Abstract. The column-averaged dry-air mole fractions of carbon dioxide and methane (XCO2 and XCH4) have been retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations and released as a SWIR L2 product from the National Institute for Environmental Studies (NIES). XCO2 and XCH4 retrieved using the version 01.xx retrieval algorithm showed large negative biases and standard deviations (−8.85 and 4.75 ppm for XCO2 and −20.4 and 18.9 ppb for XCH4, respectively) compared with data of the Total Carbon Column Observing Network (TCCON). Multiple reasons for these error characteristics (e.g., solar irradiance database, handling of aerosol scattering) are identified and corrected in a revised version of the retrieval algorithm (version 02.xx). The improved retrieval algorithm shows much smaller biases and standard deviations (−1.48 and 2.09 ppm for XCO2 and −5.9 and 12.6 ppb for XCH4, respectively) than the version 01.xx. Also, the number of post-screened measurements is increased, especially at northern mid- and high-latitudinal areas.

(2015). The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets. Remote Sensing of Environment: an interdisciplinary journal, 162 344-362. The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets Abstract The GHG-CCI project is one of several projects of the European Space Agency's (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The "ECV Greenhouse Gases" (ECV GHG) is the global distribution of important climate relevant gases-atmospheric CO2 and CH4-with a quality sufficient to obtain information on regional CO2 and CH4 sources and sinks. Two satellite instruments deliver the main input data for GHG-CCI: SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. The first order priority goal of GHG-CCI is the further development of retrieval algorithms for near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 and XCH4, to meet the demanding user requirements. GHG-CCI focuses on four core data products: XCO2 from SCIAMACHY and TANSO and XCH4 from the same two sensors. For each of the four core data products at least two candidate retrieval algorithms have been independently further developed and the corresponding data products have been quality-assessed and inter-compared. This activity is referred to as "Round Robin" (RR) activity within the CCI. The main goal of the RR was to identify for each of the four core products which algorithms should be used to generate the Climate Research Data Package (CRDP). The CRDP will essentially be the first version of the ECV GHG. This manuscript gives an overview of the GHG-CCI RR and related activities. This comprises the establishment of the user requirements, the improvement of the candidate retrieval algorithms and comparisons with ground-based observations and models. The manuscript summarizes the final RR algorithm selection decision and its justification. Comparison with ground-based Total Carbon Column Observing Network (TCCON) data indicates that the "breakthrough" single measurement precision requirement has been met for SCIAMACHY and TANSO XCO2 (< 3 ppm) and TANSO XCH4 (< 17 ppb). The achieved relative accuracy for XCH4 is 3-15 ppb for SCIAMACHY and 2-8 ppb for TANSO depending on algorithm and time period. Meeting the 0.5 ppm systematic error requirement for XCO2 remains a challenge: approximately 1 ppm has been achieved at the validation sites but also larger differences have been found in regions remote from TCCON. More research is needed to identify the causes for the observed differences. In this context GHG-CCI suggests taking advantage of the ensemble of existing data products, for example, via the EnseMble Median Algorithm (EMMA). Abstract 41 The GHG-CCI pr...

Abstract. This paper presents a detailed description of LIRIC (LIdar-Radiometer Inversion Code) algorithm for simultaneous processing of coincident lidar and radiometric (sun photometric) observations for the retrieval of the aerosol concentration vertical profiles. As the lidar/radiometric input data we use measurements from European Aerosol Research Lidar Network (EARLINET) lidars and collocated sun-photometers of Aerosol Robotic Network (AERONET). The LIRIC data processing provides sequential inversion of the combined lidar and radiometric data. The algorithm starts with the estimations of column-integrated aerosol parameters from radiometric measurements followed by the retrieval of height dependent concentrations of fine and coarse aerosols from lidar signals using integrated column characteristics of Published by Copernicus Publications on behalf of the European Geosciences Union. A. Chaikovsky et al.: Lidar-Radiometer Inversion Code (LIRIC)aerosol layer as a priori constraints. The use of polarized lidar observations allows us to discriminate between spherical and non-spherical particles of the coarse aerosol mode.The LIRIC software package was implemented and tested at a number of EARLINET stations. Intercomparison of the LIRIC-based aerosol retrievals was performed for the observations by seven EARLINET lidars in Leipzig, Germany on 25 May 2009. We found close agreement between the aerosol parameters derived from different lidars that supports high robustness of the LIRIC algorithm. The sensitivity of the retrieval results to the possible reduction of the available observation data is also discussed.

We present an original method that accounts for thin clouds in carbon dioxide retrievals from space‐based reflected sunlight observations in near‐infrared regions. This approach involves a reasonable, simple parameterization of effective transmittance using a set of parameters that describe the path‐length modification caused by clouds. The complete retrieval scheme included the following: estimation of cloud parameters from the 0.76‐μm O2 A‐band and from the H2O‐saturated absorption area of the 2.0‐μm band; a necessary correction to utilize these parameters at the target CO2 1.58‐μm band using estimated ground surface albedo outside of gas absorption lines in this band; and retrieval of CO2 amount at the 1.58‐μm band using a maximum a posteriori method of inversion. The primary retrieved parameters refer to the CO2 volume mixing ratio vertical profile that is then transformed to an averaged‐column amount under a pre‐defined increment of pressure. A set of numerical simulations with synthetic radiance spectra particular to Greenhouse Gases Observing Satellite (GOSAT) observations showed that the proposed method provides acceptably accurate CO2 retrievals from an atmosphere that includes thin cirrus clouds. Efficiency of the aerosol and cloud corrections was demonstrated by comparing it with a modified iterative maximum a posteriori‐DOAS (IMAP‐DOAS) that neglects path length modifications due to the scattering effects.

The column-averaged dry-air mole fractions of carbon dioxide and methane (XCO₂ and XCH₄) have been retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations. XCO₂ and XCH₄ retrieved using the version 01.xx retrieval algorithm showed large negative biases and standard deviations (−8.85 ppm and 4.75 ppm for XCO₂ and −20.4 ppb and 18.9 ppb for XCH₄, respectively) compared with data of the Total Carbon Column Observing Network (TCCON). Multiple reasons for these error characteristics (e.g. solar irradiance database, handling of aerosol scattering) are identified and corrected in a revised version of the retrieval algorithm (version 02.xx). The improved retrieval algorithm shows much smaller biases and standard deviations (−1.48 ppm and 2.10 ppm for XCO₂ and −6.0 ppb and 12.5 ppb for XCH₄, respectively) than the version 01.xx. Also, the number of post-screened measurements is increased especially at northern mid- and high-latitudinal area