Comparison Between Envisat SAR and 3-D Laser Scanner Statistics for the Baltic Sea Ice

Author(s):  
Markku Simila ◽  
Marko Makynen ◽  
Istvan Heiler ◽  
Martti Hallikainen
Author(s):  
Nikolay Lugovoy ◽  
Nikolay Lugovoy ◽  
Askar Ilyasov ◽  
Askar Ilyasov ◽  
Elena Pronina ◽  
...  

The paper describes application of the terrestrial laser scanner for investigation of coastal dynamics of the Svetlogorskaya Bay, Baltic Sea. Methods of investigation and results of surveys repeated over the two consecutive years for quantification of coastal erosion and slope processes within the coastal zone are presented.


Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 473-483 ◽  
Author(s):  
J. Karvonen

Abstract. An algorithm for computing ice drift from pairs of synthetic aperture radar (SAR) images covering a common area has been developed at FMI. The algorithm has been developed based on the C-band SAR data over the Baltic Sea. It is based on phase correlation in two scales (coarse and fine) with some additional constraints. The algorithm has been running operationally in the Baltic Sea from the beginning of 2011, using Radarsat-1 ScanSAR wide mode and Envisat ASAR wide swath mode data. The resulting ice drift fields are publicly available as part of the MyOcean EC project. The SAR-based ice drift vectors have been compared to the drift vectors from drifter buoys in the Baltic Sea during the first operational season, and also these validation results are shown in this paper. Also some navigationally useful sea ice quantities, which can be derived from ice drift vector fields, are presented.


2006 ◽  
Vol 45 (7) ◽  
pp. 982-994 ◽  
Author(s):  
Matthias Drusch

Abstract Sea ice concentration plays a fundamental role in the exchange of water and energy between the ocean and the atmosphere. Global real-time datasets of sea ice concentration are based on satellite observations, which do not necessarily resolve small-scale patterns or coastal features. In this study, the global National Centers for Environmental Prediction (NCEP) 0.5° sea ice concentration dataset is compared with a regional high-resolution analysis for the Baltic Sea produced 2 times per week by the Swedish Meteorological and Hydrological Institute (SMHI). In general, the NCEP dataset exhibits less spatial and temporal variability during the winter of 2003/04. Because of the coarse resolution of the NCEP dataset, ice extent is generally larger than in the SMHI analysis. Mean sea ice concentrations derived from both datasets are in reasonable agreement during the ice-growing and ice-melting periods in January and April, respectively. For February and March, during which the sea ice extent is largest, mean sea ice concentrations are lower in the NCEP dataset relative to the SMHI product. Ten-day weather forecasts based on the NCEP sea ice concentrations and the SMHI dataset have been performed, and they were compared on the local, regional, and continental scales. Turbulent surface fluxes have been analyzed based on 24-h forecasts. The differences in sea ice extent during the ice-growing period in January cause mean differences of up to 30 W m−2 for sensible heat flux and 20 W m−2 for latent heat flux in parts of the Gulf of Bothnia and the Gulf of Finland. The comparison between spatially aggregated fluxes yields differences of up to 36 and 20 W m−2 for sensible and latent heat flux, respectively. The differences in turbulent fluxes result in different planetary boundary height and structure. Even the forecast cloud cover changes by up to 40% locally.


Author(s):  
Marjan Marbouti ◽  
Oleg Antropov ◽  
Jaan Praks ◽  
Patrick B. Eriksson ◽  
Vahid Arabzadeh ◽  
...  
Keyword(s):  
Sea Ice ◽  

2020 ◽  
Author(s):  
Jaromir Jakacki ◽  
Maciej Muzyka ◽  
Marta Konik ◽  
Anna Przyborska ◽  
Jan Andrzejewski

<p>During the last decades remote sensing observations as well as modelling tools has been developed and become key elements of oceanographic research. One of the main advantages of both tools is a possibility of measuring large-scale areas.</p><p>The remote sensing measurements deliver only snapshots of the ice situation with no information about backgroundconditions. Moreover, providing picture of the whole area requires sometimes combining various datasets that increases uncertainties.  Modelling simulations provide full history of external conditions, but they also introduce errors that are the result of parameterizations. Also, an inaccuracy provided by forcing fields at the top and bottom boundaries are accumulated in the model.</p><p>In this work sea ice parameters such as sea ice concentration, thickness and volume obtained from both – satellite measurements and modelling has been compared. Numerical simulations were performed using standalone Community Ice Code (CICE) model (v. 6.0). It is a descendant of the basin scale dynamic-thermodynamic and thickness distribution sea ice model. The model is well known by scientific community and was widely used in a global as well as regional research, even operationally. The satellite derived ice thickness products were based on the C band HH-polarized SAR measurements originating from the satellites Sentinel-1 and RADARSAT-2. The sea ice concentration maps contain also visual and infrared information from MODIS and NOAA.</p><p>The ice extent, thickness and volume were compared in several regions within the Baltic Sea.  Seasonal changes were analyzed with a particular attention to ice formation and melting time. The sea ice extent datasets were compatible. Inconsistencies were observed for the sea ice thickness delivered by satellite measurements, especially during the ice melt. The work presents direction for ignoring satellite data with an error related to ice melting that allows for excluding erroneous satellite maps and obtain reliable intercalibration.</p><p> </p><p>This work was partly funded by Polish National Science Centre, project number 2017/25/B/ST10/00159</p>


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