ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
Collection
Keywords
Publisher
Years
  • 1
    facet.materialart.
    Unknown
    The Arctic combined model and satellite sea ice thickness (CMST) dataset (2018)
    PANGAEA
    In:  Supplement to: Mu, Longjiang; Losch, Martin; Yang, Qinghua; Ricker, Robert; Losa, Svetlana N; Nerger, Lars (2018): Arctic-wide sea ice thickness estimates from combining satellite remote sensing data and a dynamicice-ocean model with data assimilation during the CryoSat-2 period. Journal of Geophysical Research: Oceans, 123(11), 7763-7780, https://doi.org/10.1029/2018JC014316
    Details
    Publication Date: 2023-01-13
    Description: An Arctic sea ice thickness record covering from 2010 to 2016 is generated by assimilating satellite thickness from CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS). The model is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and the assimilation is performed by a local Error Subspace Transform Kalman filter (LESTKF) coded in the Parallel Data Assimilation Framework (PDAF).
    Keywords: File content; File format; File name; File size; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 35 data points
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 2
    facet.materialart.
    Unknown
    A data set of collocated satellite remote sensing reflectance from GlobColour merged products, chlorophyll a concentration of phytoplankton functional types derived from in situ pigment data, and CMEMS sea surface temperature from 2002 to 2012 (2021)
    PANGAEA
    Details
    Publication Date: 2024-02-14
    Description: This data set provides the collocated data of remote sensing reflectance (Rrs) at 9 bands extracted from the merged ocean color products from GlobColour archive (https://www.globcolour.info/), satellite sea surface temperature from CMEMS (https://marine.copernicus.eu/), and chlorophyll a concentrations (Chl-a) derived from a global database of in situ HPLC pigment data collected from 2002 to 2012. The total Chl-a, Chl-a of six phytoplankton functional types (PFTs) that are diatoms, dinoflagellates, haptophytes, green algae, prokaryotes and Prochlorococcus, and two fractions of prokaryotes and Prochlorococcus are included in this data set. PFT Chl-a and fractions are derived using an updated diagnostic pigment analysis (DPA) method (Soppa et al., 2014; Losa et al., 2017), that was originally developed by Vidussi et al. (2001), adapted in Uitz et al. (2006) and further refined by Hirata et al. (2011) and Brewin et al. (2015). Matchups of satellite Rrs to in situ PFT data (which were also matchups to SST) were extracted from global 4-km daily merged products. Extraction and averaging protocol including quality control were described in detail in Xi et al. (2020).
    Keywords: Chlorophyll a; Chlorophyll a, Diatoms; Chlorophyll a, Dinoflagellata; Chlorophyll a, Green algae; Chlorophyll a, Haptophyta; Chlorophyll a, Prochlorococcus; Chlorophyll a, Prokaryotes; Chlorophyll a, total; DATE/TIME; DEPTH, water; GlobColour; LATITUDE; LONGITUDE; ocean color; OLCI-PFT; ORDINAL NUMBER; particulate matter; PFT; Prochlorococcus, fractional; Prokaryotes, fractional; Remote sensing reflectance at 412 nm; Remote sensing reflectance at 443 nm; Remote sensing reflectance at 490 nm; Remote sensing reflectance at 510 nm; Remote sensing reflectance at 531 nm; Remote sensing reflectance at 547 nm; Remote sensing reflectance at 555 nm; Remote sensing reflectance at 670 nm; Remote sensing reflectance at 678 nm; Rrs; Sea surface temperature; SST
    Type: Dataset
    Format: text/tab-separated-values, 9015 data points
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 3
    facet.materialart.
    Unknown
    Global data sets of Chlorophyll a concentration for diatoms, coccolithophores (haptophytes) and cyanobacteria obtained from in situ observations and satellite retrievals (2017)
    PANGAEA
    In:  Supplement to: Losa, Svetlana N; Soppa, Mariana A; Dinter, Tilman; Wolanin, Aleksandra; Brewin, Robert J W; Bricaud, Annick; Oelker, Julia; Peeken, Ilka; Gentili, Bernard; Rozanov, Vladimir V; Bracher, Astrid (2017): Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT). Frontiers in Marine Science, 4(203), 22 pp, https://doi.org/10.3389/fmars.2017.00203
    Details
    Publication Date: 2024-02-14
    Description: We derive the chlorophyll a concentration (Chla)for three main phytoplankton functional types (PFTs)-- diatoms, coccolithophores and cyanobacteria- by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm (Hirata et al. 2011) applied to the Ocean Colour Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm (Bracher et al. 2009, Sadeghi et al. 2012) is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 ? March 2012 and evaluated against in situ HPLC pigment data and satellite information on phytoplankton size classes (PSC) (Brewin et al. 2010, Brewin et al. 2015) and the size fraction (Sf) by Ciotti and Bricaud (2006. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.
    Keywords: AC3; Arctic Amplification
    Type: Dataset
    Format: application/zip, 3 datasets
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 4
    facet.materialart.
    Unknown
    Global chlorophyll a surface concentrations for diatoms, coccolithophores and cyanobacteria as the synergistic SynSenPFT product combined PhytoDOAS and OC-PFT for the period of time August 2002 - April 2012, links to NetCDF files (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-11
    Description: The SynSenPFT product is presented as chlorophyll "a" concentrations (Chla) for diatoms, coccolithophores and cyanobacteria (some of the phytoplankton functional types, PFT) obtained globally over the World Ocean on a 4 km sinusoidal grid on a daily basis over the period of August 2002 - March 2012. The SynSenPFT is a synergistic combination of the PFT products of initial-input OC-PFT (Hirata et al., 2011, Soppa et al., 2014) applied to total chlorophyll "a" (TChla) data of Ocean Colour Climate Change Initiative (OC-CCI, Version 2, ESA) and PhytoDOAS (Bracher et al., 2009, Sadeghi et al., 2012) version 3.3 available at doi:10.1594/PANGAEA.870486 with an optimal interpolation (OI). The OI method is applied to OC-PFT and PhytoDOAS Chla products of diatoms, cyanobacteria (called prokarytoes by the OC-PFT method) and haptophytes (for OC-PFT) and coccolithophores (for PhytoDOAS). Note that OC-PFT retrieves haptophytes while PhytoDOAS retrieves coccolithophores, a (often dominating) sub-group of haptophytes. Algorithmically, the SynSenPFT is an update of OC-PFT Chla with PhytoDOAS Chla values weighted in accordance to our degree of belief to both initial-input data products. Within the current version of SynSenPFT algorithm the update is done for every sub-pixel of OC-PFT within a PhytoDOAS pixel. Thus, SynSenPFT in every OC-PFT sub-pixel on average is nudged towards PhytoDOAS values as close as allowed by the prescribed PhytoDOAS and OC-PFT error statistics.
    Keywords: AC3; Arctic Amplification; DATE/TIME; File name; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 7030 data points
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 5
    facet.materialart.
    Unknown
    Global model output of chlorophyll-a concentration, coloured dissolved organic matter absorption, sea-ice concentration, sea surface and subsurface temperature, surface heat flux, ice-covered days, mixed layer depth, meridional advection of temperature (2020)
    PANGAEA
    Details
    Publication Date: 2024-04-20
    Description: This data set is composed of model output from Darwin-MITgcm, of chlorophyll-a concentration, coloured dissolved organic matter absorption, sea-ice concentration, sea surface and subsurface temperature, surface heat flux, ice-covered days, mixed layer depth, meridional advection of temperature. It covers a time period from January 2007 to January 2017, while some fields cover only parts of the summer of 2012.
    Keywords: Arctic Ocean; Binary Object; Binary Object (File Size); CDOM; Chl-a; File content; MITgcm; MLD; particulate matter; sea ice concentration; SST
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 6
    facet.materialart.
    Unknown
    Mean spectral diffuse attenuation coefficients averaged for 320 nm to 338.5 nm (UVAB), 356.5 nm to 390 nm (UVA) and 390 nm to 423 nm (blue) in the Atlantic Ocean from Sentinel-5P instrument TROPOMI (2022)
    PANGAEA
    Details
    Publication Date: 2024-04-20
    Description: This data set contains the mean diffuse attenuation coefficient of the downwelling plane irradiance over the first optical depth and over three different wavelength regions: 312.5 - 338 nm (Kd-UVAB), 356.5 - 390 nm (Kd-UVA), and 390 - 423 nm (KD-blue) as retrieved from the Sentinel-5P TROPOMI sensor from 11 May to 9 June 2018 in the Atlantic Ocean. The retrieval for the products is based on Differential Optical Absorption Spectroscopy (DOAS) extended to the ocean domain (PhytoDOAS). The spectral integrated Kd are derived from the Vibrational Raman Scattering (VRS) signal of the ocean which is retrieved by DOAS fits in three different fit windows. Kd-UVAB corresponds to DOAS VRS fits in the wavelength regions of 349.5 - 382 nm, Kd-UVA to 405 - 450 nm, and Kd-blue to 450 - 493 nm. VRS fit factors in the blue fit window (450 - 493 nm) were offset-corrected (an offset of 0.186 was added to the VRS fit factor of all processed S5P ground pixels). Derived Kd-blue are otherwise unrealistically high. The offset was determined with the help of Kd data at 490 nm from the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A. Fit results from the DOAS retrieval are converted into physical quantities using look-up-tables which were established with coupled atmosphere-ocean radiative transfer modeling using the software SCIATRAN version 4.0.8 (Rozanov et al. 2017, https://www.iup.uni-bremen.de/sciatran/). Only TROPOMI data with a cloud fraction smaller 0.01 were processed by the algorithm. Output data within the Atlantic Ocean (55°N-55°S, 70°W-10°E) were gridded daily into 0.083° latitudinal/longitudinal bins. Details on the algorithm can be found in the related publication by Oelker et al. (2022).
    Keywords: AC3; Arctic Amplification; AtlanticOcean; Atlantic Ocean; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); blue radiation; Differential Optical Absorption Spectroscopy; diffuse attenuation coefficient; Exploitation of Sentinel-5-P for Ocean Colour Products; FRAM; FRontiers in Arctic marine Monitoring; optical satellite data; S5POC; SAT; Satellite remote sensing; UV radiation
    Type: Dataset
    Format: text/tab-separated-values, 3 data points
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 7
    facet.materialart.
    Unknown
    Global monthly mean chlorophyll a surface concentrations from August 2002 to April 2012 for diatoms, coccolithophores and cyanobacteria from PhytoDOAS algorithm version 3.3 applied to SCIAMACHY data, link to NetCDF files in ZIP archive (2017)
    PANGAEA
    Details
    Publication Date: 2024-05-11
    Description: This phytoplankton group (PFT) concentration a (Chl a) data are output from the algorithm PhytoDOAS version 3.3 applied to SCIAMACHY data from 2 Aug 2002 to 8 Apr 2012. Data have been gridded monthly on 0.5° latitude to 0.5°. For cyanobacteria (includes all prokaryotic phytoplankton) and diatoms the PhytoDOAS PFT retrieval algorithm by Bracher et al. (2009) and for coccolithophores the algorithm by Sadeghi et al. (2012) have been used. However, these methods have slightly been improved which includes: - Data during SCIAMACHY instrument decontamination are excluded in the analysis. - SCIAMACHY level-1b input data for PhytoDOAS are now version 7.04 data (instead of version 6.0). - The wavelength window for all three phytoplankton groups (PFTs) fit factor starts at 427.5 nm (instead of 429 nm). - Coccolithophores fit factors are retrieved in a retrieval fitting simultaneously diatoms and coccolithophores (instead of a triple fit with also fitting dinoflagellates as in Sadeghi et al. 2012). - Vibrational Raman Scattering (VRS) is now fitted directly in the blue spectrum (450 to 495 nm), following Dinter et al. (2015), (instead of in the UV—A region as in Vountas et al. 2007) except that here the daily solar background spectrum measured by SCIAMACHY and the VRS pseudo absorption spectrum calculated based on a SCIAMACHY solar spectrum following Vountas et al. (2007) was used in order to correct for the variation of instrumental effects over time (this is not achieved when using the RTM simulated background spectrum as done in Dinter et al. 2015). - The PFT Chl a are derived from the ratio of the PFT fit factor to the VRS fit factor multiplied by a LUT (Look Up Table). The LUT is based on radiative transfer model (RTM) SCIATRAN simulations (see Rozanov et al. 2014) accounting also for changing solar zenith angle (SZA).
    Keywords: AC3; Arctic Amplification
    Type: Dataset
    Format: application/zip, 109.9 MBytes
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    DATA PANGAEA
  • 8
    facet.materialart.
    Unknown
    Taking into Account Atmospheric Uncertainty Improves Sequential Assimilation of SMOS Sea Ice Thickness Data in an Ice–Ocean Model (2016)
    American Meteorological Society
    Details
    Publication Date: 2016-03-01
    Description: The sensitivity of assimilating sea ice thickness data to uncertainty in atmospheric forcing fields is examined using ensemble-based data assimilation experiments with the Massachusetts Institute of Technology General Circulation Model (MITgcm) in the Arctic Ocean during November 2011–January 2012 and the Met Office (UKMO) ensemble atmospheric forecasts. The assimilation system is based on a local singular evolutive interpolated Kalman (LSEIK) filter. It combines sea ice thickness data derived from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) satellite and Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data with the numerical model. The effect of representing atmospheric uncertainty implicit in the ensemble forcing is assessed by three different assimilation experiments. The first two experiments use a single deterministic forcing dataset and a different forgetting factor to inflate the ensemble spread. The third experiment uses 23 members of the UKMO atmospheric ensemble prediction system. It avoids additional ensemble inflation and is hence easier to implement. As expected, the model-data misfits are substantially reduced in all three experiments, but with the ensemble forcing the errors in the forecasts of sea ice concentration and thickness are smaller compared to the experiments with deterministic forcing. This is most likely because the ensemble forcing results in a more plausible spread of the model state ensemble, which represents model uncertainty and produces a better forecast.
    Print ISSN: 0739-0572
    Electronic ISSN: 1520-0426
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 9
    facet.materialart.
    Unknown
    Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter (2017)
    American Meteorological Society
    Details
    Publication Date: 2017-09-01
    Description: Sea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service are assimilated into a pan-Arctic ice–ocean coupled model using the ensemble-based local singular evolutive interpolated Kalman (LSEIK) filter. This study found that the SST deviation between model hindcasts and independent SST observations is reduced by the assimilation. Compared with model results without data assimilation, the deviation between the model hindcasts and independent SST observations has decreased by up to 0.2°C at the end of summer. The strongest SST improvements are located in the Greenland Sea, the Beaufort Sea, and the Canadian Arctic Archipelago. The SST assimilation also changes the sea ice concentration (SIC). Improvements of the ice concentrations are found in the Canadian Arctic Archipelago, the Beaufort Sea, and the central Arctic basin, while negative effects occur in the west area of the eastern Siberian Sea and the Laptev Sea. Also, sea ice thickness (SIT) benefits from ensemble SST assimilation. A comparison with upward-looking sonar observations reveals that hindcasts of SIT are improved in the Beaufort Sea by assimilating reliable SST observations into light ice areas. This study illustrates the advantages of assimilating SST observations into an ice–ocean coupled model system and suggests that SST assimilation can improve SIT hindcasts regionally during the melting season.
    Print ISSN: 0739-0572
    Electronic ISSN: 1520-0426
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 10
    facet.materialart.
    Unknown
    Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation (2016)
    Copernicus
    Details
    Publication Date: 2016-04-06
    Description: Data assimilation experiments that aim at improving summer ice concentration and thickness forecasts in the Arctic are carried out. The data assimilation system used is based on the MIT general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter. The effect of using sea ice concentration satellite data products with appropriate uncertainty estimates is assessed by three different experiments using sea ice concentration data of the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI) which are provided with a per-grid-cell physically based sea ice concentration uncertainty estimate. The first experiment uses the constant uncertainty, the second one imposes the provided SICCI uncertainty estimate, while the third experiment employs an elevated minimum uncertainty to account for a representation error. Using the observation uncertainties that are provided with the data improves the ensemble mean forecast of ice concentration compared to using constant data errors, but the thickness forecast, based on the sparsely available data, appears to be degraded. Further investigating this lack of positive impact on the sea ice thicknesses leads us to a fundamental mismatch between the satellite-based radiometric concentration and the modeled physical ice concentration in summer: the passive microwave sensors used for deriving the vast majority of the sea ice concentration satellite-based observations cannot distinguish ocean water (in leads) from melt water (in ponds). New data assimilation methodologies that fully account or mitigate this mismatch must be designed for successful assimilation of sea ice concentration satellite data in summer melt conditions. In our study, thickness forecasts can be slightly improved by adopting the pragmatic solution of raising the minimum observation uncertainty to inflate the data error and ensemble spread.
    Print ISSN: 1994-0416
    Electronic ISSN: 1994-0424
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...