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opencc-by-4.0Aug 2024View details →

A global gridded CO2 flux dataset inferred from OCO-2 retrievals using the GONGGA inversion system (v2025)

Data Description Here we provide a global monthly CO2 flux dataset at 1° × 1° spatial resolution for the period 2014.9-2024.12. The dataset is generated using the GONGGA (Global ObservatioN-based system for monitoring Greenhouse GAs) inversion system by assimilating OCO-2 (Observing Carbon Observatory 2) v11.2r column CO2 retrievals that scaled to the WMO X2019 standard. The dataset contains fluxes from biosphere (Net Ecosystem Exchange, NEE) (both prior and posterior), ocean (both prior and posterior), biomass burning emissions and fossil fuel emissions. We also provide the posterior model simulated values corresponding to all measurements contained in the lastest release of NOAA’s ObsPack database (obspack_co2_1_GLOBALVIEWplus_v10.1_2024-11-13 and obspack_co2_1_NRT_v10.1_2025-02-07). Change from v2024 <ul> <li>Assimilation of OCO-2 v11.2r retrievals</li> <li>Update of prior fluxes</li> </ul> Data version specification v202x.ori refers to original GONGGA flux data with 3-hourly time resolution and  2° latitude × 2.5° longitude spatial resolution, v202x refers to GONGGA flux data resampled to monthly time resolution and 1° latitude × 1° longitude spatial resolution for facilitating comparisons with other GCP inversion results. Article citation Jin, Z., Wang, T., Zhang, H., Wang, Y., Ding, J., Tian, X., Constraint of satellite CO2 retrieval on the global carbon cycle from a Chinese atmospheric inversion system. Science China Earth Sciences, 2023, 66: 609-618, doi: 10.1007/s11430-022-1036-7. Jin, Z., Tian, X., Wang, Y., Zhang, H., Zhao, M., Wang, T., Ding, J., and Piao, S.: A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system, Earth System Science Data, 2024, 16: 2857-2876, doi: 10.5194/essd-16-2857-2024.

opencc-by-4.0Dec 2021View details →

Data associated with the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data".

This dataset refers to the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data". https://doi.org/10.5194/acp-2022-15.

opencc-by-4.0May 2024View details →

OCO-2 v11.1 10-second average data, early release

The file archived here, "OCO2_b11.1_10sec_GOOD_r0.nc4", contains dry air column CO2 mixing ratio (XCO2) data from the Orbiting Carbon Obervatory (OCO-2) averaged over 10-second spans.  It is a preliminary version of files that will be used in the upcoming OCO-2 v11 flux inversion model intercomparison project (MIP), a study designed to quantify various error sources and analysis differences that lead to different surface CO2 flux estimates when the OCO-2 data are used in global CO2 flux inversions.  In particular, this file is the source of the OCO-2 XCO2 10-second average data used in the recently-completed study "An Error Model for Evaluating satellite-based XCO2 products" by Yadav et al. The Orbiting Carbon Observatory (OCO-2) is a satellite that measures solar radiance reflected from the Earth's surface in two CO2 absorption bands (1.6 and 2.0 um), as well as in the O2 A-band.  By comparing the CO2 / O2 absorption ratio, the dry air mixing ratio of CO2 may be estimated along the observed path, i.e. averaged across the full atmospheric column, though with sensitivity peaking near the Earth's surface (where the impact of surface CO2 fluxes is the greatest).  A radiative transfer model that accounts for the scattering effects of thin clouds and aerosols, water vapor, surface albedo variations, is used, and the vertical profile of CO2 mixing ratio is estimated from the radiance data.  This CO2 profile is then collapsed to a scalar vertical average (XCO2), which is then bias corrected post-hoc against Earth-based Fourier spectrometer data from the Total Carbon Column Observation Network (TCCON), which itself is tied to CO2 measurement standards using in situ aircraft CO2 profile measurements. OCO-2 takes measurements in a thin swath (up to 10-km wide) underneath the satellite, with a 3 Hz scan rate.  Each cross-scan is divided into 8 individual fields of view (FOVs) of size 2.25km x 1.25km, though the shape of the FOVs is distorted due to the pirouetting of the satellite to keep the sensor slit oriented perpendicular to the Sun-Earth-satellite plane.  These small FOVs increase the chances of seeing through clouds and can reveal details of point-source CO2 emissions, but are generally much finer-scale than can be modeled by the atmospheric transport models used in global CO2 flux inversions, which typically use grid boxes 100s of km on a side.  Rather than assimilating each fine-scale FOV XCO2 value individually and comparing them to modeled XCO2 values that change much more slowly, it is convenient to average the original OCO-2 data to coarser scales beforehand, then assimilating these averaged values in the flux inversions.  This averaging has the beneficial side-effect of reducing the OCO-2 data volume considerably.  In the file presented here, the data have been averaged across 10-second spans, equivalent to an along-track distance of ~67 km on the Earth's surface. The source of the data averaged in the attached file is the data collection "OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V11.1r (OCO2_L2_Lite_FP)", available at NASA's Goddard Earth Sciences (GES) Data and Information Services Center (DISC):  https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_11.1r/summary?keywords=OCO2_L2_Lite_FP.   The data span for this preliminary version ('Release 0') of the 10-second average file is 20140906-20230430.  The original XCO2 values contained in this data collection, along with other auxiliary variables provided for analysis purposes, have been averaged in the same manner across each 10-second span, with the approach also used with the previous (Version 10) release of the OCO-2 XCO2 data, as described in Section 3.2.1 of Byrne et al. (2023) and Section 3.1.1 of Baker et al. (2022): each value in the span is weighted with the inverse square of its retrieved XCO2 uncertainty value, taken from variable 'xco2_uncertainty' from the v11.1r 'Lite" file.  Data inside each 10-second span are averaged separately based on viewing mode and surface type, as indicated by variable 'data_type'.  Only data that pass the retrieval quality flag (variable 'xco2_quality_flag' in the 'Lite' file equal to zero) are included in the average;  the number of such 'good' data values (1-240) included in each average value is indicated in variable 'N_total_shots'.  It has been found that 10-second spans with fewer 'good' retrievals tend to be affected more by the unwanted effects of aerosols and undetected clouds: these may be mitigated somewhat by not using 10-sec averages for spans with low 'N_total_shots' values.  Variable 'assimilate_flag' separates those data that are not taken in glint viewing mode over either land or water, or in nadir mode over land, from data taken in other modes (target mode, or in transition to/from target mode, or nadir mode over water, or mixed land/water scenes) that are generally not assimilated in flux inversions; it also flags 5% of the assimilable data by orbit for possible use as withheld evaluation data.  The uncertainty on the 10-sec average XCO2 value is given in variable 'xco2_uncertainty'; this accounts for correlations in error between individual scenes (+0.3 over land, +0.6 over ocean) as described in Section 3.2.1 of Baker et al (2022), as well as variability in the averaged XCO2 values not captured by the retrieval uncertainties.  Finally, variable 'model_error' provides an example of errors incurred in attempting to model the computed 10-second average XCO2 value: this could be added in quadrature to the uncertainty from 'xco2_uncertainty' to get the value used in the flux inversions.

opencc-by-4.0Aug 2022View details →

Global net ecosystem exchange of CO2 inferred from the OCO-2 XCO2 retrievals (GCAS OCO-2 inversion)

Here is a dataset of global carbon flux estimates over 2015-2019 using the OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS XCO2 v10) by the global carbon assimilation system (GCAS v2) (Jiang et al., 2021).    Citations: Jiang, F. et al., 2021. Regional CO2 fluxes from 2010 to 2015 inferred from GOSAT XCO2 retrievals using a new version of the Global Carbon Assimilation System. Atmos. Chem. Phys., 21(3): 1963-1985. Jiang, F. et al., 2022. A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021), Earth Syst. Sci. Data., 14, 3013–3037. He, W., Jiang, F., Ju, W., et al. Improved constraints on the recent terrestrial carbon sink over China by assimilating OCO-2 XCO2 retrievals, JGR-Atmopsheres, 2022, under review. Contacts: Wei He (weihe@nju.edu.cn); Fei Jiang (jiangf@nju.edu.cn) Note:  If you want to use this dataset for your researches, please contact us in advances. Thank you!

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3D cloud metrics for OCO-2 observations

The dataset contain contains supplementary data and the 3D cloud metrics described and analyzed in "Analysis of 3D Cloud Effects in OCO-2 XCO2 Retrievals”, Massie, S. T., Cronk, H., Merrelli, A., Schmidt, K. S., Chen, H., and Baker, D., Atmospheric Measurement Techniques, 14, 1475–1499, 2021. https://doi.org/10.5194/amt-14-1475-2021 The supplementary data files include the average OCO-2 - TCCON XCO2 differences from several key figures in the manuscript (6, 7, 12). The OCO-2 data was extracted from Version 10 XCO2 retrievals from a subset of the data record used for testing and development by the OCO-2 algorithm team. figure6_ocean_distkm.dat figure6_land_distkm.dat These text files contain the average OCO-2 - TCCON XCO2 differences for several variations of the OCO2 XCO2 (raw and bias corrected, quality filter 0 or 1) as a function of the cloud distance metric. The first file includes only OCO-2 ocean glint data, and corresponds to Figure 6 from the manuscript. The second file contains the equivalent data for OCO-2 land (not plotted in the manuscript).   figure7_ocean_csnoiseratio.dat figure7_land_csnoiseratio.dat These text files contains the average OCO-2 - TCCON XCO2 differences for several variations of the OCO-2 XCO2 (raw and bias corrected, quality filter 0 or 1) as a function of the color slice noise ratio. The first file includes only OCO-2 ocean glint data, and corresponds to Figure 7 from the manuscript. The second file contains the equivalent data for OCO-2 land (not plotted in the manuscript).   figure12_ocean.dat figure12_land.dat These text files contain the average OCO-2 - TCCON XCO2 differences for several variations of the OCO-2 XCO2, same as above, but as a function of both the cloud distance metric and the color slice noise ratio. The x-variable is the cloud distance metric, and the y-variable is the colorslice noise ratio. The first file includes only OCO-2 glint data, and corresponds to Figure 12 from the manuscript. The second file contains the equivalent data for OCO-2 land (not plotted in the manuscript).   3D cloud metric data: 3d_cloud_metrics_oco2_v9_2014.zip 3d_cloud_metrics_oco2_v9_2015.zip 3d_cloud_metrics_oco2_v9_2016.zip 3d_cloud_metrics_oco2_v9_2017.zip 3d_cloud_metrics_oco2_v9_2018.zip 3d_cloud_metrics_oco2_v9_2019.zip These files are created with observation arrays that match those contained within the version 9 OCO-2 Lite XCO2 product available at NASA Earthdata: OCO-2 Science Team/Michael Gunson, Annmarie Eldering (2018), OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V9r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), 10.5067/W8QGIYNKS3JC Each file is a daily aggregrate, with matched observation lists to the parent version 9 Lite XCO2 files. Each file contains a copy of the sounding_id from the original Lite product files. These files are grouped into yearly zip files to facilitate easier file transfer. Each daily file is in netCDF4 format with appropriate metadata to describe the fields and their units. The filenames are derived from the parent version 9 Lite XCO2 file with "3Dmetrics" added as a suffix. For example, the first file from 2014 is named "oco2_LtCO2_140906_B9003r_180927215925s_3Dmetrics.nc4" which is derived from the Version 9 Lite product file "oco2_LtCO2_140906_B9003r_180927215925s.nc4".

opencc-by-4.0Sep 2020View details →

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Data associated with the publication "Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements"

This dataset refers to the publication "Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements". https://doi.org/10.5194/acp-2021-16.

opencc-by-4.0Nov 2021View details →

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COLA-hires: High-resolution (0.5x0.625) regional carbon fluxes inferred from in-situ and OCO-2 data

This dataset contains high-resolution CO2 inversion estimate in North America, East Asia, and Europe at 0.5x0.625 resolution from 2015 to 2018 using the Carbon in Ocean-Land-Atmosphere (COLA) system. The in-situ observations obtained from NOAA obspack and the land-nadir/land-glint retrevials from OCO-2 are assimilated.

opencc-by-4.0Apr 2023View details →

opencc-by-4.0Dec 2019View details →

OCO-2 lowermost troposphere partial column

This archive contains daily netcdf files with two vertically resolved partial columns from OCO-2: The LMT (lowermost troposphere) partial column contains the 5 levels nearest the Earth’s surface, and the U (upper) partial column contains the upper 15 levels (described in Kulawik et al., 2017). The bias correction of LMT was done by comparisons to aircraft observations using a similar process to the OCO-2 XCO2 bias correction (described in O’Dell et al., 2018). The LMT bias correction is described in detail in Kulawik et al. (2017, 2019). The U partial column is set by subtracting the bias-corrected LMT from the bias corrected XCO2 (with appropriate airmass factors). The LMT product contains screening additionally to XCO2. The LMT quality flag is found in lmt -> quality_flag, and all values contain good quality (0).

opencc-by-4.0Nov 2023View details →

Global continuous 0.05 degree atmospheric carbon dioxide dataset (GCXCO2) based OCO-2 satellite, CAMS and CarbonTracker simulation data from 2000 to 2020

This dataset provides global seamless 8-day XCO2 (column-averaged CO2 dry air mole fraction) with a spatial resolution of 0.05 degree from 2000 to 2020. The unit is ppm. The detailed process and product validation accuracy can be found in our paper  at https://doi.org/10.1016/j.scitotenv.2024.177051

opencc-by-4.0Jun 2019View details →

Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2

The files in this data repository provide the inputs required to run an inverse modeling case study. This case study will estimate CO2 fluxes across North America for July 2015 using synthetic observations that have been created to resemble observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. This data repository is specifically linked to a GitHub code repository (http://doi.org/10.5281/zenodo.3241524 or <a href="https://github.com/greenhousegaslab/geostatistical_inverse_modeling">https://github.com/greenhousegaslab/geostatistical_inverse_modeling</a>). That GitHub repository provides scripts for constructing a geostatistical inverse model that will estimate greenhouse gas fluxes or air pollution emissions using atmospheric observations. The GitHub repository includes a case study that can be run out-of-the-box; the case study provides users an opportunity to test out and explore the inverse modeling code. All of the input data files for that case study are provided for download here. Here is a brief explanation of the different files included in this data repository, but refer to the linked GitHub repository for greater details. All of these files are in a ".mat" file that can be read into Matlab using the load function or can be read into R using the R.matlab package. <ul> <li>H.tar.gz: This tar file contains the H matrices or sensitivity matrices required by the inverse model. These inputs were generated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model as part of NOAA's CarbonTracker-Lagrange program (<a href="https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/">https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/</a>). The H matrix is too large to store in a single file. We have therefore split up the matrix into 328 different files (all contained within H.tar.gz). Each file contains a vertical strip of the H matrix that corresponds to a different time period of fluxes to be estimated as part of the inverse model.</li> <li>Z.mat: This file contains the synthetic OCO-2 observations used in the case study. </li> <li>areas_us.mat: This file lists the area of each model grid box used in the case study in units of meters2. This file only includes grid box area for model grid boxes that fall within the continental United States. We estimate CO2 fluxes across terrestrial North America on a 1 degree latitude by 1 degree longitude grid as part of the case study. Each of these model grid boxes will have a different area, depending upon the latitude of that model grid box. </li> <li>distmat.mat: This file contains a matrix that lists the distance (in kilometers) between the center of each model grid box used in the case study. </li> <li>land_mask.mat: We only estimate CO2 fluxes for terrestrial regions of North America as part of the case study. This land mask is used to convert the fluxes estimated by the inverse model to a latitude-longitude grid that can then be plotted.</li> <li>H_all_OCO2.mat: This file contains the H matrices summed across differnt time periods. I.e., this file is the sum of all the individual H files contained within H.tar.gz.</li> <li>Xvar.tar.gz: This file contains different environmental variables from ERA5 meteorology that have been reformatted to match the H footprint matrices. These different variables can be used as predictors of CO2 fluxes in an inverse model. The different variables included in this file are as follows: <ul> <li>Xvar_e.mat                     Evaporation</li> <li>Xvar_msdwswrf.mat      Mean surface downward short-wave radiation flux</li> <li>Xvar_q.mat                     Specific humidity</li> <li>Xvar_stl1.mat                 Soil temperature level 1</li> <li>Xvar_stl3.mat                Soil temperature level 3</li> <li>Xvar_swvl1.mat             Volumetric soil water layer 1</li> <li>Xvar_t2m.mat               2 metre temperature</li> <li>Xvar_tp.mat                  Total precipitation</li> <li>Xvar_mer.mat               Mean evaporation rate</li> <li>Xvar_pev.mat               Potential evaporation</li> <li>Xvar_r.mat                   Relative humidity</li> <li>Xvar_swvl3.mat          Volumetric soil water layer 1</li> <li>Xvar_tcc.mat              Total cloud cover</li> </ul> </li> </ul>

opencc-by-4.0Dec 2022View details →

Global long-term (2010-2020) daily seamless fused XCO2 and XCH4 from GOSAT, OCO-2, and CAMS-EGG4

A novel spatiotemporally self-supervised fusion method is proposed to establish long-term daily seamless XCO2 and XCH4 products from 2010 to 2020 over the globe at grids of 0.25°. More details are provided in https://doi.org/10.5194/essd-2023-28.

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Computing a correlation length scale from MFLL-OCO2 CO2 differences, and accounting for correlated errors when assimilating OCO-2 data

This dataset contains code and data used in  'A new exponentially-decaying error correlation model for assimilating OCO-2 column-average CO2 data, using a length scale computed from airborne lidar measurements'  by David F. Baker, Emily Bell, Kenneth J. Davis, Joel F. Campbell, Bing Lin, and Jeremy Dobler, submitted to Geoscientific Model Development. In particular, the MATLAB script used to compute the autocorrelation spectrum of  Multi-functional Fiber Laser LiDAR (MFLL) and Orbiting Carbon Observatory (OCO-2) column CO2 differences (in Section 2.2 of the paper) is given here as file comp_MFLL_OCO2_autocorrl_spectrum.m along with the needed MFLL and OCO-2 CO2 data for each of the six flights analyzed (as described in Section 2.1 of the paper) in files: 20160727_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20160805_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20170215_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20170308_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20171022_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20171027_mfll_averaged_L1_RA_GMAO_ACTadj.h5 20160727_oco_averaged_B9_GMAO_ACTadj.h5 20160805_oco_averaged_B9_GMAO_ACTadj.h5 20170215_oco_averaged_B9_GMAO_ACTadj.h5 20170308_oco_averaged_B9_GMAO_ACTadj.h5 20171022_oco_averaged_B9_GMAO_ACTadj.h5 20171027_oco_averaged_B9_GMAO_ACTadj.h5 The MFLL data given here was downloaded in late 2018 in the form of L1b files (calibrated radiances), as described in Bell et al (2020). In the second part of the paper, different error correlation models are presented and applied to the averaging of OCO-2 column CO2 data.  The original bias-corrected OCO-2 data, in the form of daily OCO-2 version 10 "Lite" files, have been from obtained from NASA's GES DISC data repository, here: https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_10r/summary?keywords=OCO2_L2_Lite_FP The bias-corrected column CO2 retrievals, their uncertainties, and other parameters needed for this analysis were extracted from these "Lite" files and saved to daily files, which have been packaged up in the following compressed tarball: OCO2_XCO2_2014_2020.tar.gz These daily files are read in and averaged across 2-second and 10-second spans (as described in Section 3.5 of the paper), using the different error correlation models outlined in the paper.  The code that implements these averages is given in the following two FORTRAN programs: Make_OCO2_2sec_averages.f90 Make_OCO2_10sec_averages.f90 which need the following list of days having good OCO-2 data: OCO2_dates.txt Program "Make_OCO2_2sec_averages.f90" averages the OCO-2 data across a 2-second (~13.5 km long) span along the groundtrack, collapsing the relatively thin data swath into a one-dimensional data record, upon which the one-dimensional averaging models describe in Sections 3.1 and 3.2 of the paper may be applied.  Program "Make_OCO2_10sec_averages.f90" implements these averaging models, which average the 2-second averages across longer, 10-second (~67.5 km) spans.  Please see the manuscript for more information on the data and methods provided here.

opencc-by-4.0Dec 2020View details →

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Data for "Toward Robust Estimates of Net Ecosystem Exchanges in Mega-Countries using GOSAT and OCO-2 Observations"

This dataset contains carbon fluxes for the 10 largest countries in the world (here EU27 is treated as a country) using GOSAT and OCO-2 observational constraints for 2017-2019.

opencc-by-4.0Jun 2024View details →

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Global OCO-2 SIF from 2001 to 2022

Citation:  Li, X., Xiao, J. (2019) A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sensing, 11, 517; doi:10.3390/rs11050517 Website: https://globalecology.unh.edu/data/GOSIF.html Metadata: Spatial resolution: 0.05 degree Spatial extent: globe Temporal resolution: 8 day (and monthly) Contact: Drs. Jingfeng Xiao (j.xiao@unh.edu) and Xing Li (zxwlxty@163.com).

opencc-by-4.0Mar 2019View details →

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CarbonTracker-Lagrange North America OCO-2 Vertical Profile of Footprints V1 (CMS_CTL_NA_OCO2_FOOTPRINTS)

This data set provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory data products for particle receptors co-located with atmospheric column observations from the OCO-2 satellite. Meteorological fields from the WRF model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio per surface flux, quantifies the influence of upwind surface fluxes on greenhouse gas concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. For each column observation location, the receptors are located at 14 discrete vertical levels throughout the atmospheric column. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.

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CarbonTracker-Lagrange South America OCO-2 Vertical Profile of Footprints V1 (CMS_CTL_SA_OCO2_FOOTPRINTS)

OCO-3 Level 2 CO2 prior based on CO2 monthly flask record, global meteorology, and age of air, Retrospective Processing V11r (OCO3_L2_CO2Prior) at GES DISC

Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.

OCO-2 GEOS Level 3 daily, 0.5x0.625 assimilated CO2 V10r (OCO2_GEOS_L3CO2_DAY) at GES DISC

This is the Gridded Daily OCO-2 Carbon Dioxide assimilated dataset. The OCO-2 mission provides the highest quality space-based XCO2 retrievals to date. However, the instrument data are characterized by large gaps in coverage due to OCO-2’s narrow 10-km ground track and an inability to see through clouds and thick aerosols. This global gridded dataset is produced using a data assimilation technique commonly referred to as state estimation within the geophysical literature. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO2 to reflect observed values, thus gap-filling observations when and where they are unavailable based on previous observations and short transport simulations by GEOS. Compared to other methods, data assimilation has the advantage that it makes estimates based on our collective scientific understanding, notably of the Earth’s carbon cycle and atmospheric transport. OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 L2 retrievals every 6 hours with GEOS CoDAS, a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation approach for solving the state estimation problem. GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem.

OCO-2 Gridded bias-corrected XCO2, SIF, and other select fields aggregated as Level 3 daily files V4 (OCO2GriddedXCO2_SIF)

Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) bias corrected data.This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.

OCO-2 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V11r (OCO2_L2_Lite_SIF) at GES DISC

Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.