Data Assimilation Improves Estimates of Climate-Sensitive Seasonal Snow

Abstract

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.

Estimating Fractional Snow Cover in Open Terrain from Sentinel-2 Using the Normalized Difference Snow Index

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Evaluation the Effects of Climate Change on the Flow of the Arkansas River – United States

“…The coarse resolution bounds the general circulation models (GCMs) to reproduce a similarly complex spatial environment that simulates the actual precipitation. Besides, the discrepancies in the precipitation projections are larger than the ones in the temperature projections [21]- [23].…”

Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps

“…A strong asset is the assured long-term continuity of S1 C-band SAR observations over the coming decades, which will allow for analyzing trends in snow mass impacted by climate variability or climate change. Finally, the S1 snow depth retrievals could be of high value for data assimilation into land surface models (Girotto et al, 2020). Not only could the assimilation ensure improved and continuous (in time and space) estimates of various snow variables, it is likely to also benefit applications such as flood forecasting (Dechant and Moradkhani, 2011;Griessinger et al, 2019) or numerical weather prediction (de Rosnay et al, 2014).…”