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An original method is suggested for solving the inverse problem of hyperspectral satellite sounding of the atmosphere for retrieving the HDO/H2O ratio vertical profile in the atmosphere with simultaneous inversion of thermal and near-IR spectra. A computational experiment was performed to retrieve the vertical profile of the relative content of the HDO isotopologue in atmospheric water vapor from high-resolution spectra simulated with parameters of the TANSO-FTS spectrometer onboard the GOSAT-2 satellite. The inverse problem was solved by the optimal estimation method with simultaneous inversion of thermal and near-IR spectra. The output data of the isotopic version of the ECHAM6-wiso atmospheric general circulation model were used as an a priori statistical set of water vapor isotopologue vertical profiles.

Japan is now operating a series of two earth observation satellites for greenhouse gas measurement from space: GOSAT launched in 2009 and GOSAT-2 launched in 2018. In this presentation, products and latest science achievements of GOSAT Satellite Series will be introduced.

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.

The relative 13С content in atmospheric carbon dioxide contains information about sources and sinks of this gas and their impact on Earth’s carbon cycle. This paper represents the study on feasibility of 13CO2 atmospheric vertical profile retrieval from satellite thermal infrared spectra measured with high spectral resolution. In IASI/METOP spectra, the spectral interval is determined, in which molecular absorption is mainly bound by the given isotopologue. A comparison was made of variations in the magnitude of spectral radiance due to variations in air temperature and in relative content of 13CO2 within the limits of known from studies on the direct measurement of the isotopic composition of atmospheric carbon dioxide. Prospects for building a robust algorithm is discussed.

Remote sensing data from GOSAT (Greenhouse gases Observing SATellite) and GOSAT-2 in the future ameliorate inversion analysis of greenhouse gas (GHG) emissions[1],[2]. Meso-scale atmospheric transport model AIST-MM (National Institute of Advanced Industrial Science and Technology-Mesoscale Model)[3],[4] and global-scale transport model NICAM-TM (Nonhydrostatic ICosahedral Atmospheric Model-Transport Model)[5] have been coupled for data assimilation in order to estimate CO 2 emission from mega-city Tokyo. However, forests west and north of Tokyo Metropolis in the Kantō plain generate significant biogenic CO 2 fluxes and such atmosphere-biosphere gas exchange remains to be properly calculated during the modeling processes. In this study, we use MODIS products[6],[7] to simulate regional gross primary production (GPP), vegetational and soil respirations based on Biosphere model integrating Eco-physiological And Mechanistic approaches using Satellite data (BEAMS) algorithms[8],[9]. By integrating this atmosphere-terrestrial ecosystem carbon balance module to our regional inversion analysis, we aim at more precise estimation of CO 2 emission from Tokyo.

Hyperspectral thermal infrared sounders enable us to grasp the global behavior of minor atmospheric constituents. Ammonia, which imparts large impacts on the atmospheric environment by reacting with other species, is one of them. In this work, we present an ammonia retrieval system that we developed for the Greenhouse Gases Observing Satellite (GOSAT) and the estimates of global atmospheric ammonia concentrations that we derived from 2009 to 2014. The horizontal distributions of the seasonal columnar ammonia concentrations represent significantly high concentrations stemming from six anthropogenic emission source areas and four biomass burning ones. The monthly mean time series of these sites were investigated, and their seasonality was clearly revealed. A comparison with the Infrared Atmospheric Sounding Interferometer (IASI) ammonia product showed a good agreement spatially and seasonally, though there are some differences in detail. The values from GOSAT tend to be slightly larger than those from the IASI for low concentrations, especially in spring and summer. On the other hand, they are lower for particularly high concentrations during summer, such as Eastern China and Northern India. In addition, the largest differences were observed in central Africa. These differences seem to stem from the temporal gaps in observations and the fundamental differences in the retrieval systems.

An algorithm based on CO2 slicing, which has been used for cirrus cloud detection using thermal infrared data, was developed for high-resolution radiance spectra from satellites. The channels were reconstructed based on sensitivity height information of the original spectral channels to reduce the effects of measurement errors. The selections of the reconstructed channel pairs were optimized for several atmospheric profile patterns using simultaneous studies assuming cloudy sky. That algorithm was applied to data by the Greenhouse gases Observing SATellite (GOSAT). The results were compared with those obtained from space-borne lidar instrument onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Monthly mean cloud amounts from the slicing generally agreed with those from CALIPSO observations despite some differences caused by surface temperature biases, optically very thin cirrus, multilayer structures of clouds, extremely low cloud tops, and specific atmospheric conditions. Comparison of coincident data showed good agreement except some cases and revealed that the improved slicing method is more accurate than the traditional slicing method. Results also imply that improved slicing can detect low-level clouds with cloud top heights as low as approximately 1.5 km.

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: 18-11-00024 Acknowledgments: 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.

Greenhouse gases Observing SATellite (GOSAT) and its successor, GOSAT-2, are Japanese earth observation satellites for greenhouse gas measurement from space using Fourier transform spectrometers. Instrument specifications, together with GOSAT achievements and GOSAT-2 expectations, will be introduced.

A series of ground-based high-resolution Fourier-transform measurements of atmospheric transmittance in the near infrared region (4000–10000 cm−1) recorded at the Ural Atmospheric Station in 2012–2013 was processed in order to retrieve relative concentrations of CO2 and CH4 in the atmospheric column. Retrieved values of methane concentration do not show a noticeable seasonal cycle, while retrieved CO2 concentrations show clear seasonal variations with high amplitude, which are also observed in GOSAT measurements over the Ural region. The estimated amplitude of CO2 seasonal variations is 14–15 ppm. The comparison between CO2 ground-based and GOSAT measurements shows a good agreement, while satellite values are overestimated by approximately 3 ppm. There is no noticeable correlation between CH4 values, which could be explained by the presence of local methane sources in the area of GOSAT observations.

The space and time variabilities of methane (CH4) total column and upper tropospheric mixing ratios are analysed above the Mediterranean Basin (MB) as part of the Chemical and Aerosol Mediterranean Experiment (ChArMEx) programme. Since the analysis of the mid-to-upper tropospheric CH4 distribution from spaceborne sensors and model outputs is challenging, we have adopted a climatological approach and have used a wide variety of data sets. We have combined spaceborne measurements from the Thermal And Near infrared Sensor for carbon Observations – Fourier Transform Spectrometer (TANSO-FTS) instrument on the Greenhouse gases Observing SATellite (GOSAT) satellite, the Atmospheric InfraRed Spectrometer (AIRS) on the AURA platform and the Infrared Atmospheric Sounder Interferometer (IASI) instrument aboard the MetOp-A platform with model results from the Chemical Transport Model (CTM) MOCAGE, and the Chemical Climate Models (CCMs) CNRM-AOCCM and LMDz-OR-INCA (according to different emission scenarios). In order to minimize systematic errors in the spaceborne measurements, we have only considered maritime pixels over the MB. The period of interest spans from 2008 to 2011 considering satellite and MOCAGE data and, regarding the CCMs, from 2001 to 2010. Although CH4 is a long-lived tracer with lifetime of ~12 years and is supposed to be well mixed in the troposphere, an east–west gradient in CH4 is observed and modelled in the mid-to-upper troposphere with a maximum in the Western MB in all seasons except in summer when CH4 accumulates above the Eastern MB. The peak-to-peak amplitude of the east–west seasonal variation in CH4 above the MB in the upper troposphere (300 hPa) is weak but almost twice as great in the satellite measurements (~25 ppbv) as in the model data (~15 ppbv). The maximum of CH4 in summer above the eastern MB can be explained by a series of dynamical processes only occurring in summer. The Asian monsoon traps and uplifts high amounts of CH4 to the upper troposphere where they build up. The Asian Monsoon Anticyclone redistributes these elevated CH4 amounts towards North Africa and the Middle East to finally reach and descend in the eastern MB. In the lower troposphere, the CH4 variability is mainly driven by the local sources of emission in the vicinity of the MB.

In this paper we used VLIDORT procedures as a forward model in FIRE-ARMS code for modeling the spectra and Jacobian calculation in both thermal infrared (TIR) and short wavelength infrared spectral bands (SWIR). Simulated spectra are compared with spectra measured under clear sky conditions over Western Siberia by the Fourier spectrometer TANSO-FTS, on board of the GOSAT satellite. With the new software, operating simultaneously in both bands, Jacobians were calculated in order to retrieve vertical profiles of carbon dioxide and methane in TIR and SWIR. Data from NCEP/NCAR reanalysis were used as initial guess model atmosphere.

We present further development of the empirical orthogonal functions (EOF)-based retrieval algorithm. The algorithm output is a regression formula that relates principal components of the reflected sunlight spectra with CO2 total column amount. The algorithm was implemented and tested for the observations from the Japanese satellite Greenhouse gases Observing Satellite (GOSAT). Training of the EOF-based algorithm with the collocated ground-based and space-borne data (e.g., Total Carbon Column Observing Network and GOSAT observations, respectively) was shown to impose some errors that were interpreted as a result of implicit averaging over the collocation area. Alternative training with the small subset (∼5 % to 10%) of the full-physics algorithm is free of such errors; however, this option requires additional filtering of the space-borne observations that are strongly affected by atmospheric light scattering. This filtering was implemented by the comparison of the EOF-regression estimates of surface pressure with corresponding meteorological data.

Remote sensing of the atmospheric greenhouse gases, methane (CH4) and carbon dioxide (CO2), contributes to the understanding of global warming and climate change. A portable ground-based instrument consisting of a commercially available desktop optical spectrum analyzer and a small sun tracker has been applied to measure the column densities of atmospheric CH4 and CO2 at Yanting observation station in a mountainous paddy field of the Sichuan Basin from September to November 2013. The column-averaged dry-air molar mixing ratios, XCH4/XCO2, are compared with those retrieved by satellite observations in the Sichuan Basin and by ground-based network observations in the same latitude zone as the Yanting observation station.

The space and time variabilities of methane (CH4) total column and upper tropospheric mixing ratios are analyzed above the Mediterranean Basin (MB) as part of the Chemical and Aerosol Mediterranean Experiment (ChArMEx) programme. Spaceborne measurements from the Thermal And Near infrared Sensor for carbon Observations-Fourier Transform Spectrometer (TANSO-FTS) instrument on the Greenhouse gases Observing SATellite (GOSAT) satellite, the Atmospheric InfraRed Spectrometer (AIRS) on the AURA platform and the Infrared Atmospheric Sounder Interferometer (IASI) instrument aboard the MetOp-A platform are used in conjunction with model results from the Chemical Transport Model (CTM) MOCAGE, and the Chemical Climate Models (CCMs) CNRM-AOCCM and LMDz-OR-INCA (according to different emission scenarios). In order to minimize systematic errors in the spaceborne measurements, we have only considered maritime pixels over the MB. The period under interest spans from 2008 to 2011 considering satellite and MOCAGE data and, regarding the CCMs, from 2001 to 2010. An East-West gradient in CH4 is observed and modelled whatever the season considered. In winter, air masses mainly originating from Atlantic Ocean and Europe tend to favour an elevated amount of mid-to-upper tropospheric CH4 in the West vs. the East of the MB, with a general upward transport above the MB. In summer, the meteorological state of the MB is changed, favouring air from Northern Africa and Middle East together with Atlantic Ocean and Europe, with a general downward motion above the MB. The Asian Monsoon traps and uplifts high amounts of CH4 that are transported towards North Africa and Middle East by the Asian Monsoon Anticyclone to finally reach and descent in the East of the MB. Consequently, the mid-to-upper tropospheric CH4 is much greater in the East than in the West of the MB. The seasonal variation of the difference in CH4 between the East and the West MB does show a maximum in summer for pressures from 500 to 100 hPa considering both spaceborne measurements and model results whatever the emission scenarios used. From this study, we can conclude that CH4 in the mid-to-upper troposphere over the MB is mainly affected by long-range transport, particularly intense in summer from Asia. In the low-to-mid troposphere, the local sources of emission in the vicinity of the MB mainly affect the CH4 variability.

Interferometric Monitor for Greenhouse gases (IMG) is a Fourier Transform Spectrometer (FTS) which has been developed for measuring greenhouse gases in the atmosphere, particularly in the troposphere. It was operated for about 7 months from November 1996 to the end of June 1997. During the operational period IMG measured over 23000 terrestrial thermal emission spectra of which the signal to nose ratio was sufficient for retrieving atmospheric parameters such as temperature and gas concentrations. The most serious problem in measuring tropospheric gases using an infrared spectrometer is the cloud contamination. We used a cloud detection and correction (screening) method which was based on the analysis of the initially retrieved temperature profiles for retrieving temperature and gas profiles. Preliminary results on global distribution of methane and carbon monoxide were obtained using the cloud detection method. However the results were seemed to be contaminated with clouds even after the cloud correction (screening) process particularly in the polar regions. We need more effective cloud detection method using visible and near-infrared imagery that was simultaneously obtained with IMG data such as those from OCTS (Ocean Color and Temperature Scanner) for raising the accuracy of the retrieval.

[1] The Greenhouse Gases Observing Satellite (GOSAT) was successfully launched in January 2009, with the aim of providing global observations of greenhouse gases. We developed an algorithm to retrieve CO2 vertical profiles from the terrestrial radiation spectra at 700–800 cm−1 and assessed its validity. For this purpose, we first computed GOSAT pseudomeasurement spectra and then performed CO2 retrieval simulations using the maximum a posteriori (MAP) method, with analytical data for temperature information. Our simulations with no uncertainty in the estimates of atmospheric conditions such as surface temperature, surface emissivity, and profiles of temperature, water vapor, and ozone showed that the retrieved CO2 profiles had an accuracy of 1% above 800 hPa, with little dependence on the a priori profiles. Introducing correlations between layers in an a priori error covariance matrix was important for CO2 retrieval especially above 200 hPa. Enhancing the correlations below 800 hPa was important for CO2 retrieval there. Selecting 100 channels based on CO2 information content for all layers, 10 channels for the region above 55 hPa, and 50 channels for the region below 800 hPa was sufficient to achieve CO2 retrieval with 1% accuracy from the troposphere through the stratosphere. Our simulations with possible errors in the atmospheric conditions showed that 1% accuracy was also achieved at 600–100 hPa in every latitude region, although the retrieved CO2 concentrations probably included up to 4% positive and negative biases at 30°S–30°N above 100 hPa and at mid- and high latitudes below 600 hPa, respectively.

The ability to reliably estimate CO2 fluxes from current in situ atmospheric CO2 measurements and future satellite CO2 measurements is dependent on transport model performance at synoptic and shorter timescales. The TransCom continuous experiment was designed to evaluate the performance of forward transport model simulations at hourly, daily, and synoptic timescales, and we focus on the latter two in this paper. Twenty-five transport models or model variants submitted hourly time series of nine predetermined tracers (seven for CO2) at 280 locations. We extracted synoptic-scale variability from daily averaged CO2 time series using a digital filter and analyzed the results by comparing them to atmospheric measurements at 35 locations. The correlations between modeled and observed synoptic CO2 variabilities were almost always largest with zero time lag and statistically significant for most models and most locations. Generally, the model results using diurnally varying land fluxes were closer to the observations compared to those obtained using monthly mean or daily average fluxes, and winter was often better simulated than summer. Model results at higher spatial resolution compared better with observations, mostly because these models were able to sample closer to the measurement site location. The amplitude and correlation of model-data variability is strongly model and season dependent. Overall similarity in modeled synoptic CO2 variability suggests that the first-order transport mechanisms are fairly well parameterized in the models, and no clear distinction was found between the meteorological analyses in capturing the synoptic-scale dynamics.

The Greenhouse gases Observing Satellite (GOSAT) is a Japanese satellite that is intended to observe CO 2 concentrations from space and to contribute to advancement of research related to CO 2 source/sink estimation. The GOSAT main sensor is a Fourier Transform Spectrometer (FTS) named "TANSO-FTS", which covers a wide terrestrial radiation spectrum including CO 2 absorption bands at 1.6 μm (Short Wavelength InfraRed, SWIR), and 15 μm (Thermal InfraRed, TIR). The former band is used to estimate columnar concentration of CO 2 ; the latter is used to retrieve the vertical profile of CO 2 in the upper atmosphere above the ca. 700 hPa pressure level. We adopt the maximum a posteriori method (MAP) to retrieve the vertical profile of CO 2 concentrations using the meteorological analysis data for temperature profiles. Key techniques for retrieving CO 2 concentrations are 1) reduction of temperature estimation error through channel selection, 2) optimization of the a priori CO 2 profile based on the output from a CO 2 transport model, and 3) usage of SWIR data as an additional constraint in retrieval of vertical profiles of CO 2 . Simulation studies using the output from a CO 2 transport model show that, although thermal infrared spectrum has poor sensitivity to the CO 2 concentration change in the lower atmosphere, particularly in the boundary layer, we expect that CO 2 concentration profiles in the lower atmosphere can be reproduced statistically by combining CO 2 columnar data derived from SWIR as an additional constraint in retrieving a CO 2 concentration profile from TIR data.

The Greenhouse gases Observing Satellite (GOSAT) is a Japanese satellite that is intended to observe CO2 concentration from space and to contribute to advancement of research of the source/sink estimation of CO2. The GOSAT main sensor is a Fourier Transform Spectrometer (FTS) named "TANSO-FTS", which covers a wide terrestrial radiation spectrum including CO2 absorption bands at 1.6 μm, 2.0 μm, and 15 μm. The former two bands are used to estimate columnar concentration of CO2; the latter is used to retrieve the vertical profile of CO2 in the upper atmosphere above about 700 hPa pressure level. In addition, another installed on the satellite is an imaging sensor that will be used to detect clouds and aerosols: Cloud and Aerosol Imager (CAT). The Center for Climate System Research (CCSR) has contracted with the Japan Aerospace Exploration Agency (JAXA) to develop an algorithm to retrieve CO2 concentration profiles from data measured by the thermal infrared (TIR) band of the TANSO-FTS sensor. We adopt the maximum a posteriori method (MAP) to retrieve the vertical profile of atmospheric parameters from thermal infrared spectra. Key techniques for retrieving CO2 concentrations are 1) reduction of temperature estimation error through channel selection, 2) optimization of the initial guess for CO2 profile based on the output from a chemical transport model (CTM), and 3) usage of data from the 1.6 μm band of TANSO-FTS as an additional constraint in retrieval of vertical profiles of CO2. Although thermal infrared spectrum data have poor vertical resolving power for CO2 concentration in the lower atmosphere, particularly in the boundary layer, we expect that CO2 amount in the lower atmosphere can be deduced by substituting the upper level concentration from the columnar concentration estimated from the 1 .6 μm band data.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The Greenhouse Gases Observing Satellite (GOSAT) will be launched in 2008 for global observations of greenhouse gases such as CO 2 and methane. This study examines the feasibility of retrieving CO 2 concentrations from the infrared spectra of the GOSAT/Thermal and near infrared Sensor for Carbon Observation (TANSO)-FTS. Retrieval simulations in which the maximum a posteriori (MAP) method was applied to pseudo-spectra at 700-800 cm -1 from TANSO-FTS (CO 2 15-μm band) showed that retrieved CO 2 profiles agreed with true CO 2 profiles to within the total errors throughout the troposphere above 700-800 hPa when atmospheric conditions such as temperature used in the computation of the spectra were known. In contrast, discrepancies between retrieved CO 2 and true CO 2 concentrations increased if temperatures used in the retrieval included random errors; a random scatter of ±0.5 K caused a discrepancy that was 12 times larger at ∼750 hPa. However, appropriate channel selection based on CO 2 and temperature information could reduce the effect of temperature uncertainty on CO 2 retrievals in this spectral region: the discrepancy between retrieved and true concentrations at ∼750 hPa in the case with channel selection was about one-third of the discrepancy without any channel selection.

This study presents an analysis of atmospheric temperature and water vapor using interferometric monitor for greenhouse gases (IMG) spectrum data and its retrieval procedure. The IMG is a high-resolution infrared sensor of the Fourier transform spectrometer (FTS) type that was launched aboard the Advanced Earth Observing Satellite (ADEOS) satellite in August 1996. Upwelling infrared radiation from the Earth was measured to examine the effects of greenhouse gases in the troposphere until June 1997. In the procedure to retrieve trace gas profiles from such satellite-based FTS data, accurate information on temperature, water vapor and surface properties is essential for precise retrieval. The instrument line shape (ILS) function, which generally depends on many factors of its sensor system, must also be determined accurately. In order to estimate the optimal ILS function, the "effective optical path difference (OPD)", which is assumed in retrieval analyses, is tuned to obtain the most optimal retrieved results in comparison with the sonde data. This method was applied to IMG spectrum data.

Reducing temperature and water vapor estimation errors is indispensable for retrieving a CO 2 concentration profile or columnar amount from thermal infrared spectrum data because spectral radiance in the thermal infrared region is much more sensitive to temperature and water vapor concentration changes than it is to CO 2 concentration changes. This study presents a data analysis procedure to estimate the CO 2 columnar amount from the thermal infrared spectrum. The fi rst step retrieves the temperature vertical profile, water vapor vertical profile, and the surface temperature from spectra in the strong absorption bands of CO 2 and H 2 O. Then the spectral biases that are attributable to temperature and water vapor retrieval errors are reduced by comparing observed and synthesized radiances in the atmospheric window region. The final step estimates the CO 2 columnar amount from the corrected sp ectra of a weak absorption band of CO 2 that is located around 940 cm -1 .This method was applied to analysis of spectrum data from IMG sensor aboard the ADEOS satellite. Some preliminary results are shown. Keywords : Carbon dioxide, CO

[1] Latitudinal distribution of the columnar deuterium/hydrogen ratio of atmospheric water vapor, δDvap, was retrieved from high-resolution infrared spectra. Interferometric Monitor for Greenhouse gases (IMG) sensor aboard the ADvanced Earth Observing Satellite (ADEOS) observed the spectra over the ocean during the operational period from December 1996 through June 1997. The latitudinal mean δDvap was relatively large with values around −100‰ in the tropical region decreasing down to minimal values of −800‰ at high latitudes. For retrieving δDvap, a type of spectral fitting method was adopted using a forward/retrieval calculation code developed by Gribanov et al. [2001]. This method is expected to be applicable for data analyses of future satellite sensors such as IASI and TES.

EarthCARE(Earth Clouds, Aerosols and Radiation Explorer) project is a candidate of the ESA (European Space Agency) Earth Explorer Core Missions. EarthCARE is the joint proposal between ESA, National Space Development Agency of JAPAN (NASDA) and Communications Research Laboratory (CRL). THe Phase-A study is started in NOvember 2001. The EarthCARE satellite has five sensors, CLoud Profiling Radar (CPR), ATmospheric LIDar (ATLID), Multi-Spectral Imager (MSI), Broad Band Radiometer (BBR) and Fourier Transform Spectromter (FTS). Main objective of EarthCARE FTS is to provide spectrally resolved outgoing radiance. Another objecitve of EarthCARE FTS is to retrieve temperature and water vapor profiles in clear air and above the clouds. NASDA is carrying out the Phase A study of EarthCARE. Preliminary Concept Review (PCR) was held at March 2003. We describe the objectives and instrument design concept of EarthCARE FTS in this paper.

EarthCARE(Earth Clouds, Aerosol and Radiation Explorer) project is a candidate ofthe ESA(European Space Agency)Earth Explorer Core Missions. There are many uncertainties mainly caused by aerosols, clouds and their interactionwith radiation in predictions of climate change using numerical models. EarthCARE will provide vertical and horizontaldistributions and physical characteristics of clouds and aerosols, and also provide the Earth radiation budget. Earth-CARE is the joint proposal between ESA, National Space Development Agency of JAPAN (NASDA) and Communica-tions Research Laboratory (CRL). The Phase-A study is going on. The EarthCARE satellite has five sensors, CloudProfiling Radar (CPR), ATmospheric LIDar (ATLID), Multi-Spectral Imager (MSI), Broad Band Radiometer (BBR)and Fourier Transform Spectrometer (FTS).NASDA is studying FTS design. Main objective of EarthCARE FTS is to provide spectrally resolved outgoing radi-ance. This spectrum has many useful signatures from the surface/cloud/aerosol/water which can not get from spectrallyintegrated measurement. Another objective of EarthCARE FTS is to retrieve temperature and water vapor profiles inclear air and above the clouds. EarthCARE FTS is a compact Michelson interferometer, which covers from 5.6psn to25 tm with 0.5 cm' spectral resolution. The FOV(Field OfView) is 10km so that the data can be used in conjunctionwith BBR.Keywords: EarthCARE, FTS, fourier transform spectrometer, Infrared

IPCC third report says that we have still a lot of uncertainties to predict global warming even using latest GCMs. Regarding atmospheric radiation, uncertainty of the radiative forcing is still large, which is mainly caused by aerosols, clouds, and water vapor interacting among them. National Space Development Agency of JAPAN (NASDA) and Communications Research Laboratory (CRL) started Phase-A study with European Space Agency (ESA) in the EarthCARE project. The objectives of EarthCARE project are to observe vertical and horizontal distributions and physical characteristics of aerosols and clouds from a satellite, and also to measure the precise Earth radiation budget simultaneously. Finally we will be able to evaluate physical processes of clouds and aerosols regarding the radiative budget and forcing. The EarthCARE satellite carries 5 sensors, namely Cloud Profiling RADAR (CPR), Atmospheric LIDAR (ATLID), Multi-Spectral Imager (MSI), Broad Band Radiometer (BBR) and Fourier Transform Spectrometer (FTS). The result of the pre-Phase A study shows the synergy observation benefits using some compensative combinations of sensors, such as CPR/ATLID for clouds, ATLID/MSI for aerosols, BBR/FTS for the radiation budget. NASDA and CRL are studying FTS and CPR, respectively. CPR is a 94GHz RADAR using 2.5m diameter reflector with Doppler measurement mode. The sensitivity is -38dBZ. The vertical and horizontal resolution is 100 m, 1 km, respectively. FTS is a Michelson interferometer of which spectral measurement range is from 5.7 μm to 25 μm with 0.5 cm-1 unapodized spectral resolution. FOV is 10 km by 10 km. EarthCARE is planned to be launched in 2008 for 2 years mission. Phase-A study will continue until the end of 2003.

Interferometric Monitor for Greenhouse gases aboard advanced earth observing satellite (ADEOS) is a Fourier transform type spectrometer which had been developed for measuring greenhouse gases in the atmosphere, particularly in the troposphere. It had been operated for about 7 months from November 1996 up to the end of the life time of the ADEOS on June 1997. During the operational period IMG had measured over 138000 terrestrial thermal emission spectra of which the signal to noise ratio is sufficient for retrieving atmospheric parameters such as temperature and gas concentrations. As most of the data had been obtained during the 4-days operational period which had been scheduled twice in each system request period, we have obtained about 15 global data sets of the IMG data during the whole IMG operational period. A cloud detection and correction method which was based on the analysis of the initially retrieved temperature profiles were presented. Using the cloud correction method, latitudinal distribution of methane was preliminary analyzed and an example of the result was shown.