Microwave scattering coefficient of snow in MEMLS and DMRT-ML revisited: the relevance of sticky hard spheres and tomography-based estimates of stickiness

Löwe, Henning; Picard, Ghislain

doi:10.5194/tcd-9-2495-2015

Cited by 21 publications

(39 citation statements)

References 51 publications

(63 reference statements)

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“…The interaction of microwaves with snow is commonly interpreted as scattering at permittivity fluctuations in the microstructure, which can be described by the two-point correlation function (Vallese and Kong, 1981;Mätzler, 1998;Ding et al, 2010;Löwe and Picard, 2015). The two-point correlation function can be derived from the spatial distribution of ice and air that is characterized by the ice-phase indicator function I(x), which is equal to 1 for a point x in ice and 0 for x in air.…”

Section: Q Krol and H Löwe: Relating Optical And Microwave Grain Mementioning

confidence: 99%

“…For the following analysis we used an existing µCT data set of 3-D microstructure images described and used in Löwe et al (2013) for a thermal conductivity analysis and Löwe and Picard (2015) for a comparison of microwave scattering coefficients. All samples were classified according to Fierz et al (2009) as described in the supplement of Löwe et al (2013).…”

Section: Datamentioning

confidence: 99%

“…In this regard the two-point correlation function has become a key quantity for the prediction of various properties such as thermal conductivity, permeability and electromagnetic properties of snow Löwe et al, 2013;Calonne et al, 2014b;Löwe and Picard, 2015). The two-point correlation function carries, in essence, information about a distribution of relevant sizes in the microstructure.…”

Section: Introductionmentioning

confidence: 99%

“…The recently gained interest in two-point correlation functions is mainly driven by available data from µCT, from which the two-point correlation function can be conveniently estimated. The relevance of the two-point correlation function for microwave modeling originates from the connection between its Fourier transform and the scattering phase function in the Born approximation for small scatterers (Mätzler, 1998;Ding et al, 2010;Löwe and Picard, 2015) or the connection to the effective dielectric tensor via depolarization factors (Leinss et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Relating optical and microwave grain metrics of snow: the relevance of grain shape

Krol¹,

Löwe²

2016

The Cryosphere

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Abstract. Grain shape is commonly understood as a morphological characteristic of snow that is independent of the optical diameter (or specific surface area) influencing its physical properties. In this study we use tomography images to investigate two objectively defined metrics of grain shape that naturally extend the characterization of snow in terms of the optical diameter. One is the curvature length λ 2 , related to the third-order term in the expansion of the two-point correlation function, and the other is the second moment µ 2 of the chord length distributions. We show that the exponential correlation length, widely used for microwave modeling, can be related to the optical diameter and λ 2 . Likewise, we show that the absorption enhancement parameter B and the asymmetry factor g G , required for optical modeling, can be related to the optical diameter and µ 2 . We establish various statistical relations between all size metrics obtained from the two-point correlation function and the chord length distribution. Overall our results suggest that the characterization of grain shape via λ 2 or µ 2 is virtually equivalent since both capture similar aspects of size dispersity. Our results provide a common ground for the different grain metrics required for optical and microwave modeling of snow.

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Section: Q Krol and H Löwe: Relating Optical And Microwave Grain Mementioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Relating optical and microwave grain metrics of snow: the relevance of grain shape

Krol¹,

Löwe²

2016

The Cryosphere

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“…While measurements of snow specific surface area (SSA) can be applied to estimate the snow optical grain diameter, empirical scaling is required to translate this value to one explaining the observed microwave response [23]. Recent efforts have focused on simulating snow as a bicontinuous medium, simulating the resulting active and passive microwave response with some success [24].…”

mentioning

confidence: 99%

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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Simulation and Assimilation of Passive Microwave Data Using a Snowpack Model Coupled to a Calibrated Radiative Transfer Model Over Northeastern Canada

Larue

Royer

Sève

et al. 2018

Water Resources Research

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Over northern snowmelt‐dominated basins, the snow water equivalent (SWE) is of primary interest for hydrological forecasting. This paper evaluates first the performance of a detailed multilayer snowpack model (Crocus), driven by meteorological predictions generated by the Canadian Global Environmental Multiscale model, for hydrological applications. Simulations were compared to daily snow depth and SWE measurements over Québec, northeastern Canada (56–45°N), for 2012–2016, highlighting an overestimation of the annual maximum snow depth (35%) and of the annual maximum SWE (16%), which is not accurate enough for hydrological applications. To improve SWE simulations, a chain of models is implemented to simulate and to assimilate passive microwave satellite observations. The snowpack model is coupled to a microwave snow emission model (Dense Media Radiative Transfer‐Multilayers model, DMRT‐ML), and the comparison of simulated brightness temperatures (TBs) with surface‐based TB measurements (at 11, 19 and 37 GHz) shows best results when the snow stickiness parameter is set to 0.17 in DMRT‐ML. The overall root‐mean‐square error (RMSE) obtained by the calibrated coupling reaches 27 K, significantly better than the RMSE obtained by considering nonsticky spheres in DMRT‐ML (43.0 K). The relevance of TB assimilation is tested with synthetic observations to evaluate the information content of each frequency for SWE estimates. The assimilation scheme is a Sequential Importance Resampling Particle filter using an ensemble of perturbed meteorological forcing data. The results show a SWE RMSE reduced by 82% with TB assimilation compared to without assimilation.

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Microwave scattering coefficient of snow in MEMLS and DMRT-ML revisited: the relevance of sticky hard spheres and tomography-based estimates of stickiness

Cited by 21 publications

References 51 publications

Relating optical and microwave grain metrics of snow: the relevance of grain shape

Relating optical and microwave grain metrics of snow: the relevance of grain shape

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

Simulation and Assimilation of Passive Microwave Data Using a Snowpack Model Coupled to a Calibrated Radiative Transfer Model Over Northeastern Canada

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