A method for sea ice thickness and concentration analysis based on SAR data and  a thermodynamic model

Karvonen, Juha; Cheng, Bin; Vihma, Timo; Arkett, M.; Carrières, Tom

doi:10.5194/tc-6-1507-2012

Cited by 31 publications

(20 citation statements)

References 44 publications

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“…Further studies have extended the use of HIGHTSI in investigating various processes. By combining the modeling with HIGHTSI and remote sensing data, an analysis that can better reveal the sea ice thickness and concentration information has been conducted (Karvonen et al, 2012). Moreover, HIGHTSI has also been used in lake ice studies , and its benefits from a detailed treatment of snow and ice thermodynamics were confirmed when compared with a simple lake model (Semmler et al, 2012).…”

Section: Hightsimentioning

confidence: 97%

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

et al. 2016

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Abstract. Sea ice plays an important role in the air-iceocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice scheme, which is the only option in the current Weather Research and Forecasting (WRF) model (version 3.6.1), has a problem of energy imbalance due to its simplification in snow processes and lack of ablation and accretion processes in ice. Validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations, Noah underestimates the sea ice temperature which can reach −10 • C in winter. Sensitivity tests show that this bias is mainly attributed to the simulation within the ice when a time-dependent ice thickness is specified. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGH-TSI) uses more realistic thermodynamics for snow and ice. Most importantly, HIGHTSI includes the ablation and accretion processes of sea ice and uses an interpolation method which can ensure the heat conservation during its integration. These allow the HIGHTSI to better resolve the energy balance in the sea ice, and the bias in sea ice temperature is reduced considerably. When HIGHTSI is coupled with the WRF model, the simulation of sea ice temperature by the original Polar WRF is greatly improved. Considering the bias with reference to SHEBA observations, WRF-HIGHTSI improves the simulation of surface temperature, 2 m air temperature and surface upward long-wave radiation flux in winter by 6, 5 • C and 20 W m −2 , respectively. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information results in the best simulation among the available methods. If no observational information is available, we present a new method in which the sea ice thickness is initialized from empirical estimation and its further change is predicted by a complex thermodynamic sea ice model. The ice thickness simulated by this method depends much on the quality of the initial guess of the ice thickness and the role of the ice dynamic processes.

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Section: Hightsimentioning

confidence: 97%

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

et al. 2016

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“…The model has been further developed with respect to the formation of snow-ice and superimposed ice and adapted for lake ice too. For seasonal sea ice cover, HIGHTSI can provide quite accurate basin scale evolution of IT [2]. In this study, atmospheric forcing for HIGHTSI was taken from the operational analyses and short-term forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF).…”

Section: Hightsi Modelmentioning

confidence: 99%

“…Several high-resolution synthetic aperture radars (SAR), such as Seasat, ERS, ENVISAT and RADARSAT, have been used in the sea ice monitoring; a number of parameters, such as ice type, ice edge location, ice concentration and open leads can be extracted. A numerical sea ice model can be applied together with remote sensing data to provide analyses of sea ice thickness (IT) [1][2][3]. A methodology adapted from [2] was used in this study to estimate IT over the Bohai Sea.…”

Section: Introductionmentioning

confidence: 99%

“…A numerical sea ice model can be applied together with remote sensing data to provide analyses of sea ice thickness (IT) [1][2][3]. A methodology adapted from [2] was used in this study to estimate IT over the Bohai Sea. The latest version of the HIGHTSI (HIGH-resolution Thermodynamic Snow/Ice) model [4,5] and Radarsat-2 data were used in this study.…”

Section: Introductionmentioning

confidence: 99%

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Sea ice thickness retrieval from SAR imagery over Bohai sea

Shi

Karvonen

Cheng

et al. 2014

2014 IEEE Geoscience and Remote Sensing Symposium

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The Bohai sea is a semi-enclosed sea located in the northeast of China. Safety of ship navigation and exploration platforms are important issues. Reliable near real time ice information is necessary and the ice thickness distribution is still a challenge. Here we present an ice thickness estimation method combining numerical sea ice model and SAR data. A high resolution thermodynamic snow and ice model (HIGHTSI) is applied to calculate the thermodynamic ice growth and used as the ice thickness background. SAR images are used to express the local ice statistics and to redistribute the modeled ice thickness. The results are evaluated by comparing with in-situ observations from oil platforms and ice forecast results from National Marine Environmental Forecasting Center (NMEFC).

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“…In order to overcome these limitations of SAR data for improving sea-ice analysis, early studies used SAR backscatter characteristics and the robust classifier combined with texture features for discrimination between ice and water in the Arctic. Local binary patterns (LBP) are exploited in sea-ice analysis [12]. Frequently used texture features are the gray-level cooccurrence matrix (GLCM) textures for SAR-based sea-ice analysis [13].…”

Section: Introductionmentioning

confidence: 99%

Antarctic Sea-Ice Classification Based on Conditional Random Fields From RADARSAT-2 Dual-Polarization Satellite Images

Zhu

Heygster

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In January 2014, Chinese National Antarctic Research Expedition (CHINARE) 30th cruise raised public concern since the Xuelong, the Chinese polar research vessel, was trapped in the sea-ice zone (66°39 20.88 S, 144°25 2.28 E) in the vicinity of the Adélie Depression area on the east Antarctic continent. This event highlighted the importance of an operational sea-ice classification map for ice routing to serve ship navigation. In this paper, unprecedented Antarctic sea-ice classification algorithms from RADARSAT-2 satellite dual-polarization synthetic aperture radar (SAR) images were developed using the conditional random fields (CRF) approach by including multiple features from sea-ice concentration, gray-level cooccurrence matrix textures, polarization ratio, backscatter coefficients, and intensity data. Coincident RADARSAT-2 Satellite SAR datasets with five scenes were collected for ice classification into categories such as open water, thin ice, smooth first year ice, deformed first year ice, and old ice during the CHINARE-30th cruise. The effects of deformation, rafting, and ridging during the spring-summer transition period were overwhelmed by the spatial and contextual CRF models in combination with the rich features extracted for sea-ice classification. Four strategies including statistical distribution and region connection, multiple features and support vector machine (SVM) integrated into the CRF model are proposed to describe the sea-ice-type relationships among pixels. By conducting comparative experiments between the proposed methods and state-of-the-art sea-ice classification based on the SVM algorithm, the best was obtained from the SVM-based CRF (SVM-CRF) algorithm for sea-ice classification with respect to the three scenes from the Indian Ocean sector and two scenes of Pacific Ocean sectors including medium-resolution dual-polarization SAR imagery with a pixel spacing of 50 m and higher resolution dual-polarization SAR imagery with a pixel spacing of 6.25 m. Results indicate that the SVM-CRF approach has the capacity for improving sea-ice classification, which can provide accurate and reliable sea-ice class information for sea-ice analysis.

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A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

Cited by 31 publications

References 44 publications

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Sea ice thickness retrieval from SAR imagery over Bohai sea

Antarctic Sea-Ice Classification Based on Conditional Random Fields From RADARSAT-2 Dual-Polarization Satellite Images

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