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Abstract The Japanese Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) will be an Earth-observing satellite to conduct global observations of atmospheric carbon dioxide (CO2), methane (CH4), and nitrogen dioxide (NO2) simultaneously from a single platform. GOSAT-GW is the third satellite in the series of the currently operating Greenhouse gases Observing SATellite (GOSAT) and GOSAT-2. It will carry two sensors, the Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3) and the Advanced Microwave Scanning Radiometer 3 (AMSR3), with the latter dedicated to the observation of physical parameters related to the water cycle. TANSO-3 is a high-resolution grating spectrometer designed to measure reflected sunlight in the visible to short-wave infrared spectral ranges. It aims to retrieve the column-averaged dry-air mole fractions of CO2 and CH4 (denoted as XCO2 and XCH4, respectively), as well as the vertical column density of tropospheric NO2. The TANSO-3 sensor onboard GOSAT-GW will utilize the wavelength bands of 0.45, 0.76, and 1.61 µm for NO2, O2, and CO2 and CH4 retrievals, respectively. GOSAT-GW will fly in a sun-synchronous orbit with a local overpass time of approximately 13:30 and a 3-day ground-track repeat cycle. The TANSO-3 sensor has two observation modes in the push-broom operation: Wide Mode, which provides globally covered maps with a 10-km spatial resolution within 3 days, and Focus Mode, which provides snapshot maps over targeted areas with a high spatial resolution of 1–3 km. The objectives of the GOSAT-GW mission include (1) monitoring atmospheric global-mean concentrations of greenhouse gasses (GHGs), (2) verifying national anthropogenic GHG emissions inventories, and (3) detecting GHG emissions from large sources, such as megacities and power plants. A comprehensive validation exercise will be conducted to ensure that the sensor products’ quality meets the required precision to achieve the above objectives. With a projected operational lifetime of seven years, GOSAT-GW will provide vital space-based constraints on both anthropogenic and natural GHG emissions. These measurements will contribute significantly to climate change mitigation efforts, particularly by supporting the Global Stocktake (GST) mechanism, a key element of the Paris Agreement.

As an important greenhouse gas (GHG) in the atmosphere, carbon dioxide (CO2) has a great impact on global climate change. Accurate knowledge of the spatiotemporal variations of CO2 is of great significance for understanding the carbon cycle and evaluating the effectiveness of carbon emission reduction. In recent years, several satellites with CO2 sensors have been launched and a series of atmospheric CO2 concentration products have been developed using different retrieval algorithms. This study validated nine satellite XCO2 products derived from Greenhouse gases Observing SATellite (GOSAT), GOSAT-2, Orbiting Carbon Observatory-2 (OCO-2), and OCO-3: including ACOS-GOSAT, NIES-GOSAT, BESD-GOSAT, OCFP-GOSAT, SRFP-GOSAT, EMMA, GOSAT-2, OCO-2, and OCO-3 XCO2. The remotely sensed XCO2 products were compared with the XCO2 observations from six Total Carbon Column Observing Network (TCCON) stations in East Asia for validation. The results showed that the OCO-2 XCO2 product outperformed other products, with the highest R2 of 0.94 and the lowest MAE of 1.24 ppm. The ACOS-GOSAT and EMMA-GOSAT XCO2 products also showed favorable accuracies, both achieving R2 of 0.93 and corresponding MAE values of 1.29 and 1.31 ppm, respectively. The GOSAT-2 XCO2 product showed the poorest accuracy, with an R2 of 0.77 and a mean absolute error of 3.28 ppm. There was a significant overestimation of the bias-uncorrected GOSAT-2 XCO2 product in East Asia, and it indicated that bias correction must be performed for this XCO2 product. The accuracy of TCCON XCO2 was not consistent with remotely sensed XCO2 at different stations. The RJ, JS, AN, and TK TCCON stations generally showed better agreements between satellite estimates and TCCON observations, except for the GOSAT-2 XCO2 product.

Abstract The Greenhouse gases Observing SATellite 2 (GOSAT-2) was launched in October 2018 as a successor to GOSAT (launched in 2009), the first satellite to specialize in greenhouse gas observations. Compared to the GOSAT sensors, the sensors of GOSAT-2 offer higher performance in most respects. The quality and quantity of data from observations are expected to be improved accordingly. The signal-to-noise ratio (SNR) is better in both the SWIR and TIR bands of TANSO-FTS-2, which is the main sensor of GOSAT-2. This improvement ultimately enhances the accuracy of greenhouse gas concentration analysis. Furthermore, because of the improved SNR in the SWIR band, the northern limit at which data are obtainable in high-latitude regions of the Northern Hemisphere in winter, where observation data have remained unavailable because of weak signal strength, has moved to higher latitudes. As better data are obtained in greater quantities, progress in carbon cycle research for high-latitude regions is anticipated. Moreover, the improvement of SNR in the TIR band is expected to be considerable. Particularly, the resolutions of the vertical concentration distributions of CO2 and CH4 have been improved drastically. The first function introduced for GOSAT-2 that is not in GOSAT is an intelligent pointing mechanism: a cloud area avoidance function using the in-field camera of TANSO-FTS-2. This function can increase the amounts of observation data globally and can improve the accuracy of CO2 emissions estimation and measurements of uptake intensity. The effects are expected to be strong, especially for the tropics because cumulus clouds are the most common cloud type. The intelligent pointing system can avoid the clouds effectively. Another important benefit of TANSO-FTS-2 is that the wavelength range of Band 3 of SWIR has been expanded for measuring carbon monoxide (CO). Because CO originates from combustion, it is used to evaluate some effects of human activities in urban areas and biomass burning in fields. Particularly, black carbon-type aerosols can be measured by the sub-sensor, TANSO-CAI-2, to assess biomass burning along with CO2 and CO by TANSO-FTS-2.

The eruption of the submarine Hunga volcano in January 2022 led to a 13% increase in global stratospheric water mass and a 5-fold increase in stratospheric aerosol load, according to satellite data complemented by ground-based observations and atmospheric transport modelling.

Abstract The dramatic increase of natural gas use in China, as a substitute for coal, helps to reduce CO2 emissions and air pollution, but the climate mitigation benefit can be offset by methane leakage into the atmosphere. We estimate methane emissions from 2010 to 2018 in four regions of China using the GOSAT satellite data and in-situ observations with a high-resolution (0.1° × 0.1°) inverse model and analyze interannual changes of emissions by source sectors. We find that estimated methane emission over the north-eastern China region contributes the largest part (0.77 Tg CH4 yr−1) of the methane emission growth rate of China (0.87 Tg CH4 yr−1) and is largely attributable to the growth in natural gas use. The results provide evidence of a detectable impact on atmospheric methane observations by the increasing natural gas use in China and call for methane emission reductions throughout the gas supply chain and promotion of low emission end-use facilities.

Satellite measurements of the atmospheric concentrations of carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO) require careful validation. In particular for the greenhouse gases CO2 and CH4, concentration gradients are minute challenging the ultimate goal to quantify and monitor anthropogenic emissions and natural surface-atmosphere fluxes. The upcoming European Copernicus Carbon Monitoring mission (CO2M) will focus on anthropogenic CO2 emissions, but it will also be able to measure CH4. There are other missions such as the Sentinel-5 Precursor and the Sentinel-5 series that target CO which helps attribute the CO2 and CH4 variations to specific processes. Here, we review the capabilities and use cases of a mobile ground-based sun-viewing spectrometer of the type EM27/SUN. We showcase the performance of the mobile system for measuring the column-average dry-air mole fractions of CO2 (XCO2), CH4 (XCH4) and CO (XCO) during a recent deployment (Feb./Mar. 2021) in the vicinity of Japan on research vessel Mirai which adds to our previous campaigns on ships and road vehicles. The mobile EM27/SUN has the potential to contribute to the validation of 1) continental-scale background gradients along major ship routes on the open ocean, 2) regional-scale gradients due to continental outflow across the coast line, 3) urban or other localized emissions as mobile part of a regional network and 4) emissions from point sources. Thus, operationalizing the mobile EM27/SUN along these use cases can be a valuable asset to the validation activities for CO2M, in particular, and for various upcoming satellite missions in general.

Nitrous oxide (N2O) is the third most abundant anthropogenous greenhouse gas (after carbon dioxide and methane), with a long atmospheric lifetime and a continuously increasing concentration due to human activities, making it an important gas to monitor. In this work, we present a new method to retrieve N2O concentration profiles (with up to two degrees of freedom) from each cloud-free satellite observation by the Infrared Atmospheric Sounding Interferometer (IASI), using spectral micro-windows in the N2O ν3 band, the Radiative Transfer for TOVS (RTTOV) tools and the Tikhonov regularization scheme. A time series of ten years (2011–2020) of IASI N2O profiles and integrated partial columns has been produced and validated with collocated ground-based Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) data. The importance of consistency in the ancillary data used for the retrieval for generating consistent time series has been demonstrated. The Nitrous Oxide Profiling from Infrared Radiances (NOPIR) N2O partial columns are of very good quality, with a positive bias of 1.8 to 4% with respect to the ground-based data, which is less than the sum of uncertainties of the compared values. At high latitudes, the comparisons are a bit worse, due to either a known bias in the ground-based data, or to a higher uncertainty in both ground-based and satellite retrievals.

This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO2 total column (XCO2) using spatio-temporal geostatistics, which makes full use of the joint spatial and temporal dependencies between observations. The mapping approach considers the latitude-zonal seasonal cycles and spatio-temporal correlation structure of XCO2, and obtains global land maps of XCO2, with a spatial grid resolution of 1° latitude by 1° longitude and temporal resolution of 3 days. We evaluate the accuracy and uncertainty of the mapping dataset in the following three ways: (1) in cross-validation, the mapping approach results in a high correlation coefficient of 0.94 between the predictions and observations, (2) in comparison with ground truth provided by the Total Carbon Column Observing Network (TCCON), the predicted XCO2 time series and those from TCCON sites are in good agreement, with an overall bias of 0.01 ppm and a standard deviation of the difference of 1.22 ppm and (3) in comparison with model simulations, the spatio-temporal variability of XCO2 between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent. The generated mapping XCO2 data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming.

The metropolitan area of Tokyo, the capital of Japan, emits a large amount of carbon dioxide (CO2), an anthropogenic greenhouse gas. We measured the day-time column-averaged dry-air molar mixing ratios of atmospheric CO2, XCO2, in the central area of Tokyo during September 2014 – August 2016 using a desktop optical spectrum analyzer. The observed seasonal cycle is compared with the seasonal cycle that is observed at the Total Carbon Column Observing Network (TCCON) site in Tsukuba, which is located 60 km to the northeastern direction of Tokyo. The differences in XCO2 between the two sites are high (∼5 ppm) during December – February and low (∼0.5 ppm) during June – September. The characteristic variations of XCO2 in Tokyo are interpreted in terms of local emission sources and surface meteorological data, by referring to the variations in the concentrations of surface CO2. The sharp peaks in XCO2 at both the Tokyo and Tsukuba sites in July are interpreted in terms of local air retention in the areas, as indicated by the aircraft profiles and wind-flow forward trajectory calculations. Finally, we compare the ground-based column measurements with the top-down satellite column observations. Keywords: Emission, Megacity, Urban area, Seasonal cycle, Power plant

In Asia, much effort is put into reducing methane (CH _4 ) emissions due to the region’s contribution to the recent rapid global atmospheric CH _4 concentration growth. Accurate quantification of Asia’s CH _4 budgets is critical for conducting global stocktake and achieving the long-term temperature goal of the Paris Agreement. In this study, we present top-down estimates of CH _4 emissions from 2009 to 2018 deduced from atmospheric observations from surface network and GOSAT satellite with the high-resolution global inverse model NIES-TM-FLEXPART-VAR. The optimized average CH _4 budgets are 63.40 ± 10.52 Tg y ^−1 from East Asia (EA), 45.20 ± 6.22 Tg y ^−1 from Southeast Asia (SEA), and 64.35 ± 9.28 Tg y ^−1 from South Asia (SA) within the 10 years. We analyzed two 5 years CH _4 emission budgets for three subregions and 13 top-emitting countries with an emission budget larger than 1 Tg y ^−1 , and interannual variabilities for these subregions. Statistically significant increasing trends in emissions are found in EA with a lower emission growth rate during 2014–2018 compared to that during 2009–2013, while trends in SEA are not significant. In contrast to the prior emission, the posterior emission shows a significant decreasing trend in SA. The flux decrease is associated with the transition from strong La Ninña (2010–2011) to strong El Ninño (2015–2016) events, which modulate the surface air temperature and rainfall patterns. The interannual variability in CH _4 flux anomalies was larger in SA compared to EA and SEA. The Southern Oscillation Index correlates strongly with interannual CH _4 flux anomalies for SA. Our findings suggest that the interannual variability in the total CH _4 flux is dominated by climate variability in SA. The contribution of climate variability driving interannual variability in natural and anthropogenic CH _4 emissions should be further quantified, especially for tropical countries. Accounting for climate variability may be necessary to improve anthropogenic emission inventories.

This paper provides an overview of the validation of National Oceanic and Atmospheric Administration (NOAA) operational retrievals of atmospheric carbon trace gas profiles, specifically carbon monoxide (CO), methane (CH4) and carbon dioxide (CO2), from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), a NOAA enterprise algorithm that retrieves atmospheric profile environmental data records (EDRs) under global non-precipitating (clear to partly cloudy) conditions. Vertical information about atmospheric trace gases is obtained from the Cross-track Infrared Sounder (CrIS), an infrared Fourier transform spectrometer that measures high resolution Earth radiance spectra from NOAA operational low earth orbit (LEO) satellites, including the Suomi National Polar-orbiting Partnership (SNPP) and follow-on Joint Polar Satellite System (JPSS) series beginning with NOAA-20. The NUCAPS CO, CH4, and CO2 profile EDRs are rigorously validated in this paper using well-established independent truth datasets, namely total column data from ground-based Total Carbon Column Observing Network (TCCON) sites, and in situ vertical profile data obtained from aircraft and balloon platforms via the NASA Atmospheric Tomography (ATom) mission and NOAA AirCore sampler, respectively. Statistical analyses using these datasets demonstrate that the NUCAPS carbon gas profile EDRs generally meet JPSS Level 1 global performance requirements, with the absolute accuracy and precision of CO 5% and 15%, respectively, in layers where CrIS has vertical sensitivity; CH4 and CO2 product accuracies are both found to be within ±1%, with precisions of ≈1.5% and ⪅0.5%, respectively, throughout the tropospheric column.

The proxy method, using the ratio of total column CH4 to CO2 to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH4 from satellite data. The present study characterizes the remaining scattering effects in the CH4/CO2 ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction—namely, airmass, 2 μm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH4 for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH4 and CO2 bands. The ratio of partial column CH4 reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites.

We present a global 0.1° × 0.1° high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010–2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH4 yr−1 for Russia, 12.8 to 14.3 Tg CH4 yr−1 for temperate South America, and 43.2 to 44.9 Tg CH4 yr−1 for contiguous USA, and the values decrease from 54 to 51.3 Tg CH4 yr−1 for China, 26.2 to 25.5 Tg CH4 yr−1 for Europe, and by 12.4 Tg CH4 yr−1 for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC.

We have developed a simple measuring system prototype that uses an unmanned aerial vehicle (UAV) and a non-dispersive infrared (NDIR) analyzer to detect regional carbon dioxide (CO2) concentrations and obtain vertical CO2 distributions. Here, we report CO2 measurement results for the lower troposphere above Ogata Village, Akita Prefecture, Japan (about 40° N, 140° E, approximately −1 m amsl), obtained with this UAV system. The actual flight observations were conducted at 500, 400, 300, 200, 100, and 10 m above the ground, at least once a month during the daytime from February 2018 to February 2019. The raw CO2 values from the NDIR were calibrated by two different CO2 standard gases and high-purity nitrogen (N2) gas (as a CO2 zero gas; 0 ppm). During the observation period, the maximum CO2 concentration was measured in February 2019 and the minimum in August 2018. In all seasons, CO2 concentrations became higher as the flight altitude was increased. The monthly pattern of observed CO2 changes is similar to that generally observed in the Northern Hemisphere as well as to surface CO2 changes simulated by an atmospheric transport model of the Japan Meteorological Agency. It is highly probable that these changes reflect the vegetation distribution around the study area.

Methane ( CH 4) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane (XCH 4). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4. These differences are linked to the regional CH 4 sources and sinks, and call for further research.

This study evaluated three bias correction methods of systematic biases in column-averaged dry-air mole fraction of water vapor (XH2O) data retrieved from Greenhouse Gases Observing Satellite (GOSAT) Short-Wavelength Infrared (SWIR) observations compared with ground-based data from the Total Carbon Column Observing Network (TCCON). They included an empirically multilinear regression method, altitude bias correction method, and combination of altitude and empirical correction for three cases defined by the temporal and spatial collocation around TCCON site. The results showed that large altitude differences between GOSAT observation points and TCCON instruments are the main cause of bias, and the altitude bias correction method is the most effective bias correction method. The lowest biases result from GOSAT SWIR XH2O data within a 0.5° × 0.5° latitude × longitude box centered at each TCCON site matched with TCCON XH2O data averaged over ±15 min of the GOSAT overpass time. Considering land data, the global bias changed from −1.3 ± 9.3% to −2.2 ± 8.5%, and station bias from −2.3 ± 9.0% to −1.7 ± 8.4%. In mixed land and ocean data, global bias and station bias changed from −0.3 ± 7.6% and −1.9 ± 7.1% to −0.8 ± 7.2% and −2.3 ± 6.8%, respectively, after bias correction. The results also confirmed that the fine spatial and temporal collocation criteria are necessary in bias correction methods.

A 1.6 μm differential absorption Lidar (DIAL) system for measurement of vertical CO2 mixing ratio profiles has been developed. A comparison of CO2 vertical profiles measured by the DIAL system and an aircraft in situ sensor in January 2014 over the National Institute for Environmental Studies (NIES) in Tsukuba, Japan, is presented. The DIAL measurement was obtained at an altitude range of between 1.56 and 3.60 km with a vertical resolution of 236 m (below 3 km) and 590 m (above 3 km) at an average error of 1.93 ppm. An in situ sensor for cavity ring-down spectroscopy of CO2 was installed in an aircraft. CO2 mixing ratio measured by DIAL and the aircraft sensor ranged from 398.73 to 401.36 ppm and from 399.08 to 401.83 ppm, respectively, with an average difference of −0.94 ± 1.91 ppm below 3 km and −0.70 ± 1.98 ppm above 3 km between the two measurements.

The Total Carbon Column Observing Network (TCCON) is a global network dedicated to the precise and accurate measurements of greenhouse gases (GHG) in the atmosphere. The TCCON station in Burgos, Ilocos Norte, Philippines was established with the primary purpose of validating the upcoming Greenhouse gases Observing SATellite-2 (GOSAT-2) mission and in general, to respond to the need for reliable ground-based validation data for satellite GHG observations in the region. Here, we present the first 4 months of data from the new TCCON site in Burgos, initial comparisons with satellite measurements of C O 2 and model simulations of C O . A nearest sounding from Japan’s GOSAT as well as target mode observations from NASA’s Orbiting Carbon Observatory 2 (OCO-2) showed very good consistency in the retrieved column-averaged dry air mole fractions of C O 2 , yielding TCCON - satellite differences of 0.86 ± 1.06 ppm for GOSAT and 0.83 ± 1.22 ppm for OCO-2. We also show measurements of enhanced C O , probably from East Asia. GEOS-Chem model simulations were used to study the observed C O variability. However, despite the model capturing the pattern of the C O variability, there is an obvious underestimation in the C O magnitude in the model. We conclude that more measurements and modeling are necessary to adequately sample the variability over different seasons and to determine the suitability of current inventories.

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.

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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.

Himawari-8/9 is a next-generation Japanese geostationary earth orbit (GEO) meteorological satellite with an onboard sensor – the Advanced Himawari Imager (AHI). Because Himawari-8/9 AHI observe the Earth’s hemispheres every 10 min with multiple spectral bands, AHI providing an unprecedented opportunity to facilitate its observation datasets are expected to be a new data source for terrestrial monitoring in terms of mitigating cloud contaminations. Estimation of land surface reflectance (LSR) is crucial in quantitative terrestrial monitoring. In this study, we aimed to develop a method for estimating the LSR and angular-adjusted LSR of the Himawari-8/9 AHI using the look-up table (LUT) based Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) Radiative Transfer Model (RTM) and kernel-driven semi-empirical bidirectional reflectance distribution function (BRDF) model. The estimated LSR underwent evaluation and inter-comparison through two distinct approaches: ray-matching and estimating angular-adjusted LSR. Ray-matching of the obtained data pairs with the MODIS LSR product shows that the correlation coefficients (r) for all bands are greater than 0.86 at low latitudes. Angularadjusted LSRs estimated using AHI time-series data at mid-latitudes also show good agreement with MODIS (r > 0.5), particularly the red and near-infrared bands (r > 0.9). The results obtained by our method are in high agreement with those calculated using the reference aerosol optical thickness (AOT) (r > 0.98). Our findings highlight the potential application of our methodology to other GEO satellites for high-frequency terrestrial monitoring at continental to global scales. [ABSTRACT FROM AUTHOR]

The increase in greenhouse gas concentrations, particularly CO2, has significant implications for global climate patterns and various aspects of human life. Spaceborne remote sensing satellites play a crucial role in high-resolution monitoring of atmospheric CO2. However, the next generation of greenhouse gas monitoring satellites is expected to face challenges such as low retrieval efficiency and insufficient retrieval accuracy. To address these challenges, this study focuses on enhancing the retrieval of column-averaged dry air mole fraction of carbon dioxide (XCO2) using spectral data from the OCO-2 satellite. A novel approach based on neural network (NN) models is proposed to tackle the nonlinear inversion problems associated with XCO2 retrieval. The study employs a data-driven supervised learning method and explores two distinct training strategies. Firstly, training is conducted using experimental data obtained from the inversion of traditional optimization models, which are released as the OCO-2 satellite products. Secondly, training is performed using a simulated dataset generated by an accurate forward calculation model. The inversion and prediction performance of the machine learning model for XCO2 is compared, analyzed, and discussed for the observed region. The results demonstrate that the model trained on simulated data accurately predicts XCO2 in the target area. Furthermore, when compared to OCO-2 satellite product data, the developed XCO2 retrieval model achieves rapid predictions (<1 ms) with high precision (2 ppm or approximately 0.5 %). The accuracy of the machine learning model's retrieval results is validated against reliable data from TCCON sites, demonstrating its capability to capture CO2 seasonal variations and annual growth trends effectively. [ABSTRACT FROM AUTHOR]

The presented study investigates the efficiency of heterogeneous ice formation in natural clouds over Lauder, New Zealand/Aotearoa. Aerosol conditions in the middle troposphere above Lauder are subject to huge contrasts. Clean, pristine airmasses from Antarctica and the Southern Ocean arrive under southerly flow conditions while high aerosol loads can occur when air masses are advected from nearby Australia. This study assesses how these contrasts in aerosol load affect the ice formation efficiency in stratiform midlevel clouds in the heterogeneous freezing range (-40°C to 0°C). For this purpose, an 11-year dataset was analyzed from a dual-wavelength polarization lidar system operated by National Institute of Water & Atmospheric Research (NIWA) at Lauder in collaboration with the National Institute for Environmental Studies in Japan and the Meteorological Research Institute of the Japan Meteorological Agency. These data were used to investigate the efficiency of heterogeneous ice formation in clouds over the site as a function of cloud-top temperature as in previous studies at other locations. The Lauder cloud dataset was put into context with lidar studies from contrasting regions such as Germany and southern Chile. The ice formation efficiency found at Lauder is lower than in polluted mid-latitudes (i.e., Germany) but higher than for example in southern Chile. Both, Lauder and southern Chile are subject to generally low free-tropospheric aerosol loads, which suggests that the low ice formation efficiency at these two sites is related to low ice-nucleating particle (INP) concentrations. However, Lauder sees episodes of continental aerosol, more than does southern Chile, which seems to lead to the moderately increased ice formation efficiency. Trajectory-based tools and aerosol model re-analyses are used to relate this cloud dataset to the aerosol load and the air mass sources. Both analyses point clearly to higher ice formation efficiency for clouds which are more strongly influenced by continental aerosol, and to lower ice formation efficiency for clouds which are more influenced by Antarctic/marine aerosol and air masses. [ABSTRACT FROM AUTHOR]

Satellite based estimations of dry-air column-average mixing ratios of methane (XCH4) contribute to a better understanding of changes in CH4 emission sources and variations in its atmospheric growth rates. High accuracy of the satellite measurements is required, and therefore, extensive validation is performed, mainly against the Total Carbon Column Observing Network (TCCON). However, validation opportunities at open ocean areas outside the coastal regions are sparse. We propose a new approach to assess the accuracy of satellite derived XCH4 trends and variations. We combine various ship and aircraft observations with the help of atmospheric chemistry models, mainly used for the stratospheric column, to derive observation-based XCH4 (obs. XCH4). Based on our previously developed approach for the application to XCO2, we investigated 3 different advancements from a simple to more elaborate approaches (approach 1, 2, and 3) to account for the higher tropospheric and stratospheric variability of CH4 as compared to CO2. Between 2014–2018, at 20–40° N of the western Pacific, we discuss the uncertainties of the approaches and the derived obs. XCH4 within 10° by 20° latitude–longitude boxes. Uncertainties were 22 ppb for approach 1, and 17 ppb for approach 2 and 3. We analysed the consistency with the nearest TCCON stations and found agreement of approach 3 with Saga of 1 ± 12 ppb, and −1 ± 11 ppb with Tsukuba for the northern and southern latitude box, respectively. Furthermore, we discuss the impact of the modelled stratospheric column on the derived obs. XCH4 by applying 3 different models in our approaches. Depending on the models, the difference can be more than 0.5 %, showing the importance for the appropriate choice. We show that our obs. XCH4 dataset accurately captures seasonal variations of CH4 over the ocean. Using different retrievals of the Greenhouse gases Observing Satellite (GOSAT) from the National Institute for Environmental Studies (NIES), the RemoTeC full-physics retrieval operated at the Netherlands Institute for Space Research (SRON), and the full-physics retrieval of the University of Leicester (UoL-OCFP), we demonstrate the applicability of the dataset for satellite evaluation. The comparison with results of approach 3 revealed that NIES showed a difference of −0.04 ± 13 ppb and strong scatter at 20–30° N, while RemoTeC and OCFP have rather systematic negative bias of −12.1 ± 8.1 ppb and −10.3 ± 9.6 ppb. Our new approach to derive XCH4 reference datasets over the ocean can contribute to the validation of existing and upcoming satellite missions in future. [ABSTRACT FROM AUTHOR]