Thermal And Near-infrared Sensor For Carbon Observation-Fourier Transform Spectrometer Research Articles

Examining partial-column density retrieval of lower-tropospheric CO2 from GOSAT target observations over global megacities

We retrieved and examined the partial-column densities of carbon dioxide (CO2) in the lower (LT, typically 0–4 km) and upper (UT, typically 4–12 km) troposphere (XCO2LT and XCO2UT) collected over six global megacities: Beijing, New Delhi, New York City, Riyadh, Shanghai, and Tokyo. The radiance spectra were collected using the Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT). Our retrieval method uniquely utilizes reflected sunlight with two orthogonal components of polarization and thermal emissions. We defined megacity concentration enhancement due to surface CO2 emissions as XCO2LT minus XCO2UT, allowing us to overcome some of the challenges in the enhancement analysis using existing column density data. We examined the relationship between the XCO2LT enhancements from the time series of intensive target observations over megacities and the inverse of simulated wind speed, which could be potentially used to estimate surface emissions. Next, we attempted to estimate the average emission intensity for each city from the linear regression slope. We also compared our obtained emission estimates with the Open-Data Inventory for Anthropogenic CO2 (ODIAC) inventory for evaluation. Our results demonstrate the potential utility of the new partial-column density retrievals for estimating megacity CO2 emissions. More frequent and comprehensive coverage characterizing the spatial distribution of emissions is necessary to reduce random error and bias associated with the obtained estimate.

An 11-year record of XCO<sub>2</sub> estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm

Abstract. The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (<1 ×106 out of 24×106). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (1σ) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Global mean biases against TCCON and models are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of the order of 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) in both the per-orbit full format (https://doi.org/10.5067/OSGTIL9OV0PN, OCO-2 Science Team et al., 2019b) and in the per-day lite format (https://doi.org/10.5067/VWSABTO7ZII4, OCO-2 Science Team et al., 2019a). In addition, a new set of monthly super-lite files, containing only the most essential variables for each satellite observation, has been generated to provide entry level users with a light-weight satellite product for initial exploration (CaltechDATA, https://doi.org/10.22002/D1.2178, Eldering, 2021). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the GOSAT data (O'Dell et al., 2020). The GOSAT v9 data set should be especially useful for studies of carbon cycle phenomena that span a full decade or more and may serve as a useful complement to the shorter OCO-2 v10 data set, which begins in September 2014.

GOSAT CH4 Vertical Profiles over the Indian Subcontinent: Effect of a Priori and Averaging Kernels for Climate Applications

We examined methane (CH4) variability over different regions of India and the surrounding oceans derived from thermal infrared (TIR) band observations (TIR CH4) by the Thermal and Near-infrared Sensor for carbon Observation—Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) for the period 2009–2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, on the basis of the averaging kernel (AK) functions and a priori assumptions, as applied to the simulated concentrations by the MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM). We stress that this is of particular importance when the satellite-derived products are analyzed using different ACTMs other than those used as retrieved a priori. A comparison of modeled and retrieved CH4 vertical profiles shows that the GOSAT/TANSO-FTS TIR instrument has sufficient sensitivity to provide critical information about the transport of CH4 from the top of the boundary layer to the upper troposphere. The mean mismatch between TIR CH4 and model is within 50 ppb, except for the altitude range above 150 hPa, where the sensitivity of TIR CH4 observations becomes very low. Convolved model profiles with TIR CH4 AK reduces the mismatch to less than the retrieval uncertainty. Distinct seasonal variations of CH4 have been observed near the atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (300 hPa) over the northern and southern regions of India, corresponding to the southwest monsoon (July–September) and post-monsoon (October–December) seasons. Analysis of the transport and emission contributions to CH4 suggests that the CH4 seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. The major contrast between monsoon, and pre- and post-monsoon profiles of CH4 over Indian regions are noticed near the boundary layer heights, which is mainly caused by seasonal change in local emission strength with a peak during summer due to increased emissions from the paddy fields and wetlands. A strong difference between seasons in the middle and upper troposphere is caused by convective transport of the emission signals from the surface and redistribution in the monsoon anticyclone of upper troposphere. TIR CH4 observations provide additional information on CH4 in the region compared to what is known from in situ data and total-column (XCH4) measurements. Based on two emission sensitivity simulations compared to TIR CH4 observations, we suggest that the emissions of CH4 from the India region were 51.2 ± 4.6 Tg year−1 during the period 2009–2014. Our results suggest that improvements in the a priori profile shape in the upper troposphere and lower stratosphere (UT/LS) region would help better interpretation of CH4 cycling in the earth’s environment.

Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model

Abstract. The GEOS-Chem simulation of atmospheric CH4 was evaluated against observations from the Thermal and Near Infrared Sensor for Carbon Observations Fourier Transform Spectrometer (TANSO-FTS) on the Greenhouse Gases Observing Satellite (GOSAT), the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and the Total Carbon Column Observing Network (TCCON). We focused on the model simulations at the 4∘×5∘ and 2∘×2.5∘ horizontal resolutions for the period of February–May 2010. Compared to the GOSAT, TCCON, and ACE-FTS data, we found that the 2∘×2.5∘ model produced a better simulation of CH4, with smaller biases and a higher correlation to the independent data. We found large resolution-dependent differences such as a latitude-dependent XCH4 bias, with higher column abundances of CH4 at high latitudes and lower abundances at low latitudes at the 4∘×5∘ resolution than at 2∘×2.5∘. We also found large differences in CH4 column abundances between the two resolutions over major source regions such as China. These differences resulted in up to 30 % differences in inferred regional CH4 emission estimates from the two model resolutions. We performed several experiments using 222Rn, 7Be, and CH4 to determine the origins of the resolution-dependent errors. The results suggested that the major source of the latitude-dependent errors is excessive mixing in the upper troposphere and lower stratosphere, including mixing at the edge of the polar vortex, which is pronounced at the 4∘×5∘ resolution. At the coarser resolution, there is weakened vertical transport in the troposphere at midlatitudes to high latitudes due to the loss of sub-grid tracer eddy mass flux in the storm track regions. The vertical air mass fluxes are calculated in the model from the degraded coarse-resolution wind fields and the model does not conserve the air mass flux between model resolutions; as a result, the low resolution does not fully capture the vertical transport. This produces significant localized discrepancies, such as much greater CH4 abundances in the lower troposphere over China at 4∘×5∘ than at 2∘×2.5∘. Although we found that the CH4 simulation is significantly better at 2∘×2.5∘ than at 4∘×5∘, biases may still be present at 2∘×2.5∘ resolution. Their importance, particularly in regards to inverse modeling of CH4 emissions, should be evaluated in future studies using online transport in the native general circulation model as a benchmark simulation.

Detection of Methane Emission from a Local Source Using GOSAT Target Observations

Emissions of atmospheric methane (CH4), which greatly contributes to radiative forcing, have larger uncertainties than those for carbon dioxide (CO2). The Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) launched in 2009 has demonstrated global grid observations of the total column density of CO2 and CH4 from space, and thus reduced uncertainty in the global flux estimation. In this paper, we present a case study on local CH4 emission detection from a single-point source using an available series of GOSAT data. By modifying the grid observation pattern, the pointing mechanism of TANSO-FTS targets a natural gas leak point at Aliso Canyon in Southern California, where the clear-sky frequency is high. To enhance local emission estimates, we retrieved CO2 and CH4 partial column-averaged dry-air mole fractions of the lower troposphere (XCO2 (LT) and XCH4 (LT)) by simultaneous use of both sunlight reflected from Earth’s surface and thermal emissions from the atmosphere. The time-series data of Aliso Canyon showed a large enhancement that decreased with time after its initial blowout, compared with reference point data and filtered with wind direction simulated by the Weather Research and Forecasting (WRF) model.

Calibration, Level 1 Processing, and Radiometric Validation for TANSO-FTS TIR on GOSAT

The Greenhouse gases Observing SATellite (GOSAT) carries the Thermal and Near-Infrared Sensor for carbon Observations Fourier Transform Spectrometer (TANSO-FTS). TANSO-FTS covers a wide spectral range from the shortwave infrared to the thermal infrared (TIR). This paper describes the updated calibration algorithm of the TANSO-FTS Level 1B TIR spectra and the radiometric validation of the new V210.210 products by comparison with the previous version of V201.202. The revised nonlinearity correction for V210.210 product creates a decade-long, well-calibrated radiance set while minimizing the effect of two major anomalies: rotation stop of the one of the solar paddles in 2014 and a cryocooler shutdown in 2015, which caused abrupt changes in the thermal environment of the TANSO-FTS sensor. To check the improved nonlinearity correction and onboard calibration in TANSO-FTS V210.210 processing, we validated the entire spectral range by comparing with aircraft-based Scanning High-resolution Interferometer Sounder coincident in time with the GOSAT overpass. Also selected CO2 and CH4 channels are validated with the Atmospheric infrared sounder and the window channel with the in situ SST Quality Monitor data at temporally coincident and spatially collocated points. We have confirmed that the new V210.210 products exhibit no significant time trend in the window channel and a reduced spectral bias in the CO2 and CH4 channels. There remains some spectral bias, especially in the CO $_{2} \nu _{2}$ channel and CH4 channel, which are attributed to the uncertainty of orbital and seasonal variations in the average direct current level of TANSO-FTS without photon input.

A high-level cloud detection method utilizing the GOSAT TANSO-FTS water vapor saturated band

Abstract. A detection method for high-level clouds, such as ice clouds, is developed using the water vapor saturated channels of the solar reflected spectrum observed by the Greenhouse gases Observing SATellite (GOSAT) Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS). The clouds detected by this method are optically relatively thin (0.01 or less) and located at high altitude. Approximately 85 % of the results from this method for clouds with cloud-top altitude above 5 km agree with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud classification. GOSAT has been operating since April 2009 with a 3-day repeat cycle for a pointwise geolocation pattern, providing a spectral data record that exceeds 9 years. Cloud information derived from GOSAT TANSO-FTS spectra could be powerful data for understanding the variability in cirrus cloud on temporal scales from synoptic to interannual.

Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN.

CO2 concentration (XCO2) shows the spatial and temporal variation in Iran. The major purpose of this investigation is the assessment of the spatial distribution of carbon dioxide concentration in the different seasons of 2013 based on the Thermal And Near Infrared Sensor for Carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) level 2 GOSAT data by implementing the ordinary kriging (OK) method. In this study, the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) data from the MODerate resolution Imaging Spectroradiometer (MODIS), and metrological parameters (temperature and precipitation) were used for the analysis of the spatial distribution of CO2 over Iran in 2013. The spatial distribution maps of XCO2 show the highest concentration of this gas in the south and south-east and the lowest concentration in the north and north-west. These results indicate that the concentration of carbon dioxide decreased with the increase of LST and temperature and a decrease of NDVI and humidity in the study area. Therefore, the existence of vegetation has an effective role in capturing carbon from the atmosphere by photosynthesis phenomena, and sustainable land management can be effective for carbon absorption from the atmosphere and mitigation of climate change in arid and semi-arid regions.

Assessment of seasonal variations of carbon dioxide concentration in Iran using GOSAT data

Global warming and climate change have been identified as the most important challenges of the 21st century. Greenhouse Gases Observation Satellite (GOSAT) measures the concentrations of carbon dioxide (CO 2) and methane (CH 4) in the atmosphere column from the earth's surface to the upper atmosphere. In this research, GOSAT Thermal And Near Infrared Sensor for Carbon Observation – Fourier Transform Spectrometer (TANSO‐FTS) level 2 data and meteorological parameters were used in the assessment of changes in CO 2 concentration (XCO 2) from 2009 to 2015. We investigated the relationship between XCO 2 and meteorological parameters (temperature and precipitation) obtained from weather stations and the Normalized Difference Vegetation Index (NDVI) in the year 2013 in Iran. The results reveal a steady increase in the mean atmospheric CO 2 concentration, from 384.89 to 400.39 ppm. It was observed that the XCO 2 varied significantly depending on the month, with the highest concentration of CO 2 in April/May and the lowest concentration in August/September. The correlation between XCO 2 and average monthly air temperature is negative, which means that a reduction in XCO 2 with an increase in temperature is dependent on photosynthetic activities in the growing seasons. The highest and lowest correlation coefficient between the NDVI and XCO 2 was obtained in the spring and in the fall, respectively. These findings are useful for recognizing factors that affect CO 2 concentration in different seasons in arid and semi‐arid regions, and as an initial step toward sustainable management.

Intercomparison of XH2O Data from the GOSAT TANSO-FTS (TIR and SWIR) and Ground-Based FTS Measurements: Impact of the Spatial Variability of XH2O on the Intercomparison

Spatial and temporal variability of atmospheric water vapor (H2O) is extremely high, and therefore it is difficult to accurately evaluate the measurement precision of H2O data by a simple comparison between the data derived from two different instruments. We determined the measurement precisions of column-averaged dry-air mole fractions of H2O (XH2O) retrieved independently from spectral radiances in the thermal infrared (TIR) and the short-wavelength infrared (SWIR) regions measured using a Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT), by an intercomparison between the two TANSO-FTS XH2O data products and the ground-based FTS XH2O data. Furthermore, the spatial variability of XH2O was also estimated in the intercomparison process. Mutually coincident XH2O data above land for the period ranging from April 2009 to May 2014 were intercompared with different spatial coincidence criteria. We found that the precisions of the TANSO-FTS TIR and TANSO-FTS SWIR XH2O were 7.3%–7.7% and 3.5%–4.5%, respectively, and that the spatial variability of XH2O was 6.7% within a radius of 50 km and 18.5% within a radius of 200 km. These results demonstrate that, in order to accurately evaluate the measurement precision of XH2O, it is necessary to set more rigorous spatial coincidence criteria or to take into account the spatial variability of XH2O as derived in the present study.

An advanced retrieval algorithm for greenhouse gases using polarization information measured by GOSAT TANSO‐FTS SWIR I: Simulation study

AbstractWe present an algorithm for retrieving column‐averaged dry air mole fraction of carbon dioxide ( ) and methane ( ) from reflected spectra in the shortwave infrared (SWIR) measured by the TANSO‐FTS (Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer) sensor on board the Greenhouse gases Observing SATellite (GOSAT). The algorithm uses the two linear polarizations observed by TANSO‐FTS to improve corrections to the interference effects of atmospheric aerosols, which degrade the accuracy in the retrieved greenhouse gas concentrations. To account for polarization by the land surface reflection in the forward model, we introduced a bidirectional reflection matrix model that has two parameters to be retrieved simultaneously with other state parameters. The accuracy in and values retrieved with the algorithm was evaluated by using simulated retrievals over both land and ocean, focusing on the capability of the algorithm to correct imperfect prior knowledge of aerosols. To do this, we first generated simulated TANSO‐FTS spectra using a global distribution of aerosols computed by the aerosol transport model SPRINTARS. Then the simulated spectra were submitted to the algorithms as measurements both with and without polarization information, adopting a priori profiles of aerosols that differ from the true profiles. We found that the accuracy of and , as well as profiles of aerosols, retrieved with polarization information was considerably improved over values retrieved without polarization information, for simulated observations over land with aerosol optical thickness greater than 0.1 at 1.6 μm.

Seasonal variation of the O3–CO correlation derived from remote sensing measurements over western Japan

We used a lower tropospheric ozone column (LTOC) and column-averaged dry-air mole fraction of carbon monoxide (XCO) data observed in the area around Saga, which is located in western Japan and is close to the Asian continent, with an aim to investigate whether these data can characterize the seasonal variation of the photochemical ozone (O3) formation in the northeast Asian Pacific rim region. The LTOC data after April 2009 were retrieved from thermal infrared spectra measured by the Thermal and Near Infrared Sensor for Carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse Gases Observing Satellite (GOSAT). The XCO data after July 2011 were obtained from ground-based high-resolution FTS measurements at Saga. The retrieved LTOCs were validated with those derived from a differential absorption lidar for O3 at Saga. The LTOCs showed a distinct seasonal variation that reached a maximum in late spring (May or June) and a local minimum in winter. In addition to the general seasonal pattern, we observed pronounced minimums in July or August. The XCO concentrations showed a maximum in spring and a minimum in summer. These seasonal patterns are consistent with those observed from mountainous sites in Japan. The origins of the air masses reaching Saga were characterized for each season according to backward trajectories, and the factors causing the temporal variations of the LTOCs and the XCO were identified based on the transport paths of the air masses. The enhancement of the LTOC relative to the XCO (ΔO3/ΔCO ratio) reveals significant positive correlations in the spring and summer seasons with slopes of 0.21 and 0.45 ppb/ppb, respectively. The effects of stratospheric air intrusion on the observed ΔO3/ΔCO ratio in spring were investigated using meteorological data (backward trajectory and potential vorticity) and column-averaged hydrogen fluoride data derived from the ground-based FTS measurements. It was found that there was little contribution of the stratospheric intrusion. This suggests that the positive correlations of the ΔO3/ΔCO ratio are likely to be a result of photochemical O3 production in the troposphere.

XCO2 satellite retrieval experiments in short-wave and infrared spectra with SCIATRAN model for Sahara Desert

The spectra of O2 A-band (0.76 μm) and CO2 near-infrared emissions (1.6 μm) are simulated by the SCIATRAN radiative transfer model (V3.1.23), and compared with those observed by GOSAT-FTS (Greenhouse gases Observing SATellite-Fourier Transform Spectrometer). Systematic deviations between the observed and simulated spectra are found to exist, especially for the O2 A-band. The discrepancies are characterized by their mean differences averaged over the observed spectral ranges. A correction is applied to the observed GOSAT-FTS L1B (V141.141) spectra by scaling the spectral intensity measured by TANSO-FTS (Thermal and Near infrared Sensor for carbon Observation Fourier Transform Spectrometer) onboard GOSAT. The average columnar CO2 concentrations (XCO2) are retrieved from the observed and the corrected GOSAT-FTS spectra by using the SCIATRAN inversion algorithm. Compared with the GOSAT-FTS L2 XCO2 data products retrieved from the observed spectra of GOSAT-FTS, the SCIATRAN retrievals from the corrected spectra show a much better agreement, with the relative error less than 1%. But the results of GOSAT TANSO-FTS (V161.160) show smaller residuals than GOSAT TANSO-FTS (V141.141) without mean residual correction. The results indicate that the mean residual correction would increase the precision of XCO2 retrieval for spectra with systematic deviations.

Atmospheric CH<sub>4</sub> and CO<sub>2</sub> enhancements and biomass burning emission ratios derived from satellite observations of the 2015 Indonesian fire plumes

Abstract. The 2015–2016 strong El Niño event has had a dramatic impact on the amount of Indonesian biomass burning, with the El Niño-driven drought further desiccating the already-drier-than-normal landscapes that are the result of decades of peatland draining, widespread deforestation, anthropogenically driven forest degradation and previous large fire events. It is expected that the 2015–2016 Indonesian fires will have emitted globally significant quantities of greenhouse gases (GHGs) to the atmosphere, as did previous El Niño-driven fires in the region. The form which the carbon released from the combustion of the vegetation and peat soils takes has a strong bearing on its atmospheric chemistry and climatological impacts. Typically, burning in tropical forests and especially in peatlands is expected to involve a much higher proportion of smouldering combustion than the more flaming-characterised fires that occur in fine-fuel-dominated environments such as grasslands, consequently producing significantly more CH4 (and CO) per unit of fuel burned. However, currently there have been no aircraft campaigns sampling Indonesian fire plumes, and very few ground-based field campaigns (none during El Niño), so our understanding of the large-scale chemical composition of these extremely significant fire plumes is surprisingly poor compared to, for example, those of southern Africa or the Amazon.Here, for the first time, we use satellite observations of CH4 and CO2 from the Greenhouse gases Observing SATellite (GOSAT) made in large-scale plumes from the 2015 El Niño-driven Indonesian fires to probe aspects of their chemical composition. We demonstrate significant modifications in the concentration of these species in the regional atmosphere around Indonesia, due to the fire emissions.Using CO and fire radiative power (FRP) data from the Copernicus Atmosphere Service, we identify fire-affected GOSAT soundings and show that peaks in fire activity are followed by subsequent large increases in regional greenhouse gas concentrations. CH4 is particularly enhanced, due to the dominance of smouldering combustion in peatland fires, with CH4 total column values typically exceeding 35 ppb above those of background “clean air” soundings. By examining the CH4 and CO2 excess concentrations in the fire-affected GOSAT observations, we determine the CH4 to CO2 (CH4 ∕ CO2) fire emission ratio for the entire 2-month period of the most extreme burning (September–October 2015), and also for individual shorter periods where the fire activity temporarily peaks. We demonstrate that the overall CH4 to CO2 emission ratio (ER) for fires occurring in Indonesia over this time is 6.2 ppb ppm−1. This is higher than that found over both the Amazon (5.1 ppb ppm−1) and southern Africa (4.4 ppb ppm−1), consistent with the Indonesian fires being characterised by an increased amount of smouldering combustion due to the large amount of organic soil (peat) burning involved. We find the range of our satellite-derived Indonesian ERs (6.18–13.6 ppb ppm−1) to be relatively closely matched to that of a series of close-to-source, ground-based sampling measurements made on Kalimantan at the height of the fire event (7.53–19.67 ppb ppm−1), although typically the satellite-derived quantities are slightly lower on average. This seems likely because our field sampling mostly intersected smaller-scale peat-burning plumes, whereas the large-scale plumes intersected by the GOSAT Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) footprints would very likely come from burning that was occurring in a mixture of fuels that included peat, tropical forest and already-cleared areas of forest characterised by more fire-prone vegetation types than the natural rainforest biome (e.g. post-fire areas of ferns and scrubland, along with agricultural vegetation).The ability to determine large-scale ERs from satellite data allows the combustion behaviour of very large regions of burning to be characterised and understood in a way not possible with ground-based studies, and which can be logistically difficult and very costly to consider using aircraft observations. We therefore believe the method demonstrated here provides a further important tool for characterising biomass burning emissions, and that the GHG ERs derived for the first time for these large-scale Indonesian fire plumes during an El Niño event point to more routinely assessing spatiotemporal variations in biomass burning ERs using future satellite missions. These will have more complete spatial sampling than GOSAT and will enable the contributions of these fires to the regional atmospheric chemistry and climate to be better understood.

Satellite observation of atmospheric methane: intercomparison between AIRS and GOSAT TANSO-FTS retrievals

Abstract. Space-borne observations of atmospheric methane (CH4) have been made using the Atmospheric Infrared Sounder (AIRS) on the EOS/Aqua satellite since August 2002 and the Thermal and Near-infrared Sensor for Carbon Observation Fourier Transform Spectrometer (TANSO-FTS) on the Greenhouse Gases Observing Satellite (GOSAT) since April 2009. This study compared the GOSAT TANSO-FTS thermal infrared (TIR) version 1.0 CH4 product with the collocated AIRS version 6 CH4 product using data from 1 August 2010 to 30 June 2012, including the CH4 mixing ratios and the total column amounts. The results show that at 300–600 hPa, where both AIRS and GOSAT-TIR CH4 have peak sensitivities, they agree very well, but GOSAT-TIR retrievals tend to be higher than AIRS in layer 200–300 hPa. At 300 hPa the CH4 mixing ratio from GOSAT-TIR is, on average, 10.3 ± 31.8 ppbv higher than that from AIRS, and at 600 hPa GOSAT-TIR retrieved CH4 is −16.2 ± 25.7 ppbv lower than AIRS CH4. Comparison of the total column amount of CH4 shows that GOSAT-TIR agrees with AIRS to within 1 % in the mid-latitude regions of the Southern Hemisphere and in the tropics. In the mid to high latitudes in the Northern Hemisphere, comparison shows that GOSAT-TIR is ∼ 1–2 % lower than AIRS, and in the high-latitude regions of the Southern Hemisphere the difference of GOSAT from AIRS varies from −3 % in October to +2 % in July. The difference between AIRS and GOSAT TANSO-FTS retrievals is mainly due to the difference in retrieval algorithms and instruments themselves, and the larger difference in the high-latitude regions is associated with the low information content and small degrees of freedom of the retrieval. The degrees of freedom of GOSAT-TIR retrievals are lower than that of AIRS, which also indicates that the constraint in GOSAT-TIR retrievals may be too strong. From the good correlation between AIRS and GOSAT-TIR retrievals and the seasonal variation they observed, we are confident that the thermal infrared measurements from AIRS and GOSAT-TIR can provide valuable information to capture the spatial and temporal variation of CH4, especially in the mid-upper troposphere, in most periods and regions.

Large XCH<sub>4</sub> anomaly in summer 2013 over northeast Asia observed by GOSAT

Abstract. Extremely high levels of column-averaged dry-air mole fractions of atmospheric methane (XCH4) were detected in August and September 2013 over northeast Asia (∼ 20 ppb above the averaged summertime XCH4 over 2009–2012, after removing a long-term trend), as being retrieved from the Short-Wavelength InfraRed (SWIR) spectral data observed with the Thermal And Near-infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) onboard Greenhouse Gases Observing Satellite (GOSAT). Similar enhancements of XCH4 were also observed by the ground-based measurements at two Total Carbon Column Observing Network (TCCON) sites in Japan. The analysis of surface CH4 concentrations observed at three monitoring sites around the Japan archipelago suggest that the extreme increase of XCH4 has occurred in a limited area. The model analysis was conducted to investigate this anomalously high XCH4 event, using an atmospheric transport model. The results indicate that the extreme increase of XCH4 is attributed to the anomalous atmospheric pressure pattern over East Asia during the summer of 2013, which effectively transported the CH4-rich air to Japan from the strong CH4 source areas in east China. The two Japanese TCCON sites, ∼ 1000 km east–west apart each other, coincidentally located along the substantially CH4-rich air flow from east China. This analysis demonstrates the capability of GOSAT to monitor an XCH4 event on a synoptic scale. We anticipate that the synoptic information of XCH4 from GOSAT data contributes to improve our understanding of regional carbon cycle and the regional flux estimation.

Radiometric Comparison of 1.6-μm CO2 Absorption Band of Greenhouse Gases Observing Satellite (GOSAT) TANSO-FTS with Suomi-NPP VIIRS SWIR Band

AbstractAn atmospheric CO2 increase has become a progressively important global concern in recent past decades. Since the 1950s, the Keeling curve has documented the atmospheric CO2 increase as well as seasonal variations, which also intrigued scientists to develop new methods for global CO2 measurements from satellites. One of the dedicated satellite missions is the CO2 measurement in the 1.6-μm shortwave infrared spectra by the Greenhouse Gases Observing Satellite (GOSAT) Thermal and Near Infrared Sensor for Carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument. While this spectral region has unique advantages in detecting lower-trophosphere CO2, there are many challenges because it relies on accurate measurements of reflected solar radiance from Earth’s surface. Therefore, the calibration of the TANSO-FTS CO2 has a direct impact on the CO2 retrievals and its long-term trends. Coincidently, the Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) 1.6-μm band spectrally overlaps with the TANSO-FTS CO2 band, and both satellites are in orbit with periodical simultaneous nadir overpass measurements. This study performs an intercomparison of VIIRS and the TANSO-FTS CO2 band in an effort to evaluate and improve the radiometric consistency. Understanding the differences provides feedback on how well the GOSAT TANSO-FTS is performing over time, which is critical to ensure a well-calibrated, stable, and bias-free CO2 product.

Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space

Abstract. A data set containing more than 6 years (February 2009 to present) of radiance spectra for carbon dioxide (CO2) and methane (CH4) observations has been acquired by the Greenhouse gases Observing SATellite (GOSAT, available at http://data.gosat.nies.go.jp/GosatUserInterfaceGateway/guig/GuigPage/open.do), nicknamed “Ibuki”, Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS). This paper provides updates on the performance of the satellite and TANSO-FTS sensor and describes important changes to the data product, which has recently been made available to users. With these changes the typical accuracy of retrieved column-averaged dry air mole fractions of CO2 and CH4 (XCO2 and XCH4, respectively) are 2 ppm or 0.5 % and 13 ppb or 0.7 %, respectively. Three major anomalies of the satellite system affecting TANSO-FTS are reported: a failure of one of the two solar paddles in May 2014, a switch to the secondary pointing system in January 2015, and most recently a cryocooler shutdown and restart in August 2015. The Level 1A (L1A) (raw interferogram) and the Level 1B (L1B) (radiance spectra) of version V201 described here have long-term uniform quality and provide consistent retrieval accuracy even after the satellite system anomalies. In addition, we discuss the unique observation abilities of GOSAT made possible by an agile pointing mechanism, which allows for optimization of global sampling patterns.

Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network

Understanding the atmospheric distribution of water (H 2 O) is crucial for global warming studies and climate change mitigation. In this context, reliable satellite data are extremely valuable for their global and continuous coverage, once their quality has been assessed. Short-wavelength infrared spectra are acquired by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) aboard the Greenhouse gases Observing Satellite (GOSAT). From these, column-averaged dry-air mole fractions of carbon dioxide, methane and water vapor (XH 2 O) have been retrieved at the National Institute for Environmental Studies (NIES, Japan) and are available as a Level 2 research product. We compare the NIES XH 2 O data, Version 02.21, with retrievals from the ground-based Total Carbon Column Observing Network (TCCON, Version GGG2014). The datasets are in good overall agreement, with GOSAT data showing a slight global low bias of −3.1% ± 24.0%, good consistency over different locations (station bias of −1.53% ± 10.35%) and reasonable correlation with TCCON (R = 0.89). We identified two potential sources of discrepancy between the NIES and TCCON retrievals over land. While the TCCON XH 2 O amounts can reach 6000–7000 ppm when the atmospheric water content is high, the correlated NIES values do not exceed 5500 ppm. This could be due to a dry bias of TANSO-FTS in situations of high humidity and aerosol content. We also determined that the GOSAT-TCCON differences directly depend on the altitude difference between the TANSO-FTS footprint and the TCCON site. Further analysis will account for these biases, but the NIES V02.21 XH 2 O product, after public release, can already be useful for water cycle studies.

Validation of TANSO-FTS/GOSAT XCO<sub>2</sub> and XCH<sub>4</sub> glint mode retrievals using TCCON data from near-ocean sites

Abstract. The thermal And near infrared sensor for carbon observations Fourier transform spectrometer (TANSO-FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) applies the normal nadir mode above the land (“land data”) and sun glint mode over the ocean (“ocean data”) to provide global distributions of column-averaged dry-air mole fractions of CO2 and CH4, or XCO2 and XCH4. Several algorithms have been developed to obtain highly accurate greenhouse gas concentrations from TANSO-FTS/GOSAT spectra. So far, all the retrieval algorithms have been validated with the measurements from ground-based Fourier transform spectrometers from the Total Carbon Column Observing Network (TCCON), but limited to the land data. In this paper, the ocean data of the SRPR, SRFP (the proxy and full-physics versions 2.3.5 of SRON/KIT's RemoTeC algorithm), NIES (National Institute for Environmental Studies operational algorithm version 02.21) and ACOS (NASA's Atmospheric CO2 Observations from Space version 3.5) are compared with FTIR measurements from five TCCON sites and nearby GOSAT land data.For XCO2, both land and ocean data of NIES, SRFP and ACOS show good agreement with TCCON measurements. Averaged over all TCCON sites, the relative biases of ocean data and land data are −0.33 and −0.13 % for NIES, 0.03 and 0.04 % for SRFP, 0.06 and −0.03 % for ACOS, respectively. The relative scatter ranges between 0.31 and 0.49 %. For XCH4, the relative bias of ocean data is even less than that of the land data for the NIES (0.02 vs. −0.35 %), SRFP (0.04 vs. 0.20 %) and SRPR (−0.02 vs. 0.06 %) algorithms. Compared to the results for XCO2, the XCH4 retrievals show larger relative scatter (0.65–0.81 %).