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5 results for “BGC-Argo”
BGC-Argo matchups with Ocean Color Satellite Sensors and MERRA-2 updated for 2023
<p>Updated matchup dataset as described in "Begouen Demeaux et al., Algorithms to Retrieve the Spectral Diffuse Attenuation Coefficient of Light in the Ocean from Remote Sensing, Optics Express, 2023".</p> <p>Composed of Satellites matchup from the MODIS, VIIRS and OLCI sensors with BGC-Argo floats, including Kds derived from float measurements (Kd_WV_Xing), Rrs at all wavelengths from each sensor, solar zenith angle and information on the atmospheric composition from Merra-2 matchups. </p> <p>New recomputed Kds using the Lee et al., 2005 algorithm with individual sensor coefficients are also listed (new_kd_WV_Lee_indiv), as well as recomputed Kds for a new global m2 coefficient (new_kd_WV_Lee_global). Recomputed Kds for the new coefficients of the NASA/ESA algorithm are also available (new_Kd_Aus). Lastly, Kds for the new GF algorithm depending on the MERRA inputs and IOPs is listed : (kd_WV_f). </p> <p>For any questions, do not hesitate to be in touch. </p>
BGC-Argo Satellite matchup to compute variability in the Chl:C ratio of phytoplankton.
<p>This dataset provides matchups between BGC-Argo and MODIS satellites (both atmospheric and ocean color products). This dataset allows users to compare the variability of the Chlorophyll (Chl) to Phytoplankton Carbon ratio from BGC-Argo floats depending on the light in the mixed layer and link to information obtained from satellites about cloud coverage. </p> <p>Quality control previously performed on this dataset and matchup criteria are described in the associated publication.</p> <p>Here are some of the column headers detailed for clarity:</p> <p>Columns 1-25 represent data from the BGC-Argo floats:</p> <ul> <li>ID: Float WMO ID number</li> <li>dt: Datetime in datenum format.</li> <li>biomes: Biomes according to Fay & McKinley, 2014 (with West Mediterranean biome 18 and East Mediterranean biome 19)</li> <li>zenith: Sun zenith angle at time of surfacing.</li> <li>kd_490_Xing: Downwelling diffuse attenuation coefficient at 490nm from Xing et al.,2021 method. </li> <li>kd_PAR_Xing: Downwelling diffuse attenuation coefficient of PAR from Xing et al.,2021 method. </li> <li>chla: Median chlorophyll from fluorescence in the mixed layer (corrected for Non-Photochemical Quenching following Xing et al., 2012)</li> <li>F_indiv: Calibration factor for chla (chlorophyll from fluorescence) according to the method described in Xing et al., 2011. </li> <li>F_median: Median Correction factor (F) for all the floats in a biome</li> <li>F_median_season: Median Correction factor (F) for all the floats in a biome in a given season</li> <li>Chl_cor: Chla from floats corrected using the F_median factor according to Xing et al., 2011. </li> <li>PAR_0_Argo: PAR(0-) right below the surface also from Xing et al., 2021.</li> <li>Z_iso : Depth of the 0.415 mol/quanta/m-2/d-1 isolume. </li> <li>Zeu: Euphotic depth, 1% of surface light.</li> <li>Eg_Argo: Median light level in the mixed layer during a float's profile, bounded by the surface and the MLD (in mol quanta m^-2 h^-1).</li> <li>MLD: Mixed layer depth, determined using the 0.03 density criteria from de Boyer Montégut, et al.,2004.</li> <li>bbp_XXX: Backscattering at a specific wavelength</li> <li>Cphyto: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Graff et al., 2015.</li> <li>Cphyto_B: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Behrenfeld et al., 2005.</li> <li>Cphyto_M: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Martinez-Vincente et al., 2013.</li> <li>ratio_cor: Chl_cor /Cphyto.</li> </ul> <p>Columns 26-45 have products from matchups with ocean-color MODIS files:</p> <ul> <li>sat_dt: Datetime of satellite overpass in datenum format. </li> <li>chlor_a: Satellite chl obtained from NASA's OBPG hybrid algorithm.</li> <li>sat_IPAR: Instantaneous PAR at time of overpass.</li> <li>sat_PAR: MODIS Daily PAR product above the surface.</li> <li>sat_Daily_PARminus: MODIS Daily PAR product propagated right below the surface (0-)</li> <li>sat_Daily_Eg: Daily median light in the mixed layer computed as sat_DailyPAR_minus * exp(-Kd_PAR*MLD/2) ( in mol quanta m^-2 d^-1).</li> </ul> <p>Columns 46-49 have products from matchups with atmospheric MODIS files: </p> <ul> <li>a_lat, a_lon, a_dt: Same as above but for the atmospheric file</li> <li>Confident Cloudy: Number of pixels (Out of 25) with the Confident Cloudy flag. </li> </ul> <p> </p>
BGC-Argo radiometry matchups with L2 satellite images from MODIS, VIIRS and OLCI sensors
<p> Diffuse attenuation coefficients(Kd) were computed from measured downwelling irradiance measurements from BGC-Argo floats. Matchups between satellite images and float profiles were then performed. Estimates of Kd at two different wavelengths and<br> band-integrated (PAR) were obtained from Remote Sensing Reflectance using different published algorithms developed for open ocean waters spanning in type from explicit-empirical, semi-analytical and implicit-empirical and applied to data from spectral radiometers on board six different satellites (MODIS-Aqua, MODIS-Terra, VIIRS–SNPP, VIIRS-JPSS, OLCI-Sentinel 3A and OLCI-Sentinel 3B).</p>
Mediterranean Quality checked BGC-Argo 2013-2022 dataset
<p>BGC Argo profiles were downloaded from the Coriolis GDAC (Argo, 2022; last visited July 2022). We collected both AM and DM synthetic profiles and selected data labelled as good, probably good, changed, and interpolated (quality check - QC - flags 1, 2, 5, and 8).</p> <p>Moreover, if the vertical resolution of the profiles exceeded 2m, the data were averaged to 2m resolution. Finally, a moving weighted average was calculated to remove eventual small fluctuations in the data profiles. This moving average takes into account the 2 upper and 2 lower neighbouring points and a Gaussian function of the distance to the central point for the weights.</p> <p>Specific quality check procedures were applied to the variables based on our knowledge and use of BGC-Argo data in the Mediterranean Sea. For chlorophyll, the profiles were shifted to have a value of 0 at 400-600 m (Cossarini et al., 2019; Teruzzi et al., 2021); for oxygen a check for anomalous drifts at depth was computed (Amadio et al., 2024); and for nitrate negative surface values were replaced by 0.05 mmol/m3 (Teruzzi et al., 2021).<span> </span>The Python package “Bit.Sea” (/Float), available at <a href="https://doi.org/10.5281/zenodo.8283692"><span>https://doi.org/10.5281/zenodo.8283692</span></a> (Bolzon et al., 2023), was used for the quality check.</p> <p>The dataset is used in the Mediterranean Biogeochemical Center of the Copernicus Marine Service (Coppini et al., 2023) for assimilation and validation (Amadio et al., 2024), and neural network application (Pietropolli et al., 2023). </p>
Datasets for the comparison between POC estimated from BGC-Argo floats and PISCES model simulations
<p>This dataset is composed of two main folders.</p> <p><strong>clim_3D</strong>: contains 4 files in 3 directories:</p> <ul> <li>BGC-Argo/bbp700_nemo_clim.nc: global monthly climatology of BGC-Argo b<sub>bp700</sub> measurements on the ORCA2_L31 NEMO grid.</li> <li>BGC-Argo/bbp700_nemo_climseas.nc: seasonal climatology (JFM, ...OND) of BGC-Argo b<sub>bp700</sub> measurements on the ORCA2_L31 NEMO grid.</li> <li>biomes/nemobiomes.nc: biomes of Fay and McKinley 2014 (ESSD) reprojected onto the ORCA2_L31 NEMO grid.</li> <li>PISCES/PISCES_1m_19600101_19601231_ptrc_T.nc: PISCES tracers (living organisms and detrial organic carbon), monthly climatology based on pre-industrial simulation described by Aumont et al. 2017 (Biogeosciences).</li> </ul> <p><strong>CATS_1D:</strong> contains 66 folders. The folders are named as <fwmo>_<yyyy>_<orca1cell>, where</p> <ul> <li>fwmo = World Meteorological Organization (WMO) float number</li> <li>yyyy = year</li> <li>orca1cell = number of the NEMO model horizontal grid cell (ORCA1 grid) used to run the PISCES 1D offline simulation.</li> </ul> <p>Each folder contains:</p> <ul> <li>BGC-Argo observations for a given Argo float and year, bined onto the model vertical grid and 5-day peiods: 'Mprof*.nc'</li> <li>Dynamical fields used to run PISCES 1D offline simulations: 'dyna_grid_T.nc'</li> <li>Output of the simulations: PISCES tracers at 5-day resolution: 'PISCES_5d_*_ptrc_T.nc'</li> <li>List of horizontal model grid cells that were visited by the Argo float on a given year: 'xymatch_<fwmo>.csv'. If the list is queried for the grid cell number <orca1cell>, one can obtain the corresponding longitude (xmap), latitude (ymap), and the x and y indices of the grid (plus/minus 1). These indices correspond to the 3x3 horizontal grid of the dynamical fields and the PISCES 1D output.</li> </ul> <p> </p> <ul> </ul>
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