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

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.

[1]  Justyna Meler,et al.  Seasonal and spatial variability of light absorption by suspended particles in the southern Baltic: A mathematical description , 2017 .

[2]  A. Höglund,et al.  Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6-based ocean–sea-ice model setup for the North Sea and Baltic Sea , 2017 .

[3]  U. Gräwe,et al.  Fresh oxygen for the Baltic Sea — An exceptional saline inflow after a decade of stagnation , 2015 .

[4]  Mati Kahru,et al.  Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea , 2014 .

[5]  T. M. Chin,et al.  A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies , 2016 .

[6]  Jens Schröter,et al.  A comparison of error subspace Kalman filters , 2005 .

[7]  J. Andersen,et al.  Eutrophication in the Baltic Sea – An integrated thematic assessment of the effects of nutrient enrichment and eutrophication in the Baltic Sea region. , 2009 .

[8]  C. Giardino,et al.  Drivers of Cyanobacterial Blooms in a Hypertrophic Lagoon , 2018, Front. Mar. Sci..

[9]  S. Maritorena,et al.  Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues , 2010 .

[10]  K. Bradtke,et al.  Spatial and interannual variations of seasonal sea surface temperature patterns in the Baltic Sea , 2010 .

[11]  Malgorzata Stramska,et al.  Satellite Remote Sensing Signatures of the Major Baltic Inflows , 2019, Remote. Sens..

[12]  D. Stramski,et al.  An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea , 2004 .

[13]  C. Domingues,et al.  Ocean Reanalyses: Recent Advances and Unsolved Challenges , 2019, Front. Mar. Sci..

[14]  A. Omstedt,et al.  Assessment of Eutrophication Abatement Scenarios for the Baltic Sea by Multi-Model Ensemble Simulations , 2018, Front. Mar. Sci..

[15]  P. J. Werdell,et al.  A multi-sensor approach for the on-orbit validation of ocean color satellite data products , 2006 .

[16]  G. Schernewski,et al.  Eutrophication in the Baltic Sea and shifts in nitrogen fixation analyzed with a 3D ecosystem model , 2008 .

[17]  Marek Kowalewski,et al.  The operational method of filling information gaps in satellite imagery using numerical models , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[18]  R. Majchrowski,et al.  SatBałtyk – A Baltic environmental satellite remote sensing system – an ongoing project in Poland. Part 1: Assumptions, scope and operating range , 2011 .

[19]  M. Stramska,et al.  Spatial and temporal variability of sea surface temperature in the Baltic Sea based on 32-years (1982–2013) of satellite data , 2015 .

[20]  H. Siegel,et al.  Sea surface temperature development of the Baltic Sea in the period 1990-2004 , 2006 .

[21]  Antoine Mangin,et al.  The CMEMS GlobColour chlorophyll a product based on satellite observation: multi-sensor merging and flagging strategies , 2019, Ocean Science.

[22]  The Baltic and North Seas Climatology (BNSC)—A Comprehensive, Observation-Based Data Product of Atmospheric and Hydrographic Parameters , 2019, Front. Earth Sci..

[23]  Bozena Wojtasiewicz,et al.  Bio-optical characterization of selected cyanobacteria strains present in marine and freshwater ecosystems , 2016, Journal of Applied Phycology.

[24]  Ye Liu,et al.  Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013 , 2016 .

[25]  R. Doerffer,et al.  The MERIS Case 2 water algorithm , 2007 .