scholarly journals Operational Service for Mapping the Baltic Sea Landfast Ice Properties

2020 ◽  
Vol 12 (24) ◽  
pp. 4032
Author(s):  
Marko Mäkynen ◽  
Juha Karvonen ◽  
Bin Cheng ◽  
Mwaba Hiltunen ◽  
Patrick B. Eriksson
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Prediction Models ◽  
Winter Season ◽  
Free Access ◽  
The Baltic Sea ◽  
Usable Information ◽  
Landfast Ice ◽  
Sar Imagery ◽  
The Baltic

The Baltic Sea is partly covered by sea ice in every winter season. Landfast ice (LFI) on the Baltic Sea is a place for recreational activities such as skiing and ice fishing. Over thick LFI ice roads can be established between mainland and islands to speed up transportation compared to the use of ferries. LFI also allows transportation of material to or from islands without piers for large ships. For all these activities, information on LFI extent and sea ice thickness, snow thickness and degree of ice deformation on LFI is very important. We generated new operational products for these LFI parameters based on synthetic aperture radar (SAR) imagery and existing products and prediction models on the Baltic Sea ice properties. The products are generated daily and have a 500 m pixel size. They are visualized in a web-portal titled “Baltic Sea landfast ice extent and thickness (BALFI)” which has free access. The BALFI service was started in February 2019. Before the BALFI service, information on the LFI properties in fine scale (<1 km) was not available from any single source or product. We studied the accuracy and quality of the BALFI products for the ice season 2019–2020 using ice charts and in-situ coastal ice station data. We suggest that the current products give usable information on the Baltic LFI properties for various end-users. We also identify some topics for the further development of the BALFI products.

scholarly journals Ventilation of the Northern Baltic Sea

2019 ◽  
Author(s):  
Thomas Neumann ◽  
Herbert Siegel ◽  
Matthias Moros ◽  
Monika Gerth ◽  
Madline Kniebusch ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Deep Water ◽  
Bottom Water ◽  
Ice Core ◽  
Winter Season ◽  
The Baltic Sea ◽  
The Baltic ◽  
Bothnian Bay ◽  
Bothnian Sea

Abstract. The Baltic Sea is a semi-enclosed, brackish water sea in northern Europe. The deep basins of the central Baltic Sea regularly show hypoxic conditions. In contrast, the northern parts of the Baltic Sea, the Bothnian Sea and Bay, are well oxygenated. Lateral inflows or a ventilation due to convection are possible mechanisms for high oxygen concentrations in the deep water of the northern Baltic Sea. Owing to the high latitudes of the northern Baltic, this region is regularly covered by sea ice during the winter season. In March 2017, the RV Maria S. Merian was for two days in the Bothnian Bay collecting ice core samples, brine water, and CTD profiles. The bulk sea ice salinity was on average 0.6 g/kg and in brine samples, a salinity of 11.5 g/kg and 17.8 g/kg have been measured. At one station, the CTD profiles indicated a recent ventilation event of the deep water. A water mass analysis showed that the ventilation is most probably due to mixing of Bothnian Sea and Bothnian Bay surface water which results in sufficient dense water able to replace older bottom water. However, the high salinity of brine provides the potential for forming dense bottom water masses as well.

scholarly journals Operational SAR-based sea ice drift monitoring over the Baltic Sea

Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 473-483 ◽  
Author(s):  
J. Karvonen
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Vector Fields ◽  
Phase Correlation ◽  
The Baltic Sea ◽  
Sar Images ◽  
Envisat Asar ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
The Baltic

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.

scholarly journals Sea Ice Concentration Analyses for the Baltic Sea and Their Impact on Numerical Weather Prediction

2006 ◽  
Vol 45 (7) ◽  
pp. 982-994 ◽  
Author(s):  
Matthias Drusch
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Baltic Sea ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
The Baltic Sea ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Concentration ◽  
The Baltic

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.

scholarly journals TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

2020 ◽  
pp. 1-20
Author(s):  
Marjan Marbouti ◽  
Oleg Antropov ◽  
Jaan Praks ◽  
Patrick B. Eriksson ◽  
Vahid Arabzadeh ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
The Baltic Sea ◽  
The Baltic

Comparison of the main sea ice parameters received from satellite measurements and based on numerical simulation for the Baltic Sea region

2020 ◽  
Author(s):  
Jaromir Jakacki ◽  
Maciej Muzyka ◽  
Marta Konik ◽  
Anna Przyborska ◽  
Jan Andrzejewski
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Baltic Sea ◽  
Melting Time ◽  
Ice Thickness ◽  
Satellite Measurements ◽  
The Baltic Sea ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Baltic

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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. &amp;#160;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.&lt;/p&gt;&lt;p&gt;In this work sea ice parameters such as sea ice concentration, thickness and volume obtained from both &amp;#8211; 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.&lt;/p&gt;&lt;p&gt;The ice extent, thickness and volume were compared in several regions within the Baltic Sea.&amp;#160; 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.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;This work was partly funded by Polish National Science Centre, project number 2017/25/B/ST10/00159&lt;/p&gt;

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