As the Earth warms, the spatial and temporal response of seasonal snow remains uncertain. The global snow science community estimates snow cover and mass with information from land surface models, numerical weather prediction, satellite observations, surface measurements, and combinations thereof. Accurate estimation of snow at the spatial and temporal scales over which snow varies has historically been challenged by the complexity of land cover and terrain and the large global extent of snow-covered regions. Like many Earth science disciplines, snow science is in an era of rapid advances as remote sensing products and models continue to gain granularity and physical fidelity. Despite clear progress, the snow science community continues to face challenges related to the accuracy of seasonal snow estimation. Namely, advances in snow modeling remain limited by uncertainties in modeling parameterization schemes and input forcings, and advances in remote sensing techniques remain limited by temporal, spatial, and technical constraints on the variables that can be observed. Accurate monitoring and modeling of snow improves our ability to assess Earth system conditions, trends, and future projections while serving highly valued global interests in water supply and weather forecasts. Thus, there is a fundamental need to understand and improve the errors and uncertainties associated with estimates of snow. A potential method to overcome model and observational shortcomings is data assimilation, which leverages the information content in both observations and models while minimizing their limitations due to uncertainty. This article proposes data assimilation as a way to reduce uncertainties in the characterization of seasonal snow changes and reviews current modeling, remote sensing, and data assimilation techniques applied to the estimation of seasonal snow. Finally, remaining challenges for seasonal snow estimation are discussed.

中文翻译：

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数据同化可改善对气候敏感的季节性降雪的估算

随着地球变暖，季节性降雪的时空响应仍然不确定。全球雪科学界利用陆面模型，数值天气预报，卫星观测，地表测量及其组合的信息来估计积雪和积雪。过去，由于土地覆盖和地形的复杂性以及全球大面积冰雪覆盖地区的挑战，在积雪变化的时空尺度上准确估算积雪一直受到挑战。像许多地球科学学科一样，随着遥感产品和模型的不断发展，粒度和物理保真度不断提高，雪科学正处于飞速发展的时代。尽管取得了明显的进展，但雪科学界仍面临与季节性降雪估计准确性有关的挑战。即 降雪建模的进展仍然受到建模参数化方案和输入强迫的不确定性的限制，而遥感技术的进展仍然受到对可观测变量的时间，空间和技术约束的限制。对雪的精确监视和建模可以提高我们评估地球系统状况，趋势和未来预测的能力，同时可以满足全球对供水和天气预报的高度重视。因此，根本需要理解和改善与降雪估计有关的误差和不确定性。克服模型和观测缺点的一种潜在方法是数据同化，它可以利用观测和模型中的信息内容，同时最大限度地减少由于不确定性导致的局限性。本文提出了数据同化方法，以减少季节性积雪变化特征中的不确定性，并回顾了当前的模型，遥感技术以及用于估计季节性积雪的数据同化技术。最后，讨论了季节性降雪估计的剩余挑战。