Data Assimilation Improves Estimates of Climate-Sensitive Seasonal Snow
Data Assimilation Improves Estimates of Climate-Sensitive Seasonal Snow
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DOI：
10.1007/s40641-020-00159-7
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发表时间：
2020-05-15
影响因子：
9.5
通讯作者：
Essery, Richard L. H.
中科院分区：
文献类型：
--
作者：
Girotto, Manuela;Musselman, Keith N.;Essery, Richard L. H.
随着地球变暖，季节性降雪的时空响应仍然不确定。全球雪科学界利用来自陆地表面模型、数值天气预报、卫星观测、表面测量及其组合的信息来估计雪覆盖和质量。在积雪变化的空间和时间尺度上准确估计积雪，历来受到土地覆盖和地形复杂性以及全球积雪覆盖区范围大的挑战。与许多地球科学学科一样，雪科学正处于一个快速发展的时代，因为遥感产品和模型不断获得粒度和物理保真度。尽管取得了明显的进展，但雪科学界仍然面临着与季节性雪估计准确性有关的挑战。也就是说，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，遥感技术的进展仍然受到可以观察到的变量的时间，空间和技术限制。雪的准确监测和建模提高了我们评估地球系统状况、趋势和未来预测的能力，同时为全球在供水和天气预报方面的高度重视提供服务。因此，有一个基本的需要，以了解和改善与雪的估计相关的误差和不确定性。克服模型和观测缺陷的一个潜在方法是数据同化，它利用观测和模型中的信息内容，同时最大限度地减少不确定性造成的局限性。本文提出了数据同化的一种方法，以减少不确定性的表征季节性降雪的变化和审查目前的建模，遥感和数据同化技术应用于估计季节性降雪。最后，季节性降雪估计的剩余挑战进行了讨论。
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.