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The Greenhouse gases Observing SATellite-2 (GOSAT-2, launched on October 29, 2018) has shown immense potential in CO2 and CH4 research, as a successor of GOSAT; however, the retrieval of tropospheric ozone is yet to be analyzed. Meanwhile, metropolitan areas are regarded as tropospheric ozone source for emission of ozone precursors (NOx and VOC) by anthropogenic activities. The extreme surface temperature due to urban heat island effect can potentially modify the averaging kernel (AK) of satellite retrieval; this effect, "thermal contrast", is expected to favor the analysis of ozone in urban areas. In the paper, we aim at providing the first results of 3-year tropospheric ozone retrieval using thermal infrared (TIR) band from the nadir-looking instrument Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer-2 (TANSO-FTS-2), which is onboard GOSAT-2. The retrieval is focused on the urban area of Tokyo, Japan by the targeting mode of FTS-2 with around 40×40 km2 domain. In the retrieval process, we basically apply the optimal estimation and the Line-By-Line Radiative Transfer Model (LBLRTM) as the forward model, with the spectroscopic line parameters from HITRAN 2016 database. The climatological mean of the tropospheric ozone in Tokyo is applied as the a priori profile. For validation, we use ozonesonde data in Tsukuba, Japan (36.06° N, 140.13° E) with averaging kernel smoothing method. The results are also compared with ground measurements obtained from Atmospheric Environmental Regional Observation System (AEROS) of Japan. The performance of retrieval is evaluated by the degree of freedom of signal (DOFS), root-mean square error (RMSE) and mean bias (BIAS), in comparison with previous satellite missions such as TES and IASI. Our results present day-to-day and seasonal variation of tropospheric ozone in metropolitan area, while elucidating the urban heat island effect on the sensitivity of satellite retrieval and the characteristics of retrieved ozone episodes in view of meteorological conditions. Our results facilitate the assessment of urban tropospheric ozone trend and lead to better understanding of ozone chemistry using new satellite observations. Global validation and analysis of GOSAT-2 retrieval will be presented in a subsequent paper.

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.

A novel, practical observation system for measuring tropospheric carbon dioxide (CO2) concentrations using a non-dispersive infrared analyzer carried by a small helium-filled balloon (CO2 sonde) has been developed for the first time. Vertical profiles of atmospheric CO2 can be measured with a 240–400 m altitude resolution through regular onboard calibrations using two different CO2 standard gases. The standard deviations (1σ) of the measured mole fractions in the laboratory experiments using a vacuum chamber at a temperature of 298 K were approximately 0.6 ppm at 1010 hPa and 1.2 ppm at 250 hPa. Two CO2 vertical profile data obtained using the CO2 sondes, which were launched on 31 January and 3 February 2011 at Moriya, were compared with the chartered aircraft data on the same days and the commercial aircraft data obtained by the Comprehensive Observation Network for TRace gases by Airliner (COTRAIL) program on the same day (31 January) and 1 d before (2 February). The difference between the CO2 sonde data and these four sets of in situ aircraft data (over the range of each balloon altitude ±100 m) up to the altitude of 7 km was 0.6±1.2 ppm (average ±1σ). In field experiments, the CO2 sonde detected an increase in CO2 concentration in an urban area and a decrease in a forested area near the surface. The CO2 sonde was shown to be a useful instrument for observing and monitoring the vertical profiles of CO2 concentration in the troposphere.

We investigate surface-layer characteristics over a mountainous ridge in the Central Himalayas, utilizing tower-based fast-response micrometeorological observations (at 12 and 27 m above ground level) for the winter months November 2013–January 2014. During this period, the site generally experienced clear skies and weak synoptic flow (wind speed

We present a method and algorithm for inverse problem solution in satellite sounding of the atmosphere for retrieval of vertical profiles of optically active gases by joint use of high-resolution thermal and near-infrared atmospheric spectra. To show the capabilities of the method, a computational experiment was performed on retrieving the vertical profile of the main methane isotopologue with simultaneous use of the simulated thermal and near-infrared spectra. The spectra simulated are similar to those measured by TANSO-FTS/GOSAT IR spectrometer. The signal-to-noise ratio in the simulated spectra was set to 350. The model experiments show higher accuracy of retrieval of the methane vertical profile and its total column in the case when both spectral bands (thermal and near-IR) are used in comparison with the case when each band is used separately.

COVID-19 induced restrictions resulted in a lowered fine aerosol particle and trace gas concentration across several urban places around the world, providing a natural opportunity to study effects of anthropogenic activities on emissions of air pollutants. Impact of sudden suspension of human activities on air pollution was analyzed by studying the change in satellite retrieved NO2 concentrations and top-down NOx emission over the urban and rural areas around Delhi. NO2 was chosen for being the most indicative of emission intensity due to its short lifetime of the order of a few hours in the planetary boundary layer. We present a robust temporal comparison of Ozone Monitoring Instrument (OMI) retrieved NO2 column density during the lockdown with the counterfactual baseline concentrations, extrapolated from the long-term trend and seasonal cycle components of NO2 using observations during 2015 to 2019. NO2 columns in the urban area of Delhi experienced an anomalous relative change ranging from 60.0% decline during the Phase 1 of lockdown (March 25-April 13, 2020) to 3.4% during the post-lockdown Phase 5. In contrast, we find no substantial reduction in NO2 concentrations over the rural areas. To clarify the quantitative impact of the lockdown measures, weekly top-down NOx emissions were estimated from TROPOspheric Monitoring Instrument (TROPOMI) retrieved NO2 by accounting for horizontal advection derived from the steady state continuity equation. NOx emissions from urban areas and power plants exhibited a mean decline of 72.19% and 53.4% respectively in Phase 1 compared to the pre-lockdown business-as-usual phase. Due to absence of confounding emission source activity during lockdown, emission estimates over urban areas and power-plants were validated with respective electricity generation reports and Google’s mobility reports. A higher anomaly in emission estimates suggests that comparison of only concentration change, without accounting for the dynamical and photochemical conditions, may mislead evaluation of lockdown impact. Our results are also useful for optimizing bottom-up emission inventories.

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.

FIRE-ARMS software was supplemented with the vector radiative transfer model VLIDORT. The new version of the software allows simulating outgoing thermal infrared radiation (TIR) from the Earth and solar shortwave infrared radiation (SWIR) reflected from the surface taking into account multiple scattering for the same atmospheric model and sensing geometry. We performed simulations of the spectra of outgoing TIR and SWIR radiation with multiple scattering in a cloudless atmosphere and compared them with the spectra measured by GOSAT satellite spectrometers in the cloudless atmosphere over Western Siberia. Analysis of calculated weighting functions shows that simultaneous use of the TIR and SWIR spectral regions will improve the height resolution in vertical profiling of methane concentrations in the atmosphere.

A four-dimensional variational method (4D-Var) is a popular technique for source/sink inversions of atmospheric constituents, but it is not without problems. Using an icosahedral grid transport model and the 4D-Var method, a new atmospheric greenhouse gas (GHG) inversion system has been developed. The system combines offline forward and adjoint models with a quasi-Newton optimization scheme. The new approach is then used to conduct identical twin experiments to investigate optimal system settings for an atmospheric CO2 inversion problem, and to demonstrate the validity of the new inversion system. In this paper, the inversion problem is simplified by assuming the prior flux errors to be reasonably well known and by designing the prior error correlations with a simple function as a first step. It is found that a system of forward and adjoint models with smaller model errors but with nonlinearity has comparable optimization performance to that of another system that conserves linearity with an exact adjoint relationship. Furthermore, the effectiveness of the prior error correlations is demonstrated, as the global error is reduced by about 15 % by adding prior error correlations that are simply designed when 65 weekly flask sampling observations at ground-based stations are used. With the optimal setting, the new inversion system successfully reproduces the spatiotemporal variations of the surface fluxes, from regional (such as biomass burning) to global scales. The optimization algorithm introduced in the new system does not require decomposition of a matrix that establishes the correlation among the prior flux errors. This enables us to design the prior error covariance matrix more freely.

A four-dimensional variational (4D-Var) method is a popular algorithm for inverting atmospheric greenhouse gas (GHG) measurements. In order to meet the computationally intense 4D-Var iterative calculation, offline forward and adjoint transport models are developed based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). By introducing flexibility into the temporal resolution of the input meteorological data, the forward model developed in this study is not only computationally efficient, it is also found to nearly match the transport performance of the online model. In a transport simulation of atmospheric carbon dioxide (CO2), the data-thinning error (error resulting from reduction in the time resolution of the meteorological data used to drive the offline transport model) is minimized by employing high temporal resolution data of the vertical diffusion coefficient; with a low 6-hourly temporal resolution, significant concentration biases near the surface are introduced. The new adjoint model can be run in discrete or continuous adjoint mode for the advection process. The discrete adjoint is characterized by perfect adjoint relationship with the forward model that switches off the flux limiter, while the continuous adjoint is characterized by an imperfect but reasonable adjoint relationship with its corresponding forward model. In the latter case, both the forward and adjoint models use the flux limiter to ensure the monotonicity of tracer concentrations and sensitivities. Trajectory analysis for high CO2 concentration events are performed to test adjoint sensitivities. We also demonstrate the potential usefulness of our adjoint model for diagnosing tracer transport. Both the offline forward and adjoint models have computational efficiency about 10 times higher than the online model. A description of our new 4D-Var system that includes an optimization method, along with its application in an atmospheric CO2 inversion and the effects of using either the discrete or continuous adjoint method, is presented in an accompanying paper Niwa et al.(2016).

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.

Dust aerosols, which have diverse and strong influences on the environment, must be monitored. Satellite data are effective for monitoring atmospheric conditions globally. In this work, the modified CO2 slicing method, a cloud detection technique using thermal infrared data from space, was applied to GOSAT data to detect the dust aerosol layer height. The results were compared using lidar measurements. Comparison of horizontal distributions found for northern Africa during summer revealed that both the relative frequencies of the low level aerosol layer from the slicing method and the dust frequencies of CALIPSO are high in northern coastal areas. Comparisons of detected layer top heights using collocated data with CALIPSO and ground-based lidar consistently showed high detection frequencies of the lower level aerosol layer, although the slicing method sometimes produces overestimates. This tendency is significant over land. The main causes of this tendency might be uncertainty of the surface skin temperature and a temperature inversion layer in the atmosphere. The results revealed that obtaining the detailed behavior of dust aerosols using the modified slicing method alone is difficult.

The present study shows spatio-temporal variability in carbon dioxide $$(\hbox {CO}_{2})$$ in the mid-tropospheric region over India (0–32 $$^{\mathrm{o}}\hbox {N}$$ , 60–100 $$^{\mathrm{o}}\hbox {E}$$ ) during 2003–2011. The $$\hbox {CO}_{2}$$ data used in the study is retrieved from Atmospheric Infra-Red Sounder (AIRS). Analysis of 9 yrs of data shows that the $$\hbox {CO}_{{2}}$$ exhibits a linear increasing trend of 2.01 ppm/year. Besides displaying the linear increasing trend, data show strong seasonal and annual variability. Concentration of $$\hbox {CO}_{{2}}$$ is observed to be highest around April–May (summer months), which decreases by 4–5 ppm during the monsoon months. Seasonal decrease in $$\hbox {CO}_{2}$$ concentration appeared to be influenced by the monsoonal activity. Low OLR (proxy of convection) associated with high rainfall during summer monsoon via increasing vegetation index (NDVI) appears to be the primary cause for the seasonal decrease in $$\hbox {CO}_{{2}}$$ through photosynthesis. Correlation coefficient between $$\hbox {CO}_{{2}}$$ and NDVI is of the order of –0.90 suggesting vegetation as a seasonal sink of $$\hbox {CO}_{{2}}$$ . Decrease in $$\hbox {CO}_{{2}}$$ concentration takes place at a delay of 2–3 months of rainfall. However, convection seems to be another component, which causes uplifting of $$\hbox {CO}_{{2}}$$ during dry summer (April and May) making high concentration in the mid-troposphere as shown by increase in the planetary boundary layer (PBL) height in this period. Eastward propagating intra-seasonal oscillations with period 30–40 days in OLR anomalies are found to modulate (with a fluctuation of 1–2 ppm) mid-tropospheric $$\hbox {CO}_{{2}}$$ . Analysis of seasonal anomalies in $$\hbox {CO}_{{2}}$$ over four different regions (northern, southern, western and eastern) of India is also being investigated. The regional variability of $$\hbox {CO}_{2}$$ in northern region show marginal larger values suggesting more anthropogenic activities especially during late winter.

COVID-19 related restrictions lowered particulate matter and trace gas concentrations across cities around the world, providing a natural opportunity to study effects of anthropogenic activities on emissions of air pollutants. In this paper, the impact of sudden suspension of human activities on air pollution was analyzed by studying the change in satellite retrieved NO2 concentrations and top-down NOx emission over the urban and rural areas around Delhi. NO2 was chosen for being the most indicative of emission intensity due to its short lifetime of the order of a few hours in the planetary boundary layer. We present a robust temporal comparison of Ozone Monitoring Instrument (OMI) retrieved NO2 column density during the lockdown with the counterfactual baseline concentrations, extrapolated from the long-term trend and seasonal cycle components of NO2 using observations during 2015 to 2019. NO2 concentration in the urban area of Delhi experienced an anomalous relative change ranging from 60.0% decline during the Phase 1 of lockdown (March 25–April 13, 2020) to 3.4% during the post-lockdown Phase 5. In contrast, we find no substantial reduction in NO2 concentrations over the rural areas. To segregate the impact of the lockdown from the meteorology, weekly top-down NOx emissions were estimated from high-resolution TROPOspheric Monitoring Instrument (TROPOMI) retrieved NO2 by accounting for horizontal advection derived from the steady state continuity equation. NOx emissions from urban Delhi and power plants exhibited a mean decline of 72.2% and 53.4% respectively in Phase 1 compared to the pre-lockdown business-as-usual phase. Emission estimates over urban areas and power-plants showed a good correlation with activity reports, suggesting the applicability of this approach for studying emission changes. A higher anomaly in emission estimates suggests that comparison of only concentration change, without accounting for the dynamical and photochemical conditions, may mislead evaluation of lockdown impact. Our results shall also have a broader impact for optimizing bottom-up emission inventories.

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.

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.

A novel, practical observation system for measuring tropospheric carbon dioxide (CO2) concentrations using a non-dispersive infrared analyzer carried by a small helium-filled balloon (CO2 sonde), has been developed for the first time. Onboard calibrations, using CO2 standard gases, is possible to measure the vertical profiles of atmospheric CO2 accurately with a 240–400 m altitude resolution. The standard deviations (1σ) of the measured mixing ratios in the laboratory experiments using a vacuum chamber at a temperature of 298 K were approximately 0.6 ppm at 1010 hPa and 1.2 ppm at 250 hPa. Compared with in situ aircraft data, although the difference up to the altitude of 7 km was 0.6 ± 1.2 ppm, this bias and difference were within the precision of the CO2 sonde. In field experiments, the CO2 sonde detected an increase in CO2 concentration in an urban area and a decrease in a forested area near the surface. The CO2 sonde was shown to be a useful instrument for observing and monitoring the vertical profiles of CO2 concentration in the troposphere.

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.

The photon path length probability density function-simultaneous (PPDF-S) algorithm is effective for retrieving column-averaged concentrations of carbon dioxide (XCO2) and methane (XCH4) 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 XCO2 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 CO2 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 XCO2 adversely. By optimizing the constraint, it was possible to obtain a stable solution of XCO2. The new optimization was applied to retrieval analysis of the GOSAT data measured in Western Siberia. First, we assumed clear sky conditions and retrieved XCO2 from GOSAT data obtained near Yekaterinburg in the target area. The retrieved XCO2 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 CO2 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.

Site environments and instrumental characteristics of carbon dioxide (CO2) measurements operated by local governments in the Kanto Plain, the center of which is Tokyo were summarized for this study. The observation sites were classified into environments of three types: urban, suburban, and woodland. Based on a few decades of accumulated hourly data, the diurnal and seasonal variations of CO2 concentrations were analyzed as a composite of anomalies from annual means recorded for each site. In urban areas, the highest concentrations appear before midnight in winter. The second peak corresponds to the morning rush hour and strengthening of the inversion layer. Suburban areas can be characterized as having the highest concentration before dawn and the lowest concentration during the daytime in summer in association with the activation of respiration and photosynthesis of vegetation. In these areas, concentration peaks also appear during the morning rush hour. Woodland areas show background features, with the highest concentration in early spring: higher than the global background by about 5 ppmv.

We have validated CO2 and CH4 data retrieved from TIR band of GOSAT/TANSO-FTS based on comparisons with aircraft observations. The accuracy of GHG measurements by TIR bands of GOSAT-2/TANSO-FTS-2 are expected to be much improved.