Abstract. This work describes the NASA Atmospheric CO 2 Observations from Space (ACOS) X CO 2 retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ∼0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small.
[1] We present global, vertical profile estimates of the HDO/H 2 O ratio from the Tropospheric Emission Spectrometer (TES) on the Earth Observing System (EOS) Aura satellite. We emphasize in this paper the estimation approach and error characterization, which are critical to determining the very small absolute concentration of HDO relative to H 2 O and its uncertainty. These estimates were made from TES nadir-viewing (downlooking) thermal infrared spectral radiances observed on 20 September 2004. Profiles of HDO and H 2 O are simultaneously estimated from the observed radiances and a profile of the ratio is then calculated. This simultaneous, or ''joint,'' estimate is regularized with an a priori covariance matrix that includes expected correlations between HDO and H 2 O. This approach minimizes errors in the profile of the HDO/H 2 O ratio that are due to overlapping HDO and H 2 O spectroscopic lines. Under clear-sky conditions in the tropics, TES estimates of the HDO/H 2 O ratio are sensitive to the distribution of the actual ratio between the surface and about 300 hPa with peak sensitivity at 700 hPa. The sensitivity decreases with latitude through its dependence on temperature and water amount. We estimate a precision of approximately 1% to 2% for the ratio of the HDO/H 2 O tropospheric densities; however, there is possibly a bias of approximately 5% in the ratio due to the HDO spectroscopic line strengths. These global observations clearly show increased isotopic depletion of water vapor at higher latitudes as well as increased depletion in the upper troposphere versus the lower troposphere.
Abstract. The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O 2 ) Aband at 0.765 microns and the carbon dioxide (CO 2 ) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO 2 dry-air mole fraction, XCO 2 . Variations of XCO 2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO 2 . This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (< 0.25 %) changes in the background XCO 2 field. High measurement precision is therefore essential to resolve these small variations, and high accuracy is needed because small biases in the retrieved XCO 2 distribution could be misinterpreted as evidence for CO 2 fluxes.To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s −1 across a narrow (< 10 km) swath as the observatory flies over the sunlit hemisphere, yielding almost 1 million soundings each day. On monthly timescales, between 7 and 12 % of these soundings pass the cloud screens and other data quality filters to yield full-column estimates of XCO 2 . Each of these soundings has an unprecedented combination of spatial resolution (< 3 km 2 /sounding), spectral resolving power (λ/ λ > 17 000), dynamic range (∼ 10 4 ), and sensitivity (continuum signal-to-noise ratio > 400).The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.
Abstract. Since September 2014, NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite has been taking measurements of reflected solar spectra and using them to infer atmospheric carbon dioxide levels. This work provides details of the OCO-2 retrieval algorithm, versions 7 and 8, used to derive the column-averaged dry air mole fraction of atmospheric CO2 (XCO2) for the roughly 100 000 cloud-free measurements recorded by OCO-2 each day. The algorithm is based on the Atmospheric Carbon Observations from Space (ACOS) algorithm which has been applied to observations from the Greenhouse Gases Observing SATellite (GOSAT) since 2009, with modifications necessary for OCO-2. Because high accuracy, better than 0.25 %, is required in order to accurately infer carbon sources and sinks from XCO2, significant errors and regional-scale biases in the measurements must be minimized. We discuss efforts to filter out poor-quality measurements, and correct the remaining good-quality measurements to minimize regional-scale biases. Updates to the radiance calibration and retrieval forward model in version 8 have improved many aspects of the retrieved data products. The version 8 data appear to have reduced regional-scale biases overall, and demonstrate a clear improvement over the version 7 data. In particular, error variance with respect to TCCON was reduced by 20 % over land and 40 % over ocean between versions 7 and 8, and nadir and glint observations over land are now more consistent. While this paper documents the significant improvements in the ACOS algorithm, it will continue to evolve and improve as the CO2 data record continues to expand.
Abstract. We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO 2 (X CO 2 ) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO 2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in X CO 2 south of 25 • S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use Correspondence to: D. Wunch (dwunch@gps.caltech.edu) the observed correlation between free-tropospheric potential temperature and X CO 2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TC-CON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25 • S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.
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