高分辨率积雪建模的要求仍然不够可靠。在本研究中，通过基于所有这些可用产品的组合的具体分析，获得了更好的降水量估计。首先，提出了一个预处理步骤，以减轻雷达和降水量估算产品的主要缺陷，重点是减少不切实际的空间模式。该方法还根据雷达和 NWP 估计降水的气候学分析，提供山区相关误差的空间估计。然后提出了三种集合日降水分析方法，首先仅使用修正的降水估计和相关误差，然后将它们与基于粒子滤波器和集合卡尔曼滤波器数据同化算法的集合 NWP 模拟相结合。使用独立的滑雪场降水观测在局部范围内评估不同降水分析方法的性能。预处理步骤的评估表明其能够消除来自雷达测量的主要空间伪影并改进局部尺度的降水估计。集合分析的局部尺度评估并没有证明集合数值天气预报的额外好处，但它们对比的空间模式很难用现有数据进行评估。