Snow plays a crucial role in the water balance of mountainous regions by affecting the timing and magnitude of runoff and, thus, water availability and flood hazards. However, estimating snow water equivalent (SWE) in mountainous regions is challenging due to its substantial spatial variability, the lack of accurate distributed measurements, and the uncertainties of snow models. Model uncertainties are primarily bound to uncertainties in the meteorological forcings. This study proposes an assimilation scheme to identify and correct spatiotemporal error patterns in the meteorological forcing data. Using a particle filter, we assimilated in situ snow depth observations from 444 stations across Switzerland into an ensemble simulated by the multi-layer, physics-based snow model FSM2OSHD. The ensemble is created by applying traceable, fixed perturbations to the energy input and the amount and phase of precipitation. This allows us to identify and correct errors in the meteorological forcing data for each station site and each 3-day assimilation window. Leveraging spatial correlation in these errors, we distribute the corrections across the entire model domain using a weighted three-dimensional spatial interpolation method. The refined meteorological data then serve as forcing for improved model runs, allowing unobserved grid points to benefit from the point assimilation. A leave-one-station-out cross-validation shows marked improvements in root-mean-squared error and bias for estimates of snow depth and SWE over the entire elevation range and multiple winter seasons. The proposed scheme is a promising step in developing comprehensive data assimilation solutions for large-scale, fully distributed, near real-time snow modeling applications, taking into account operational constraints and practical considerations.

中文翻译：

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通过绘制从积雪监测数据的粒子过滤器同化推断的气象强迫扰动来改进完全分布式积雪模拟


积雪通过影响径流的时间和大小，从而影响水的可用性和洪水危害，在山区的水平衡中起着至关重要的作用。然而，由于山区空间变异性大、缺乏精确的分布式测量以及雪模型的不确定性，估计山区的雪水当量 （SWE） 具有挑战性。模式不确定性主要与气象强迫中的不确定性相关联。本研究提出了一种同化方案来识别和校正气象强迫数据中的时空误差模式。使用粒子过滤器，我们将来自瑞士 444 个站点的原位积雪深度观测数据同化到一个由多层、基于物理的雪模型FSM2OSHD模拟的集合中。该集成是通过对能量输入以及降水量和相位应用可追踪的固定扰动来创建的。这使我们能够识别并纠正每个站点和每个 3 天同化窗口的气象强迫数据中的错误。利用这些误差中的空间相关性，我们使用加权三维空间插值方法将校正分布在整个模型域中。然后，精炼的气象数据将作为改进模型运行的强制，从而允许未观测的格网点从点同化中受益。留一站出交叉验证显示，在整个海拔范围和多个冬季，积雪深度和 SWE 估计的均方根误差和偏差都有显著改善。 考虑到操作限制和实际考虑，拟议的方案是为大规模、完全分布式、近乎实时的雪建模应用程序开发综合数据同化解决方案的有前途的一步。