Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals

Langlois, Alexandre; Royer, A.; Derksen, Chris; Montpetit, Benoît; Dupont, Florent; Goı̈ta, Kalifa

doi:10.1029/2012wr012133

Cited by 72 publications

(51 citation statements)

References 92 publications

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“…A demonstration of different microstructural parameterizations and their use in emission models using a suite of different emission and physical models is given in [47]. Similar schemes have been demonstrated previously for SWE estimation [48] and for forward model simulations [41,49] using other datasets.…”

Section: Discussionmentioning

confidence: 84%

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

et al. 2018

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Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical snow models, is necessary to further improve retrieval skill, in particular for snow regimes with larger temporal variability in snow microstructure and a more pronounced layered structure.

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Section: Discussionmentioning

confidence: 84%

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

et al. 2018

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“…The use of a correlation length correction scheme for microwave modelling has also been demonstrated by previous studies. Wiesmann et al (2000) and the natural logarithm of the maximum grain diameter, while Langlois et al (2012) and Montpetit et al (2013) used an approach similar to Eq. (1), including an additional factor of 2/3 according to Mätzler (2002), and obtained scaling coefficients of 0.1 and 1.3, respectively.…”

Section: Combined Model Studymentioning

confidence: 99%

The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study

2014

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Abstract. Satellite observations of microwave brightness temperatures between 19 GHz and 85 GHz are the main data sources for operational sea-ice monitoring and retrieval of ice concentrations. However, microwave brightness temperatures depend on the emissivity of snow and ice, which is subject to pronounced seasonal variations and shows significant hemispheric contrasts. These mainly arise from differences in the rate and strength of snow metamorphism and melt. We here use the thermodynamic snow model SNTHERM forced by European Re-Analysis (ERA) interim data and the Microwave Emission Model of Layered Snowpacks (MEMLS), to calculate the sea-ice surface emissivity and to identify

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“…This is a major limitation for the assimilation of passive microwave brightness temperature (T B ) data for the improvement of snow simulations. In the context of data assimilation, where physical and emission models of snow are coupled, estimates of snow grain size are needed (Durand et al, 2009;Toure et al, 2011;Langlois et al, 2012). The implementation of snow grain metamorphism within CLASS is thus of particular interest for assimilation purposes.…”

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confidence: 99%

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

et al. 2013

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Abstract. Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and the temperature gradient under dry snow conditions, while it considers the liquid water content of the snowpack for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, arctic and subarctic) with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.0 m 2 kg −1 , and a root mean square error (RMSE) of 5.1 m 2 kg −1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

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Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals

Cited by 72 publications

References 92 publications

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

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