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

Tian-Kunze, Xiangshan; Kaleschke, Lars; Maaß, Nina; Mäkynen, M.; Serra, Nuno; Drusch, Matthias; Krumpen, Thomas

doi:10.5194/tc-8-997-2014

Cited by 190 publications

(265 citation statements)

References 62 publications

(99 reference statements)

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“…Since the launch of ESA's Soil Moisture and Ocean Salinity (SMOS) mission in 2009, it has been demonstrated that passive microwave measurements at L-band are well suited for sea ice thickness retrieval [2][3][4][5]. Having a large sensitivity to thin sea ice of 0.5 to 1 m, L-band measurements nicely complement Cryosat-2 altimeter data, which show increasingly large relative uncertainties for thin ice below 1 m [6].…”

Section: Introductionmentioning

confidence: 74%

A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications

Tetzlaff

Kaleschke

2018

Remote Sensing

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Abstract:Passive microwave measurements at L-band from ESA's Soil Moisture and Ocean Salinity (SMOS) mission can be used to retrieve sea ice thickness of up to 0.5-1.0 m. Since 2015, NASA's Soil Moisture Active Passive (SMAP) mission provides brightness temperatures (TB) at the same frequency. Here, we explore the possibility of combining SMOS and SMAP TBs for sea ice thickness retrieval. First, we compare daily TBs over polar ocean and sea ice regions. For this purpose, the multi-angular SMOS measurements have to be fitted to the SMAP incidence angle of 40 • . Using a synthetical dataset for testing, we evaluate the performance of different fitting methods. We find that a two-step regression fitting method performs best, yielding a high accuracy even for a small number of measurements of only 15. Generally, SMOS and SMAP TBs agree very well with correlations exceeding 0.99 over sea ice but show an intensity bias of about 2.7 K over both ocean and sea ice regions. This bias can be adjusted using a linear fit resulting in a very good agreement of the retrieved sea ice thicknesses. The main advantages of a combined product are the increased number of daily overpasses leading to an improved data coverage also towards lower latitudes, as well as a continuation of retrieved timeseries if one of the sensors stops delivering data.

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Section: Introductionmentioning

confidence: 74%

A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications

Tetzlaff

Kaleschke

2018

Remote Sensing

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“…Still, large uncertainties remain in the accuracy of the resulting SIT estimates (larger than 0.5 m) due to uncertainties in the snow depth and the sea ice density (Zygmuntowska et al, 2014). A new database based on CryoSat-2 has been provided (Laxon, 2013;Ricker et al, 2014) and has been made available in near-real time (Tilling et al, 2016). Finally, methods for SIT retrieval based on measurements of the brightness temperature at a low microwave frequency of 1.4 GHz (L-band: wavelength λ a = 21 cm) have been developed in preparation for the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission (Heygster et al, 2009;Kaleschke et al, 2010Kaleschke et al, , 2013.…”

Section: Introductionmentioning

confidence: 99%

Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system

Xie¹,

Counillon²,

Bertino³

et al. 2016

The Cryosphere

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Abstract. An observation product for thin sea ice thickness (SMOS-Ice) is derived from the brightness temperature data of the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. This product is available in near-real time, at daily frequency, during the cold season. In this study, we investigate the benefit of assimilating SMOS-Ice into the TOPAZ coupled ocean and sea ice forecasting system, which is the Arctic component of the Copernicus marine environment monitoring services. The TOPAZ system assimilates sea surface temperature (SST), altimetry data, temperature and salinity profiles, ice concentration, and ice drift with the ensemble Kalman filter (EnKF). The conditions for assimilation of sea ice thickness thinner than 0.4 m are favorable, as observations are reliable below this threshold and their probability distribution is comparable to that of the model. Two parallel Observing System Experiments (OSE) have been performed in March and November 2014, in which the thicknesses from SMOS-Ice (thinner than 0.4 m) are assimilated in addition to the standard observational data sets. It is found that the root mean square difference (RMSD) of thin sea ice thickness is reduced by 11 % in March and 22 % in November compared to the daily thin ice thicknesses of SMOS-Ice, which suggests that SMOS-Ice has a larger impact during the beginning of the cold season. Validation against independent observations of ice thickness from buoys and ice draft from moorings indicates that there are no degradations in the pack ice but there are some improvements near the ice edge close to where the SMOS-Ice has been assimilated. Assimilation of SMOS-Ice yields a slight improvement for ice concentration and degrades neither SST nor sea level anomaly. Analysis of the degrees of freedom for signal (DFS) indicates that the SMOS-Ice has a comparatively small impact but it has a significant contribution in constraining the system (> 20 % of the impact of all ice and ocean observations) near the ice edge. The areas of largest impact are the Kara Sea, Canadian Archipelago, Baffin Bay, Beaufort Sea and Greenland Sea. This study suggests that the SMOS-Ice is a good complementary data set that can be safely included in the TOPAZ system.

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“…But results of the study can enlighten several aspects of the snow on ice problem which is important today. The difference between MY and FY regarding snow cover, estimation of energy fluxes from ocean to atmosphere in coupled models, the impact on snow cover on satellite altimeter retrievals of ice thickness and 20 passive microwave retrievals of thin ice thickness (Tian-Kunze et al;.…”

Section: Resultsmentioning

confidence: 99%

Snow depth on Arctic sea ice from historical in situ data

Shalina¹,

Sandven²

2017

Preprint

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Abstract.In this paper we analyze snow data from Soviet airborne expeditions Sever that was collected in the Arctic around places of landings in March, April and May and cover much wider area than the region of observations of Soviet North Pole drifting stations. Particularly, there were a lot of Sever observations in the Eurasian seas. We investigate the following snow parameters: average snow depth on the level ice, height and area of sastrugi, depth of snow dunes attached to ice ridges and 10 depth of snow on hummocks. We have built new snow depth climatology for the late winter that was calculated using both Sever expedition and North Pole drifting station observations. Our result refines the description of snow depth in the central Arctic and provides detailed information on snow depth in the marginal seas. In the 1970s-80s the snow cover in the central Arctic had the following characteristics: the snow depth of the undisturbed snow was 21.2 cm, the depth of sastrugi (that occupied about 36% of the ice surface) was 36.2 cm and the depth of snow assembled near the hummocks and ridges was 15 about 65 cm. For the marginal seas Sever observations revealed that the average depth of undisturbed snow on the level ice changed from 9.8 cm in the Laptev Sea to 15.3 cm in the East Siberian Sea, the topmost value in the East Siberian Sea is explained by the highest proportion of multiyear ice there. Observations demonstrated a very high spatial variability of snow depth in the marginal seas characterized by standard deviation changing from 66 to 100%. Average height of sastrugi in the Eurasian seas varied from 23 cm to about 32 cm with standard deviation from 50 to 56%. Average area covered by sastrugi 20 in the marginal seas was estimated as 36.5% of the area of the ice floe where those features have been observed. The snow map introduced here as a new climatology is built from Sever and North Pole data, with the latter amounted to 6.1% of the whole data set. On the whole, our snow depth map reveals lower values comparing to Warren climatology in the central Arctic and shows refined information for the Eurasian seas.

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

Cited by 190 publications

References 62 publications

A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications

A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications

Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system

Snow depth on Arctic sea ice from historical in situ data

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