scholarly journals Comparisons of Satellite and Modeled Surface Temperature and Chlorophyll Concentrations in the Baltic Sea with In Situ Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3049
Author(s):  
Malgorzata Stramska ◽  
Marta Konik ◽  
Paulina Aniskiewicz ◽  
Jaromir Jakacki ◽  
Miroslaw Darecki
Keyword(s):  
Baltic Sea ◽  
Satellite Data ◽  
In Situ Measurements ◽  
Data Sets ◽  
The Baltic Sea ◽  
Data Set ◽  
In Situ Data ◽  
The Baltic ◽  
Good Agreement

Among the most frequently used satellite data are surface chlorophyll concentration (Chl) and temperature (SST). These data can be degraded in some coastal areas, for example, in the Baltic Sea. Other popular sources of data are reanalysis models. Before satellite or model data can be used effectively, they should be extensively compared with in situ measurements. Herein, we present results of such comparisons. We used SST and Chl from model reanalysis and satellites, and in situ data measured at eight open Baltic Sea stations. The data cover time interval from 1 January 1998 to 31 December 2019, but some satellite data were not always available. Both the model and the satellite SST data had good agreement with in situ measurements. In contrast, satellite and model estimates of Chl concentrations presented large errors. Modeled Chl presented the lowest bias and the best correlation with in situ data from all Chl data sets evaluated. Chl estimates from a regionally tuned algorithm (SatBaltic) had smaller errors in comparison with other satellite data sets and good agreement with in situ data in summer. Statistics were not as good for the full data set. High uncertainties found in chlorophyll satellite algorithms for the Baltic Sea highlight the importance of continuous regional validation of such algorithms with in situ data.

scholarly journals Remote sensing algorithm for sea surface CO<sub>2</sub> in the Baltic Sea

2014 ◽  
Vol 11 (8) ◽  
pp. 12255-12294 ◽  
Author(s):  
G. Parard ◽  
A. A. Charantonis ◽  
A. Rutgerson
Keyword(s):  
Baltic Sea ◽  
High Variability ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Accurate Estimation ◽  
The Baltic Sea ◽  
Validation Data ◽  
Data Set ◽  
The Baltic

Abstract. Studies of coastal seas in Europe have brought forth the high variability in the CO2 system. This high variability, generated by the complex mechanisms driving the CO2 fluxes makes their accurate estimation an arduous task. This is more pronounced in the Baltic Sea, where the mechanisms driving the fluxes have not been as highly detailed as in the open oceans. In adition, the joint availability of in-situ measurements of CO2 and of sea-surface satellite data is limited in the area. In this paper, a combination of two existing methods (Self-Organizing-Maps and Multiple Linear regression) is used to estimate ocean surface pCO2 in the Baltic Sea from remotely sensed surface temperature, chlorophyll, coloured dissolved organic matter, net primary production and mixed layer depth. The outputs of this research have an horizontal resolution of 4 km, and cover the period from 1998 to 2011. The reconstructed pCO2 values over the validation data set have a correlation of 0.93 with the in-situ measurements, and a root mean square error is of 38 μatm. The removal of any of the satellite parameters degraded this reconstruction of the CO2 flux, and we chose therefore to complete any missing data through statistical imputation. The CO2 maps produced by this method also provide a confidence level of the reconstruction at each grid point. The results obtained are encouraging given the sparsity of available data and we expect to be able to produce even more accurate reconstructions in the coming years, in view of the predicted acquisitions of new data.

scholarly journals Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3609 ◽  
Author(s):  
Kyryliuk ◽  
Kratzer
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Attenuation Coefficient ◽  
Secchi Depth ◽  
Local Algorithm ◽  
The Baltic Sea ◽  
In Situ Data ◽  
The Baltic ◽  
Level 2

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data.

scholarly journals Upwelling-induced trace gas dynamics in the Baltic Sea inferred from 8 years of autonomous measurements on a ship of opportunity

2021 ◽  
Vol 18 (8) ◽  
pp. 2679-2709
Author(s):  
Erik Jacobs ◽  
Henry C. Bittig ◽  
Ulf Gräwe ◽  
Carolyn A. Graves ◽  
Michael Glockzin ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Gas Dynamics ◽  
Coastal Upwelling ◽  
Regional Distribution ◽  
Trace Gas ◽  
Data Sets ◽  
The Baltic Sea ◽  
Spatial And Temporal Scales ◽  
In Situ Data ◽  
The Baltic

Abstract. Autonomous measurements aboard ships of opportunity (SOOP) provide in situ data sets with high spatial and temporal coverage. In this study, we use 8 years of carbon dioxide (CO2) and methane (CH4) observations from SOOP Finnmaid to study the influence of upwelling on trace gas dynamics in the Baltic Sea. Between spring and autumn, coastal upwelling transports water masses enriched with CO2 and CH4 to the surface of the Baltic Sea. We study the seasonality, regional distribution, relaxation, and interannual variability in this process. We use reanalysed wind and modelled sea surface temperature (SST) data in a newly established statistical upwelling detection method to identify major upwelling areas and time periods. Large upwelling-induced SST decrease and trace gas concentration increase are most frequently detected around August after a long period of thermal stratification, i.e. limited exchange between surface and underlying waters. We found that these upwelling events with large SST excursions shape local trace gas dynamics and often lead to near-linear relationships between increasing trace gas levels and decreasing temperature. Upwelling relaxation is mainly driven by mixing, modulated by air–sea gas exchange, and possibly primary production. Subsequent warming through air–sea heat exchange has the potential to enhance trace gas saturation. In 2015, quasi-continuous upwelling over several months led to weak summer stratification, which directly impacted the observed trace gas and SST dynamics in several upwelling-prone areas. Trend analysis is still prevented by the observed high variability, uncertainties from data coverage, and long water residence times of 10–30 years. We introduce an extrapolation method based on trace gas–SST relationships that allows us to estimate upwelling-induced trace gas fluxes in upwelling-affected regions. In general, the surface water reverses from CO2 sink to source, and CH4 outgassing is intensified as a consequence of upwelling. We conclude that SOOP data, especially when combined with other data sets, enable flux quantification and process studies addressing the process of upwelling on large spatial and temporal scales.

Wind conditions over the Baltic Sea – comparing reanalysis data sets with observations

2020 ◽  
Author(s):  
Christoffer Hallgren ◽  
Erik Sahlée ◽  
Stefan Ivanell ◽  
Heiner Körnich ◽  
Ville Vakkari
Keyword(s):  
Baltic Sea ◽  
Wind Power ◽  
Reanalysis Data ◽  
Data Sets ◽  
The Baltic Sea ◽  
Data Set ◽  
Low Level ◽  
Seasonal And Diurnal Variations ◽  
Low Level Jets ◽  
The Baltic

&lt;p&gt;The potential of increasing the amount of offshore wind energy production in the Baltic Sea has been of great interest for many countries and wind power companies for a long time. From a meteorological point of view, there are several special wind characteristics that are observed in this area that needs to be taken into consideration when planning for a wind farm. For example, as the Baltic Sea is a semi-enclosed basin surrounded by coastlines in all directions, phenomenon such as low-level jets occur frequently.&lt;/p&gt;&lt;p&gt;In order to create a climatology of the wind conditions over the Baltic Sea, with wind power applications in mind, four different state-of-the-art reanalysis data sets (MERRA2, ERA5, UERRA and NEWA) have been compared with measurements from LIDAR systems and high meteorological towers (Anholt, Finnish Ut&amp;#246;, FINO2 and &amp;#214;stergarnsholm). The performance of the data sets has been analyzed in terms of stability and governing synoptic weather conditions as well as seasonal and diurnal variations. By selecting the most suitable reanalysis data set and using the observations to make corrections, a climatology for wind conditions over the Baltic Sea, focusing on the low-level jets, has then been constructed.&lt;/p&gt;

scholarly journals Remote sensing the sea surface CO<sub>2</sub> of the Baltic Sea using the SOMLO methodology

2015 ◽  
Vol 12 (11) ◽  
pp. 3369-3384 ◽  
Author(s):  
G. Parard ◽  
A. A. Charantonis ◽  
A. Rutgerson
Keyword(s):  
Baltic Sea ◽  
High Variability ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Accurate Estimation ◽  
The Baltic Sea ◽  
Validation Data ◽  
The Baltic ◽  
Self Organizing

Abstract. Studies of coastal seas in Europe have noted the high variability of the CO2 system. This high variability, generated by the complex mechanisms driving the CO2 fluxes, complicates the accurate estimation of these mechanisms. This is particularly pronounced in the Baltic Sea, where the mechanisms driving the fluxes have not been characterized in as much detail as in the open oceans. In addition, the joint availability of in situ measurements of CO2 and of sea-surface satellite data is limited in the area. In this paper, we used the SOMLO (self-organizing multiple linear output; Sasse et al., 2013) methodology, which combines two existing methods (i.e. self-organizing maps and multiple linear regression) to estimate the ocean surface partial pressure of CO2 (pCO2) in the Baltic Sea from the remotely sensed sea surface temperature, chlorophyll, coloured dissolved organic matter, net primary production, and mixed-layer depth. The outputs of this research have a horizontal resolution of 4 km and cover the 1998–2011 period. These outputs give a monthly map of the Baltic Sea at a very fine spatial resolution. The reconstructed pCO2 values over the validation data set have a correlation of 0.93 with the in situ measurements and a root mean square error of 36 μatm. Removing any of the satellite parameters degraded this reconstructed CO2 flux, so we chose to supply any missing data using statistical imputation. The pCO2 maps produced using this method also provide a confidence level of the reconstruction at each grid point. The results obtained are encouraging given the sparsity of available data, and we expect to be able to produce even more accurate reconstructions in coming years, given the predicted acquisition of new data.

scholarly journals Satellite Remote Sensing Signatures of the Major Baltic Inflows

2019 ◽  
Vol 11 (8) ◽  
pp. 954
Author(s):  
Malgorzata Stramska ◽  
Paulina Aniskiewicz
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Large Scale ◽  
Spatial Information ◽  
The Baltic Sea ◽  
Salty Water ◽  
In Situ Data ◽  
The North ◽  
The Baltic

Variability of sea level in the North and Baltic Seas, enforced by weather patterns, affects the intensity of water exchange between these seas. Transfer of salty water from the North Sea is very important for the hydrography of the Baltic Sea. The volume of inflowing salty water can occasionally increase remarkably. Such incidents, called the Major Baltic Inflows (MBIs), are unpredictable, of relatively short duration, and difficult to observe using in situ data. We have shown that remote sensing altimetry can be used as a complementary source of information about the MBI events. The advantage of using such data is that large-scale spatial information about SLA is available with daily resolution. We have described changes in SLA during several MBI events observed in 1993–2017. The net volume of water transported into the Baltic Sea varied between the events due to differences in atmospheric forcing. Based on SLA data, the largest inflow of water happened during the 2014 MBI. This is in agreement with previously published results, based on in situ data.

scholarly journals Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multisensor data

2015 ◽  
Vol 12 (5) ◽  
pp. 2283-2313
Author(s):  
J. Pitarch ◽  
G. Volpe ◽  
S. Colella ◽  
H. Krasemann ◽  
R. Santoleri
Keyword(s):  
Time Series ◽  
Baltic Sea ◽  
Late Winter ◽  
The Baltic Sea ◽  
In Situ Data ◽  
Basin Scale ◽  
Gulf Of Bothnia ◽  
Summer Bloom ◽  
The Baltic

Abstract. Fifteen-year (1997–2012) time series of chlorophyll a (CHL) in the Baltic Sea, based on merged multisensor satellite data provided by the European projects Globcolour and ESA-OC-CCI were analysed. Several available CHL algorithms were sea-truthed against a large in situ CHL dataset consisting of data by Seadatanet, HELCOM and NOAA. Matchups were calculated for three separate areas (1) Skagerrak and Kattegat, (2) Baltic Proper plus gulfs of Riga and Finland, called here "Central Baltic", (3) Gulf of Bothnia, and for the three areas as a whole. Statistics showed low linearity. The OC4v6 algorithm (R2 = 0.46, BIAS = +60 %, RMS = 79 % for the whole dataset) was linearly transformed by using the best linear fit (OC4corr). By construction, the bias was corrected, but RMS was increased instead. Despite this shortcoming, we demonstrated that errors between OC4corr and in situ data were log-normally distributed and centred at zero. Consequently, unbiased estimators of the horizontally-averaged CHL could be obtained, the error of which tends to zero when a large amount of pixels is averaged. From the basin-wide time series, the climatology and the annual anomalies were separated. The climatologies revealed completely different CHL dynamics among regions: in Skagerrak and Kattegat, CHL strongly peaks in late winter, with a minimum in summer and a secondary peak in spring. In the Central Baltic, CHL follows a dynamics of a spring CHL peak, followed by a much stronger summer bloom, with decreasing CHL towards winter. The Gulf of Bothnia shows a similar CHL dynamics as the central Baltic, although the summer bloom is absent. Across years, CHL showed great variability. Supported by auxiliary satellite sea-surface temperature (SST) data, we found that phytoplankton growth was inhibited in the central Baltic Sea in the years of colder summers or when the SST happened to increase later in the season. Extremely high CHL in spring 2008 was detected and linked to an exceptionally warm preceding winter. Sharp SST changes were found to induce CHL changes in the same direction. This phenomenon was appreciated best by overlaying the time series of the CHL and SST anomalies.

scholarly journals Upwelling-induced trace gas dynamics in the Baltic Sea inferred from 8 years of autonomous measurements on a ship of opportunity

2020 ◽  
Author(s):  
Erik Jacobs ◽  
Henry C. Bittig ◽  
Ulf Gräwe ◽  
Carolyn A. Graves ◽  
Michael Glockzin ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Gas Dynamics ◽  
Coastal Upwelling ◽  
Regional Distribution ◽  
Trace Gas ◽  
Data Sets ◽  
The Baltic Sea ◽  
Spatial And Temporal Scales ◽  
In Situ Data ◽  
The Baltic

Abstract. Autonomous measurements aboard ships of opportunity (SOOP) provide in situ data sets with high spatial and temporal coverage. In this study, we use 8 years of carbon dioxide (CO2) and methane (CH4) observations from SOOP Finnmaid to study the influence of upwelling on trace gas dynamics in the Baltic Sea. Between spring and autumn, coastal upwelling transports water masses enriched with CO2 and CH4 to the surface of the Baltic Sea. We study the seasonality, regional distribution, relaxation, and interannual variability of this process. We use reanalysed wind and modelled sea surface temperature (SST) data in a newly established statistical upwelling detection method to identify major upwelling areas and time periods. Strong upwelling events are most frequently detected around August after a long period of thermal stratification, i.e. limited exchange between surface and underlying waters. We found that these strong upwelling events with large SST excursions shape local trace gas dynamics and often lead to near-linear relationships between increasing trace gas levels and decreasing temperature. Upwelling relaxation is mainly driven by mixing and modulated by air–sea gas exchange and possibly primary production. Subsequent warming through air–sea heat exchange has the potential to enhance trace gas saturation. In 2015, quasi-continuous upwelling over several months led to weak summer stratification, which directly impacted the observed trace gas and SST dynamics in several upwelling-prone areas. We introduce an extrapolation method based on trace gas – SST relationships that allows us to estimate upwelling-induced trace gas fluxes in upwelling-affected regions. In general, the surface water reverses from CO2 sink to source and CH4 outgassing is intensified as a consequence of upwelling. We conclude that upwelling is an important and relevant process controlling trace gas dynamics in near-coastal environments in the Baltic Sea, and that SOOP data, especially when combined with other data sets, enable flux quantification and process studies on larger spatial and temporal scales.

Comparison sea ice data for Baltic Sea region based on modelling simulations and remote sensing measurements. 

2021 ◽  
Author(s):  
Jaromir Jakacki ◽  
Maciej Muzyka ◽  
Marta Konik ◽  
Anna Przyborska ◽  
Małgorzata Stramska
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Satellite Data ◽  
Ice Thickness ◽  
Data Sets ◽  
Operational Mode ◽  
The Baltic Sea ◽  
Baltic Sea Region ◽  
Atmospheric Data ◽  
The Baltic

&lt;p&gt;A comprehensive analysis of the results of remote measurements of the Baltic Sea ice cover has been performed. For this purpose, two modelling integrations were made. Two modelling simulations have been compared with two satellite data sets. As a modelling tool Community Ice Code (CICE) was implemented for Baltic Sea region. It was forced by two independent atmospheric data sets.&amp;#160; In the first simulation, the eBalticGrid system was the source of the atmospheric data, which has been operating in operational mode for almost five years. The second simulation used data from the SatBa&amp;#322;tyk system. The satellite data differed in the method of evaluating the quality of the results - in some cases, the result was supervised by ice experts, and in the other, the quality was assessed automatically. &amp;#160;Comparisons with model we have performed using the daily ice concentration and ice thickness maps over the Baltic Sea. Datasets are produced by the Finnish Meteorological Institute (FMI) and disseminated through the central dissemination unit: Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/services-portfolio/ access-to-products/). The analysis showed an unnatural increase in the average ice thickness obtained from satellite data at the end of the ice season, for selected regions. The possibility of water appearance on the surface of the analyzed cells was assumed as the source of the potential error, which has a significant impact on the optical properties of the surface. It was proposed to eliminate cells containing a specific surface wetting fraction. However, the results do not allow this approach to be considered correct and therefore the work needs to be continued.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;

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