Large-scale inverse Ku-band backscatter modeling of sea ice

Remund, Q.P.; Long, D.G.

doi:10.1109/tgrs.2003.813495

Cited by 18 publications

(11 citation statements)

References 29 publications

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“…SeaWinds/QuikSCAT (QuikSCAT) Scatterometer Image Reconstruction (SIR) active microwave data has large areal coverage (1800 km swath) and high spatial and temporal resolution making it ideally suited for mapping sea ice melt dynamics over broad‐scale regions. QuikSCAT data has been used to monitor sea ice extent [ Remund and Long , 1999, 2003], detect the onset of snowmelt over the Arctic Ocean Polar Pack [ Forster et al , 2001], and provide advanced sea ice melt information within the CAA [ Howell et al , 2005].…”

Section: Introductionmentioning

confidence: 99%

Application of a SeaWinds/QuikSCAT sea ice melt algorithm for assessing melt dynamics in the Canadian Arctic Archipelago

Howell¹,

Tivy²,

Yackel³

et al. 2006

J. Geophys. Res.

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A remotely sensed sea ice melt algorithm utilizing SeaWinds/QuikSCAT (QuikSCAT) data is developed and applied to sea ice the Canadian Arctic Archipelago (CAA) from 2000 to 2004. The extended advanced very high resolution radiometer Polar Pathfinder (APP‐x) data set is used to identify spatially coupled relationships between sea ice melt and radiative forcings. In situ data from the Collaborative Interdisciplinary Cryospheric Experiment (C‐ICE) (2000, 2001, and 2002) and the Canadian Arctic Shelf Exchange Study (CASES) (2004) are used to validate APP‐x data during the melt period. QuikSCAT‐detected maps of melt onset, pond onset, and drainage are created from 2000 to 2004, and results indicate considerable interannual variability of melt dynamics within the CAA. In some years, melt stages are positively spatially autocorrelated, whereas other years exhibit a negative or no spatial autocorrelation. QuikSCAT‐detected stages of melt are found to be influenced by interannual varying amounts and timing of radiative forcing making prediction difficult. The spatiotemporal variability of ice melt also influences the distribution of ice within the CAA. The lower‐latitude regions of the CAA are shown to have accumulated increasing concentrations of multiyear ice from 2000 to 2005. This paper concludes with a discussion of the interplay between thermodynamic and dynamic sea ice processes likely to have contributed to this trend.

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Section: Introductionmentioning

confidence: 99%

Application of a SeaWinds/QuikSCAT sea ice melt algorithm for assessing melt dynamics in the Canadian Arctic Archipelago

Howell¹,

Tivy²,

Yackel³

et al. 2006

J. Geophys. Res.

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“…Sea ice is commonly classified as either first‐year ice (FYI) or multiyear ice (MYI). Low salinity and large air bubbles in MYI promote volume scattering, whereas surface scattering dominates in FYI because of reduced air bubble size and high salinity, including at the snow‐ice interface [ Remund and Long , 2003; Yueh et al , 1997]. The outcome is a backscattering persistently 3 dB stronger in MYI than in FYI [ Kwok , 2004] and different intra‐annual patterns.…”

Section: Satellite Measurement Analysis Methodsmentioning

confidence: 99%

Mapping of seasonal freeze‐thaw transitions across the pan‐Arctic land and sea ice domains with satellite radar

Mortin¹,

Schröder²,

Hansen³

et al. 2012

J. Geophys. Res.

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[1] To monitor the pan-Arctic seasonal freeze-thaw transitions of the land surface and sea ice, we analyze daily backscatter data from satellite scatterometry to examine the time series on an annual basis by applying an optimal edge detection scheme, and iterate against an internal median climatology to mitigate unreasonable outliers. By applying this novel algorithm to resolution-enhanced QuikSCAT data from 1999 to 2009, we have mapped a decade of seasonal freeze-thaw transitions across the landmass and sea ice north of 60 N at a spatial resolution better than 5 km. The data set has been validated against surface air temperature measurements and snow depth obtained from a distributed network of weather stations and drift buoys. Most retrieved timings from surface and QuikSCAT measurements agree to less than a week at thaw transition for both land and sea ice and at freeze transition for sea ice, indicating successful retrieval over a range of surface covers. While the spatial pattern of freeze-thaw transition changes substantially from year to year, the interannual variability of the mean transition timing over a particular surface is small.

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“…Here more in situ observations and a better understanding of ocean-ice-atmosphere fluxes and boundary layer physics are necessary (Jung et al, 2016). While there has been research into inverse modeling of raw values (Lee et al, 2017;Remund & Long, 2003) to provide model outputs directly comparable with the satellite observations, the additional parameterizations to calculate these still require detailed understanding of the processes involved. Drift measurements, both in situ from buoys and derived from satellites (Löptien & Axell, 2014;Schweiger & Zhang, 2015) especially high-resolution SAR (Karvonen, 2012;Korosov & Rampal, 2017) and optical, are another underutilized resource that could improve drift forecasts.…”

Section: Next Steps and New Technologies For Derived Productsmentioning

confidence: 99%

Sea-ice information and forecast needs for industry maritime stakeholders

et al. 2020

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Profound changes in Arctic sea-ice, a growing desire to utilize the Arctic's abundant natural resources, and the potential competitiveness of Arctic shipping routes, all provide for increased industry marine activity throughout the Arctic Ocean. This is anticipated to result in further challenges for maritime safety. Those operating in ice-infested waters require various types of information for sea-ice and iceberg hazards. Ice information requirements depend on regional needs and whether the stakeholder wants to avoid ice all together, operate near or in the Marginal Ice Zone, or areas within the ice pack. An insight into user needs demonstrates how multiple spatial and temporal resolutions for sea-ice information and forecasts are necessary to provide information to the marine operating community for safety, planning, and situational awareness. Although ship-operators depend on sea-ice information for tactical navigation, stakeholders working in route and capacity planning can benefit from climatological and long-range forecast information at lower spatial and temporal resolutions where the interest is focused on open-water season. The advent of the Polar Code has brought with it additional information requirements, and exposed gaps in capacity and knowledge. Thus, future satellite data sources should be at resolutions that support both tactical and planning activities.

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Large-scale inverse Ku-band backscatter modeling of sea ice

Cited by 18 publications

References 29 publications

Application of a SeaWinds/QuikSCAT sea ice melt algorithm for assessing melt dynamics in the Canadian Arctic Archipelago

Application of a SeaWinds/QuikSCAT sea ice melt algorithm for assessing melt dynamics in the Canadian Arctic Archipelago

Mapping of seasonal freeze‐thaw transitions across the pan‐Arctic land and sea ice domains with satellite radar

Sea-ice information and forecast needs for industry maritime stakeholders

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