Contributors: Miriam Kosmale, Kari Luojus, Mikko Moisander, Matias Takala, Pinja Venäläinen (Finnish Meteorological Institute)

Issued by: Finnish Meteorological Institute / Miriam Kosmale, Kari Luojus

Date: 30 Jun 2025 

Ref: C3S2_313d_ENVEO.WP3-DDP-SWE-v1.0_202506_SWE_PUGS

Official reference number service contract: 2024/C3S2_313d_ENVEO/SC1

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History of modifications

List of datasets covered by this document

Acronyms

General definitions

Accuracy: is defined as the “closeness of the agreement between a measured quantity value and a true quantity value of the measurand”. The concept “measurement accuracy” is not a quantity and is not given a numerical quantity value. (GCOS-200, p293)

Brightness Temperature is the measurand of “passive“ microwave remote sensing system (radiometers). Brightness temperature (in degree Kelvin) is a function of kinetic temperature and emissivity. Depending on the depth of a snowback the brightness temperature observed from the satellite and channel varies. The emissivity of a snowpack originates from its snow surface, characteristics of its deeper snow layers and from the ground beneath. Attenuation in emissivity and hence brightness temperature depending on the underlying snow depth is lower at lower frequencies. Means of a brightness temperature gradient between two channels allow for estimating snow characteristics. 

Backscatter is the measurand of “active” microwave remote sensing systems (radar). As the energy pulses emitted by the radar hit the surface, a scattering effect occurs and part of the energy is reflected back. 

Bias: “Bias is defined as an estimate of the systematic measurement error” (GCOS-200, p293)

Error: “The term error refers to the deviation of a single measurement (estimate) from the true value of the quantity being measured (estimated), which is always unknown” (Gruber et al., 2020)

Freshet: Freshet is used to describe snowmelt, which is an annual high water event on rivers resulting from snow and river ice melting

GCOS Breakthrough (B): An Essential Climate Variable (ECV) requirement level set by Global Climate Observing System (GCOS) which "[...], if achieved, would result in a significant improvement for the targeted application. The breakthrough value may also indicate the level at which specified uses within climate monitoring become possible. It may be appropriate to have different breakthrough values for different uses." (GCOS-244)  

GCOS Goal (G): An Essential Climate Variable (ECV) requirement level set by Global Climate Observing System (GCOS) which "[...], if achieved, would be an ideal requirement above which further improvements are not necessary." (GCOS-244)  

GCOS Threshold (T): An Essential Climate Variable (ECV) requirement level set by Global Climate Observing System (GCOS) which "[...], if achieved, would be the minimum requirement to be met to ensure that data are useful." (GCOS-244)  

Radiometer: Spaceborne radiometers are satellite-carried sensors that measure energy in the microwave domain emitted by the Earth. The amount of radiation emitted by an object in the microwave domain (~1-20 GHz). The observed quantity is called “brightness temperature” and depends on kinetic temperature of an object and its emissivity. Due to the high emissivity of water compared to dry matter, radiometer measurements of Earth’s surface contain information in the water content in the observed area.

Satellite processing level 0: Raw measured signal at satellite: Reconstructed, unprocessed instrument and payload data at full resolution, with any and all communications artifacts (e.g., synchronization frames, communications headers, duplicate data) removed.

Satellite processing level 1: Level 1A (L1A) data are reconstructed, unprocessed instrument data at full resolution, time-referenced, and annotated with ancillary information. L1C data are L1B data that include new variables to describe the measured spectra. 

Satellite processing level 2: Derived geophysical variables at the same resolution and location as L1 source data. For SWE products derived from PMR satellite observations the projection is the Equal-Area Scalable Earth (EASE) Grid, which is a system of projections that is used by NASA for distribution of some of the polar orbiting satellite sensors. (ref to EPSG definition: https://epsg.io/6933 and https://epsg.io/6933 and https://nsidc.org/data/user-resources/help-center/guide-ease-grids). EASE grids utilise an equal-area projection which minimises the amount of distortion over the poles, using the Northern and Southern Hemisphere projections, and on other key areas of the globe, using the temperate and global projections.

Satellite processing level 3: Variables mapped on uniform space-time grid scales; for C3S SWE is a regular 0.1 degree latitude-longitude grid and daily temporal resolution.

SMMR: Scanning Multichannel Microwave Radiometer sensor onboard Nimbus-7 satellites in space between 1979-1987. The passive microwave radiometer data is used to derive Snow Water Equivalent.

SSM/I: Special Sensor Microwave/Imager is a seven-channel, four-frequency, linearly polarized passive microwave radiometer system. It is flown on board the United States Air Force Defense Meteorological Satellite Program running from 1987-2008. The passive microwave radiometer data is used to derive Snow Water Equivalent.

SSMIS: Special Sensor Microwave Imager / Sounder is a 24-channel, 21-frequency, linearly polarized passive microwave radiometer system. The instrument is flown on board the United States Air Force Defense Meteorological Satellite Program F-16, F-17, F-18 and F-19 satellites, which were launched in October 2003, November 2006, October 2009, and April 2014, respectively. It is the successor to the Special Sensor Microwave/Imager (SSM/I). The passive microwave radiometer data is used to derive Snow Water Equivalent.

Snow Water Equivalent retrieval algorithm: The here described snow water equivalent (SWE) retrieval algorithm combines information from satellite-based microwave radiometer and optical spectrometer observations with ground-based weather station snow depth (SD) measurements and produces daily hemispherical scale SWE estimates.

Snow Water Equivalent (SWE): SWE represents the resulting water column of a snowpack melt in place. SWE is the product of depth and density of a snowpack. => Snow water equivalent (SWE) = snow depth (SD) * snow density (ρ)

Snowpack: An accumulation of precipitated snow at the surface that compresses with time and melts seasonally. Snowpacks are an important water resource for the surrounding area.

Stability may be thought of as the extent to which the uncertainty of measurement remains constant with time. [...] “stability” refer to the maximum acceptable change in systematic error, usually per decade. (GCOS-200, p293)

Uncertainty: Uncertainty (of measurement) non-negative parameter, associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand. (GCOS-200). Satellite retrievals of SWE usually contain considerable systematic errors which, especially for model calibration and refinement, provide better insight when estimated separate from random errors. Therefore, we use the term bias to refer to systematic errors only and the term uncertainty to refer to random errors only, specifically to their standard deviation (or variance).

Executive summary

This Product User Guide and Specification (PUGS) is part of the Copernicus Climate Change Service (C3S) C3S2_313d for the cryosphere. The document summarises the Climate Data Record (CDR) and Intermediate Climate Data Record (ICDR) for Snow Water Equivalent (SWE), where the ICDR represent consistent extensions of the CDR. 

Snow water equivalent (SWE) is a measure indicating the amount of accumulated snow on land surfaces. SWE describes the amount of liquid water in a snowpack that would be formed if the snowpack had completely melted.  This CDR provides daily estimates on Snow Water Equivalent (SWE) with a spatial coverage of the Northern hemisphere. The data is derived from passive microwave radiometer (PMR) measurements onboard polar-orbiting satellites, which were available since 1979 to present. In addition to the daily product monthly means of SWE are available. The C3S Snow Water Equivalent products are accessible from the European Centre for Medium Range Weather Forecasting (ECMWF) C3S Climate Data Store (CDS). Both the CDR and the ICDR datasets contain information on the snow water equivalent, including uncertainty measures for each grid pixel. All datasets are provided in NetCDF4 format and include daily and monthly mean maps of SWE in a 0.1° regular latitude-longitude grid.

This product user guide provides comprehensive guidance on the content and structure of the SWE products. It includes information on input data sources, an overview on the processing methods, quality indicators, and known issues and limitations, as well as instructions on how to access the data.

Product description

Snow Water Equivalent description

Snow water equivalent (SWE) is a measure indicating the amount of accumulated snow on land surfaces, so-called snowpacks. SWE describes the amount of liquid water in a snowpack that would be formed if it was completely melted. SWE represents the resulting water column in units mm. Typically SWE values range from 0-500mm. In certain mountain areas with extreme high snowpack values would exceed 500mm. 

The dataset of C3S SWE covers the Northern hemisphere for the period from 1979 to present derived from satellite observations. As the method utilises passive microwave radiometer (PMR) data for retrieval of the snow, it is completely unaffected by clouds or requires sunlight conditions. Unlike observations within the visible spectrum, satellite measurements in the radiometer frequency channels are not affected by any absorption of water vapour and other atmospheric trace gases. It measured signal is only affected by the underlying earth's surface emission. Furthermore measurements of surface brightness temperatures as derived from PMR are independent of incoming solar radiation. Therefore PMR observations offer a good spatial coverage, without gaps due to clouds, and no significant temporal gaps during polar night. All of what is essential for monitoring winter snow conditions. 

Snow Water Equivalent (SWE) is provided as daily product as well as monthly mean in NetCDF4 format. The product is provided on a regular 0.1° latitude-longitude grid with a spatial resolution of 0.1°x 0.1° grid size. The SWE products are available at daily temporal resolution. Mountain areas are masked out. The time series excludes observations during June until September for all years and focuses on winter seasons in the Northern hemisphere only (October - May).

The Copernicus Climate Change Service (C3S) C3S2_313d on cryosphere offers Climate Data Record (CDR) and consecutively updated Intermediate Climate Data Record (ICDR) products on Snow Water Equivalent (SWE). Details of the algorithm for producing the CDR is given in Snow Water Equivalent (SWE) version 1.0: Algorithm Theoretical Basis Document (ATBD) and is updated in case of changes on the algorithm. The ICDR represents a consistent extension of the CDR. The generation of the ICDR uses the same algorithms and parameters, which are used to create the CDR.

Table 1 below contains the expected product updates for Snow Water Equivalent CDR and ICDRs, as well as the respective time coverage upon data delivery. The time series is excluding summer months from June to September for all years.

Table 1: Schedule of CDR and ICDR available within C3S and the corresponding product time coverage they include.

The SWE retrieval algorithm combines information from satellite-based microwave radiometer and optical spectrometer observations with ground-based weather station snow depth (SD) measurements and produces daily hemispherical scale SWE estimates. The product is based on passive microwave radiometer measurements from the SMMR aboard NIMBUS-7, and SSM/I and SSMIS sensors aboard DMSP 5D F-series satellites. The algorithm retrieval scheme consists of the full processing chain from Level-1 PMR satellite K- and Ka-band (19 GHz and 37 GHz respectively) measurements over Level-2 native satellite EASE-Grid projection to the final Level-3 processing stage. The provision on such level-3 stage, which is a regular latitude-longitude grid, simplifies usage of those dataset, compared to the more complicated in EASE grid projected level-2 stage data products.

The original algorithm to derive SWE goes back to research done by Pulliainen et al. 2006, and Takala et al. 2011. The snow water equivalent production system is built on the GlobSnow SWE and ESA CCI production systems. The adapted production scheme for this C3S products includes latest improvements in the algorithm by improving various aspects of the processing chain. Improvements to the GlobSnow algorithm implemented for CCI SWE include the update for the dry snow detection algorithm (Zschenderlein et al., 2023), the implementation of a dynamic snow densities in the SWE retrieval (Venäläinen et al., 2023) and an improved complex terrain. For C3S an improved snow masking is added for post processing and consistent daily coverage is ensured by temporal interpolation for older SMMR-based products. For more information, the theoretical and algorithmic base of the CDRs is described in Algorithm theoretical baseline document (ATBD).

As described before the advantage of using PMR measurements for Earth observations lies in the fact that at those frequencies the signal is not affected by clouds and does not require incoming sunlight. This combined with wide swaths width of the satellite instruments allow sufficient spatial and temporal coverage, such as daily observations of snow from space. The older sensor SMMR onboard of Nimbus-7 had only observations bi-daily for the period 1979–1987 due to power constraints. An additional limiting factor is that SMMR had a narrower swath width (780 km), compared to the later instruments with more than double of swath widths (SSM/I and SSMIS with ~1400km). To allow a consistent dataset for C3S, the missing dates are filled and SWE is estimated as temporal interpolated value between consecutive days, with available observations. Figure 1 shows the timeline of the available measurements for each sensor and satellite.

The CDR SWE dataset is validated against independent in-situ snow measurements. Details on the validation procedure and results are summarised in the Product quality assurance document (PQAR). 

Figure 1: Schedule of satellite Passive Microwave Radiometer (PMR) sensors used for production of C3S SWE (ref.: https://nsidc.org/sites/default/files/documents/technical-reference/smmr-ssmi-ssmis-sensors.pdf)

Table 2 gives an exact listing on the incoming data from the various sensors and satellites and to which C3S SWE data record processing they are contributing to. CDR and ICDR for the snow water equivalent are available as daily and monthly products. The monthly mean datasets are calculated from daily files, which represent the daily observation derived by the relevant PMR sensors (table 3). 

Table 2: Satellite sensors data used for production of CDR and ICDRs

Table 3: CDR / ICDR products and data sets. 

Overview of Product Target Requirements

Table 4 shows the C3S ECV Snow Water Equivalent product target requirements adopted from the Global Climate Observing System (GCOS)-245 target requirements (GCOS, Plan 2022) and shows to what extent these requirements are currently met by the products. As it can be seen, the CDR product currently provided by the system are compliant in most cases with the GCOS target requirements. Further details on product accuracy and validation results are provided in the Product Quality Assessment Report (PQAR).

Table 4: Summary of C3S ECV Snow Water Equivalent requirements. Compliance with GCOS-245 Requirements, 2022 edition - updated in 2025 for the ECV product "Snow-Water Equivalent".

Example visualisation of key variables

All product files are available as netCDF files. NetCDF4-classic files are following the CF1.8 convention. Data are readable by any software tool able to open and read netCDF file format. A list of open source and commercial software tools for reading, displaying and working with netCDF files is available online, at  https://www.unidata.ucar.edu/software/netcdf/software.html

It can be therefore visualised with typical graphic software tools like Panoply: https://www.giss.nasa.gov/tools/panoply/ 

The data standard allows easy implementation into data manipulation tools, such as CDO (https://code.mpimet.mpg.de/projects/cdo/wiki) and GDAL (https://gdal.org/en/stable/drivers/raster/netcdf.html), in order to implement the dataset to new applications. Gdal can be also embedded as module into existing python-scripts.

Figure 2 gives an example plot for SWE observation of the daily C3S product, including flagged pixels for mountain, glacier areas and water bodies.

Figure 2: Northern hemisphere SWE observation of date 01.01.2023 (left) and monthly mean SWE distribution for January 2023 (right).

Data usage information

Data format and file naming

Both the CDR and the ICDR comprise snow water equivalent (SWE) data products. While the monthly mean represents the temporal mean SWE for each month, the daily files are created directly through the merging of SWE data from multiple satellite instruments. The monthly means are calculated from averaging these daily files. The SWE products are provided in NetCDF-4 classic file format with metadata meeting the Climate and Forecast (CF) Metadata Convention v1.8. The daily products are prepared in a geographic latitude-longitude grid (EPSG code 4326). 

The file names for SWE products meet the following file name convention, with the tokens listed in table 5: AAA_BBB_CCC_DDD.nc

Table 5: Tokens of SWE filenames.

Examples:

• daily product: C3S_SWE_20231201_v1.0.nc
• monthly product: C3S_SWE_202312_v1.0.nc

File contents

For each SWE dataset, the latitude, longitude and time dimensions are provided, and complemented with the associated uncertainty estimation. Snow water equivalent has typically values between 0 and 500mm. Negative values in the data layer corresponds to flagged pixels as described in table 7. Details on the layers per product are provided in table 6. The uncertainty layer (swe_unc) gives information on how reliable the SWE retrieval has been for the given pixel in the data layer (SWE); it represents the statistical standard deviation of the SWE estimates. If a user has a known threshold for the retrieval accuracy needed to utilise the SWE data in their respective end-user applications, the swe_unc field can be used to select the SWE data to be utilised for the users’ needs.

Table 6: Layers of SWE product.

The grid is a 0.1° x 0.1° latitude-longitude global array of points, based on the World Geodetic System 1984 (WGS 84) reference system, EPSG 4326 (https://epsg.io/4326). Its dimension is 3600x1800, where the first dimension, latitude is incremental from South (-90°) to North (90°), and the second dimension, longitude is incremental from West (-180°) to East (180°). Each coordinate of a grid-pixel is represented by its grid centre. An exact definition on the grid and projection is given in the metadata of the netCDF file.

In total, there are 3600x1800 gridpoints available. Even though the Southern hemisphere is included in the netCDF file, the SWE product is flagged as no_data for this area. Both SWE and pixel-level uncertainty SWE_unc include certain flag-values, which describing why a pixel does not provide information on snow. As described in the Algorithm theoretical baseline document (ATBD) certain conditions do not allow retrieval of SWE, such as glacier areas, mountain areas or water pixels. All flag-values can be distinguished from SWE, as they are negative values, while the valid range of SWE is between 0-500mm. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. Those mountain pixels are flagged and do not provide values on SWE. All land ice and water bodies are also masked; retrievals are not produced for Greenland.

Values of SWE=0 corresponds to a retrieval result of no snow observed according to the algorithm.

The flag-values are defined as snow in table 7.

Table 7: List of flag values for SWE product

All files include metadata information on: satellite, sensor, observation timestamp, crs attributes and information on image projection (WGS84 longitude coordinates, center of pixel).  The netCDF file consists of the retrieved estimate of snow water equivalent, based on the underlying PMR input datastream. Additionally an estimate of its pixel-level uncertainty of SWE is included. Both variables are on a 0.1 degree regular latitude-longitude grid. Additionally the variables contain flag values noting glacier, mountain or water pixels, which do not contains information on snow due to the various masks applied in the retrieval. 

Examples of know climate applications and best practices

Snow Water Equivalent observations from space can be used for various application developments. Service provider Finnish Meteorological Institute, Arctic Space centre has developed several service applications, which include information on snow, in particular Snow Water Equivalent (SWE).

SWE for forest management service and application

Snow information is important for forest application. Typically forest operations, such like harvesting is conducted during winter season, when the soil is either frozen or snow covered, in order to not damage the underlying soil despite the heavy machinery used. Finnish Meteorological Institute has developed Harvester Seasons, a service for the forestry sector aiming to pave the way for climate smart operations (Kosmale et al. 2022). This service is supporting the forestry sector by offering a tailored trafficability and soil information service based on weather and seasonal forecast models. This service is operationally running and directly supporting the Finnish forestry sector by providing tailored information on this web app service. Data from C3S with its satellite observations on snow, such as snow water equivalent helps to improve the winter trafficability mapping for forestry operations offered in this service. More information on the service can be found here. The link to the operational forestry service "Harvester Seasons" can be found here.

SWE for hydropower production: service application

The Finnish Meteorological Institute has developed a web-service that provides detailed hydro-meteorological information to support more efficient hydropower production operations in Northern Finland. This service aims to reduce uncertainties in freshet forecasts through assimilation of satellite data-based cryosphere products to an in-house developed hydrological modelling system (HOPS) and an in-house developed machine learning streamflow forecasting algorithm. The service leverages common European efforts in climate monitoring by linking data from the Copernicus Climate Data Store with Copernicus seasonal forecasts data records as well as Copernicus Earth Observations (Ikonen et al. 2022). More information on the service can be found here.

Cross-ECVs and application of SWE for C3S hyrdology on groundwater

Groundwater management decisions rely on an adequate monitoring at representative places and with a proper frequency. The depletion of groundwater does not only impact freshwater availability but has also numerous serious consequences to all groundwater dependent socio-economic and ecological systems. Typical examples include agricultural productivity, land subsidence, sea level rise, seawater intrusion in estuaries and coastal aquifers, loss of springs and wetlands, ecosystem degradation, regional climate feedbacks following reduced evapotranspiration, and even political unrest (ref.: https://www.g3p.eu/fileadmin/g3p/G3P_Lecture_Notes.pdf).

Since snow water equivalent describe the amount of water in a snowpack, SWE products can help in applications estimating other hydrological variables, such as groundwater. C3S hydrology will provide new products on satellite-based ECV on groundwater. Snow water equivalent is one of the relevant water contents needed for retrieving groundwater from gravimetric satellite measurements. Within previous H2020 G3P project an algorithm had been developed addressing groundwater monitoring around the world based on satellite technology, in particular Earth gravimetric measurements by the GRACE and GRACE-FO satellites. Those R&D developments offer the baseline for the C3S hydrology service on groundwater. Observations on Snow Water Equivalent (SWE) from satellite is part of this new service development and complements the groundwater processing chain.

Longterm observations of snow masses: investigating patterns and trends for climate research

Warming surface temperatures have driven a substantial reduction in the extent and duration of Northern Hemisphere snow cover. These changes in snow cover affect Earth’s climate system via the surface energy budget, and influence freshwater resources across a large proportion of the Northern Hemisphere. In contrast to snow extent, reliable quantitative knowledge on seasonal snow mass and better understanding of its trend is crucial for climate research. An extensive study on this matter is given in Nature by Pulliainen et al. (2020). Continuous and long-term data records, such as those provided within C3S on Snow Water Equivalent help such kind of research activities tremendously. 

Known Issues and Limitations

Strengths

• C3S Snow Water Equivalent is based on satellite observations available since 1979, which allows for long-term continuous observations from space in monitoring winter conditions on snow.
• The product is provided as Level-3 processing stage, which is a regular latitude-longitude grid. This simplifies application of those datasets, compared to the more complicated level-2 native satellite products.
• As the method utilises passive microwave radiometer data for retrieval of the snow, it is completely unaffected by clouds or polar night conditions.
• Wide swaths of the satellite instruments allow for continuous daily observations of snow without any undue gaps in the time series.
• Using background snow depth, acquired from weather station observations, as an initial guess, allows for much more accurate estimates of snow mass than any method utilising only one source of input data.
• The dataset is validated against independent snow transect data from Canada, Russia and Finland, which provides a quality controlled long time series for SWE dependent snow mass concentrations of the Northern Hemisphere.

Known issues

• The algorithm can be used to measure snow packs roughly between 0.05 m and 1.00 m in thickness and only under dry snow conditions. Depth less than 0. 05 m cannot be reliable retrieved because the brightness temperature difference between the two frequencies, falls within 2 K detection precision of the radiometer instruments used.
• With snow depths greater than 1 m, the brightness temperature signal starts to saturate. Even relatively small amount of liquid water will contaminate the signal making retrieval of SWE difficult.

Because of known limitations with such high snow depths for alpine terrain, a complex-terrain mask is applied. Additionally the native satellite pixel size of approximate 25km is not able to resolve such small-scale features in elevation change. A high-resolution digital elevation model excludes therefore mountain pixels for the retrieval. 

Limitations

• The retrieval methodology combines satellite PMR measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. Due to this assimilation scheme of in-situ observation data, the algorithm is limited to the Northern hemisphere only and can't yet provide SWE for Southern hemisphere latitudes. 
• Water bodies are masked.
• Retrieval values are not produced for glaciers and Greenland; pixels are flagged accordingly.

Data access information

Climate Data Store 

C3S satellite snow water equivalent CDRs and ICDRs data are made available through the Copernicus Climate Data Store (CDS), which is the sole data distributor. The store provides not only consistent estimates of ECVs, but also climate indicators, and other relevant information about the past, present, and future evolution of the coupled climate system, on global, continental, and regional scales. Registration (free) is required to access the CDS and its toolbox software suite. Data can be downloaded from the website and used under the License to Use Copernicus Products (included on download page). Data may also be viewed online. All requests for information or further data should be channelled through the CDS Knowledge Base.

User Support

A dedicated service desk has been set up, the Copernicus User Support (CUS) team, which provides support to users of the Copernicus Atmosphere Monitoring Service (CAMS) and C3S services at ECMWF. All enquiries about the snow water equivalent dataset must be submitted through the service desk where appropriate agents will deal with it: Link to the ECMWF Support Portal

There is a forum where users can browse issues or can also submit direct enquiries. Once submitted, the user may add comments or further information to the issue, including responding to questions / requests for additional information from the support team.

References

GCOS-200, The Global Observing System for Climate, Implementation Needs, https://library.wmo.int/idurl/4/55469

GCOS-244, Climate Observing System (GCOS), ECVs on snow, https://gcos.wmo.int/site/global-climate-observing-system-gcos/essential-climate-variables/snow

GCOS-245, The 2022 GCOS ECVs Requirements, 2022 edition - Updated in 2025

Ikonen J., C. Tanis, M. Kämäräinen, M. Kosmale, H. Poikela, and M. Strahlendorff (2022) The Kemijoki Hydrological Forecast System – a service supporting hydropower production in Northern Finland, FMI’s Clim. Bull. Res. Lett., 4(1), 21–23, https://doi.org/10.35614/ISSN-2341-6408-IK-2022-07-RL

Kosmale M., J. Ikonen, M. Moisander, T. Smolander, H. Ovaskainen, A. Poikela, and M. Strahlendorff (2022) Harvester Seasons – a forestry service supporting climate smart operations, FMI’s Clim. Bull. Res. Lett., 4(1), 14–16, https://doi.org/10.35614/ISSN-2341-6408-IK-2022-05-RL

Pulliainen, J., Luojus, K., Derksen, C. et al. (2020). Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature 581, 294–298, https://doi.org/10.1038/s41586-020-2258-0

Pulliainen, J. (2006) Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations. Remote Sensing of Environment, 101, 257-269, DOI: 10.1016/j.rse.2006.01.002.

Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T. and Norberg, J. (2020) Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature 581, 294–298. https://doi.org/10.1038/s41586-020-2258-0.

Takala, M, K. Luojus, J. Pulliainen, C. Derksen, J. Lemmetyinen, J.-P. Kärnä, J. Koskinen, B. Bojkov (2011) Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sensing of Environment, 115, 12, 3517-3529, doi:10.1016/j.rse.2011.08.014.

Venäläinen, P., Luojus, K., Mortimer, C., Lemmetyinen, J., Pulliainen, J., Takala, M., Moisander, M. & Zschenderlein, L. (2023) Implementing spatially and temporally varying snow densities into the GlobSnow snow water equivalent retrieval. The Cryosphere, 17(2), 719-736.

Zschenderlein, L., Luojus, K., Takala, M., Venäläinen, P., & Pulliainen, J. (2023) Evaluation of passive microwave dry snow detection algorithms and application to SWE retrieval during seasonal snow accumulation. Remote Sensing of Environment, 288, 113476.

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