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 subgrid method with a downscaling tool and data assimilation method with the purpose to improve 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 by 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 utilises 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 experimental scales using this approach. We propose 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 EO missions.

TopoCLIM: rapid topography-based downscaling of regional climate model output in complex terrain v1.1

“…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 time series globally while accounting for the main topographic effects on atmospheric forcing.…”

“…and discussion of TopoSCALE uncertainties is given by des 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.…”

Reply on RC1

“…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.…”

“…We have shown in previous studies that variables driving the energy balance (TA, ILWR, ISWR) are downscaled with good skill by TopoSCALE. One method we have explored to reduce (and quantify) meteorological forcing uncertainty and precipitation uncertainty in particular, is through Bayesian data assimilation of globally available satellite products using a particle batch smoother, which has shown promising results (Fiddes et al, 2019;Alonso-González et al, 2020). The coupling of this scheme with TopoCLIM will be the subject of subsequent work.…”