Evolution of electromagnetic signatures of sea ice from initial formation to the establishment of thick first-year ice

Grenfell, Thomas C.; Barber, David G.; Fung, A.K.; Gow, Anthony J.; Jezek, Kenneth C.; Knapp, Eric J.; Nghiem, S. V.; Onstott, R.G.; Perovich, Donald K.; Roesler, Collin S.; Swift, C.T.; Tanis, F.J.

doi:10.1109/36.718636

Cited by 56 publications

(49 citation statements)

References 31 publications

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“…The least sensitivity to thickness of thin ice was observed for the N90 algorithm; the SIC obtained by this algorithm was independent of SIT values already at thicknesses of 20-25 cm. This is caused most likely by a smaller penetration depth in the near-90 GHz channels (shorter wave length) (see also Grenfell et al, 1998). OSISAF and CV had the second least sensitivity (levelled off at 25-30 cm), which adds more weight to the choice of an OSISAF-like combination as an optimal algorithm.…”

Section: Thin Icementioning

confidence: 99%

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Ivanova¹,

et al. 2015

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Abstract. Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.

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Section: Thin Icementioning

confidence: 99%

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Ivanova¹,

et al. 2015

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“…Frost flower growth on the newly forming ice was ubiquitous during this study [Perovich and Richter-Menge, 1994]. Laboratory optical studies showed that the presence of frost flowers on young ice caused an increase of 0.1-0.2 in albedo [Grenfell et al, 1998]. Their influence was simulated by adding a thin surface layer with the optical properties of wet snow [Grenfell and Maykut, 1977] …”

Section: Ice Productionmentioning

confidence: 98%

Ice growth and solar heating in springtime leads

Perovich¹,

Richter‐Menge²

2000

J. Geophys. Res.

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Abstract. The large thermal contrast between the cold atmosphere and the relatively warm ocean in springtime leads results in rapid ice growth and large fluxes of heat from the ocean to the atmosphere and of salt from the ice to the ocean. However, the magnitude of the ice growth and of the fluxes is moderated by solar radiation absorbed in the ice and upper ocean. During the Arctic lead experiment (LeadEx) we monitored ice conditions at four springtime leads during the first few days of growth. The experiment took place in March-April 1992 in the Beaufort Sea (73øN, 146øW). Two of the leads were <200 rn in width, one was approximately a kilometer wide, and the fourth was quite large, more than a few kilometers wide. Ice thickness typically increased rapidly, with 15-20 cm of growth in the first few days. The crystallographic analysis of a series of ice cores taken across and along the edge of one of the smaller leads indicated that granular ice was more abundant along the edges (30%) of the lead than in the central part of the lead (10%). Observations suggest that thermodynamics processes dominated ice growth in these leads. Additional thickening of the ice at the edge of the leads was common because of rafting of the young ice and the accumulation of blowing snow. Ice temperature profiles exhibited a diurnal cycle induced by solar radiation, with daily oscillations of roughly 5øC On the basis, in part, of these earlier studies our expectations prior to the field experiment were straightforward. We believed that springtime leads were areas with great amounts of frazil ice production and tremendous heat fluxes from the ocean to the atmosphere. However, the observations we made during LeadEx indicate that this simple picture is incomplete and that the actual behavior of springtime leads is more complex. In this paper we use field observations from four springtime leads, along with a radiative transfer model, to explore ice production in leads and the impact of solar radiation on springtime leads. Field ExperimentLeadEx was a large interdisciplinary program directed at assessing the impact of leads on the atmosphere and ocean and understanding the processes of heat transfer to the atmosphere and salt to the upper ocean associated with freezing leads. The research team included oceanographers, atmospheric scientists, microwave remote sensing researchers, and ice physicists. Be-6541

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“…Later laboratory and field studies (Barber et al [74], Grenfell [75]) showed that the backscatter of sea ice undergoes a profound evolution as the ice grows and changes. However, because multiple sea ice states can have the same backscatter values (Grenfell et al [76]), backscatter at a single pixel is insufficient to distinguish between ice categories. Steffen and Heinrichs [77], using Landsat data as "truth", found that first-year and old ice in winter can be separated using single-point C-band SAR backscatter but that performance is much worse in other seasons and for the thinner ice types.…”

Section: Analysis Of Sar Data For Sea-ice Classificationmentioning

confidence: 99%

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

et al. 2016

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Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line.

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Evolution of electromagnetic signatures of sea ice from initial formation to the establishment of thick first-year ice

Cited by 56 publications

References 31 publications

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

Ice growth and solar heating in springtime leads

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

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