NASA’s Orbiting Carbon Observatory-2 (OCO-2) has the goal of accurately measuring column-averaged dry-air mole fractions of carbon dioxide (XCO2). In order to fit the measured radiances, many parameters besides CO2 are included in the optimal estimation state vector, including atmospheric water vapor and temperature. The current operational XCO2 retrieval algorithm (V11) solves for a multiplicative scaling factor on an a priori water vapor profile and an additive offset on an a priori temperature profile. However, simulations have indicated that water vapor and temperature each have 1.5-3 degrees of freedom in the vertical column. This means that the retrieval is limited in its ability to fit the true profiles of temperature and water vapor. Here, we use singular value decomposition (SVD) to determine the three most explanatory profile ”shapes” of water vapor and temperature error, then retrieve a single scaling factor applied to each shape. We assess retrieval errors by comparing to the Total Carbon Column Observing Network (TCCON) and a collection of CO2 models. We find that after applying quality filtering using Data Ordering Genetic Optimization (DOGO) and a custom bias correction, the scatter of the XCO2 error versus TCCON is reduced from 1.02 to 1.01 ppm (2.3% reduction in variance) for land glint observations, 1.04 to 0.96 ppm (14.5% reduction in variance) for land nadir observations, and 0.68 to 0.66 ppm (4.7% reduction in variance) for ocean glint observations. We also see a small improvement in the agreement between OCO-2 XCO2 and CO2 models over oceans and the Amazon.

Tropical lands play an important role in the global carbon cycle yet their contribution remains uncertain owing to sparse observations. Satellite observations of atmospheric carbon dioxide (CO2) have greatly increased spatial coverage over tropical regions, providing the potential for improved estimates of terrestrial fluxes. Despite this advancement, the spread among satellite-based and in-situ atmospheric CO2 flux inversions over northern tropical Africa (NTA), spanning 0-24◦N, remains large. Satellite-based estimates of an annual source of 0.8-1.45 PgC yr−1 challenge our understanding of tropical and global carbon cycling. Here, we compare posterior mole fractions from the suite of inversions participating in the Orbiting Carbon Observatory 2 (OCO-2) Version 10 Model Intercomparison Project (v10 MIP) with independent in-situ airborne observations made over the tropical Atlantic Ocean by the NASA Atmospheric Tomography (ATom) mission during four seasons. We develop emergent constraints on tropical African CO2 fluxes using flux-concentration relationships defined by the model suite. We find an annual flux of 0.14 ± 0.39 PgC yr−1 (mean and standard deviation) for NTA, 2016-2018. The satellite-based flux bias suggests a potential positive concentration bias in OCO-2 B10 and earlier version retrievals over land in NTA during the dry season. Nevertheless, the OCO-2 observations provide improved flux estimates relative to the in situ observing network at other times of year, indicating stronger uptake in NTA during the wet season than the in-situ inversion estimates.

The Orbiting Carbon Observatories-2 and 3 make space-based measurements in the oxygen A-band and the weak and strong carbon dioxide (CO2) bands. A Bayesian optimal estimation approach is employed to retrieve the column averaged CO2 dry air mole fraction from these measurements. This retrieval requires a large number of polarized, multiple-scattering radiative transfer (RT) calculations for each iteration. These RT calculations take up the majority of the processing time for each retrieval, and slow down the algorithm to the point that reprocessing data from the mission over multiple years becomes very expensive. To accelerate the RT calculations and thereby ease this bottleneck, we have developed a novel approach that enables reproduction of the spectra for the three OCO-2/3 instrument bands from radiances calculated at a small subset of monochromatic wavelengths. This allows reduction of the number of monochromatic RT calculations by a factor of 20 and can be achieved with radiance errors of less than 0.1% with respect to the existing algorithm. The technique is applicable to similar retrieval algorithms for other greenhouse gas sensors with large data volumes, such as GeoCarb, GOSAT-3, and CO2M.

Previous studies done for the TROPOMI mission (e.g. Meister et al (2017), Hu et al (2016), Landgraf et al (2016)) has shown that inhomogeneous illumination within the field of view causes distortions in the instrument spectral response function (ISRF). The differences in the distorted ISRF and the ISRF measured in the pre-flight calibration lead to retrieval biases. It is known through laboratory trials that these distortions increase with wavelength and various methods have been employed to mitigate these issues, including continuous within-slit scanning and temporal oversampling. Recently, a hardware solution, called a slit homogenizer, has shown promise in reducing distortions due to inhomogeneous illumination. In this work, we used an instrument model of the Geostationary Carbon Cycle Observatory, GeoCarb, to simulate observations in the 0.76um, 1.6um, 2.05um, and 2.3um spectral bands with varying illuminations. We found that the posterior scatter and bias of Greenhouse Gas retrievals grow linearly with the coefficient of variation of illumination within a footprint. We also found that the ISRF distortion effects of inhomogeneous illumination are significantly reduced through the use of a slit homogenizer, though not completely resolved.

The ACT-America project is a NASA Earth Venture Suborbital-2 mission designed to study the transport and fluxes of greenhouse gases. The open and freely available ACT-America datasets provide airborne in-situ measurements of atmospheric carbon dioxide, methane, trace gases, aerosols, clouds, and meteorological properties, airborne remote sensing measurements of aerosol backscatter, atmospheric boundary layer height and columnar content of atmospheric carbon dioxide, tower-based measurements, and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over the Central and Eastern United States. We conducted 121 research flights during five campaigns in four seasons during 2016-2019 over three regions of the US (Mid-Atlantic, Midwest and South) using two NASA research aircraft (B-200 and C-130). We performed three flight patterns (fair weather, frontal crossings, and OCO-2 underflights) and collected more than 1,140 hours of airborne measurements via level-leg flights in the atmospheric boundary layer, lower, and upper free troposphere and vertical profiles spanning these altitudes. We also merged various airborne in-situ measurements onto a common standard sampling interval, which brings coherence to the data, creates geolocated data products, and makes it much easier for the users to perform holistic analysis of the ACT-America data products. Here, we report on detailed information of datasets collected, and the workflow for datasets including storage and processing of the quality controlled and quality assured harmonized observations, and their archival and formatting for users. Finally, we provide some important information on the dissemination of data products including metadata and highlights of applications of datasets for future investigations.