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Dataset results
14 results for “MAtchUP”
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>
A Novel Framework to Harmonise Satellite Data Series for Climate Applications: Matchups, Calibration Parameters and Residuals
<p>The datasets included with this archive supplement the journal article:</p> <p>Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. <em>Remote Sens. 2019</em>, <strong>11</strong>, 1002. doi:<a href="https://doi.org/10.3390/rs11091002">10.3390/rs11091002</a>.</p> <p>The archive includes a README file with further explanations.</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>
Data package for opportunistic constant target matchup study
<p>Opportunistic constant target matching is a new method for satellite<br> intercalibration.</p> <p>It is complementary to the traditional simultaneous nadir overpass<br> (SNO) method because it can provide warm matchups in cases where the<br> SNO method provides only cold matchups.</p> <p>A geostationary infrared sensor (SEVIRI) is used to select constant<br> target matches for two different microwave sensors (NOAA 18 and Metop<br> A). This is the data package for a publication where we discuss the<br> main assumptions and limitations of the new method and explore its<br> statistical properties with a simple Monte Carlo simulation and with<br> real observations from NOAA 18 and Metop A.</p>
Dataset containing all action and intervention indicators (KPI) in Valencia Demo Site for the MAtchUP project.
<p>KPIs added for interventions I1A, I2A and I2B, from June 2023 till September 2023.</p>
Dataset containing all action and intervention indicators (KPI) in Valencia Demo Site for the MAtchUP project.
Open the record for dataset details and reuse information.
Aqua AIRS-MODIS Matchup Indexes V1.0 (AIRS_MDS_IND) at GES_DISC
This is Aqua AIRS-MODIS collocation indexes, in netCDF-4 format. These data map AIRS profile indexes to those of MODIS.The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records.The short name for this collections is AIRS_MDS_IND
Aqua AIRS-MODIS 1-km Matchup Indexes V1 (Aqua_AIRS_MODIS1km_IND) at GES_DISC
This dataset includes Aqua AIRS to MODIS 1-km collocation index product, within the framework of the Multidecadal Satellite Record of Water Vapor, Temperature, and Clouds (PI: Eric Fetzer) funded by NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2017. The dataset is built upon work by Wang et al. (doi: 10.3390/rs8010076) and Yue (doi:10.5194/amt-15-2099-2022).The short name for this collections is Aqua_AIRS_MODIS1km_IND
Aqua AIRS-MLS Matchup Indexes V1.0 (AIRS_MLS_IND) at GES_DISC
This dataset is part of MEaSUREs 2012 Program, and represent Aqua/AIRS-Aura/MLS collocation indexes, in netCDF-4 format. These data map AIRS profile indexes to those of MLS.The A-Train provides water vapor (H2O) retrievals from both the Atmospheric Infrared Sounder (AIRS) and Microwave Limb Sounder (MLS). While AIRS loses sensitivity to H2O at the elevated portions of the upper troposphere (UT), MLS cannot detect H2O below 316 hPa. Therefore, to obtain a full profile of H2O in the whole column of air, this dataset manages to join the two products together by utilizing their own averaging kernels (AK). In doing so, the dataset builds a solid H2O of the whole column of air, which will help understand the H2O budget and many processes governing the humidity around the upper troposphere and lower stratosphere (UTLS). The short name for this collections is AIRS_MLS_IND
AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC
This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project.The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information,and develop a merged, multi-sensor climatology of atmospheric water vapor as afunction of altitude, stratified by the cloud classes. This is a large scienceanalysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, findspace/time "matchups" between pairs of instruments, and process years ofsatellite data to produce the climate data records.The short name for this collection is AIRSM_CPR_MATParameters contained in the data files include the following:Variable Name|Description|Units CH4_total_column|Retrieved total column CH4| (molecules/cm2) CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None) CloudLayers| Number of hydrometeor layers| (count) clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2) CO_total_column|Retrieved total column CO| (molecules/cm2) CPR_Cloud_mask| CPR Cloud Mask |(None) Data_quality| Data Quality |(None) H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg) H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air) MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None) MODIS_scene_var |MODIS scene variability| (None) nSurfStd|1-based index of the first valid level|(None) O3VMRStd|Ozone Volume Mixing Ratio|(vmr) olr|All-sky Outgoing Longwave Radiation|(Watts/m**2) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) Sigma-Zero| Sigma-Zero| (dB*100) TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K) TCldTopStd|Cloud top temperature|(K) totH2OStd|Total precipitable water vapor| (kg/m**2) totO3Std|Total ozone burden| (Dobson) TSurfAir|Atmospheric Temperature at Surface|(K) TSurfStd|Surface skin temperature|(K)End of parameter information
JPSS1 CrIS-VIIRS 750-m Matchup Indexes V1 (J1_CrIS_VIIRS750m_IND) at GES_DISC
This dataset includes JPSS-1 VIIRS-CrIS collocation index product, within the framework of the Multidecadal Satellite Record of Water Vapor, Temperature, and Clouds (PI: Eric Fetzer) funded by NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2017. The dataset is built upon work by Wang et al. (doi: 10.3390/rs8010076) and Yue (doi:10.5194/amt-15-2099-2022).The short name for this collections is J1_CrIS_VIIRS750m_IND_1
AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT) at GES DISC
This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records.The short name for this collection is AIRS_CPR_MATParameters contained in the data files include the following:Variable Name|Description|Units CldFrcStdErr|Cloud Fraction|(None) CloudLayers| Number of hydrometeor layers| (count) CPR_Cloud_mask| CPR Cloud Mask| (None) DEM_elevation| Digital Elevation Map| (m) dust_flag|Dust Flag|(None) latAIRS|AIRS IR latitude|(deg) Latitude|CloudSat Latitude |(degrees) LayerBase| Height of Layer Base| (m) LayerTop| Height of layer top| (m) lonAIRS|AIRS IR longitude|(deg) Longitude|CloudSat Longitude| (degrees) MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) radiances|Radiances|(milliWatts/m**2/cm**-1/steradian) Sigma-Zero| Sigma-Zero| (dB*100) spectral_clear_indicator|Spectral Clear Indicator|(None) Vertical_binsize|CloudSat vertical binsize| (m)End of parameter information
SNPP CrIS-VIIRS 750-m Matchup Indexes V1 (SNPP_CrIS_VIIRS750m_IND) at GES_DISC
This dataset includes SNPP VIIRS-CrIS collocation index product, within the framework of the Multidecadal Satellite Record of Water Vapor, Temperature, and Clouds (PI: Eric Fetzer) funded by NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, 2017. The dataset is built upon work by Wang et al. (doi: 10.3390/rs8010076) and Yue (doi:10.5194/amt-15-2099-2022).The short name for this collections is SNPP_CrIS_VIIRS750m_IND
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