Abstract
Abstract. Nine gridded Northern Hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Intercomparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS-2; the European Centre for Medium-Range Weather Forecasts (ECMWF) interim land surface reanalysis – ERA-Interim/Land and ERA5; the NASA Modern-Era Retrospective Analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2); the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) stand-alone passive microwave retrievals (NASA AMSR-E SWE versions 1.0 and 2.0) which do not utilize surface snow observations. Evaluation included validation against independent snow course measurements from Russia, Finland, and Canada and product intercomparison through the calculation of spatial and temporal correlations in SWE anomalies. The stand-alone passive microwave SWE products (AMSR-E v1.0 and v2.0 SWE) exhibit low spatial and temporal correlations to other products and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides performance comparable to the reanalysis-based products; RMSE over Finland and Russia for all but the AMSR-E products is ∼50 mm or less, with the exception of ERA-Interim/Land over Russia. Using a seven-dataset ensemble that excluded the stand-alone passive microwave products reduced the RMSE by 10 mm (20 %) and increased the correlation from 0.67 to 0.78 compared to any individual product. The overall performance of the best multiproduct combinations is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.
Highlights
(∼ 20–30 years) and spatially (∼ 10–20 km) consistent estimates of daily snow water equivalent (SWE) over seasonal snow-covered land are required for many applications including climate model evaluation (Mudryk et al, 2018a), verification of seasonal forecasts
Both snow depth and snowfall measurements from single point locations are intrinsically limited by a lack of confidence in how they capture the landscape mean across coarse grid cells (Meromy et al, 2012), which is problematic in areas of mixed forest vegetation, open areas prone to wind redistribution, and complex topography
We evaluate three categories of Northern Hemisphere gridded SWE products: (1) stand-alone passive microwave retrievals (AMSR-E SWE v1.0 and v2.0), (2) passive microwave estimates combined with surface snow depth observations (GlobSnow v2.0), and (3) products which utilize some form of reanalysis (Crocus, GLDAS-2, ERAInterim/Land, ERA5, MERRA, MERRA-2)
Summary
(∼ 20–30 years) and spatially (∼ 10–20 km) consistent estimates of daily snow water equivalent (SWE) over seasonal snow-covered land are required for many applications including climate model evaluation (Mudryk et al, 2018a), verification of seasonal forecasts Meaningful spatially continuous information can be derived from surface observations for regions and time periods with a sufficiently dense observing network (Dyer and Mote, 2006; Brown and Derksen, 2013); as an alternative to snow depth, snowfall measurements can be integrated (Broxton et al, 2016) Both snow depth and snowfall measurements from single point locations are intrinsically limited by a lack of confidence in how they capture the landscape mean across coarse grid cells (Meromy et al, 2012), which is problematic in areas of mixed forest vegetation, open areas prone to wind redistribution, and complex topography (most snow-covered regions fall into at least one of these categories). There remain expansive alpine and northern regions with insufficient coverage by conventional observing networks (Brown et al, 2019)
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