Thermal And Near Infrared Sensor For Carbon Observation Research Articles

Evaluating Radiometric Responsivity Degradations of TANSO-FTS/GOSAT Using Principal Component Analysis

AbstractThe short-wavelength infrared bands of the Thermal and Near-Infrared Sensor for Carbon Observation (TANSO)–Fourier transform spectrometer (FTS) instrument on board theGreenhouse Gas Observing Satellite(GOSAT) have degraded, which affects the retrieval of data for CO2and CH4. Herein, a new algorithm that uses principal component analysis (PCA) to evaluate these degradations from on-orbit solar calibration spectra has been developed. The datasets of the spectra were decomposed using PCA, and the temporal variations of their components were fitted using the appropriate functions. Our results show that PCA is effective to construct a suitable degradation model for TANSO-FTS. Comparisons of CO2data retrieved using the new degradation model with that using the ground-based FTS indicate that the new model improves the measurement biases.

The use of the 1.27 µm O<sub>2</sub> absorption band for greenhouse gas monitoring from space and application to MicroCarb

Abstract. Monitoring CO2 from space is essential to characterize the spatiotemporal distribution of this major greenhouse gas and quantify its sources and sinks. The mixing ratio of CO2 to dry air can be derived from the CO2∕O2 column ratio. The O2 column is usually derived from its absorption signature on the solar reflected spectra over the O2 A band (e.g. Orbiting Carbon Observatory-2 (OCO-2), Thermal And Near infrared Sensor for carbon Observation (TANSO)/Greenhouse Gases Observing Satellite (GOSAT), TanSat). As a result of atmospheric scattering, the atmospheric path length varies with the aerosols' load, their vertical distribution, and their optical properties. The spectral distance between the O2 A band (0.76 µm) and the CO2 absorption band (1.6 µm) results in significant uncertainties due to the varying spectral properties of the aerosols over the globe. There is another O2 absorption band at 1.27 µm with weaker lines than in the A band. As the wavelength is much closer to the CO2 and CH4 bands, there is less uncertainty when using it as a proxy of the atmospheric path length to the CO2 and CH4 bands. This O2 band is used by the Total Carbon Column Observing Network (TCCON) implemented for the validation of space-based greenhouse gas (GHG) observations. However, this absorption band is contaminated by the spontaneous emission of the excited molecule O2*, which is produced by the photo-dissociation of O3 molecules in the stratosphere and mesosphere. From a satellite looking nadir, this emission has a similar shape to the absorption signal that is used. In the frame of the CNES (Centre National d'Études Spatiales – the French National Centre for Space Studies) MicroCarb project, scientific studies have been performed in 2016–2018 to explore the problems associated with this O2* airglow contamination and methods to correct it. A theoretical synthetic spectrum of the emission was derived from an approach based on A21 Einstein coefficient information contained in the line-by-line high-resolution transmission molecular absorption (HITRAN) 2016 database. The shape of our synthetic spectrum is validated when compared to O2* airglow spectra observed by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY)/Envisat in limb viewing. We have designed an inversion scheme of SCIAMACHY limb-viewing spectra, allowing to determine the vertical distribution of the volume emission rate (VER) of the O2* airglow. The VER profiles and corresponding integrated nadir intensities were both compared to a model of the emission based on the Reactive Processes Ruling the Ozone Budget in the Stratosphere (REPROBUS) chemical transport model. The airglow intensities depend mostly on the solar zenith angle (both in model and data), and the model underestimates the observed emission by ∼15 %. This is confirmed with SCIAMACHY nadir-viewing measurements over the oceans: in such conditions, we have disentangled and retrieved the nadir O2* emission in spite of the moderate spectral resolving power (∼860) and found that the nadir SCIAMACHY intensities are mostly dictated by solar zenith angle (SZA) and are larger than the model intensities by a factor of ∼1.13. At a fixed SZA, the model airglow intensities show very little horizontal structure, in spite of ozone variations. It is shown that with the MicroCarb spectral resolution power (25 000) and signal-to-noise ratio (SNR), the contribution of the O2* emission at 1.27 µm to the observed spectral radiance in nadir viewing may be disentangled from the lower atmosphere/ground absorption signature with a great accuracy. Indeed, simulations with 4ARCTIC radiative transfer inversion tool have shown that the CO2 mixing ratio may be retrieved with the accuracy required for quantifying the CO2 natural sources and sinks (pressure-level error ≤1 hPa; XCO2 accuracy better than 0.4 ppmv) with the O2 1.27 µm band only as the air proxy (without the A band). As a result of these studies (at an intermediate phase), it was decided to include this band (B4) in the MicroCarb design, while keeping the O2 A band for reference (B1). Our approach is consistent with the approach of Sun et al. (2018), who also analysed the potential of the O2 1.27 µm band and concluded favourably for GHG monitoring from space. We advocate for the inclusion of this O2 band on other GHG monitoring future space missions, such as GOSAT-3 and EU/European Space Agency (ESA) CO2-M missions, for a better GHG retrieval.

The Impact of Different Support Vectors on GOSAT-2 CAI-2 L2 Cloud Discrimination

Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation (TANSO)-Fourier Transform Spectrometer 2 (FTS-2) and the TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands to observe the optical properties of aerosols and clouds and to monitor the status of urban air pollution and transboundary air pollution over oceans, such as PM2.5 (particles less than 2.5 micrometers in diameter). CAI-2 has important applications for cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1), which applies sequential threshold tests to features is used for GOSAT CAI L2 cloud flag processing. If CLAUDIA1 is used with CAI-2, it is necessary to optimize the thresholds in accordance with CAI-2. However, CLAUDIA3 with support vector machines (SVM), a supervised pattern recognition method, was developed, and then we applied CLAUDIA3 for GOSAT-2 CAI-2 L2 cloud discrimination processing. Thus, CLAUDIA3 can automatically find the optimized boundary between clear and cloudy areas. Improvements in CLAUDIA3 using CAI (CLAUDIA3-CAI) continue to be made. In this study, we examined the impact of various support vectors (SV) on GOSAT-2 CAI-2 L2 cloud discrimination by analyzing (1) the impact of the choice of different time periods for the training data and (2) the impact of different generation procedures for SV on the cloud discrimination efficiency. To generate SV for CLAUDIA3-CAI from MODIS data, there are two times at which features are extracted, corresponding to CAI bands. One procedure is equivalent to generating SV using CAI data. Another procedure generates SV for MODIS cloud discrimination at the beginning, and then extracts decision function, thresholds, and SV corresponding to CAI bands. Our results indicated the following. (1) For the period from November to May, it is more effective to use SV generated from training data from February while for the period from June to October it is more effective to use training data from August; (2) In the preparation of SV, features obtained using MODIS bands are more effective than those obtained using the corresponding GOSAT CAI bands to automatically extract cloud training samples.

Comparison of Retrieved L2 Products from Four Successive Versions of L1B Spectra in the Thermal Infrared Band of TANSO-FTS over the Arctic Ocean

This paper concentrates on the calibration/validation of the Thermal and Near Infrared Sensor for Carbon Observation (TANSO)–Fourier Transform Spectrometer (FTS) spectra in the thermal infrared (TIR) spectral region (B4 band) over the Arctic Ocean. We have performed inter-comparisons of the retrieved L2 products from four successive versions of L1B products (V150, V160, V201, V203) to check the differences and the improvement in the spectral and radiometric calibration of TANSO-FTS spectra in the narrow spectral domain of 940–980 cm−1 covering CO2 lines of the so-called laser band in the rather clear 10.4 μm atmospheric window, allowing sounding down to the lowest atmospheric layers. To our knowledge, this is the first attempt to retrieve XCO2 from this spectral region. The period covered is the summer months (July, August, September) and the years from 2009 to 2015. Internal comparisons of L1B TANSO-FTS spectra, as well as comparisons of retrieved L2 products, i.e., Tsurf (sea surface temperature or SST) and the retrieved column-averaged dry air volume mixing ratio XCO2 derived with the same algorithm are presented. The overall trend in the CO2 column-averaged VMR is well captured over the six year period for Green-house Gases Observing Satellite (GOSAT), but calibration issues are still hindering the use of TANSO-FTS TIR spectra for accurate and stable XCO2 and Tsurf products. However, an internal comparison of the successive L1B versions is possible and helpful to make progress with respect to the radiometric and spectral calibration TIR spectra collected by TANSO-FTS on GOSAT.

Bias assessment of lower and middle tropospheric CO<sub>2</sub> concentrations of GOSAT/TANSO-FTS TIR version 1 product

Abstract. CO2 observations in the free troposphere can be useful for constraining CO2 source and sink estimates at the surface since they represent CO2 concentrations away from point source emissions. The thermal infrared (TIR) band of the Thermal and Near Infrared Sensor for Carbon Observation (TANSO) Fourier transform spectrometer (FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) has been observing global CO2 concentrations in the free troposphere for about 8 years and thus could provide a dataset with which to evaluate the vertical transport of CO2 from the surface to the upper atmosphere. This study evaluated biases in the TIR version 1 (V1) CO2 product in the lower troposphere (LT) and the middle troposphere (MT) (736–287 hPa), on the basis of comparisons with CO2 profiles obtained over airports using Continuous CO2 Measuring Equipment (CME) in the Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL) project. Bias-correction values are presented for TIR CO2 data for each pressure layer in the LT and MT regions during each season and in each latitude band: 40–20° S, 20° S–20° N, 20–40° N, and 40–60° N. TIR V1 CO2 data had consistent negative biases of 1–1.5 % compared with CME CO2 data in the LT and MT regions, with the largest negative biases at 541–398 hPa, partly due to the use of 10 µm CO2 absorption band in conjunction with 15 and 9 µm absorption bands in the V1 retrieval algorithm. Global comparisons between TIR CO2 data to which the bias-correction values were applied and CO2 data simulated by a transport model based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM-TM) confirmed the validity of the bias-correction values evaluated over airports in limited areas. In low latitudes in the upper MT region (398–287 hPa), however, TIR CO2 data in northern summer were overcorrected by these bias-correction values; this is because the bias-correction values were determined using comparisons mainly over airports in Southeast Asia, where CO2 concentrations in the upper atmosphere display relatively large variations due to strong updrafts.

Algorithm update of the GOSAT/TANSO-FTS thermal infrared CO<sub>2</sub> product (version 1) and validation of the UTLS CO<sub>2</sub> data using CONTRAIL measurements

Abstract. The Thermal and Near Infrared Sensor for Carbon Observation (TANSO)–Fourier Transform Spectrometer (FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) has been observing carbon dioxide (CO2) concentrations in several atmospheric layers in the thermal infrared (TIR) band since its launch. This study compared TANSO-FTS TIR version 1 (V1) CO2 data and CO2 data obtained in the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project in the upper troposphere and lower stratosphere (UTLS), where the TIR band of TANSO-FTS is most sensitive to CO2 concentrations, to validate the quality of the TIR V1 UTLS CO2 data from 287 to 162 hPa. We first evaluated the impact of considering TIR CO2 averaging kernel functions on CO2 concentrations using CO2 profile data obtained by the CONTRAIL Continuous CO2 Measuring Equipment (CME), and found that the impact at around the CME level flight altitudes (∼ 11 km) was on average less than 0.5 ppm at low latitudes and less than 1 ppm at middle and high latitudes. From a comparison made during flights between Tokyo and Sydney, the averages of the TIR upper-atmospheric CO2 data were within 0.1 % of the averages of the CONTRAIL CME CO2 data with and without TIR CO2 averaging kernels for all seasons in the Southern Hemisphere. The results of comparisons for all of the eight airline routes showed that the agreements of TIR and CME CO2 data were worse in spring and summer than in fall and winter in the Northern Hemisphere in the upper troposphere. While the differences between TIR and CME CO2 data were on average within 1 ppm in fall and winter, TIR CO2 data had a negative bias up to 2.4 ppm against CME CO2 data with TIR CO2 averaging kernels at the northern low and middle latitudes in spring and summer. The negative bias at the northern middle latitudes resulted in the maximum of TIR CO2 concentrations being lower than that of CME CO2 concentrations, which led to an underestimate of the amplitude of CO2 seasonal variation.

Methane cross-validation between three Fourier transform spectrometers: SCISAT ACE-FTS, GOSAT TANSO-FTS, and ground-based FTS measurements in the Canadian high Arctic

Abstract. We present cross-validation of remote sensing measurements of methane profiles in the Canadian high Arctic. Accurate and precise measurements of methane are essential to understand quantitatively its role in the climate system and in global change. Here, we show a cross-validation between three data sets: two from spaceborne instruments and one from a ground-based instrument. All are Fourier transform spectrometers (FTSs). We consider the Canadian SCISAT Atmospheric Chemistry Experiment (ACE)-FTS, a solar occultation infrared spectrometer operating since 2004, and the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for carbon Observation (TANSO)-FTS, a nadir/off-nadir scanning FTS instrument operating at solar and terrestrial infrared wavelengths, since 2009. The ground-based instrument is a Bruker 125HR Fourier transform infrared (FTIR) spectrometer, measuring mid-infrared solar absorption spectra at the Polar Environment Atmospheric Research Laboratory (PEARL) Ridge Laboratory at Eureka, Nunavut (80° N, 86° W) since 2006. For each pair of instruments, measurements are collocated within 500 km and 24 h. An additional collocation criterion based on potential vorticity values was found not to significantly affect differences between measurements. Profiles are regridded to a common vertical grid for each comparison set. To account for differing vertical resolutions, ACE-FTS measurements are smoothed to the resolution of either PEARL-FTS or TANSO-FTS, and PEARL-FTS measurements are smoothed to the TANSO-FTS resolution. Differences for each pair are examined in terms of profile and partial columns. During the period considered, the number of collocations for each pair is large enough to obtain a good sample size (from several hundred to tens of thousands depending on pair and configuration). Considering full profiles, the degrees of freedom for signal (DOFS) are between 0.2 and 0.7 for TANSO-FTS and between 1.5 and 3 for PEARL-FTS, while ACE-FTS has considerably more information (roughly 1 DOFS per altitude level). We take partial columns between roughly 5 and 30 km for the ACE-FTS–PEARL-FTS comparison, and between 5 and 10 km for the other pairs. The DOFS for the partial columns are between 1.2 and 2 for PEARL-FTS collocated with ACE-FTS, between 0.1 and 0.5 for PEARL-FTS collocated with TANSO-FTS or for TANSO-FTS collocated with either other instrument, while ACE-FTS has much higher information content. For all pairs, the partial column differences are within ±3 × 1022 molecules cm−2. Expressed as median ± median absolute deviation (expressed in partial column units or as a percentage), these differences are 0.11 ± 9.60 × 1020 molecules cm−2 (0.012 ± 1.018 %) for TANSO-FTS–PEARL-FTS, −2.6 ± 2.6 × 1021 cm−2 (−1.6 ± 1.6 %) for ACE-FTS–PEARL-FTS, and 7.4 ± 6.0 × 1020 molecules cm−2 (0.78 ± 0.64 %) for TANSO-FTS–ACE-FTS. The differences for ACE-FTS–PEARL-FTS and TANSO-FTS–PEARL-FTS partial columns decrease significantly as a function of PEARL partial columns, whereas the range of partial column values for TANSO-FTS–ACE-FTS collocations is too small to draw any conclusion on its dependence on ACE-FTS partial columns.

Impact of Aerosol Property on the Accuracy of a CO2 Retrieval Algorithm from Satellite Remote Sensing

Based on an optimal estimation method, an algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using Shortwave Infrared (SWIR) channels, referred to as the Yonsei CArbon Retrieval (YCAR) algorithm. The performance of the YCAR algorithm is here examined using simulated radiance spectra, with simulations conducted using different Aerosol Optical Depths (AODs), Solar Zenith Angles (SZAs) and aerosol types over various surface types. To characterize the XCO2 retrieval algorithm, reference tests using simulated spectra were analysed through a posteriori XCO2 retrieval errors and averaging kernels. The a posteriori XCO2 retrieval errors generally increase with increasing SZA. However, errors were found to be small (<1.3 ppm) over vegetation surfaces. Column averaging kernels are generally close to unity near the surface and decrease with increasing altitude. For dust aerosol with an AOD of 0.3, the retrieval loses its sensitivity near the surface due to the influence of atmospheric scattering, with the peak of column averaging kernels at ~800 hPa. In addition, we performed a sensitivity analysis of the principal state vector elements with respect to XCO2 retrievals. The reference tests with the inherent error of the algorithm showed that overall XCO2 retrievals work reasonably well. The XCO2 retrieval errors with respect to state vector elements are shown to be <0.3 ppm. Information on aerosol optical properties is the most important factor affecting the XCO2 retrieval algorithm. Incorrect information on the aerosol type can lead to significant errors in XCO2 retrievals of up to 2.5 ppm. The XCO2 retrievals using the Thermal and Near-infrared Sensor for carbon Observation (TANSO)-Fourier Transform Spectrometer (FTS) L1B spectra were biased by 2.78 ± 1.46 ppm and 1.06 ± 0.85 ppm at the Saga and Tsukuba sites, respectively. This study provides important information regarding estimations of the effects of aerosol properties on the CO2 retrieval algorithm. An understanding of these effects can contribute to improvements in the accuracy of XCO2 retrievals, especially combined with an aerosol retrieval algorithm.

Estimating CO2 concentration during the growing season from MODIS and GOSAT in East Asia

Because of the limited number of observation stations and the short time series of orbiting carbon satellite data, it is difficult to monitor CO2 concentrations (XCO2) at broad spatial scales for long time spans. Therefore, we are limited in accurately forecasting change in XCO2. Studies based on the approach of using satellite sensor-derived data as independent variables to model CO2 exchange show promising results for closed forest stands. There is a need to extend this approach to other land-cover types to monitor XCO2 at large spatial scales. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived indices were used to model XCO2. Greenhouse Gases Observing Satellite (GOSAT) data and MODIS-derived indices in 2010 and 2011 were selected to construct XCO2 models during the growing season (May–October). We selected three ground stations to assess the accuracy of the modelled XCO2 for each month from 2011 to 2013. The accuracy of the results indicates that the average bias was 2.25, 4.53, and 4.43 ppm at the three ground stations, respectively, although the largest bias was 10.03 ppm (at Shangdianzi Station in June 2013). We also used GOSAT Thermal and Near Infrared Sensor for Carbon Observation (TANSO) point data in 2012 and 2013 as the observed data to assess the accuracy of the XCO2 models, and achieved a slightly favourable result for each month, except June. The overall conclusion of this study is that the proposed new approach to obtaining XCO2 at the regional scale needs to be perfected in the future.

Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method

Because a synoptic overview facilitates understanding of the temporal and spatial changes in the global distribution of greenhouse gases, we developed a statistical spatial estimation method using kriging. Level 3 (L3) data products for the Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for Carbon Observation (TANSO) Fourier Transform Spectrometer (FTS) Short Wave Infrared (SWIR) were generated from column-averaged, dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) TANSO-FTS SWIR Level 2 (L2) products using this method. Although there have been some reports on the use of kriging for analysing GOSAT products, the kriging method used in this research was specifically adapted to the statistical characteristics of GOSAT L2 products. In the context of using data for atmospheric research, spatially interpolated data (GOSAT L3 products) cannot be more accurate than model-simulated global distributions of gas concentrations (GOSAT Level 4B (L4B) products), which are generated using an atmospheric tracer transport model. However, the L3 product takes much less time to generate than the L4B. It would take about a year to produce the L4B after generation of an L2 product. The great advantage of the L3 product is that it gives a comprehensive and reasonable monthly global distribution of gas concentrations with little delay. The L3 product using the kriging method can be generated on a monthly basis by estimating global semi-variogram curves from the L2 products for each month and interpolating spatially within a region with a radius of 1000 km from existing L2 data locations. The main purpose of this paper is to describe the methodology and characteristics of kriging used to generate the GOSAT L3 product, not for strictly scientific use of the estimated values, but for a reasonable global map of gas concentrations derived statistically from the sparsely observed L2 products within a short time frame. The characteristics of this method are compared to XCO2 products simulated with an atmospheric tracer transport model. The results show that the method proposed in this study is of practical use for generating L3 products from L2 products.

Assimilation of atmospheric methane products into the MACC-II system: from SCIAMACHY to TANSO and IASI

Abstract. The Monitoring Atmospheric Composition and Climate Interim Implementation (MACC-II) delayed-mode (DM) system has been producing an atmospheric methane (CH4) analysis 6 months behind real time since June 2009. This analysis used to rely on the assimilation of the CH4 product from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument onboard Envisat. Recently the Laboratoire de Météorologie Dynamique (LMD) CH4 products from the Infrared Atmospheric Sounding Interferometer (IASI) and the SRON Netherlands Institute for Space Research CH4 products from the Thermal And Near-infrared Sensor for carbon Observation (TANSO) were added to the DM system. With the loss of Envisat in April 2012, the DM system now has to rely on the assimilation of methane data from TANSO and IASI. This paper documents the impact of this change in the observing system on the methane tropospheric analysis. It is based on four experiments: one free run and three analyses from respectively the assimilation of SCIAMACHY, TANSO and a combination of TANSO and IASI CH4 products in the MACC-II system. The period between December 2010 and April 2012 is studied. The SCIAMACHY experiment globally underestimates the tropospheric methane by 35 part per billion (ppb) compared to the HIAPER Pole-to-Pole Observations (HIPPO) data and by 28 ppb compared the Total Carbon Column Observing Network (TCCON) data, while the free run presents an underestimation of 5 ppb and 1 ppb against the same HIPPO and TCCON data, respectively. The assimilated TANSO product changed in October 2011 from version v.1 to version v.2.0. The analysis of version v.1 globally underestimates the tropospheric methane by 18 ppb compared to the HIPPO data and by 15 ppb compared to the TCCON data. In contrast, the analysis of version v.2.0 globally overestimates the column by 3 ppb. When the high density IASI data are added in the tropical region between 30° N and 30° S, their impact is mainly positive but more pronounced and effective when combined with version v.2.0 of the TANSO products. The resulting analysis globally underestimates the column-averaged dry-air mole fractions of methane (xCH4) just under 1 ppb on average compared to the TCCON data, whereas in the tropics it overestimates xCH4 by about 3 ppb. The random error is estimated to be less than 7 ppb when compared to TCCON data.

Using ocean-glint scattered sunlight as a diagnostic tool for satellite remote sensing of greenhouse gases

Abstract. Spectroscopic measurements of sunlight backscattered by the Earth's surface is a technique widely used for remote sensing of atmospheric constituent concentrations from space. Thereby, remote sensing of greenhouse gases poses particularly challenging accuracy requirements for instrumentation and retrieval algorithms which, in general, suffer from various error sources. Here, we investigate a method that helps disentangle sources of error for observations of sunlight backscattered from the glint spot on the ocean surface. The method exploits the backscattering characteristics of the ocean surface, which is bright for glint geometry but dark for off-glint angles. This property allows for identifying a set of clean scenes where light scattering due to particles in the atmosphere is negligible such that uncertain knowledge of the lightpath can be excluded as a source of error. We apply the method to more than 3 yr of ocean-glint measurements by the Thermal And Near infrared Sensor for carbon Observation (TANSO) Fourier Transform Spectrometer (FTS) onboard the Greenhouse Gases Observing Satellite (GOSAT), which aims at measuring carbon dioxide (CO2) and methane (CH4) concentrations. The proposed method is able to clearly monitor recent improvements in the instrument calibration of the oxygen (O2) A-band channel and suggests some residual uncertainty in our knowledge about the instrument. We further assess the consistency of CO2 retrievals from several absorption bands between 6400 cm−1 (1565 nm) and 4800 cm−1 (2100 nm) and find that the absorption bands commonly used for monitoring of CO2 dry air mole fractions from GOSAT allow for consistency better than 1.5 ppm. Usage of other bands reveals significant inconsistency among retrieved CO2 concentrations pointing at inconsistency of spectroscopic parameters.

Impact of aerosol and thin cirrus on retrieving and validating XCO2 from GOSAT shortwave infrared measurements

Inadequate treatment of aerosol scattering can be a significant source of error when retrieving column‐averaged dry‐air mole fractions of CO2 (XCO2) from space‐based measurements of backscattered solar shortwave radiation. We have developed a retrieval algorithm, RemoTeC, that retrieves three aerosol parameters (amount, size, and height) simultaneously with XCO2. Here we evaluate the ability of RemoTeC to account for light path modifications by clouds, subvisual cirrus, and aerosols when retrieving XCO2 from Greenhouse Gases Observing Satellite (GOSAT) Thermal and Near‐infrared Sensor for carbon Observation (TANSO)‐Fourier Transform Spectrometer (FTS) measurements. We first evaluate a cloud filter based on measurements from the Cloud and Aerosol Imager and a cirrus filter that uses radiances measured by TANSO‐FTS in the 2 micron spectral region, with strong water absorption. For the cloud‐screened scenes, we then evaluate errors due to aerosols. We find that RemoTeC is well capable of accounting for scattering by aerosols for values of aerosol optical thickness at 750 nm up to 0.25. While no significant correlation of errors is found with albedo, correlations are found with retrieved aerosol parameters. To further improve the XCO2 accuracy, we propose and evaluate a bias correction scheme.Measurements from 12 ground‐based stations of the Total Carbon Column Observing Network (TCCON) are used as a reference in this study. We show that spatial colocation criteria may be relaxed using additional constraints based on modeled XCO2 gradients, to increase the size and diversity of validation data and provide a more robust evaluation of GOSAT retrievals. Global‐scale validation of satellite data remains challenging and would be improved by increasing TCCON coverage.

Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data

Carbon dioxide (CO2) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO2 concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO2 concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO2 concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO2 concentrations on a global scale. We assumed that CO2 concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson’s correlation coefficient (R2) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R2 was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO2 concentration (XCO2), we found that the accuracy throughout the World is between −2.56∼3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified.

First observations of CO2 absorption spectra recorded in 2005 using an airship-borne FTS (GOSAT TANSO–FTS BBM) in the SWIR spectral region

This article describes the results of a field experiment performed to confirm the feasibility of measuring the solar spectra absorbed by carbon dioxide (CO2) using a short wavelength infrared Fourier transform spectrometer (SWIR-FTS) installed onboard an airship. These airship-borne observations, conducted on 7 April 2005, represent the first aircraft-borne spectroscopic experiment on greenhouse gases (GHGs) in Japan and were performed as part of the Greenhouse Gases Observing Satellite (GOSAT) project. To develop and demonstrate an effective retrieval algorithm to derive CO2 column amounts from spectroscopic observations, ground-surface scattered solar absorption spectra were observed by SWIR-FTS installed onboard the airship. This instrument is a breadboard model (BBM) of the Thermal and Near-infrared Sensor for Carbon Observation (TANSO) of GOSAT, which was successfully launched on 23 January 2009. The spectra of CO2 observed using the BBM were compared with the simulated spectra calculated by a radiative transfer code based on airship-borne in situ measurement data, which were obtained at the time of FTS measurements. The two sets of spectra are in agreement within 5%, and we identified solutions to several technical problems related to the FTS instrument, thereby improving the signal-to-noise ratio (SNR).