New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

Gabarró, Carolina; Turiel, Antonio; Elósegui, P.; Pla-Resina, Joaquim A.; Portabella, Marcos

doi:10.5194/tc-11-1987-2017

Cited by 12 publications

(17 citation statements)

References 45 publications

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“…6). The observational sea ice concentration product ASI (ARTIST Sea Ice algorithm) (Kaleschke et al, 2001;Spreen et al, 2008) shows a rapid freeze-up to 80 % sea ice coverage in just a few days. The brightness temperatures of SMOS measurements and the KA2010 and MA2013 models show high agreement with some exceptions on the first days of the freezing period, which starts around the 25 October.…”

Section: Radiative Transfer Model Sensitivity Studymentioning

confidence: 99%

“…Consequently, sea ice thickness data sets using SMOS measurements have been produced operationally for the Arctic based on retrieval algorithm developed at the University of Hamburg (Kaleschke et al, 2012;Tian-Kunze et al, 2014). Other sea ice parameters estimated from L-band observations comprise sea ice concentration (Gabarro et al, 2017) and snow coverage on thick sea ice (Maaß et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models

et al. 2018

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Abstract. Sea ice is a crucial component for short-, medium- and long-term numerical weather predictions. Most importantly, changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-band brightness temperatures from ESA's Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to derive thin sea ice thickness. These retrieved estimates were already successfully assimilated in forecasting models to constrain the ice analysis, leading to more accurate initial conditions and subsequently more accurate forecasts. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems, reducing the data latency and providing a more consistent first guess. As a first step towards such a data assimilation system we studied the forward operator that translates geophysical parameters provided by a model into brightness temperatures. We use two different radiative transfer models to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes are most pronounced in the marginal ice zone where new sea ice is formed. There we observe the largest differences of more than 20 K over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yields high potential for forecasting models to correct for uncertainties in thin sea ice areas and suggest that information on sea ice fractional coverage from higher-frequency brightness temperatures should be used simultaneously.

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Section: Radiative Transfer Model Sensitivity Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models

et al. 2018

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“…The SIC has been retrieved with satellite microwave radiometer data since the 1970s, and the daily estimates of the global sea ice area and extent from these data are one of the longest continuous climate records (Stocker et al, ; Tonboe et al, ). Microwave frequency channels spanning from 1 to nearly 100 GHz are used for SIC retrieval (Gabarro et al, ; Ivanova et al, ). Recently, in an evaluation of over 20 different SIC algorithms (not including L band), it was found that the algorithm using 6.9‐GHz data had the lowest noise level of all the algorithms (Ivanova et al, ).…”

Section: Introductionmentioning

confidence: 99%

Expected Performances of the Copernicus Imaging Microwave Radiometer (CIMR) for an All‐Weather and High Spatial Resolution Estimation of Ocean and Sea Ice Parameters

et al. 2018

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Climate change resulting in ocean warming, sea level rise, and sea ice melting has consequences for the global economy, navigation, and security. The Copernicus Imaging Microwave Radiometer (CIMR) mission is a high priority candidate mission within the European Copernicus Expansion program. CIMR is designed to observe the ocean and sea ice and more particularly the Arctic environment. Sea surface temperature (SST), ocean wind speed, sea surface salinity (SSS), and sea ice concentration (SIC) are fundamental variables for understanding, monitoring, and predicting the state of the ocean and sea ice. CIMR is a conically scanning microwave radiometer imager that includes channels at 1.4, 6.9, 10.65, 18.7, and 36.5 GHz, in a Sun-synchronous polar orbit, to provide SST, ocean wind speed, SSS, and SIC with an increased accuracy and/or spatial resolution. Here we analyze the performances of the CIMR mission in terms of theoretical retrieval precision and spatial resolution on the SST, SSS, and SIC products. A careful information content analysis is conducted. The CIMR performances are compared with the Advanced Microwave Scanning Radiometer 2 and the Soil Moisture Active Passive current missions. Maps of the retrieval precision based on realistic conditions are computed. CIMR will provide SST, SSS, and SIC with a spatial resolution of 15, 55, and 5 km and a precision of 0.2 K, 0.3 psu, and 5%, respectively. The SST and SIC will be retrieved at better than 30 km from the coast. CIMR is currently in preparatory phase, and if selected, it is for a launch in the 2025+ time frame.Plain Language Summary Climate change resulting in ocean warming, sea level rise, and sea ice melting has consequences for the global economy, navigation, and security. The Copernicus Imaging Microwave Radiometer mission is a high priority candidate satellite mission within the European Copernicus Expansion program. It is designed to observe the ocean and sea ice and more particularly the Arctic environment. Sea surface temperature, ocean wind speed, sea surface salinity, and sea ice concentration are fundamental variables for understanding, monitoring, and predicting the state of the ocean and sea ice. Here we analyze the performances of this new satellite mission in terms of precision and spatial resolution on the sea surface temperature, sea surface salinity, and sea ice concentration and compare it with current missions. The Copernicus Imaging Microwave Radiometer will provide sea surface temperature, sea surface salinity, and sea ice concentration with a spatial resolution of 15, 55, and 5 km and a precision of 0.2 K, 0.3 psu, and 5%, respectively. This satellite mission is currently in preparatory phase, and if selected, it is for a launch in the 2025 time frame.

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“…The L1B dataset contains the Fourier components of the Brightness Temperatures (TB) at the antenna reference frame. By applying an inverse Fourier transform, we obtain TB snapshots (i.e., an interferometric TB image) [25]. TBs are referenced using an Equal-Area Scalable Earth (EASE) Northern Hemisphere grid of 25 km resolution.…”

Section: Smos Datamentioning

confidence: 99%

Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

et al. 2020

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The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

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New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

Cited by 12 publications

References 45 publications

Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models

Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models

Expected Performances of the Copernicus Imaging Microwave Radiometer (CIMR) for an All‐Weather and High Spatial Resolution Estimation of Ocean and Sea Ice Parameters

Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

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