Optimization of the photon path length probability density function-simultaneous (PPDF-S) method and evaluation of CO 2 retrieval performance under dense aerosol conditions

The photon path length probability density function-simultaneous (PPDF-S) algorithm is effective for retrieving column-averaged concentrations of carbon dioxide (XCO 2 ) and methane (XCH 4 ) from Greenhouse gases Observing Satellite (GOSAT) spectra in Short Wavelength InfraRed (SWIR). Using this method, light-path modification attributable to light reflection/scattering by atmospheric clouds/aerosols is represented by the modification of atmospheric transmittance according to PPDF parameters. We optimized PPDF parameters for a more accurate XCO 2 retrieval under aerosol dense conditions based on simulation studies for various aerosol types and surface albedos. We found a more appropriate value of PPDF parameters referring to the vertical profile of CO 2 concentration as a measure of a stable solution. The results show that the constraint condition of a PPDF parameter that represents the light reflectance effect by aerosols is sufficiently weak to affect XCO 2 adversely. By optimizing the constraint, it was possible to obtain a stable solution of XCO 2 . The new optimization was applied to retrieval analysis of the GOSAT data measured in Western Siberia. First, we assumed clear sky conditions and retrieved XCO 2 from GOSAT data obtained near Yekaterinburg in the target area. The retrieved XCO 2 was validated through a comparison with ground-based Fourier Transform Spectrometer (FTS) measurements made at the Yekaterinburg observation site. The validation results showed that the retrieval accuracy was reasonable. Next, we applied the optimized method to dense aerosol conditions when biomass burning was active. The results demonstrated that optimization enabled retrieval, even under smoky conditions, and that the total number of retrieved data increased by about 70%. Furthermore, the results of the simulation studies and the GOSAT data analysis suggest that atmospheric aerosol types that affected CO 2 analysis are identifiable by the PPDF parameter value. We expect that we will be able to suggest a further improved algorithm after the atmospheric aerosol types are identified. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.Russian Science Foundation: 18-11-00024Acknowledgments: The v3.0 ACOS/OCO-2 absorption coefficient (ABSCO) tables, used for the calculation of gas absorption coefficients, were provided by NASA and the ACOS/OCO-2 project. Vyacheslav Zakharov, Konstantin Gribanov, and Nikita Rokotyan thank the Russian Science Foundation for support of their research under the framework of grant 18-11-00024

Update on the GOSAT TANSO–FTS SWIR Level 2 retrieval algorithm

The National Institute for Environmental Studies has provided the column-averaged dry-air mole fraction of carbon dioxide and methane (XCO$_2$ and XCH$_4$) products (L2 products) obtained from the Greenhouse gases Observing SATellite (GOSAT) for more than a decade. Recently, we updated the retrieval algorithm used to produce the new L2 product, V03.00. The main changes from the previous version (V02) of the retrieval algorithm are the treatment of cirrus clouds, the degradation model of the Thermal And Near-infrared Spectrometer for carbon Observation–Fourier Transform Spectrometer (TANSO–FTS), solar irradiance spectra, and gas absorption coefficient tables. The retrieval results from the updated algorithm showed improvements in fitting accuracies in the O$_2$ A, weak CO$_2$, and CH$_4$ bands of TANSO–FTS, although the residuals increase in the strong CO$_2$ band over the ocean. The direct comparison of the new product obtained from the updated (V03) algorithm with the previous version V02.90/91 and the validations using the Total Carbon Column Observing Network revealed that the V03 algorithm increases the amount of data without diminishing the data qualities of XCO$_2$ and XCH$_4$ over land. However, the negative bias of XCO$_2$ is larger than that of the previous version over the ocean, and bias correction is still necessary. Additionally, the V03 algorithm resolves the underestimation of the XCO$_2$ growth rate compared with the in situ measurements over the ocean recently found using V02.90/91 and V02.95/96

Towards CO2 emission monitoring with passive air- and space-borne sensors

Coal-fueled power plants are responsible for 30 % of anthropogenic carbon dioxide (CO2) emissions and can therefore be considered important drivers of climate warming. The 2015 Paris Climate Accord has established a global stock take mechanism, which will assess the progress of global carbon emission reduction policies in five-yearly tallies of worldwide emissions. However, there exists no independent monitoring network, which could verify such stock takes. Remote sensing of atmospheric CO2 concentrations from air- and space-borne sensors could provide the means of monitoring localized carbon sources, if their ground sampling distance is sufficiently fine (i.e. below the kilometer scale). Increased spatial resolution can be achieved at the expense of decreasing the spectral resolution of the instrument, which in turn complicates CO2 retrieval techniques due to the reduced information content of the spectra. The present thesis aims to add to the methodology of remote CO2 monitoring approaches by studying the compromise between spectral and spatial resolution with CO2 retrievals from three different sensors. First, the trade-off between coarse spectral resolution and retrieval performance is discussed for a hypothetical imaging spectrometer which could reach a spatial resolution of ~50×50 m2 by measuring backscattered sunlight in the short wave infrared spectral range at a resolution of ∆λ ~ 1 nm. To this end, measurements of the Greenhouse gases Observing SATellite (GOSAT) at ∆λ = 0.1 nm are artificially degraded to coarser spectral resolutions to emulate the proposed sensor. CO2 column retrievals are carried out with the native and degraded spectra and the results are compared with each other, while data from the ground based Total Carbon Column Observing Network (TCCON) serve as independent reference data. This study identifies suitable retrieval windows in the short wave infrared spectral range and a favorable spectral resolution for a CO2 monitoring mission. Second, CO2 column retrievals are carried out with measurements of the air-borne AVIRIS-NG sensor at a spectral resolution of ∆λ = 5 nm. This case study identifies advantageous CO2 retrieval configurations, which minimize correlations between retrieval parameters, near two coal-fired power plants. A bias correction method is proposed for the retrievals and a plume mask is applied to the retrieved CO2 enhancements to separate the CO2 emission signal from the atmospheric background. Emission rates of the two facilities are calculated under consideration of the local wind speed, compared to a public inventory and discussed in terms of their uncertainties. Third, CO2 retrievals are extended to spectral resolutions on the order of ∆λ ~ 10 nm by analyzing spectra of the specMACS imager near a small power plant. Retrieval effects that hamper the detection of the source signal are discussed

XCO2_{2} retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm

Since 2009, the Greenhouse gases Observing SATellite (GOSAT) has performed radiance measurements in the near-infrared (NIR) and shortwave infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 have also been available. We present the first results from the application of the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL was initially developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast, it is currently being considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This particularly includes modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT and GOSAT-2 is the independent use of both S- and P-polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT\u27s wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O), and solar-induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semi-heavy water vapour (HDO). In the case of GOSAT-2, the retrieval of nitrous oxide (XN2O) and carbon monoxide (CO) may also be possible. Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal agreement of long-term temporal variations within about 1 ppm over 1 decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a station-to-station bias of the new GOSAT-FOCAL product to the ground-based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until the end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results and also TCCON

Towards CO2 Emission Monitoring with Passive Air- and Space-Borne Sensors

Coal-fueled power plants are responsible for 30% of anthropogenic carbon dioxide (CO2) emissions and can therefore be considered important drivers of climate warming. The 2015 Paris Climate Accord has established a global stock take mechanism, which will assess the progress of global carbon emission reduction policies in five-yearly tallies of worldwide emissions. However, there exists no independent monitoring network, which could verify such stock takes. Remote sensing of atmospheric CO2 concentrations from air- and space-borne sensors could provide the means of monitoring localized carbon sources, if their ground sampling distance is sufficiently fine (i.e. below the kilometer scale). Increased spatial resolution can be achieved at the expense of decreasing the spectral resolution of the instrument, which in turn complicates CO2 retrieval techniques due to the reduced information content of the spectra. The present thesis aims to add to the methodology of remote CO2 monitoring approaches by studying the compromise between spectral and spatial resolution with CO2 retrievals from three different sensors. First, the trade-off between coarse spectral resolution and retrieval performance is discussed for a hypothetical imaging spectrometer which could reach a spatial resolution of ∼ 50 × 50 m² by measuring backscattered sunlight in the short wave infrared spectral range at a resolution of ∆λ ∼ 1 nm. To this end, measurements of the Greenhouse gases Observing SATellite (GOSAT) at ∆λ = 0.1 nm are artificially degraded to coarser spectral resolutions to emulate the proposed sensor. CO2 column retrievals are carried out with the native and degraded spectra and the results are compared with each other, while data from the ground based Total Carbon Column Observing Network (TCCON) serve as independent reference data. This study identifies suitable retrieval windows in the short wave infrared spectral range and a favorable spectral resolution for a CO2 monitoring mission. Second, CO2 column retrievals are carried out with measurements of the airborne AVIRIS-NG sensor at a spectral resolution of ∆λ = 5 nm. This case study identifies advantageous CO2 retrieval configurations, which minimize correlations between retrieval parameters, near two coal-fired power plants. A bias correction method is proposed for the retrievals and a plume mask is applied to the retrieved CO2 enhancements to separate the CO2 emission signal from the atmospheric background. Emission rates of the two facilities are calculated under consideration of the local wind speed, compared to a public inventory and discussed in terms of their uncertainties. Third, CO2 retrievals are extended to spectral resolutions on the order of ∆λ ∼ 10 nm by analyzing spectra of the specMACS imager near a small power plant. Retrieval effects that hamper the detection of the source signal are discussed

Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS): Final Report of the ASCENDS Ad Hoc Science Definition Team

Improved remote sensing observations of atmospheric carbon dioxide (CO2) are critically needed to quantify, monitor, and understand the Earth's carbon cycle and its evolution in a changing climate. The processes governing ocean and terrestrial carbon uptake remain poorly understood,especially in dynamic regions with large carbon stocks and strong vulnerability to climate change,for example, the tropical land biosphere, the northern hemisphere high latitudes, and the Southern Ocean. Because the passive spectrometers used by GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Orbiting Carbon Observatory-2) require sunlit and cloud-free conditions,current observations over these regions remain infrequent and are subject to biases. These short comings limit our ability to understand and predict the processes controlling the carbon cycle on regional to global scales.In contrast, active CO2 remote-sensing techniques allow accurate measurements to be taken day and night, over ocean and land surfaces, in the presence of thin or scattered clouds, and at all times of year. Because of these benefits, the National Research Council recommended the National Aeronautics and Space Administration (NASA) Active Sensing of CO2 Emissions over Nights,Days, and Seasons (ASCENDS) mission in the 2007 report Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. The ability of ASCENDS to collect low-bias observations in these key regions is expected to address important gaps in our knowledge of the contemporary carbon cycle.The ASCENDS ad hoc Science Definition Team (SDT), comprised of carbon cycle modeling and active remote sensing instrument teams throughout the United States (US), worked to develop the mission's requirements and advance its readiness from 2008 through 2018. Numerous scientific investigations were carried out to identify the benefit and feasibility of active CO2 remote sensing measurements for improving our understanding of CO2 sources and sinks. This report summarizes their findings and recommendations based on mission modeling studies, analysis of ancillary meteorological data products, development and demonstration of candidate technologies, anddesign studies of the ASCENDS mission concept