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Column-averaged dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) measured by a solar viewing portable Fourier transform spectrometer (FTS, EM27/SUN) have been characterized and validated by comparison using in-situ profile measurements made during the transfer flights of two aircraft campaigns: Korea-United States Air Quality Study (KORUS-AQ) and Effect of Megacities on the Transport and Transformation of Pollutants on the Regional and Global Scale (EMeRGe). The aircraft flew over two Total Carbon Column Observing Network (TCCON) sites: Rikubetsu, Japan (43.46° N, 143.77° E) for the KORUS-AQ campaign and Burgos, Philippines (18.53° N, 120.65° E) for the EMeRGe campaign. The EM27/SUN was deployed at the corresponding TCCON sites during the overflights. The mole fraction profiles obtained by the aircraft over Rikubetsu differed between the ascending and the descending flights above approximately 8 km for both CO2 and CH4. Because the spatial pattern of tropopause heights based on potential vorticity values from the ERA5 reanalysis show that the tropopause height over the Rikubetsu site was consistent with the descending profile, we used only the descending profile to compare with the EM27/SUN data. Both the XCO2 and XCH4 derived from the descending profiles over Burgos were lower than those from the ascending profiles. Output from the Weather Research and Forecast Model indicate that higher CO2 for the ascending profile originated in central Luzon, an industrialized and densely populated region about 400 km south of the Burgos TCCON site. Air masses observed with the EM27/SUN overlap better with those from the descending aircraft profiles than those from the ascending aircraft profiles with respect to their properties such as origin and atmospheric residence times. Consequently, the descending aircraft profiles were used for the comparison with the EM27/SUN data. The EM27/SUN XCO2 and XCH4 data were derived by using the GGG2014 software in which air mass independent correction factors utilized for the TCCON data (0.9898 for XCO2 and 0.9765 for XCH4) were not applied. The comparison of the EM27/SUN observations with the aircraft data revealed that on average, the EM27/SUN XCO2 data were biased low by 1.22 % and the EM27/SUN XCH4 data were biased low by 1.67 %. The resulting air mass independent correction factors of 0.9878 for XCO2 and 0.9833 for XCH4 were obtained for the portable FTS. [ABSTRACT FROM AUTHOR]

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 CO2 and model simulations of CO. 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 CO2, 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 CO, probably from East Asia. GEOS-Chem model simulations were used to study the observed CO variability. However, despite the model capturing the pattern of the CO variability, there is an obvious underestimation in the CO 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. [ABSTRACT FROM AUTHOR]

We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO2 (XCO2/ and CH4 (XCH4/ derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT).We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO2 and XCH4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO2/XCH4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases. [ABSTRACT FROM AUTHOR]

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]

We present a simple approach for estimating the greenhouse gas emissions of large cities using accurate long-term data of column-averaged greenhouse gas abundances collected by a nearby FTIR (Fourier Transform InfraRed) spectrometer. We estimate the average yearly carbon emissions from the area of Tokyo to be 86 ± 33 MtCyear between 2011 and 2016, calculated using only measurements from the TCCON site in Tsukuba (north-east of Tokyo) and wind-speed data from nearby radiosondes. [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]

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]

Column-averaged dry air mole fractions of carbon dioxide and methane (XCO2 and XCH4) retrieved from SWIR (Short-Wavelength InfraRed) observations by GOSAT (Greenhouse Gases Observing SATellite) since 2009 and its successor GOSAT-2 since 2019 are available from NIES (National Institute for Environmental Studies) as SWIR L2 (Level 2) products. This paper shows the current status of the data quality of NIES SWIR L2 products and inter-satellite comparison results. Comparisons of XCO2 and XCH4 obtained from each satellite with ground-based observations from the TCCON (Total Carbon Column Observing Network) reveal that the averaged single measurement precision are less than 1.9 ppm and 10 ppb, respectively, and the site-to-site biases are less than 0.9 ppm and 5 ppb, respectively. The standard deviations of the inter-satellite differences for XCO2 and XCH4 are 2.18 ppm and 12.1 ppb, respectively, on an individual-data basis and 1.77 ppm and 11.7 ppb, respectively, on a monthly-regional-mean basis. While further improvements in the retrieval algorithm and bias-correction method are needed, GOSAT and GOSAT-2 retrievals are generally in good agreement. [ABSTRACT FROM AUTHOR]

The National Institute for Environmental Studies has provided the column-averaged dry-air mole fraction of carbon dioxide and methane (XCO2 and XCH4) products (L2 products) obtained from the Greenhouse gases Observing SATellite for more than a decade. Recently, we updated the retrieval algorithm used to produce the new L2 product, V03.00. The main changes from the previous version (V02) of the retrieval algorithm are the treatment of cirrus clouds, the degradation model of the Thermal And Near-infrared Spectrometer for carbon Observation - Fourier Transform Spectrometer (TANSO-FTS), solar irradiance spectra, and gas absorption coefficient tables. The retrieval results from the updated algorithm showed improvements in fitting accuracies in the O2 A, weak CO2, and CH4 bands of TANSO-FTS, although the residuals increase in the strong CO2 band over the ocean. The direct comparison of the new product obtained from the updated (V03) algorithm with the previous version V02.90/91 and the validations using the Total Carbon Column Observing Network revealed that the V03 algorithm increases the amount of data without diminishing the data qualities of XCO2 and XCH4 over land. Further, the negative bias of XCO2 is larger than that of the previous version over the ocean, and bias correction is still necessary. Additionally, the V03 algorithm resolves the underestimation of the XCO2 growth rate compared with the in situ measurements over the ocean recently found using V02.90/91 and V02.95/96. [ABSTRACT FROM AUTHOR]

TROPOMI (the TROPOspheric Monitoring Instrument), on-board the Sentinel-5 Precursor satellite, has been monitoring the Earth's atmosphere since October 2017, with an unprecedented horizontal resolution (initially 7×3.5 km², upgraded to 5.5×3.5 km² since August 2019). Monitoring air quality is one of the main objectives of TROPOMI, with the measurements of important pollutants such as nitrogen dioxide, carbon monoxide, and formaldehyde (HCHO). In this paper we assess the quality of the latest HCHO TROPOMI products (version 1.1.[5-7]), using ground-based solar-absorption FTIR (Fourier Transform Infrared) measurements of HCHO from twenty-five stations around the world, including high, mid, and low latitude sites. Most of these stations are part of the Network for the Detection of Atmospheric Composition Change (NDACC), and they provide a wide range of observation conditions from very clean remote sites to those with high HCHO levels from anthropogenic or biogenic emissions. The ground-based HCHO retrieval settings have been optimized and harmonized at all the stations, ensuring a consistent validation among the sites. In this validation work, we first assess the accuracy of TROPOMI HCHO tropospheric columns, using the median of the relative differences between TROPOMI and FTIR ground-based data (BIAS). We observe that, at all sites, the TROPOMI accuracy is below the upper limit of the pre-launch requirements of 80 %, and below the lower limit of 40 % for 20 of the 25 stations. The provided TROPOMI systematic uncertainties are well in agreement with the observed biases at most of the stations, except for the highest HCHO levels site where it is found to be underestimated. We find that, while the BIAS has no latitudinal dependence, it is dependent on the HCHO concentration levels: an overestimation (+26 ± 5 %) of TROPOMI is observed for very small HCHO levels (< 2.5×1015 molec cm²), while an underestimation (−30.8 % ± 1.4 %) is found for high HCHO levels (> 8.0 × 1015 molec/cm²). This demonstrates the great value of such a harmonized network covering a wide range of concentration levels, the sites with high HCHO concentrations being crucial for the determination of the satellite bias at the regions of emissions, and the clean sites allowing a small TROPOMI offset to be determined. The wide range of sampled HCHO levels within the network allows the robust determination of the significant constant and proportional TROPOMI HCHO biases (TROPOMI=+ 1.10 (± 0.05) × 1015+ 0.64 (± 0.03) × FTIR, in molec/cm²). Second, the precision of TROPOMI HCHO data is estimated by the median absolute deviation (MAD) of the relative differences between TROPOMI and FTIR ground-based data. The clean sites are especially useful to minimize a possible additional collocation error. The precision requirement of 1.2 × 1016 molec/cm² for a single pixel is reached at most of the clean sites, where it is found that the TROPOMI precision can even be twice better (0.5–0.8 × 1015 molec/cm² for a single pixel). However, we find that the provided TROPOMI random uncertainties may be underestimated by a factor of 1.6 (for clean sites) to 2.3 (for high HCHO levels). The correlation is very good between TROPOMI and FTIR data (R = 0.88 for 3 hours-mean coincidences; R = 0.91 for monthly means coincidences). Using about 17 months of data (from May 2018 to September 2019), we show that the TROPOMI seasonal variability is in very good agreement at all of the FTIR sites. The FTIR network demonstrates the very good quality of the TROPOMI HCHO products which is well within the pre-launch requirements for both accuracy and precision. This paper advises for a refinement of the TROPOMI random uncertainty budget and of the TROPOMI quality assurance values for a better filtering of the remaining outliers. [ABSTRACT FROM AUTHOR]

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term Climate Data Records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003-2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory-2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the “ensemble median algorithm” (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5ox5o data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consists of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets): single observation random error (1-sigma): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm), spatio-temporal bias or “relative accuracy” (1-sigma): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are: single observation random error (1-sigma): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb), spatio-temporal bias (1-sigma): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from December 2019 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/). [ABSTRACT FROM AUTHOR]

Carbon monoxide (CO) is an important atmospheric constituent affecting air quality and methane (CH4) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadir viewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR) combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with unprecedented level of detail on a global scale introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4 are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 μm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified DOAS (WFM-DOAS). We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine learning-based quality filter and a shallow learning calibration procedure applied in the post-processing of the XCH4 data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier Transform Spectrometer (FTS) measurements providing realistic error estimates of the satellite data: The XCO data set is characterised by a random error of 5.1 ppb (5.7 %) and a systematic error of 1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb (0.8 %) and a systematic error of 4.4 ppb (0.2 %). The natural XCO and XCH4 variations are well captured by the satellite retrievals, which is demonstrated by a high correlation to the reference data (R = 0.97 for XCO and R = 0.91 for XCH4 based on daily averages). We also present selected results from mission start until end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH4 emissions from the energy sector. [ABSTRACT FROM AUTHOR]

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument onboard the Terra spacecraft were expected to have an accuracy of 10 % prior to launch in 1999. Here we evaluate MOPITT version 7 joint TIR-NIR (V7J) accuracy and precision, and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings, and ground based measurements. 1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. 2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. 3) Using a small region approximation (SRA) a new filtering scheme is developed and applied based on additional quality indicators such as signal-to-noise. After applying these new filters, the root mean squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 ppb to 2.55 ppb. 4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. 5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground and satellite based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO, and tend to pull concentrations away from the prior, and closer to the truth. We conclude with suggestions for further improving the MOPITT data products. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Observations of stable isotopes of water vapor provide important information for water cycle. The volume mixing ratios (VMR) of H2O (XH2O) and HDO (XHDO) have been retrieved based on a high-resolution ground-based Fourier transform infrared spectroscopy (FTIR) at Hefei site, and the isotopic composition δD was calculated. Time series of XH2O were compared with the Greenhouse gases Observing Satellite (GOSAT) data, showing a good agreement. The daily averaged δD ranges from -17.02 ‰ to -282.3 ‰ between September 2015 and September 2016. Also, the relationships of meteorological parameters with stable isotopologue were analyzed. δD values showed an obvious positive correlation with temperature and ln(XH2O) and a weak correlation with relative humidity. Further, 51.35 % of airmass at Hefei site comes from the southeast of China, and the main potential sources of δD are in the east of China over the observation period based on the back trajectories model. Furthermore, the δD values of evapotranspiration were calculated based on Keeling plot. Observations of the stable isotopes of water vapor by high-resolution ground-based FTIR provide information on study of the variation of the atmospheric water vapor at Hefei site. [ABSTRACT FROM AUTHOR]

In this study we present the retrieval of the column averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) satellite observations using the RemoTeC algorithm, previously successfully applied to retrieval of greenhouse gas concentration from the Greenhouse Gases Observing Satellite (GOSAT). The XCO2 product has been validated with collocated ground based measurements from the Total Carbon Column Observing Network (TCCON) for almost 2 years of OCO-2 data from September 2014 to July 2016. We found that fitting an additive radiometric offset in all three spectral bands of OCO-2 significantly improved the retrieval. Based on a small correlation of the XCO2 error over land with fit residuals, we applied an a posteriori bias correction to our OCO-2 retrievals. In daily averaged results, XCO2 retrievals have a standard deviation ~ 1.30 ppm and a station-to-station variability of ~ 0.40 ppm among collocated TCCON sites. The seasonal relative accuracy (SRA) has a value of 0.52 ppm. The validation shows relatively larger difference with TCCON over high latitude areas and some specific regions like Japan. [ABSTRACT FROM AUTHOR]

A ground-based high-resolution Fourier transform spectrometer (FTS) station has been established in Hefei, China to remotely measure CO2, CO and other greenhouse gases based on near-infrared solar absorption spectra. Total column measurements of atmospheric CO2 and CO were successfully obtained from July 2014 to April 2016. The spectra collected with an InSb detector in the first year were compared with those collected by an InGaAs detector from July 2015, demonstrating that InGaAs spectra have better signal-to-noise ratios and rms of spectral fitting residuals relative to InSb spectra. Consequently, the measurement precision of the retrieved XCO2 and XCO for InGaAs spectra is superior to InSb spectra, with about 0.04 and 0.09% for XCO2, and 1.07 and 2.00% for XCO within clear-sky days respectively. Daily and monthly averages of columnaveraged dry air mole fraction of CO2 show a clear seasonal cycle, while the daily and monthly averages of XCO displayed no seasonal variation. Also, we analysed the relationship of the anomalies of XCO and XCO2, found that the correlations are only observable for individual days, and the data under different prevailing wind conditions during the observations displayed weak correlation. The observations based on the high-resolution FTS were also compared with the temporally coinciding measurements taken with a low-resolution solar FTS instrument, the EM27/SUN. Ratioing the daily averaged XCO2 of EM27 and FTS gives an overall calibration factor of 0.996±0.001. We also compared ground-based observations from the Tsukuba TCCON station with our observations, the results showing that the variation in phase and seasonal amplitude of XCO2 are similar to our results, but the variation of XCO in Tsukuba is quite different from our data in Hefei. To further evaluate our retrieved data, we made use of satellite measurements. The direct comparison of our observations with the Greenhouse Gases Observing Satellite (GOSAT) data shows good agreement of daily median XCO2, with a bias of -0.52 ppm and standard deviation of 1.63 ppm. The correlation coefficient (R²) is 0.79 for daily median XCO2 between our FTS and GOSAT observations. Daily median Orbiting Carbon Observatory 2 (OCO-2) data produce a positive bias of 0.81 ppm and standard deviation of 1.73 ppm relative to our ground-based data. Our daily median XCO2 also show strong correlation with OCO-2 data, with correlation coefficient (R²) of 0.83. Although there were a limited number of data during the observations due to instrument downtime and adverse weather, the results confirm the suitability of the observatory for ground-based long-term measurements of greenhouse gases with high precision and accuracy, and fulfil the requirements of the Total Carbon Column Observing Network (TCCON). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]