Reduction of weather effects in the calculation of sea-ice concentration with the DMSP SSM/I

Cavalieri, Donald J.; Germain, Karen M. St.; Swift, C.T.

doi:10.1017/s0022143000034791

Cited by 100 publications

(37 citation statements)

References 5 publications

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“…Ti, Zr and Rb contents were measured on slab sections at a 2 mm resolution along the entire core using an AAVATECH XRF core-scanner 67 . 68 at a spatial resolution of 25 Â 25 km. Averaged concentrations were calculated over two specific domains, CB and the entire MGP domain (Fig.…”

Section: Methodsmentioning

confidence: 99%

Glacial ice and atmospheric forcing on the Mertz Glacier Polynya over the past 250 years

et al. 2015

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The Mertz Glacier Polynya off George V Land, East Antarctica, is a source of Adélie Land Bottom Water, which contributes up to B25% of the Antarctic Bottom Water. This major polynya is closely linked to the presence of the Mertz Glacier Tongue that traps pack ice upstream. In 2010, the Mertz Glacier calved a massive iceberg, deeply impacting local sea ice conditions and dense shelf water formation. Here we provide the first detailed 250-year long reconstruction of local sea ice and bottom water conditions. Spectral analysis of the data sets reveals large and abrupt changes in sea surface and bottom water conditions with a B70-year cyclicity, associated with the Mertz Glacier Tongue calving and regrowth dynamics. Geological data and atmospheric reanalysis, however, suggest that sea ice conditions in the polynya were also very sensitive to changes in surface winds in relation to the recent intensification of the Southern Annular Mode.

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Section: Methodsmentioning

confidence: 99%

Glacial ice and atmospheric forcing on the Mertz Glacier Polynya over the past 250 years

et al. 2015

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“…Traditionally, the atmospheric effects on the SIC retrievals are removed by applying an open water/weather filter based on gradient ratios of Tbs for SMMR (Gloersen and Cavalieri, 1986) and SSM/I (Cavalieri et al, 1995): …”

Section: Substitution Of Weather Filters By Atmospheric Correctionmentioning

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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“…From a comparison with field data or other ground-truth references, tie points or transfer functions are deducted to allow for an inversion from microwave measurements to sea-ice concentration, or also surface properties like snow thickness or ice type (Markus and Cavalieri, 1998). Critical to this inversion are, however, seasonal and regional variations in the surface microwave emissivity that are caused by differences in atmospheric forcing and associated snow processes (Meier and Notz, 2010;Markus et al, 2006;Cavalieri et al, 1995;Gloersen and Cavalieri, 1986). As shown by Andersen et al (2007) variations in sea-ice concentration retrievals over high-concentration Arctic sea ice are dominated…”

Section: Introductionmentioning

confidence: 99%

The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study

2014

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Abstract. Satellite observations of microwave brightness temperatures between 19 GHz and 85 GHz are the main data sources for operational sea-ice monitoring and retrieval of ice concentrations. However, microwave brightness temperatures depend on the emissivity of snow and ice, which is subject to pronounced seasonal variations and shows significant hemispheric contrasts. These mainly arise from differences in the rate and strength of snow metamorphism and melt. We here use the thermodynamic snow model SNTHERM forced by European Re-Analysis (ERA) interim data and the Microwave Emission Model of Layered Snowpacks (MEMLS), to calculate the sea-ice surface emissivity and to identify

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Reduction of weather effects in the calculation of sea-ice concentration with the DMSP SSM/I

Cited by 100 publications

References 5 publications

Glacial ice and atmospheric forcing on the Mertz Glacier Polynya over the past 250 years

Glacial ice and atmospheric forcing on the Mertz Glacier Polynya over the past 250 years

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

The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study

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