Historical gridded snow water equivalent over the Northern Hemisphere from remote sensing and land surface models
This page provides observation-based estimates of daily snow water equivalent (SWE) over the Northern Hemisphere (excluding Greenland) for 1981-2020 and the associated technical documentation.
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Daily snow water equivalent (1981-1989)
Daily snow water equivalent (1990-1999)
Daily snow water equivalent (2000-2009)
Daily snow water equivalent (2010-2020)
Technical notes
Datasets of daily snow water equivalent (SWE) over the Northern Hemisphere are constructed using a multi-dataset approach for the time period 1981-2020. Datasets of monthly snow cover fraction (SCF), monthly SWE and annual maximum SWE (SWEmax) over Canada for 1981-2016 based on an older generation of source datasets can be found here.
The general methodology for the creation of these datasets follows that of Mudryk et al. (2015)Reference4.
Table 1. Main characteristics.
| Variables | Daily snow water equivalent (millimetre water equivalent) |
|---|---|
| Spatial resolution and geographical coverage | 0.5- by 0.5-degree grid resolution across the Northern Hemisphere (excluding Greenland) |
| Time period | 1981-2020 |
| Temporal resolution | Daily |
Source Datasets
Daily SWE data was downloaded from four sources for the 1981 to 2020 period.
Four sources of daily SWE data:
- The Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) (Global Modeling and Assimilation Office, 2015)Reference2 is a National Aeronautics and Space Administration (NASA) atmospheric reanalysis product generated with the Goddard Earth Observing System Model, Version 5.2.0 (GEOS-5), atmospheric general circulation model and atmospheric data assimilation system (ADAS).
- Fifth Generation European Centre for Medium-Range Weather Forecasts ( ECMWF) Land reanalysis (ERA5-Land) (Muñoz Sabater, 2019)Reference5.
- The physical snowpack model Crocus-ERA5 simulates daily SWE using meteorology from the standard ERA5 reanalysis (Decharme, 2024)Reference1.
- The Snow CCI+ SWE Version 2 (CCIv2) data product (Luojus, 2022)Reference3, which uses a combination of passive-microwave brightness temperatures and in situ snow depth measurements to estimate terrestrial SWE.
Preprocessing
The CCIv2 product contains missing days during the snow season, particularly prior to 1988. Days without data were linearly interpolated from surrounding days with available data outside of the ‘summer’ season. The product also contains no data each year for some interval between May and September. To avoid creating issues in the blended product, exponential interpolation was used to mimic spring melts and fall snow build-up from the last available spring date and the first available fall date.
Spatial interpolation
Daily SWE data over the Northern Hemisphere from each of the four products was interpolated to a common 0.5- by 0.5-degree grid using a nearest neighbor remapping function (‘remapnn’) from Climate Data Operators (CDO) (version 2.0.3).
Daily snow water equivalent
SWE values greater than 2000mm were removed from the component products to focus on seasonal snow.
The four source datasets were weighted equally in the daily SWE mean in almost all cases with two exceptions.
The CCIv2 product was partially masked over mountainous regions, defined as regions with a slope of 2 degrees or larger. SWE was replaced in grid cells which contained mountains with a blend of the CCIv2 SWE data (if any) and the mean value from the other three data sources. The weighting for the blend was determined by the fraction of the grid cell area which was mountainous. For grid cells with no mountainous terrain, unaltered CCIv2 data were used. As the fraction of mountainous terrain increases, the weight applied to the CCIv2 data was linearly reduced, reaching zero for grid cells containing only mountainous terrain.
The CCIv2 product contains no data past May 24th, 2020, and as such, the remainder of 2020 is a mean of the three remaining data sources.
Use limitation
Open Government Licence - Canada
Individual model datasets and all related derived products are subject to the terms of use of the source organization.