Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

Maaß, Nina; Kaleschke, Lars; Tian-Kunze, Xiangshan; Drusch, Matthias

doi:10.5194/tc-7-1971-2013

Cited by 87 publications

(95 citation statements)

References 45 publications

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“…Although we consider the insulation effect of snow, the radiative contribution of the snow layer to the overall brightness temperature is ignored. This effect is investigated in Maaß et al (2013b) with another radiation model based on Burke et al (1979). The quantification of the effect and uncertainty caused by snow layers is considered as future work.…”

Section: Discussionmentioning

confidence: 99%

“…Although we ignore snow layer in the sea ice radiation model, we consider its thermal insulation effect in the thermodynamic model when we calculate T ice . It is shown in Maaß et al (2013b) that the impact of a snow layer on the TB is partly caused by its insulation effect on the ice temperature. The insulation effect of a snow layer increases with snow thickness.…”

Section: The Thermodynamic Modelmentioning

confidence: 99%

“…One is the thermodynamic insulation effect, which will be discussed in the following section, the other is the radiative contribution to the overall brightness temperature. To consider the second effect, an elaborate inter-comparison with a multi-layer emission model that includes a snow layer (e.g., Maaß et al, 2013b) would be necessary. The TB over sea ice depends on the dielectric properties of the ice layer, which are a function of brine volume (Vant et al, 1978).…”

Section: The Sea Ice Radiation Modelmentioning

confidence: 99%

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SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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Abstract. Following the launch of ESA's Soil Moisture andOcean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

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Section: Discussionmentioning

confidence: 99%

Section: The Thermodynamic Modelmentioning

confidence: 99%

Section: The Sea Ice Radiation Modelmentioning

confidence: 99%

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SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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“…The model was originally designed for the modeling of radiative transfer of the X-and L-band soil moisture. In Maaß et al (2013b), this model is applied to sea ice and further used for the retrieval of snow depth over thick sea ice. In these works, a simple one-layer formulation is used for both the sea ice and the snow cover over it.…”

Section: L-band Radiation Modelmentioning

confidence: 99%

“…In Comiso et al (2003), multi-band data from AMSR-E are utilized, but only for snow cover over FYI. Maaß et al (2013b) explored the retrieval of snow depth over thick sea ice with L-band data from Soil Moisture Ocean Salinity (SMOS). SMOS provides full coverage of polar regions on a near-real-time (daily) basis.…”

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On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

Zhou

Liu

et al. 2018

The Cryosphere

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Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

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Thin Sea Ice, Thick Snow, and Widespread Negative Freeboard Observed During N‐ICE2015 North of Svalbard

et al. 2018

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In recent years, sea‐ice conditions in the Arctic Ocean changed substantially toward a younger and thinner sea‐ice cover. To capture the scope of these changes and identify the differences between individual regions, in situ observations from expeditions are a valuable data source. We present a continuous time series of in situ measurements from the N‐ICE2015 expedition from January to June 2015 in the Arctic Basin north of Svalbard, comprising snow buoy and ice mass balance buoy data and local and regional data gained from electromagnetic induction (EM) surveys and snow probe measurements from four distinct drifts. The observed mean snow depth of 0.53 m for April to early June is 73% above the average value of 0.30 m from historical and recent observations in this region, covering the years 1955–2017. The modal total ice and snow thicknesses, of 1.6 and 1.7 m measured with ground‐based EM and airborne EM measurements in April, May, and June 2015, respectively, lie below the values ranging from 1.8 to 2.7 m, reported in historical observations from the same region and time of year. The thick snow cover slows thermodynamic growth of the underlying sea ice. In combination with a thin sea‐ice cover this leads to an imbalance between snow and ice thickness, which causes widespread negative freeboard with subsequent flooding and a potential for snow‐ice formation. With certainty, 29% of randomly located drill holes on level ice had negative freeboard.

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Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

Cited by 87 publications

References 45 publications

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

Thin Sea Ice, Thick Snow, and Widespread Negative Freeboard Observed During N‐ICE2015 North of Svalbard

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