Assessment of a multiresolution snow reanalysis framework: a multidecadal reanalysis case over the upper Yampa River basin, Colorado

Abstract

Abstract. A multiresolution (MR) approach was successfully implemented in the context of a data assimilation (DA) framework to efficiently estimate snow water equivalent (SWE) over a large head water catchment in the Colorado River basin (CRB), while decreasing computational constraints by 60 %. A total of 31 years of fractional snow cover area (fSCA) images derived from Landsat TM, ETM+, and OLI sensor measurements were assimilated to generate two SWE reanalysis datasets, a baseline case at a uniform 90 m spatial resolution and another using the MR approach. A comparison of the two showed negligible differences in terms of snow accumulation, melt, and timing for the posterior estimates (in terms of both ensemble median and coefficient of variation). The MR approach underestimated the baseline peak SWE by less than 2 % and underestimated day of peak and duration of the accumulation season by a day on average. The largest differences were, by construct, limited primarily to areas of low complexity, where shallow snowpacks tend to exist. The MR approach should allow for more computationally efficient implementations of snow data assimilation applications over large-scale mountain ranges, with accuracies similar to those that would be obtained using ∼ 100 m simulations. Such uniform resolution applications are generally infeasible due to the computationally expensive nature of ensemble-based DA frameworks.

A Joint Landsat- and MODIS-Based Reanalysis Approach for Midlatitude Montane Seasonal Snow Characterization

“…The 30 m fSCA data is then aggregated to the desired reanalysis model resolution. Previous regional applications have used 90 m (Margulis et al, 2016a), 180 m (Cortes and Margulis, 2017) and a multi-resolution approach (Baldo and Margulis, 2018). For the ultimate application to the large-scale HMA domain, and to make the Landsat fSCA of consistent scale with the MODIS-based fSCA, we herein used an aggregated resolution of ∼480 m (16 arcsecond grid).…”

MuSA: The Multiscale Snow Data Assimilation System (v1.0)

“…applied a similar setup to conduct a snow reanalysis for the extratropical Andes Aalstad et al (2018). compared the performance of the PBS with ensemble Kalman-based smoothers when assimilating FSCA data from Sentinel-2 and MODIS for sites in the high-Arctic and found that the PBS markedly outperformed non-iteratve ensemble Kalman-based smoothers in line withMargulis et al (2015) Baldo and Margulis (2018). developed a multi-resolution snow reanalysis framework using the PBS and demonstrated, through tests in a basin in Colorado, that this could match the performance of the original single-resolution approach at a fraction of the computational cost.…”

Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review

“…As such, to our knowledge, 4D-Var has not been applied in snow cover applications. Other smoother methods, namely the Ensemble Kalman Smoother (EnKS) and the Particle Batch Smoother (PBS), which are essentially smoother extensions of the filtering approaches outlines above, have been applied in snow hydrology recently with a variety of data assimilated including passive/active microwave and SCA/SCF (e.g., Durand et al, 2009;Bateni et al, 2013Bateni et al, , 2015Girotto et al, 2014;Margulis et al, 2015Margulis et al, , 2016Margulis et al, , 2019bCortés and Margulis, 2017;Li et al, 2017;Aalstad et al, 2018;Baldo and Margulis, 2018). In the EnKS, a Kalman update can be performed on all states over a given window, while in the PBS, particle weights are updated by conditioning on all measurements over the assimilation window.…”