Snow and precipitation estimates in high-mountain regions typically suffer from low temporal and spatial resolution and large uncertainties. Here, we present a two-step statistically-based model to derive spatio-temporal highly-resolved estimates of snow water equivalent (SWE) across the Swiss Alps. A multiple linear regression model (Step-1 MLR) was first used to combine the CombiPrecip radar-gauge product with the precipitation and wind speed (10 m from the ground) of the numerical weather prediction model COSMO-1 in order to adjust the precipitation estimates. Step-1 MLR was trained with SWE data from a cosmic ray sensor (CRS) installed on the Plaine Morte glacier and tested with SWE data from a CRS on the Findel glacier. Step-1 MLR was then applied to the entire area of eight Swiss glaciers and evaluated with scattered end-of-season in-situ manual SWE measurements. The cumulative estimates of Step-1 MLR were found to agree well with the end-of-season measurements. The observed differences can partially be explained by considering the radar visibility, melting processes and preferential snow deposition, which are dictated by the local topography and local weather conditions. To address these limitations of Step-1 MLR, several high-resolution topographical parameters and a solar radiation parameter were included in the subsequent MLR version (Step-2 MLR). Step-2 MLR was evaluated by means of cross-validation, and it showed an overall correlation of 0.78 and a mean bias error of 4 mm with respect to end-of-season in-situ measurements. Step-2 MLR was also evaluated for non-glacierized regions by evaluating it against twice-monthly manual SWE measurements at 44 sites in the Swiss Alps. In such a setting, the Step-2 model showed an overall weaker correlation (0.53) and a higher mean bias error (31 mm). On the other hand, negative variations of the measured SWE were removed because of the lower altitude of the sites, thereby leading to more pronounced melting periods, which again increased the correlation values to 0.63 and reduced the mean bias error to 12 mm. Such results confirm the high potential of the model for applications to other mountainous regions.

中文翻译：

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瑞士阿尔卑斯山SWE的时空高分辨率连续估计-高山区地​​形的统计两步法

高山区的降雪和降水估计通常受时间和空间分辨率低以及不确定性大的困扰。在这里，我们提出了一个基于统计的两步模型，可以得出瑞士阿尔卑斯山雪水当量（SWE）的时空高度解析估计。首先使用多元线性回归模型（Step-1 MLR）将CombiPrecip雷达仪表产品与数值天气预报模型COSMO-1的降水量和风速（距地面10 m）相结合，以调节降水量估计。使用来自安装在Plaine Morte冰川上的宇宙射线传感器（CRS）的SWE数据对Step-1 MLR进行了训练，并使用来自Findel冰川上的CRS的SWE数据进行了测试。然后，将步骤1 MLR应用于8个瑞士冰川的整个区域，并使用分散的季节末期原位手动SWE测量进行评估。发现第1步MLR的累积估算值与季末测量值非常吻合。可以通过考虑雷达的能见度，融化过程和优先降雪来部分解释观测到的差异，这取决于当地的地形和当地的天气状况。为了解决第1步MLR的这些限制，在后续的MLR版本（第2步MLR）中包括了几个高分辨率的地形参数和太阳辐射参数。步骤2 MLR通过交叉验证进行了评估，相对于季末现场测量，其总体相关性为0.78，平均偏差为4 mm。通过在瑞士阿尔卑斯山的44个站点进行每月两次的SWE手动测量，对步骤2的MLR进行了非冰川地区的评估。在这种情况下，Step-2模型显示出整体较弱的相关性（0.53）和较高的平均偏差误差（31 mm）。另一方面，由于站点的高度较低，因此消除了所测SWE的负变化，从而导致更明显的熔化时间，这又将相关值增加到0.63，并将平均偏差误差减小到12 mm。这样的结果证实了该模型在其他山区应用的巨大潜力。53）和更高的平均偏差（31 mm）。另一方面，由于站点的高度较低，因此消除了所测SWE的负变化，从而导致更明显的熔化时间，这又将相关值增加到0.63，并将平均偏差误差减小到12 mm。这样的结果证实了该模型在其他山区应用的巨大潜力。53）和更高的平均偏差（31 mm）。另一方面，由于站点的高度较低，因此消除了所测SWE的负变化，从而导致更明显的熔化时间，这又将相关值增加到0.63，并将平均偏差误差减小到12 mm。这样的结果证实了该模型在其他山区应用的巨大潜力。