Showing papers on "Total Carbon Column Observing Network published in 2017"

Comparison of Satellite-Observed XCO2 from GOSAT, OCO-2, and Ground-Based TCCON

Abstract: CO2 is one of the most important greenhouse gases. Its concentration and distribution in the atmosphere have always been important in studying the carbon cycle and the greenhouse effect. This study is the first to validate the XCO2 of satellite observations with total carbon column observing network (TCCON) data and to compare the global XCO2 distribution for the passive satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT), which are on-orbit greenhouse gas satellites. Results show that since GOSAT was launched in 2009, its mean measurement accuracy was −0.4107 ppm with an error standard deviation of 2.216 ppm since 2009, and has since decreased to −0.62 ppm with an error standard deviation of 2.3 ppm during the past two more years (2014–2016), while the mean measurement accuracy of the OCO-2 was 0.2671 ppm with an error standard deviation of 1.56 ppm from September 2014 to December 2016. GOSAT observations have recently decreased and lagged behind OCO-2 on the ability to monitor the global distribution and monthly detection of XCO2. Furthermore, the XCO2 values gathered by OCO-2 are higher by an average of 1.765 ppm than those by GOSAT. Comparison of the latitude gradient characteristics, seasonal fluctuation amplitude, and annual growth trend of the monthly mean XCO2 distribution also showed differences in values but similar line shapes between OCO-2 and GOSAT. When compared with the NOAA statistics, both satellites’ measurements reflect the growth trend of the global XCO2 at a low and smooth level, and reflect the seasonal fluctuation with an absolutely different line shape.

Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics

Abstract: This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO_2 total column (XCO_2) 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 XCO_2, 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 XCO_2 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 XCO_2 between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent. The generated mapping XCO_2 data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming.

Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0

Abstract: We present a global distribution of surface methane (CH4) emission estimates for 2000–2012 derived using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, anthropogenic and biospheric CH4 emissions are simultaneously estimated based on constraints of global atmospheric in situ CH4 observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH4 to surface in situ observations, vertical profiles of CH4 made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH4) from the Total Carbon Column Observing Network (TCCON), and XCH4 from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH4 is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000–2012 are 516 ± 51 Tg CH4 yr−1, with an increase of 18 Tg CH4 yr−1 from 2000–2006 to 2007–2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations ( < 2 Tg CH4 yr−1 difference), and much smaller than suggested by EDGAR v4.2 FT2010 inventory (33 Tg CH4 yr−1), which was used for prior anthropogenic emission estimates. The result is in good agreement with other published estimates from inverse modelling studies (16–20 Tg CH4 yr−1). However, this study could not conclusively separate a small trend in biospheric emissions (−5 to +6.9 Tg CH4 yr−1) from the much larger trend in anthropogenic emissions (15–27 Tg CH4 yr−1). Finally, we find that the global and North American CH4 balance could be closed over this time period without the previously suggested need to strongly increase anthropogenic CH4 emissions in the United States. With further developments, especially on the treatment of the atmospheric CH4 sink, we expect the data assimilation system presented here will be able to contribute to the ongoing interpretation of changes in this important greenhouse gas budget.

High accuracy absorption coefficients for the Orbiting Carbon Observatory-2 (OCO-2) mission: Validation of updated carbon dioxide cross-sections using atmospheric spectra

Abstract: The accuracy of atmospheric trace gas retrievals depends directly on the accuracy of the molecular absorption model used within the retrieval algorithm. For remote sensing of well-mixed gases, such as carbon dioxide (CO 2 ), where the atmospheric variability is small compared to the background, the quality of the molecular absorption model is key. Recent updates to the 1.6 µm and 2.06 µm CO 2 absorption model used within the Orbiting Carbon Observatory (OCO-2) algorithm are described and validated. A set of 164 atmospheric spectra from the Total Carbon Column Observing Network (TCCON) is used to compare three models, both previous and current versions of absorption coefficient tables (largely derived from recent multispectrum fitting analyses targeted specifically at these bands) as well as a recent model constructed to use the HITRAN 2012 compilation. Both spectral residuals and retrieved column-averaged CO 2 mixing ratios (XCO 2 ) are included in the comparison. Absorption coefficients based on the updated multispectrum fitting analyses provide residuals comparable to or smaller than either the previous version of the multispectrum fits or the HITRAN 2012-based model. For the 2.06 µm band the updated model finds noticeably lower residuals for low water content cases. It is found that apart from a scaling factor the prior and updated absorption models result in similar retrieved values of XCO 2 for the 2.06 µm band and a slightly different airmass dependence for the 1.6 µm band.

The Total Carbon Column Observing Network site description for Lauder, New Zealand

Abstract: . In this paper we describe the retrievals of atmospheric trace gases from near-infrared, high-resolution solar absorption spectroscopy measurements at the Lauder atmospheric research station in New Zealand and submitted to the Total Carbon Column Observing Network (TCCON) archive. The Lauder site (45.034° S, 169.68° E, 370 m a.s.l.) is located within a sparsely populated region of the South Island of New Zealand and is sheltered from the prevailing wind direction by the Southern Alps, which gives the site a high number of clear-sky days and an air mass that is largely unmodified by regional anthropogenic sources. The Lauder TCCON archive consists of data from two instruments: a Bruker IFS 120HR from June 2004 to February 2010 and a Bruker IFS 125HR from February 2010 to present. The bias between the two instruments is assessed to be 0.068 % for CO2. Since measurements using the IFS 125HR began, the SD about the hourly mean has been better than 0.1 % for 96.81 % of CO2 column retrievals. The retrievals have been calibrated against in situ airborne measurements to correct for biases and provide traceability to the World Meteorological Organization (WMO) scales with an accuracy of 0.1 % for CO2. The Lauder TCCON time series of retrieved dry-air mole fractions of CO2, CH4, N2O, HF, H2O, HDO and CO are available from the TCCON data archive. The DOIs are https://doi.org/10.14291/tccon.ggg2014.lauder01.R0/1149293 for the IFS 120HR data https://doi.org/10.14291/tccon.ggg2014.lauder02.R0/1149298 for the IFS 125HR data.

A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2

Abstract: Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO 2 (XCO 2 ) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O 2 and CO 2 absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO 2 data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO 2 retrievals. Therefore, so called full physics retrieval algorithms were developed aiming to minimize scattering related errors by explicitly fitting scattering related properties such as cloud water/ice content, aerosol optical thickness, cloud height, etc. However, the computational costs for multiple scattering radiative transfer (RT) calculations can be immense. Processing all data of the Orbiting Carbon Observatory-2 (OCO-2) can require up to thousands of CPU cores and the next generation of CO 2 monitoring satellites will produce at least an order of magnitude more data. For this reason, the Fast atmOspheric traCe gAs retrievaL FOCAL has been developed reducing the computational costs by orders of magnitude by approximating multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer. Here we confront FOCAL for the first time with measured OCO-2 data and protocol the steps undertaken to transform the input data (most importantly, the OCO-2 radiances) into a validated XCO 2 data product. This includes preprocessing, adaptation of the noise model, zero level offset correction, post-filtering, bias correction, comparison with the CAMS (Copernicus Atmosphere Monitoring Service) greenhouse gas flux inversion model, comparison with NASA’s operational OCO-2 XCO 2 product, and validation with ground based Total Carbon Column Observing Network (TCCON) data. The systematic temporal and regional differences between FOCAL and the CAMS model have a standard deviation of 1.0 ppm. The standard deviation of the single sounding mismatches amounts to 1.1 ppm which agrees reasonably well with FOCAL’s average reported uncertainty of 1.2 ppm. The large scale XCO 2 patterns of FOCAL and NASA’s operational OCO-2 product are similar and the most prominent difference is that FOCAL has about three times less soundings due to the inherently poor throughput (11%) of the MODIS (moderate-resolution imaging spectroradiometer) based cloud screening used by FOCAL’s preprocessor. The standard deviation of the difference between both products is 1.1 ppm. The validation of one year (2015) of FOCAL XCO 2 data with co-located ground based TCCON observations results in a standard deviations of the site biases of 0.67 ppm (0.78 ppm without bias correction) and an average scatter relative to TCCON of 1.34 ppm (1.60 ppm without bias correction).

Sensitivity of CO2 surface flux constraints to observational coverage

Abstract: Inverse modeling of regional CO2 fluxes using atmospheric CO2 data is sensitive to the observational coverage of the observing network. Here we use the GEOS-Chem adjoint model to examine the sensitivity to CO2 fluxes of observations from the in situ surface network, the Total Carbon Column Observing Network (TCCON), the Greenhouse Gases Observing Satellite (GOSAT), and the Orbiting Carbon Observatory (OCO-2). We find that OCO-2 has high sensitivity to fluxes throughout the tropics and Southern Hemisphere, while surface observations have high sensitivity to fluxes in the northern extratropics throughout the year. For GOSAT viewing modes, ocean glint data provide the strongest constraints on fluxes in the tropics and Southern Hemisphere during Northern Hemisphere fall and winter relative to other viewing modes. In contrast, GOSAT nadir land data offer the greater sensitivity to fluxes in these regions during Northern Hemisphere spring and summer. For OCO-2 viewing modes, ocean glint data provided the dominant sensitivity to the surface fluxes in the northern subtropics, tropics, and Southern Hemisphere. We performed a series of inversion analyses using pseudodata and found that the varying sensitivities can result in large differences in regional flux estimates. However, combining measurements from different observing systems to exploit their complementarity may lead to a posteriori flux estimates with improved accuracy.

TCCON Philippines: First Measurement Results, Satellite Data and Model Comparisons in Southeast Asia

Abstract: 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.

Emissions and topographic effects on column CO2 ( XCO2) variations, with a focus on the Southern California Megacity

Abstract: Within the California South Coast Air Basin (SoCAB), X_(CO)_2 varies significantly due to atmospheric dynamics and the nonuniform distribution of sources. X_(CO)_2 measurements within the basin have seasonal variation compared to the “background” due primarily to dynamics, or the origins of air masses coming into the basin. We observe basin-background differences that are in close agreement for three observing systems: Total Carbon Column Observing Network (TCCON) 2.3 ± 1.2 ppm, Orbiting Carbon Observatory-2 (OCO-2) 2.4 ± 1.5 ppm, and Greenhouse gases Observing Satellite 2.4 ± 1.6 ppm (errors are 1σ). We further observe persistent significant differences (∼0.9 ppm) in X_(CO)_2 between two TCCON sites located only 9 km apart within the SoCAB. We estimate that 20% (±1σ confidence interval (CI): 0%, 58%) of the variance is explained by a difference in elevation using a full physics and emissions model and 36% (±1σ CI: 10%, 101%) using a simple, fixed mixed layer model. This effect arises in the presence of a sharp gradient in any species (here we focus on CO_2) between the mixed layer (ML) and free troposphere. Column differences between nearby locations arise when the change in elevation is greater than the change in ML height. This affects the fraction of atmosphere that is in the ML above each site. We show that such topographic effects produce significant variation in X_(CO)_2 across the SoCAB as well.

Comparison of Global XCO2 Concentrations From OCO-2 With TCCON Data in Terms of Latitude Zones

Abstract: This work evaluated the performance of the orbiting carbon observatory 2 (OCO-2) in terms of global atmospheric CO 2 observations for 20 months (September 2014 to April 2016). Three versions of data on CO2 are currently available, namely, version 7, version 7r, and Lite File Product (Lite_FP). For the first time, we evaluated X CO2 measurements from three versions of OCO-2 in terms of utilization efficiency, spatiotemporal coverage, and measurement accuracy compared with data (GGG2014) from the total carbon column observing network (TCCON). In data application, Lite_FP usually displayed the most efficient data volume and relatively stable spatial coverage, i.e., 42% in global scale. In addition, the spatial coverage of X CO2 measurements on land and ocean displayed opposite periodic seasonal fluctuations. However, no data were obtained in some areas where research on carbon ecology is highly significant. In terms of measurement accuracy, we considered the latitude distribution of TCCON sites and performed a site-by-site comparison at different latitude zones between X CO2 from three versions of OCO-2 and TCCON. Results demonstrated that the periodic variation trend of X CO2 from OCO-2 was consistent with that from TCCON. Moreover, the amplitude was similar to that of TCCON except that several sites had significant seasonal variation amplitude. The mean bias of OCO-2 was generally < 0.8 ppm, with 0.55% deviation. Among the three versions of OCO-2, Lite_FP showed good result in filtering and bias correction in the mid-low latitudes but still needs improvement in the high latitudes of the Northern and the Southern Hemispheres.

EOF-based regression algorithm for the fast retrieval of atmospheric CO2 total column amount from the GOSAT observations

Abstract: This paper presents a novel retrieval algorithm for the rapid retrieval of the carbon dioxide total column amounts from high resolution spectra in the short wave infrared (SWIR) range observations by the Greenhouse gases Observing Satellite (GOSAT). The algorithm performs EOF (Empirical Orthogonal Function)-based decomposition of the measured spectral radiance and derives the relationship of limited number of the decomposition coefficients in terms of the principal components with target gas amount and a priori data such as airmass, surface pressure, etc. The regression formulae for retrieving target gas amounts are derived using training sets of collocated GOSAT and ground-based observations. The precision/accuracy characteristics of the algorithm are analyzed by the comparison of the retrievals with those from the Total Carbon Column Observing Network (TCCON) measurements and with the modeled data, and appear similar to those achieved by full-physics retrieval algorithms.

Methane profiles from GOSAT thermal infrared spectra

Abstract: . This paper discusses the retrieval of atmospheric methane profiles from the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) between 1210 and 1310 cm−1 , using the RemoTeC analysis software. Approximately one degree of information on the vertical methane distribution is inferred from the measurements, with the main sensitivity at about 9 km altitude but little sensitivity to methane in the lower troposphere. For verification, we compare the GOSAT-TIR methane profile retrieval results with profiles from model fields provided by the Monitoring Atmospheric Composition and Climate (MACC) project, scaled to the total column measurements of the Total Carbon Column Observing Network (TCCON) at ground-based measurement sites. Without any radiometric corrections of GOSAT observations, differences between both data sets can be as large as 10 %. To mitigate these differences, we developed a correction scheme using a principal component analysis of spectral fit residuals and airborne observations of methane during the HIAPER pole-to-pole observations (HIPPO) campaign II and III. When the correction scheme is applied, the bias in the methane profile can be reduced to less than 2 % over the whole altitude range with respect to MACC model methane fields. Furthermore, we show that, with this correction, the retrievals result in smooth methane fields over land and ocean crossings and no differences can be discerned between daytime and nighttime measurements. Finally, a cloud filter is developed for the nighttime and ocean measurements. This filter is rooted in the GOSAT-TIR (thermal infrared) measurements and its performance, in terms of biases, is consistent with the cloud filter based on the GOSAT-SWIR (shortwave infrared) measurements. The TIR filter shows a higher acceptance rate of observations than the SWIR filter, at the cost of a higher uncertainty in the retrieved methane profiles.

Total Carbon Column Observing Network Philippines: toward quantifying atmospheric carbon in Southeast Asia

Abstract: TCCON (Total Carbon Column Observing Network) is dedicated to the precise measurements of greenhouse gases such as CO2 and CH4. TCCON measurements are used extensively for satellite validation, for atmospheric chemistry modeling, and for carbon cycle studies. With the global effort to cap greenhouse gas emissions, TCCON has taken on a vital role in validating past, current, and future satellite missions such as Japan's Greenhouse Gases Observing Satellite (GOSAT & GOSAT-2), National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2 & future OCO-3), and others. However, the lack of reliable validation data for satellite-based greenhouse gas observations in the tropics is a common limitation in global carbon-cycle studies that have a tropical component. The international CO2 modeling community has specified a requirement for expansion of the CO2 observation network within the tropics in order to reduce uncertainties in regional estimates of CO2 sources and sinks. A TCCON site in the tropical western Pacific is a logical next step in obtaining additional knowledge that would greatly contribute to the understanding of the Earth's atmosphere and the carbon cycle. In this study, we present an assessment of a planned site in the Philippines where a new TCCON station, the first in Southeast Asia, will be installed.

High-resolution Fourier transform measurements of line strengths in the 00 0 2-00 0 0 main isotopologue band of nitrous oxide.

Abstract: Nitrous oxide (N2O) is a key greenhouse gas and a major ozone-depleting anthropogenic pollutant monitored by the total carbon column observing network (TCCON) in the 0002-0000-band of its main isotopologue. Here, we present highly accurate line strengths for this window determined using Fourier transform infrared spectroscopy with pressure, temperature, and optical path length being metrologically traceable to the SI. The obtained results agree with previous studies within the given uncertainties. Depending on the respective rovibrational transition, the present uncertainties could be reduced by a factor of 5 to 83 in comparison to literature data.

A high-accuracy method for simulating the XCO 2 global distribution using GOSAT retrieval data

Abstract: A high-accuracy surface modeling (HASM) method based on the fundamental theorem of surfaces, is developed to simulate XCO2 surfaces using the GOSAT retrieval XCO2 data. Two tests are designed to investigate the simulation accuracy. The first test divides the existing satellite retrieval XCO2 data into training points and testing points, and simulates the XCO2 surface using the training points while computing the simulation error using the testing points. The absolute mean error (MAE) of the testing points is 1.189 ppmv, and the corresponding values of the comparison methods, Ordinary Kriging, IDW, and Spline are 1.203, 1.301, and 1.355 ppmv, respectively. The second test simulates the XCO2 surface using all the satellite retrieval points and uses the TCCON (Total Carbon Column Observing Network) site observation values as the ture values. For the six typical TCCON sites, the HASM simulation MAE is 1.688 ppmv, and the satellite retrieval MAE at the same sites is 2.147 ppmv. These results indicate that HASM can successfully simulate XCO2 surfaces based on satellite retrieval data.

The arctic seasonal cycle of total column CO 2 and CH 4 from ground-based solar and lunar FTIR absorption spectrometry

Abstract: . Solar absorption spectroscopy in the near infrared has been performed in Ny-Alesund (78.9° N, 11.9° E) since 2002; however, due to the high latitude of the site, the sun is below the horizon from October to March (polar night) and no solar absorption measurements are possible. Here we present a novel method of retrieving the total column dry-air mole fractions (DMFs) of CO2 and CH4 using moonlight in winter. Measurements have been taken during the polar nights from 2012 to 2016 and are validated with TCCON (Total Carbon Column Observing Network) measurements by solar and lunar absorption measurements on consecutive days and nights during spring and autumn. The complete seasonal cycle of the DMFs of CO2 and CH4 is presented and a precision of up to 0.5 % is achieved. A comparison of solar and lunar measurements on consecutive days during day and night in March 2013 yields non-significant biases of 0. 66 ± 4. 56 ppm for xCO2 and −1. 94 ± 20. 63 ppb for xCH4. Additionally a model comparison has been performed with data from various reanalysis models.

Evaluation of XCO2 from OCO-2 Lite File Product compared with TCCON data

Abstract: To evaluate the performance of the Orbiting Carbon Observatory 2 (OCO-2) Lite File Product (Lite_FP) which has the highest amount of data and the highest utilization efficiency among the three products of OCO-2, we compared global atmospheric CO 2 observations for 20 months (September 2014 to April 2016) with GGG2014 data from the Total Carbon Column Observing Network (TCCON). We considered the latitude distribution of the TCCON sites and performed a site-by-site comparison at different latitude zones. The result demonstrated that the seasonal fluctuation of X CO2 from Lite_FP is consistent with TCCON, and the biases of X CO2 measurements ranged from −3 ppm to 4 ppm, with a 1% precision. Bias distribution differed in terms of latitude zones and observing modes. In addition, we analyzed the distribution characteristic of the bias of X CO2 observations under land target mode in detail combined with surface and atmospheric properties.

Characteristics of the Greenhouse Gas Concentration Derived from the Ground-based FTS Spectra at Anmyeondo, Korea

Abstract: Since the late 1990s, the meteorological observatory established in Anmyeondo (36.5382˚ N, 126.3311˚ E, and 30 m above mean sea level), has been monitoring several greenhouse gases such as CO 2 , CH 4 , N 2 O, CFCs, and SF 6 , as part of the Global Atmosphere Watch (GAW) Program. A high resolution ground-based (g-b) Fourier Transform Spectrometer (FTS, IFS-125HR model) was installed at such observation site in 2013, and has been fully operated within the frame work of the Total Carbon Column Observing Network (TCCON) since August, 2014. The solar spectra recorded by the g-b FTS are covered in the range between 3,800 and 16,000 cm −1 at the spectral resolution of 0.02 cm −1 during the measurement period between 2013 and 2016. In this work, the GGG2014 version of the TCCON standard retrieval algorithm was used to retrieve XCO 2 concentrations from the FTS spectra. Two spectral bands (at 6220.0 and 6339.5 cm −1 center wavenumbers) were used to derive the XCO 2 concentration within the spectral residual of +0.01 %. All sources of errors were thoroughly analyzed. In this paper, we introduced a new home made OASIS (Operational Automatic System for Intensity of Sunray) system to our g-b FTS instrument and that allows reducing the solar intensity variations (SIV) below 2 %. A comparison of the XCO 2 concentration in g-b FTS and OCO-2 (Orbiting Carbon Observatory) satellite observations were presented only for the measurement period between 2014 and 2015. Nine coincident observations were selected on a daily mean basis. It was obtained that OCO-2 exhibited low bias with respect to the g-b FTS, which is about −0.065 ppm with the standard deviation of 1.66 ppm, and revealed a strong correlation (R = 0.85). Based on seasonal cycle comparisons, both instruments were generally agreed in capturing seasonal variations of the target species with its maximum and minimum levels in spring and late summer respectively. In the future, it is planned to exert further works in utilizing the FTS measurements for the evaluation of satellite observations such as Greenhouse Gases Observing Satellite (GOSAT) at observation sites. This is the first report of the g-b FTS observations of XCO 2 species over the Anmyeondo station.

CO Total Column Retrieval from SCIAMACHY: Full-Mission Validation with NDACC and TCCON

Abstract: Verification and validation are critical elements of any code development, and mandatory for the assessment of spaceborne remote sensing products. The objective was to perform intercomparisons of CO columns estimated from SCIAMACHY with temporal coincidented and co-located retrievals provided by ground-based Fourier transform infrared spectroscopy. More specifically, we used column averaged dry air mole fractions from several NDACC (Network for the Detection of Atmospheric Composition Change) and TCCON (Total Carbon Column Observing Network) stations. Like SCIAMACHY's channel 8 the TCCON instruments utilize the 2.3 mue band of carbon monoxide, whereas NDACC observes the CO mid infrared absorption. In most cases, satellite validation is based on statistical comparison (specifically true for SCIAMACHY with its large signal to noise ratio per observation (Gimeno Garcia et al. 2011)) with reference data. However, satellite and reference measurements do neither exactly match in time and space (mistime and misdistance) nor address the same volume of air (misintegration). Hence, the natural atmospheric variability leads to differences between both data sets and these differences must not be interpreted in terms of a satellites instrument malfunction (Verhoelst et al. 2015). The validation strategy presented here accounts for spatial (and in selected cases temporal) induced mismatches. The outcome of the study demonstrates that spatial (and temporal) averaging is required in order to minimize representation errors. Increased deviations of the spaceborne and ground-based columns in the later years of the mission clearly demonstrated the impact of the degrading channel 8 detector. Therefore, in order to perform a comprehensive full-mission (2003 - 2012) validation of the SCIAMACHY dataset, an approach providing more observations within a given time interval and sampling area had been utilized. The results reveal that the combination of these methods lead to acceptable agreement of SCIAMACHY CO data with most g-b reference sites (within the standard deviation). In addition the effect of whether using the mean or median of the datasets for comparison was investigated. The outcome demonstrates that the differences between the SCIAMACHY CO mean and median values are within 1-10 percent in most cases and within the standard deviation of the reference observations.

On statistical approaches to generate Level 3 products from satellite remote sensing retrievals

Abstract: Satellite remote sensing of trace gases such as carbon dioxide (CO$_2$) has increased our ability to observe and understand Earth's climate. However, these remote sensing data, specifically~Level 2 retrievals, tend to be irregular in space and time, and hence, spatio-temporal prediction is required to infer values at any location and time point. Such inferences are not only required to answer important questions about our climate, but they are also needed for validating the satellite instrument, since Level 2 retrievals are generally not co-located with ground-based remote sensing instruments. Here, we discuss statistical approaches to construct Level 3 products from Level 2 retrievals, placing particular emphasis on the strengths and potential pitfalls when using statistical prediction in this context. Following this discussion, we use a spatio-temporal statistical modelling framework known as fixed rank kriging (FRK) to obtain global predictions and prediction standard errors of column-averaged carbon dioxide based on Version 7r and Version 8r retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite. The FRK predictions allow us to validate statistically the Level 2 retrievals globally even though the data are at locations and at time points that do not coincide with validation data. Importantly, the validation takes into account the prediction uncertainty, which is dependent both on the temporally-varying density of observations around the ground-based measurement sites and on the spatio-temporal high-frequency components of the trace gas field that are not explicitly modelled. Here, for validation of remotely-sensed CO$_2$ data, we use observations from the Total Carbon Column Observing Network. We demonstrate that the resulting FRK product based on Version 8r compares better with TCCON data than that based on Version 7r.