Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering

Abstract

Abstract. Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient sub-grid method with a downscaling tool and a data assimilation method with the purpose of improving numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1–2 orders of magnitude of computational cost due to ensemble simulations, we save 4–5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilizes only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various scales using this approach that assimilates retrievals from a MODIS snow cover product. We propose that this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by Earth observation missions.

CrocO_v1.0 : a Particle Filter to assimilate snowpack observations in a spatialised framework

“…Strong and almost uniform correlations in HS might be caused by the spatial homogeneity of precipitation perturbations and because we do not account for snow transport by the wind and gravitational redistribution of snow (Wayand et al, 2018). Despite this semi-distributed framework suffers from obvious limitations, NWP models still suffer for large errors in mountainous areas, hampering the potential for high-resolution snowpack modelling (Vionnet et al, 2016;Fiddes et al, 2019).…”

Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019)

“…However, there remain biases in the ERA5 meteorological reanalyses, which could be significantly reduced by assimilating remote sensing snow cover area products (e.g., Sentinel-2 or MODIS) [25,49]. Indeed, assimilation snow cover area derived from remote sensing products in hydrological and snowpack evolution models has shown a great added value in the literature.…”

TopoCLIM: Rapid topography-based downscaling of regional climate model output in complex terrain v.1.0

“…It has been extensively tested in various geographical regions and applications e.g. permafrost in the European Alps (Fiddes et al, 2015), permafrost in the North Atlantic region (Westermann et al, 2015), Northern hemisphere permafrost (Obu et al, 2019), Antarctic permafrost (Obu et al, 2020), Arctic snow cover (Aalstad et al, 2018), Arctic climate change (Schuler and Østby, 2020), and Alpine snow cover (Fiddes et al, 2019). This approach enables us to provide a climate length pseudo-observation timeseries globally, while accounting for the main topographic effects on atmospheric forcing.…”

“…As the density of observations that are assimilated in reanalyses varies globally we expect the performance of the TopoCLIM model pipeline to be a function of how well constrained ERA5 is in any given location. A full analysis and discussion of TopoSCALE uncertainties is given by Fiddes and Gruber (2014) and to some extent in Fiddes et al (2019). The most uncertain variable is precipitation which is clearly a critical point for snow modelling studies.…”