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) present in the MODIS data. The accuracy is best for the 15-30 cm thickness range, $ $38%. The largest h i uncertainty comes from air temperature data. Our ice-thickness limits are more conservative than those in previous studies where numerical weather prediction model data were not used in the h i retrieval. Our study gives new detailed insight into the capability of T s -based h i retrieval in the Arctic marginal seas during freeze-up and wintertime, and should also benefit work where MODIS h i charts are used.

Abstract. We present methods to utilise CryoSat-2 (CS-2) synthetic aperture radar (SAR) mode data in operational ice charting. We compare CS-2 data qualitatively to SAR mosaics over the Barents and Kara seas. Furthermore, we compare the CS-2 to archived operational ice charts. We present distributions of four CS-2 waveform parameters for different ice types as presented in the ice charts. We go on to present an automatic classification method for CS-2 data which, after training with operational ice charts, is capable of determining open ocean from ice with a hit rate of > 90 %. The training data are dynamically updated every 5 days using the most recent 15 days of CS-2 data and operative ice charts. This helps the adaption of the classifier to the evolving ice/snow conditions throughout winter. The classifier is also capable of detecting three different ice classes (thin and thick firstyear ice as well as old ice) with success rates good enough for the output to be usable to support operational ice charting. Finally, we present a near-real-time CS-2 product just plotting the waveform characteristics and conclude that even such a simple product is usable for some of the needs of ice charting.

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Abstract. For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chartbased degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.

ABSTRACT. Snow and ice thickness in the coastal Kara Sea, Russian Arctic, were investigated by applying the thermodynamic sea-ice model HIGHTSI. The external forcing was based on two numerical weather prediction (NWP) models: the High Resolution Limited Area Model (HIRLAM) and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. A number of model experiments were carried out applying different snow parameterization schemes. The modelled ice thickness was compared with in situ measurements and the modelled snow thickness was compared with the NASA Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) snow thickness. The HIRLAM and ECMWF model results agreed with each other on air temperature and wind. The NWP model precipitation forecasts caught up the synoptic-scale snowfall events, but the magnitude was liable to errors. The ice growth was modelled reasonably well applying HIGHTSI either with a simple parameterization for snow thickness or with the HIRLAM or ECMWF model precipitation as input. For the latter, however, an adjustment of snow accumulation in early winter was necessary to avoid excessive accumulation and consequent underestimation of ice thickness. Applying effective snow heat conductivity improved the modelled ice thickness. The HIGHTSI-modelled snow thickness had a seasonal evolution similar to that of the AMSR-E snow thickness. New field data are urgently needed to validate NWP and ice models and remote-sensing products for snow and sea ice in the Kara Sea.

[1] A statistical analysis was performed for nearly simultaneously acquired C band synthetic aperture radar (SAR) images and ice freeboard statistics. The data analyzed were collected during a CryoSat calibration-validation campaign in March 2005 in the Baltic Sea. The 3-D ice freeboard topography along transects with a total length about 150 km and width of 300 m was constructed from cross-track scanning airborne laser scanner measurements. The SAR image data set consisted of two nearly coincident Envisat Advanced Synthetic Aperture Radar alternating polarization precision and image mode precision images at HH polarization with their incidence angle ranges deviating about 20°. The data set represents primarily low-salinity thin first year ice with high ice concentration and thin snow cover under cold weather conditions. The variance of the mean freeboard increased with increasing mean freeboard with the chosen window size (300 m) up to the mean freeboard of 15 cm. This multiplicative character of sea ice cover forms the basis by which we can expect that the C band backscattering coefficient response, which depends only on the ice surface roughness and the top part of ice medium, can provide information on the sea ice thickness. A nonlinear regression model with three control variables, the backscattering coefficient, the dominant thickness of the level ice which has deformed, and a variable accounting the effect of the SAR incidence angle, was established to predict the variation of the ice freeboard. The applied Bayesian approach also provided means to estimate the uncertainty range for the model fitting and the model predictions. The modeled predictions were mostly in good agreement with the measured values. The predictions for three different test lines accounted for 67%-85% of the measured freeboard variance in 300 m scale. In cold conditions with thin snow cover it was possible to estimate the degree of ice deformation and ice thickness quantitatively from C band SAR images.Citation: Similä, M., M. Mäkynen, and I. Heiler (2010), Comparison between C band synthetic aperture radar and 3-D laser scanner statistics for the

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