Snow Water Equivalent Products: History
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Mercedeh Tahery

Available snow water equivalent (SWE) products are classified into three main groups, including satellite, reanalysis, and data assimilation datasets.

  • Snow water equivalent
  • reanalysis
  • data assimilation
  • passive microwave

1. Introduction

Snow is a complex medium that experiences considerable changes in space and time, especially in mountainous terrains where both the accumulation process of solid precipitation and the snowpack melting process are highly variable because of the complex topography and diverse land cover types [1]. An accurate estimation of the spatiotemporal changes in snow data is crucial for modeling the global water cycle through the representation of macrophysical, microstructural, optical, and thermal characteristics of the snowpack [2][3][4][5][6]. Snow water equivalent (SWE), indicating the amount of water stored in the snowpack, and snow cover area (SCA) are the most critical snow data, which are required for a wide range of purposes, including global water cycle modeling, freshwater management, snow hydrology, meteorology, global change analysis, and risk assessment, such as drought and flood [1][7][8][9][10][11][12][13][14][15].
SCA information is mostly obtained from the near infrared (NIR) and visible spectral (VIS) wavelengths [16]. The inability of optical signals to penetrate clouds leads to spatiotemporal gaps in SCA retrieval under cloudy weather conditions and high-altitude regions [17]. In addition, SCA is incapable of estimating the snowpack meltwater runoff, which supplies a significant fraction of water resources and determines the dominant hydrological regime in mountain regions [3][10]. SWE (or snow depth) monitoring, due to a lack of illumination impact (clouds and nights) [18][19], is more useful than SCA for practical purposes under all-sky conditions. 
Generally, three approaches have been presented to simulate snowpack variations, including in situ measurements, reconstruction, and space-borne measurements. In situ SWE measurements include the direct and indirect methods [20]. Direct methods, such as snow pillows, measure SWE by weighing the mass of a snow column, while indirect methods of ground-based SWE measurement are based on the attenuation of gamma radiation by water molecules. In this way, the difference in gamma radiation levels emitted from the surface (before and after snowfall) is converted to real-time SWE via an optimized coefficient. In situ measurement is the most reliable approach for the determination of snow characteristics; however, the limited spatial extent of stations and snow spatial heterogeneities make the method inefficient on a large scale [21][22]. As an alternative method, SWE reconstruction is based on a backward melt calculation by the remotely sensed SCA and energy-based snowmelt to temporally and spatially calculate SWE for the snow accumulation season [23][24]. Although the in situ data requirement of the reconstruction method is limited, the SWE results are only valid for the melt season, leading to its inefficiency for seasonal prediction. Furthermore, the method is sensitive to some factors that can lead to uncertainties if not accurately determined. Those factors include the SCA data, dates of snow disappearance and peak SWE, and the snowmelt estimation methods. These factors can lead to uncertainties if they are not accurately determined.
Microwave remote sensing, including active and passive types, is the most significant method to represent spatiotemporal variations of snowpack properties, such as snow depth and snow water equivalent at different scales [17]. Because of the relationship between electromagnetic radiation and snow properties, the snowpack can be simulated with different operating principles and frequencies [25][26]. Compared to active microwave, passive microwave (PMW) remote sensing, which estimates SWE based on the brightness temperature (TB) differences at two different frequencies, has a long-term history due to its global coverage and short revisit time [27][28][29]. The PMW-based SWE measurements date back to 1978 by the Nimbus-7 satellite using the scanning multi-channel microwave radiometer (SMMR) sensor, followed by the inter-calibrated sensors of the special sensor microwave/imager (SSM/I) onboard the Defense Meteorological Satellite Program (DMSP), the advanced microwave scanning radiometer for Earth Observing System (AMSR-E) on the Aqua spacecraft of NASA’s Earth Observing System (EOS), AMSR2 on the Japan Aerospace Exploration Agency (JAXA)’s Global Change Observation Mission 1st-Water (GCOM-W1), and the Chinese FengYung series [15].
To understand the relation between PMW brightness temperature and snowpack properties, different inversion models have been developed based on both simple and complicated concepts. Traditional inversion models are defined as the empirical relationships between TB difference (at two low and high frequencies) and SWE [30]. Accordingly, various static and dynamic algorithms have been developed as a result of the different choices of microwave frequency channels as well as coefficient estimation. In the algorithms based on TB differences, the effect of the physical temperature of snowpack on the brightness temperature is thwarted because of the signal difference, and the saturation effect for thick snow can be also alleviated, to some extent, due to the use of two different low frequencies [31][32]. However, the interlayer scattering intensifies the saturation issue by reason of different electromagnetic characteristics, which leads to temporally inconsistent SWE results [31]. An important approach to overcome the weakness of these methods is the use of attenuation concepts of snow radiation, i.e., physically based statistical algorithms and radiative transfer equation (RTE)-based models, and energy and water balance concepts, i.e., land surface models (LSMs) and snowpack modules. The statistical SWE retrieval approach is based on a regression relationship between snow water equivalent and snowpack attenuation and radiation properties at different polarizations and frequencies [33]. The RTE-based microwave emission models, as more sophisticated SWE estimation structures, are capable of providing snow parameters such as snow depth and density by matching the simulated and observed TB [34]. These models describing the microwave emission or scattering properties in snow medium and radiative transfer (RT) in the air–snow–soil system are different in many aspects, such as the number of layers, rough or smooth interfaces, grain size parametrization, scattering coefficient, and the solution method of the RTE [35]. As a general classification, snow emission models have been categorized based on different theoretical foundations including semi-empirical, analytical, and numerical concepts [15]. The semi-empirical approach, which benefits from a simple, stable structure, is based on ground measurements with a limited range of validity. The analytical approach uses Maxwell’s equations with physical input parameters; however, it assumes a simplified structure to simulate the snow. In contrast, the numerical approach accounts for certain snow structures, and scattering or emission characteristics are achieved numerically with less simplification. This approach, despite being more time-consuming, provides more accurate simulations in comparison with other types. In addition to snow emission models, the mass and energy balance of the snowpack is used to simulate snow accumulation and melting processes. The most important LSMs are the Biosphere-Atmosphere Transfer Scheme (BATS), Simple Biosphere (SiB), Variable Infiltration Capacity (VIC), Mosaic, and Noah model, which have been discussed in [36]. It should be noted that although LSMs and snowpack simulation modules are capable of estimating SWE with some degree of accuracy, the results have shown large uncertainties in space and time [37].
Stand-alone passive microwave SWE retrievals have a coarse spatial resolution, and due to this, the spatial variability resulting from mixed land cover and topography cannot be properly captured [38]. This issue is a serious limitation especially in alpine regions with forested cover and deep and wet snowpack so that some PMW products mask out these regions completely [39][40][41][42]. To address the matter, the statistical combination of satellite data and in situ observations by data assimilation methods is proposed. The data assimilation approach, which integrates the observations based on their uncertainties, is a promising method to provide SWE at continental scales [17][43]. In addition, the nonlinear relationships between PMW brightness temperature and snow parameters can be simulated by several inversion techniques including iterative algorithms, lookup table algorithms, and machine learning algorithms.
On the other hand, the limitation of the coarse spatial resolution of passive microwave can be compensated for by active remote sensing with a higher spatial resolution. Therefore, it is recommended to use active microwave remote sensing rather than passive microwave in alpine regions due to its higher spatial resolution [15]. However, the temporal resolution of active microwave is less than that of passive remote sensing, and it requires the appropriate frequencies (Ku-band) to model the microstructure properties of the snowpack [16]. Two types of inversion algorithms, including physical backscattering and phase-based approaches, are used to estimate snow parameters by using active microwave remote sensing. The physical backscattering composed of surface and volume scattering calculates SWE by an iteratively minimized cost function. On the contrary, the phase-based approach uses the repeat-pass SAR measurements to estimate SWE through a phase shift caused by snowpack.

2. SWE Products

The generation of an accurate dataset across all snowy regions, because of widely spatial changes in snow properties, requires a physical method to robustly simulate snowpack [18] or a regional method to statistically parameterize the snowpack processes [44]. The application of the statistical approach is mostly limited to the regions with calibrated retrieval schemes while the physical approach, unlike its challenging implementation, has widely applicable potential. Generally, available SWE products are classified into three main groups, including satellite, reanalysis, and data assimilation datasets, explained as follows and summarized in Table 1.

2.1. Satellite SWE Products

The satellite SWE datasets are based on the brightness temperature observations retrieved from the passive microwave data without any ancillary data. Most SWE algorithms employed by space-borne observers use the combination of channels with 37 (sensitive to scattering by snow) and 19 (low-sensitive to scattering by snow) GHz. The difference of TB at these channels, with the benefit of reducing the physical temperature effect on measured brightness temperature relative to single-frequency analysis, shows the SWE value [45]. Furthermore, both vertically (V) and horizontally (H) polarized channels can be used to retrieve SWE with similar results; however, V-based channels are preferred due to less sensitivity to snow layering [46]. The SWE variable detected using space-borne sensors has acceptable accuracy over regions with consistent snow properties and insignificant altitudinal and vegetation variability. The most common satellite-based SWE products are listed as follows:
The Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E; [47]) is a microwave radiometer onboard the Aqua satellite with microwave frequencies of 6.9, 10.7, 18.7, 23.8, 36.5, and 89 GHz in vertical and horizontal polarization. AMSR-E produces global SWE observations based on the algorithm introduced by Chang et al. (1987) [30] and amended by Kelly et al. (2003) [48]. This product distinguishes the shallow and non-shallow dry snowpack by brightness temperature thresholds [48] and estimates SWE using the brightness temperature difference at two frequencies: 19 and 37 GHz [49]. In addition to considering the snow grain size variations, the AMSR-E product uses the 10.7 GHz frequency to take into account the vegetation influence for deeper snow. The snow density from [50] and [51] and snow climate classification from [52] are used to convert the snow depth to SWE.
The Scanning Multichannel Microwave Radiometer (SMMR; [53]) is a microwave instrument onboard NIMBUS-7 with frequencies of 6.6, 10.7, 18, 21, and 37 GHz in both vertical and horizontal polarization [54]. The microwave frequencies for SWE retrieval are 18 and 37 GHz [45].
The Special Sensor Microwave/Imager (SSM/I; [55]) is an instrument onboard the DMSP F-series satellites with microwave frequencies of 19, 22, 37, and 85 GHz. All channels, except 22 GHz, which has only vertical polarization, include both vertical and horizontal polarization [54]. In addition, the 19 and 37 GHz frequencies are employed to estimate SWE data.
The Advanced Microwave Scanning Radiometer 2 (AMSR2; [47]) is a remote sensing instrument onboard the GCOM-W1 satellite launched by the JAXA, and produces global SWE estimates by measuring weak microwave emission.
The Advanced Microwave Sounding Unit (AMSU; [56]) is a microwave radiometer onboard the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellites (POES), and has two modules: AMSU-A and AMSU-B, with the nadir resolutions of 48 km and 16 km, respectively. SWE retrieval is based on an empirical relationship that uses brightness temperature measurements at 23, 31, and 89 GHz channels. Despite the coarse spatial resolution, AMSU has larger spatial coverage and more additional channels in comparison with AMSR-E and SSM/I, which makes the SWE estimates more robust [56].

2.2. Reanalysis SWE Products

In reanalysis products, the best features of observations and models are used to recreate the climate variables field [54]. The most widely used reanalysis SWE products are presented below:
The National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR; [57]) presents a reanalysis dataset, which is achieved by combining the globally coupled land–atmosphere–ocean–sea ice system and global precipitation analyses obtained from the Climate Prediction Center Unified (CPCU) daily gauge analysis datasets [58] and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; [59]). The snowpack simulation is performed by the Noah land surface model [60], and the CFSR snow analysis is based on the Snow Depth model (SNODEP) [61]. The SNODEP assimilates the snow cover area obtained from the National Environmental Satellite Data and Information Service (NESDIS), Interactive Multi-sensor Snow and Ice Mapping System (IMS; [62]), and in situ observations to produce a global snow depth analysis through the detection algorithm of the SSM/I. The SNODEP model has low accuracy in estimating SWE due to the sparse in situ measurements [63]. It should be noted that a 10:1 ratio is utilized to convert the snow depth data to SWE.
ERA40 [64] is a common reanalysis product from the European Centre for Medium-Range Weather Forecasting (ECMWF). The data assimilated within ERA40 include observational snow depths and SSM/I brightness temperatures. Reichler and Kim (2008) [65] introduced ERA40 as one of the best available reanalysis products.
ERA-Interim (ERA-I; [66]) is a global reanalysis dataset obtained from the ECMWF. The land component of ERA-I called the Tiled ECMWF Scheme of Surface Exchanges over Land (TESSEL) is driven using the ECMWF with the snow module proposed by Douville et al. (1995) [67]. ERA-I updates the snow analysis based on the assimilated IMS snow cover and the Cressman analysis of snow depth observations [68][69]. According to [70], ERA-I detects the melt season for forested areas too early and also underpredicts snowfall values. Moreover, Kapnick and Delworth (2013) [71] reported a negative bias for ERA-Interim SWE in coastal areas and a snow underestimation for locations with midlatitude.
ERA-Interim/Land (ERA-I/L; [72]) is another reanalysis dataset achieved from the ECMWF. The land component of the ERA-I/L is based on the offline Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) model, which is a revised version of TESSEL with a simple, single layer snowpack module developed by Dutra et al. (2010) [70]. The forcing data include the ERA-Interim atmospheric dataset and precipitation modified using the Global Precipitation Climatology Project (GPCP; [73]). The ERA-I/L, unlike the ERA-I, does not assimilate any station-based snow data into the model.
ERA5, the ECMWF Reanalysis version 5, is similar to ERA-I/L in terms of its land component and snowpack module. The dataset updates the snow reanalysis using a two-dimensional optimal interpolation of in situ snow depth and IMS snow cover.
ERA5-Land [74] is the improved version of ERA5, which provides global snow data, including snow cover, albedo, density, temperature, depth, snowfall, snowmelt, and SWE from 1981 onwards.
Crocus [75] has been obtained from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model forced by ERA-Interim meteorological data. The snowpack module of the dataset is the Crocus snow scheme, which is a complex physically based snowpack model with multiple snow layers representing distinct snowfall events. Each snow layer is identified by the liquid water content, density, temperature, thickness, and grain properties.
Japanese 55-year Reanalysis (JRA-55; [76]) simulates the land component based on the offline SiB model [77] driven by precipitation that is revised using precipitable water (PW), which is obtained from the SSM/I brightness temperature [78]. The snow analysis is carried out based on the two-dimensional optimal interpolation of observed snow depth and snow cover data retrieved from the Special Sensor Microwave Imager/Sounder (SSMIS) and SSM/I. It should be noted that the SiB uses the maximum depth between 2 cm and the climatological depth to simulate ice sheets due to the lack of the physical processes for ice sheets within the SiB model.
Modern-Era Retrospective Analysis for Research and Applications (MERRA; [79][80]) is a global reanalysis product that assimilates NASA’s EOS satellite observations into a climate model [79]. The land component of the dataset is simulated using the Catchment model [81] driven by precipitation that is modified based on the CPCU and CMAP data, similar to the CFSR. The snow scheme of the model has an intermediate complexity with up to three snow layers, in which the processes of snow accumulation, melting, compaction, and refreezing are simulated without any snow data assimilation [82]. The snow depth data derived from MERRA showed a bias of 21.0 cm and a correlation of 0.56 in comparison with in situ observations of the World Meteorological Organization (WMO). By rerunning the land surface component with atmospheric data of the MERRA product, except for precipitation that is obtained from NOAA’s CPCU dataset, the MERRA-Land model has been produced.

2.3. Data Assimilation-Based SWE Products

Examples of DA-based products, which estimate SWE data by combining the land surface models and snow modules with different types of observations, are listed below:
The Global Land Data Assimilation System (GLDAS; [83]) provides a DA-based dataset using the different forcing data and land surface models. The first version of GLDAS uses the NOAA Global Data Assimilation System (GDAS) with precipitation obtained from the CPC CMAP, while the GLDAS version 2 is forced by the Global Meteorological Forcing Dataset from Princeton University. On the other hand, the land surface models used to estimate SWE include the VIC land surface model [84]; the Mosaic land surface model [85], the Community Land Model (CLM) [86], and the Noah land surface model. The structure of snowpack modules implemented in land surface models varies from simple single-layer structures (in the Mosaic and Noah models) to intermediate complex structures (in VIC and CLM). It is worth noting that no snow data assimilation is carried out in the GLDAS dataset.
The North American Land Data Assimilation System (NLDAS; [87][88][89]) is a DA-based reanalysis product, with forcing data obtained from the North American Regional Reanalysis (NARR) model [90] and in situ precipitation of the CPC adjusted by the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) [91]. Land surface models used within the NLDAS include VIC, Mosaic, and Noah.
Sierra Nevada Snow Reanalysis (SNSR; [92]) is another assimilation-based reanalysis dataset, with input data obtained from the NLDAS dataset. The SWE estimates are updated and constrained by the Landsat snow cover fraction data, leading to optimal results. Though the SNSR considers the canopy effect on the snow accumulation and melting processes, the forest cover has no significant effect on the reanalysis algorithm on a watershed scale. According to an extensive validation of the dataset against in situ data, a bias of less than 3 cm, R greater than 0.95, and RMSE less than 13 cm were achieved [92].
The Canadian Meteorological Centre (CMC; [93]) is an assimilation-based dataset providing daily snow depth across the Northern Hemisphere through integrating in situ snow depth and model simulations. Using the snow density derived from snow course measurements, the SWE data with a monthly temporal scale are produced. The results mostly depend on the model simulation in regions with few observations, although SWE is well-constrained by observational data in areas with a dense network. According to [94], the CMC, in comparison with snow course data, tends to estimate a little snow cover during the summer and begins the snowmelt in the spring too early.
North American Regional Reanalysis (NARR; [90]) is an assimilation-based dataset over North America generated by NCEP. The NARR dataset reaches peak SWE nearly three months prior to the Snowpack Telemetry (SNOTEL) data [95]. In addition, it is found that NARR is incapable of indicating an obvious annual cycle for SWE so that the melting and accumulation processes occur multiple times throughout the season. In this regard, Salzmann and Mearns (2012) [95] concluded that NARR is more appropriate for snow cover diagnosis than SWE, possibly owing to its poor correlation with SNOTEL SWE.
The National Weather Service Snow Data Assimilation System (SNODAS; [96]) is another assimilation-based dataset, developed by the National Weather Service (NWS), and produces the SWE data over the continental United States by merging station data, snow model simulations, and airborne observations. The assimilated data include satellite snow cover, radar precipitation data, airborne gamma radiation, and in situ measurements of snow courses and snow pillows. It has been shown that SNODAS performs well in forest regions, and conversely has a poor performance in alpine areas [97][98]. Furthermore, SNODAS underestimates snow depth for deep snowpacks and dense canopies [98].
Global Snow Monitoring for Climate Research (GlobSnow; [42]) is a global dataset released by the European Space Agency (ESA), which combines satellite-based PMW with ground-based snow depth measurements to quantify SWE using Bayesian non-linear iterative assimilation [42][99]. The retrieval scheme is based on the HUT model and vertically polarized observations of brightness temperature at 19 and 37 GHz obtained from the SSM/I, SMMR, and SSMIS instrument on the DMSP [100]. According to [42], the kriged snow depth field is used as the input for the forward microwave emission model to estimate the snow grain size and snow depth in two iterations. The algorithm accounts for the effect of forest transmissivity and snow density variation on the snow depth by the empirical model presented by Kruopis et al. (1999) [101] to refine the brightness temperature. It should be noted that the alpine regions are excluded from the simulation process as the approach is incapable of estimating SWE in complex terrains [42]. The comparisons of SWE retrievals with airborne or ground-based observations showed that the model is capable of simulating snow water equivalent under various land cover types and snow conditions [102][103]. In addition, the model demonstrated comparable performance with other models driven by ground-based data [104][105], and better performance than other SWE products [42]. However, it performs poorly in wet snow as well as deep dry snowpack, due to greater absorption of microwave signals rather than scattering [42][99][104].
Table 1. A summary of available SWE datasets.
Product Retrieval Approach Snow Scheme Land Model Snow Data
Assimilation		 Temporal
Resolution		 Spatial Resolution Time
Coverage		 Spatial Coverage Ref
GlobSnow Assimilation-based Simple - Ground-based SD and PMW signals Daily; weekly; monthly 25 km 1979-present Northern Hemisphere [42]
GLDAS Assimilation-based Simple
Intermediate		 Noah
Mosaic
VIC
CLM		 Meteorological data obtained from CPC CMAP and Princeton University Hourly
daily
monthly		 1° × 1°
0.25° × 0.25°		 1979–2020
1948–2014		 Global [106]
ERA-I Reanalysis Simple TESSEL IMS Hourly
Daily
monthly		 0.25° × 0.25° 1979–2019 Global [66]
CMC Assimilation-based Simple - Meteorological observations monthly 35 km 1998–2014 Global [93][107]
SNSR Assimilation-based - A land surface model (LSM) with a snow depletion curve Landsat snow cover fraction Daily 90 m 1985–2015 Sierra Nevada (United States) [92]
AMSR-E PMW - - - Daily 25 km 2002–2011 Global [47]
AMSR2 PMW + in situ - - - Daily 25 km 2012-present Global [47]
SMMR PMW - - - 2-days 25 km 1978–1987 Global [53]
SSM/I PMW - - - Daily 25 km 1987–2009 Global [55]
CFSR Reanalysis Simple Noah SNODEP, IMS 1979–2010; Version 2 updates from 2011 0.5° × 0.5° Hourly-monthly Global [57]
ERA40 Reanalysis Simple TESSEL satellite radiance data Hourly
Daily
monthly		 2.5° × 2.5° 1957–2002 Global [64]
ERA-I/L Reanalysis Simple HTESSEL - Hourly 0.25° × 0.25° 1979–2010 Global [72]
ERA5-Land Reanalysis Simple HTESSEL - Hourly
monthly		 0.1° × 0.1° 1981-present Global [74]
Crocus Reanalysis Complex ISBA - Daily 1° × 1° 1979–2016   [75]
JRA-55 Reanalysis Complex SiB Satellite radiance data Hourly
daily		 1.25° × 1.25° 1957-present Global [76]
MERRA Reanalysis Intermediate Catchment - Hourly
daily
monthly		 0.5° × 0.67° 1979–2016 Global [79]
MERRA-Land Reanalysis Intermediate Catchment - Hourly
daily
monthly		 0.5° × 0.625° 1979–2016 Global [80]
NLDAS Assimilation-based Intermediate VIC
Mosaic
Noah		 Terrestrial precipitation, space-based radiation data, and numerical model output Hourly
monthly		 12.5 km 1979-present central North America [87]
NARR Assimilation-based Complex NCEP Eta Model Observed precipitation Hourly
daily
monthly		 32 km 1979-present North America [90]
SNODAS Assimilation-based Complex Rapid Update Cycle numerical weather prediction model Satellite, airborne, and ground-based snow observations Daily 1 km 2003-present Continental US [96]
ERA5 Reanalysis Simple HTESSEL - Hourly
monthly		 0.25° × 0.25° 1979 to present Global [108]

This entry is adapted from the peer-reviewed paper 10.3390/su141811395

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