Scanning Imaging Absorption Spectrometer For Atmospheric Cartography Research Articles

Inter-comparison and evaluation of global satellite XCO2 products

ABSTRACT Carbon dioxide (CO2) is one of the main greenhouse gases and has become a major concern as its concentration has been growing in recent years. Satellite remote sensing is an efficient way to monitor CO2 in the atmosphere, and several satellites are already used for CO2 monitoring. It is imperative to investigate the spatial coverage and spatio-temporal trends of satellite products, as well as identify the satellites with higher levels of accuracy. Additionally, examining the disparities between the older and new generations of satellites would be meaningful. Therefore, this paper provides a comprehensive evaluation and inter-comparison for the commonly used satellite column-averaged dry-air mole fraction of CO2 (XCO2) products. Specifically, the temporal trends and monthly coverage of the Greenhouse Gases Observing SATellite (GOSAT), Greenhouse Gases Observing SATellite-2 (GOSAT-2), Orbiting Carbon Observatory-2 (OCO-2), Orbiting Carbon Observatory-3 (OCO-3), and SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) are investigated. The accuracy of these satellite products is evaluated and analyzed based on Total Carbon Column Observing Network (TCCON) data. The results indicate that the XCO2 of all the satellite products show a year-by-year increase, with seasonal periodicity. In terms of overall accuracy, the OCO series satellites exhibit a slightly higher level of accuracy compared to the GOSAT series. The products of the new generation of satellites are less stable than those of the older generation, probably due to the impacts of the inversion algorithm and platforms.

A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range

Abstract. We present a long-term data set of 1∘×1∘ monthly mean total column water vapour (TCWV) based on global measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. In comparison to the retrieval algorithm of Borger et al. (2020), several modifications and filters have been applied accounting for instrumental issues (such as OMI's “row anomaly”) or the inferior quality of solar reference spectra. For instance, to overcome issues related to low-quality reference spectra, the daily solar irradiance spectrum is replaced by an annually varying mean earthshine radiance obtained in December over Antarctica. For the TCWV data set, we only consider measurements with an effective cloud fraction less than 20 %, an air mass factor (AMF) greater than 0.1, a snow- and ice-free ground pixel, and an OMI row that is not affected by the row anomaly over the complete time range of the data set. The individual TCWV measurements are then gridded to a regular 1∘×1∘ lattice, from which the monthly means are calculated. The investigation of sampling errors in the OMI TCWV data set shows that these are dominated by the clear-sky bias and cause on average deviations of around −10 %, which is consistent with the findings of previous studies. However, the spatiotemporal sampling errors and those due to the row-anomaly filter are negligible. In a comprehensive intercomparison study, we demonstrate that the OMI TCWV data set is in good agreement with the global reference data sets of ERA5 (fifth-generation ECMWF atmospheric reanalysis), RSS SSM/I (Remote Sensing Systems Special Sensor Microwave Imager), and CM SAF/CCI TCWV-global (COMBI): over ocean the orthogonal distance regressions indicate slopes close to unity with very small offsets and high coefficients of determination of around 0.96. However, over land, distinctive positive deviations of more than +10 kg m−2 are obtained for high TCWV values. These overestimations are mainly due to extreme overestimations of high TCWV values in the tropics, likely caused by uncertainties in the retrieval input data (surface albedo, cloud information) due to frequent cloud contamination in these regions. Similar results are found from intercomparisons with in situ radiosonde measurements from version 2 of the Integrated Global Radiosonde Archive (IGRA2) data set. Nevertheless, for TCWV values smaller than 25 kg m−2, the OMI TCWV data set shows very good agreement with the global reference data sets. Furthermore, a temporal stability analysis proves that the OMI TCWV data set is consistent with the temporal changes in the reference data sets and shows no significant deviation trends. As the TCWV retrieval can be easily applied to further satellite missions, additional TCWV data sets can be created from past missions, such as the Global Ozone Monitoring Experiment-1 (GOME-1) or the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY); under consideration of systematic differences (e.g. due to different observation times), these data sets can be combined with the OMI TCWV data set in order to create a data record that would cover a time span from 1995 to the present. Moreover, the TCWV retrieval will also work for all missions dedicated to NO2 in the future, such as Sentinel-5 on MetOp-SG. The Max Planck Institute for Chemistry (MPIC) OMI total column water vapour (TCWV) climate data record (CDR) is available at https://doi.org/10.5281/zenodo.7973889 (Borger et al., 2023).

Historical trend of China's CH4 concentrations and emissions during 2003–2020 based on satellite observations, and their implications

China's anthropogenic CH4 emissions have been increasing before 2010, while no clear trends have been reported by previous studies regarding China's CH4 emissions post-2010. After analyzing the column-averaged dry-air concentration data of CH4 from a Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) and Greenhouse Gases Observing Satellite (GOSAT), this study found that the average concentration of CH4 in China increased from 1780 ppb in 2003 to 1873 ppb in 2020. The average growth rate of China's CH4 concentration was 2.8 ± 1.7 ppb/yr (parts per billion per year) during 2003–2009, and 8.5 ± 0.7 ppb/yr during 2010–2020, which is similar to the global concentration growth. The enhancement of the CH4 concentration in China relative to background concentrations increased by 0.94 ± 0.20 ppb/yr (4.4 ± 0.95%) during 2003–2009 and 0.09 ± 0.07 ppb/yr (0.49 ± 0.41%) during 2010–2020. This study implies that China's CH4 emissions increased rapidly during 2003–2009 but increased very slightly during 2010–2020.

A long-term global XCO2 dataset: Ensemble of satellite products

Many satellites such as SCIAMACHY (scanning imaging absorption spectrometer for atmospheric cartography), GOSAT-1/2 (greenhouse gases observing satellite), OCO-2/3 (orbiting carbon observatory) and TanSat (CarbonSat, Tan means “carbon” in Chinese) provide key observations of atmospheric CO2 concentration and different XCO2 retrieval algorithms have been developed for these satellite measurements. However, limited by the revisit period and scanning swath of the satellites, the effective daily observation coverage of satellites mentioned above is very small (<1%), which is a great challenge for high-resolution mapping of global XCO2. In this paper, we re-evaluated the uncertainty of each XCO2 pixel with 0.5° × 0.5° spatial resolution in the global based on season, AOD (aerosol optical depth), surface albedo, uncertainty parameters of SCIAMACHY, GOSAT-1/2, OCO-2/3 and TanSat XCO2 products by TCCON (Total Carbon Column Observing Network) data. Then, a 30-day time window was used to smooth XCO2 datasets from satellite observations and filled in the missing values according to CarbonTracker. Finally, we ensembled global XCO2 datasets using maximum likelihood estimation (MLE) method and optimal interpolation (OI) based on the re-evaluated uncertainty. The ensemble XCO2 dataset at 0.5° × 0.5° spatial resolution for every three hours from January 2003 to August 2020 was generated. Compared to TCCON and WDCGG (World Data Centre for Greenhouse Gases) measurements separately on the global scale, we obtained the correlation coefficient R = 0.96, the Root Mean Square Error (RMSE) of 2.62 ppm for TCCON and R = 0.82, the RMSE of 6.75 ppm for WDCGG, respectively. The RMSE of the ensemble dataset was 6 ppm lower than that of the satellites' XCO2 used for fusion compared with WDCGG and the coverage is greatly improved at a higher spatial and temporal resolution, proving the feasibility and high precision of the method.

An Entropy Framework for Evaluating Reflectance Observations for Climate Studies

AbstractThis study evaluated the comparative entropy represented in shortwave (300–1,750 nm) passive remote sensing measurements from conceptual instruments with varying systematic measurement uncertainties and spectral resolutions. The focus was on spectral and broadband reflectance averaged over large spatial scales typically relevant for climate change studies. Information theory was applied to quantify how the normalized Shannon entropy changed for different conceptual instruments and over different spatial scales. Reflectance measurements from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) were used to represent the observed reflectance, and simulated reflectance from climate Observing System Simulations Experiments (OSSEs) were used to represent decadal‐ and centennial‐length data sets free from instrument artifacts. Similar to previous studies, the simulated OSSE spectra were shown to be a sufficient proxy for the observed reflectance. As hypothesized, the normalized entropy decreased with increasing measurement uncertainty and increasing spectral bandwidth. Additionally, the entropy decreased for increasingly large spatial scales, particularly for measurement uncertainties larger than 2%. When applied to OSSE reflectance simulated from a forced CMIP3 climate model simulation, the change in entropy with measurement uncertainty and spectral resolution provided insight into measurement attributes needed to monitor a changing climate system and highlighted the importance of sufficiently high accuracy and spectral resolution for detecting and attributing climate trends. These preliminary studies illustrate the value of this information theory‐based framework in instrument design by calculating the entropy, used to represent information in measurements from different conceptual instruments.

Variations in O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;, CO, and CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; over the Bay of Bengal during the summer monsoon season: shipborne measurements and model simulations

Abstract. We present shipborne measurements of surface ozone (O3), carbon monoxide (CO), and methane (CH4) over the Bay of Bengal (BoB), the first time such measurements have been performed during the summer monsoon season, as a part of the Continental Tropical Convergence Zone (CTCZ) experiment during 2009. O3, CO, and CH4 mixing ratios exhibited significant spatial and temporal variability in the ranges of 8–54 nmol mol−1, 50–200 nmol mol−1, and 1.57–2.15 µmol mol−1, with means of 29.7 ± 6.8 nmol mol−1, 96 ± 25 nmol mol−1, and 1.83 ± 0.14 µmol mol−1, respectively. The average mixing ratios of trace gases over BoB in air masses from central/northern India (O3: 30 ± 7 nmol mol−1; CO: 95 ± 25 nmol mol−1; CH4: 1.86 ± 0.12 µmol mol−1) were not statistically different from those in air masses from southern India (O3: 27 ± 5 nmol mol−1; CO: 101 ± 27 nmol mol−1; CH4: 1.72 ± 0.14 µmol mol−1). Spatial variability is observed to be most significant for CH4 with higher mixing ratios in the air masses from central/northern India, where higher CH4 levels are seen in the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) data. O3 mixing ratios over the BoB showed large reductions (by ∼ 20 nmol mol−1) during four rainfall events. Temporal changes in the meteorological parameters, in conjunction with O3 vertical profile, indicate that these low-O3 events are associated with downdrafts of free-tropospheric O3-poor air masses. While the observed variations of O3 and CO are successfully reproduced using the Weather Research and Forecasting model with Chemistry (WRF-Chem), this model overestimates mean concentrations by about 6 and 16 % for O3 and CO, respectively, generally overestimating O3 mixing ratios during the rainfall events. An analysis of modelled O3 along air mass trajectories show mean en route O3 production rate of about 4.6 nmol mol−1 day−1 in the outflow towards the BoB. Analysis of the various tendencies from model simulations during an event on 10 August 2009, reproduced by the model, shows horizontal advection rapidly transporting O3-rich air masses from near the coast across the BoB. This study fills a gap in the availability of trace gas measurements over the BoB and, when combined with data from previous campaigns, reveals large seasonal amplitude ( ∼ 39 and ∼ 207 nmol mol−1 for O3 and CO, respectively) over the northern BoB.

Anthropogenic emissions and space-borne observations of carbon monoxide over South Asia

The focus of this study is to understand anthropogenic emissions, spatiotemporal variability and trends of carbon monoxide (CO) over South Asia by using datasets from MACCity (Monitoring Atmospheric Composition and Climate, MACC and megaCITY – Zoom for the Environment, CityZEN), REAS (Regional Emission inventory in Asia), AIRS (Atmospheric Infrared Sounder) and SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY). MACCity anthropogenic emissions show an overall increase of 16.5% during 2000–2010. Elevated levels of MACCity CO are found in Indo-Gangetic Basin (IGB), eastern mining region of India, Bangladesh and large urban areas. Some of the major contributors of these emissions have been identified as agricultural waste burning, land transport, industrial production, and energy generation and distribution. An area averaged mean value of AIRS CO at 600hPa is found to be 114±2ppbv (slope −0.48±0.2ppbvyr−1, y-intercept 117±1ppbv and r=0.68) with a minor declining trend at −0.41±0.18%yr−1 over the region during 2003–2015. A strong seasonality in AIRS CO concentration is observed with spring season peak in March 129±1.9ppbv, whereas low values have been observed in summer monsoon with sturdy dip in July 99.6±1.94ppbv. AIRS CO and SCIAMACHY CO Total Column (CO TC) over the study region show spatial patterns similar to MACCity and REAS emissions. An analysis of SCIAMACHY CO TC tendencies has been performed which indicates minor rising trends over some parts of the region. Background CO, Recent Emissions (RE), and spatial anomalies in RE over high anthropogenic activity zones of Indus Basin, Ganges Basin and Eastern Region were analyzed using AIRS and SCIAMACHY CO data.

Time-resolved observation of volcanic ash using COMS/MI: A case study from the 2011 Shinmoedake eruption

Abstract Between January 26 and 27, 2011, Shinmoedake volcano, located in the Krishima complex in Kyushu, Japan, produced a number of explosive eruptions with ash clouds reaching up to 8.5 km above sea level. Volcanic ash (VA) plumes from those eruptions affected the population near the volcano and posed a significant threat to aviation. Since its launch on June 27, 2010, the Meteorological Imager (MI) onboard Korea's first geostationary meteorological satellite, the Communication, Ocean, and Meteorological Satellite (COMS), has provided time-resolved images (less than 27-minute intervals for the full disk) at 1 km spatial resolution for the visible (VIS) channel and 4 km spatial resolution for the four infrared (IR) channels. In this study, MI operational data with one broadband VIS channel (centered at 0.68 μm) and three IR channels (centered at 3.7, 10.8, and 12.0 μm) for eruption events occurring at Shinmoedake were analyzed using the formerly developed hybrid VA detection and retrieval algorithm. We analyzed time-resolved VA plumes produced during three subplinian eruptions and found that spatio-temporal distributions of VA pixels determined by this analysis agreed with the source-receptor regions in meteorological trajectory modeling as well as with the operational products from the Global Ozone Monitoring Experiment-2 (GOME-2), Scanning Imaging Absorption spectrometer for Atmospheric Cartography (SCIAMACHY), and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For the three subplinian eruptions, VA retrieval results revealed that the maximum total ash mass loadings were 115.1, 1309.7, and 51.3 t, and the maximum height reached by the ash clouds were 5.6 ± 0.7, 10.2 ± 2.4, and 5.6 ± 0.8 km, respectively. These values imply that the second eruption emitted the largest amount of VA into the atmosphere. Overall, this is the first study that has been conducted to detect VA using the COMS/MI observations, which have been determined to provide reliable estimates of VA.

Stratospheric Aerosol Extinction Profile Retrieval from SCIAMACHY Limb Measurements

AbstractStratospheric aerosol extinction profiles are retrieved from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) limb scatter measurements. In the process of retrieval, the SCIATRAN radiative transfer model is used to simulate the limb scattering radiation received by the SCIAMACHY instrument, and an optimal estimation algorithm is used to calculate the aerosol extinction profiles. Sensitivity analyses are performed to investigate the impact of the surface albedo on the accuracy of the retrieved aerosol extinction profiles in the northern midlatitudes. It is found that the errors resulting from the bias of the assumed surface albedo in the retrieval are generally below 6%. The retrieved SCIAMACHY aerosol extinction profiles are compared with corresponding Stratospheric Aerosol and Gas Experiment (SAGE) II measurements, and the results indicate that for the zonal mean profiles, the SCIAMACHY retrievals show good agreement with SAGE II measurements, with the absolute dif...

Comparison analysis of the global carbon dioxide concentration column derived from SCIAMACHY, AIRS, and GOSAT with surface station measurements

As a significant greenhouse gas, CO2 plays an important role in not only the formation of, but also in changes to, the Earth’s climate. A series of remote-sensing detectors have been launched into space to aid the understanding of sources and sinks of CO2. Although measurements from AIRS (Atmospheric Infrared Sounder), SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography), and GOSAT (Greenhouse Gases Observing Satellite) have been frequently used to retrieve atmospheric CO2 concentrations, there are no comprehensive comparison analyses between satellite data and ground data. In this article, the characteristics of the current common observing platforms and their product data are compared and analysed. Correlation coefficient, RMSD, and bias are used to evaluate the CO2 retrieved from satellite data. The results reveal that: SCIAMACHY has limitations in detecting CO2 over the ocean; GOSAT has the poorest coverage on a global scale but has a better capability to detect CO2 over the sea than SCIAMACHY; and AIRS can reflect the distribution and changes of CO2 very well. The differences in coverage and accuracy indicate the necessity to produce consistent products with improved spatial and temporal features and indicate a future development trend for instruments including higher spectral resolution, higher spatial resolution, wider coverage, shorter revisit periods, and higher signal to noise ratios.

A GIS-based assessment of the suitability of SCIAMACHY satellite sensor measurements for estimating reliable CO concentrations in a low-latitude climate.

An assessment of the reliability of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) satellite sensor measurements to interpolate tropospheric concentrations of carbon monoxide considering the low-latitude climate of the Niger Delta region in Nigeria was conducted. Monthly SCIAMACHY carbon monoxide (CO) column measurements from January 2,003 to December 2005 were interpolated using ordinary kriging technique. The spatio-temporal variations observed in the reliability were based on proximity to the Atlantic Ocean, seasonal variations in the intensities of rainfall and relative humidity, the presence of dust particles from the Sahara desert, industrialization in Southwest Nigeria and biomass burning during the dry season in Northern Nigeria. Spatial reliabilities of 74 and 42 % are observed for the inland and coastal areas, respectively. Temporally, average reliability of 61 and 55 % occur during the dry and wet seasons, respectively. Reliability in the inland and coastal areas was 72 and 38 % during the wet season, and 75 and 46 % during the dry season, respectively. Based on the results, the WFM-DOAS SCIAMACHY CO data product used for this study is therefore relevant in the assessment of CO concentrations in developing countries within the low latitudes that could not afford monitoring infrastructure due to the required high costs. Although the SCIAMACHY sensor is no longer available, it provided cost-effective, reliable and accessible data that could support air quality assessment in developing countries.

Inverse modelling of CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; emissions for 2010–2011 using different satellite retrieval products from GOSAT and SCIAMACHY

Abstract. At the beginning of 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH4) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent {methane (CH4) retrievals} were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on board the ENVironmental SATellite (ENVISAT). The GOSAT and SCIAMACHY XCH4 retrievals can be compared during the period of overlap. We estimate monthly average CH4 emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modelling system. In addition to satellite data, high-accuracy measurements from the Cooperative Air Sampling Network of the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) are used, providing strong constraints on the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH4 retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC "Full-Physics" (FP) XCH4 retrievals available from SRON/KIT. The GOSAT-based inversions show significant reductions in the root mean square (rms) difference between retrieved and modelled XCH4, and require much smaller bias corrections compared to the inversion using SCIAMACHY retrievals, reflecting the higher precision and relative accuracy of the GOSAT XCH4. Despite the large differences between the GOSAT and SCIAMACHY retrievals, 2-year average emission maps show overall good agreement among all satellite-based inversions, with consistent flux adjustment patterns, particularly across equatorial Africa and North America. Over North America, the satellite inversions result in a significant redistribution of CH4 emissions from North-East to South-Central United States. This result is consistent with recent independent studies suggesting a systematic underestimation of CH4 emissions from North American fossil fuel sources in bottom-up inventories, likely related to natural gas production facilities. Furthermore, all four satellite inversions yield lower CH4 fluxes across the Congo basin compared to the NOAA-only scenario, but higher emissions across tropical East Africa. The GOSAT and SCIAMACHY inversions show similar performance when validated against independent shipboard and aircraft observations, and XCH4 retrievals available from the Total Carbon Column Observing Network (TCCON).

Intercomparison of daytime stratospheric NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; satellite retrievals and model simulations

Abstract. This paper evaluates the agreement between stratospheric NO2 retrievals from infrared limb sounders (Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and High Resolution Dynamics Limb Sounder (HIRDLS)) and solar UV/VIS backscatter sensors (Ozone Monitoring Instrument (OMI), Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) limb and nadir) over the 2005–2007 period and across the seasons. The observational agreement is contrasted with the representation of NO2 profiles in 3-D chemical transport models such as the Whole Atmosphere Community Climate Model (WACCM) and TM4. A conclusion central to this work is that the definition of a reference for stratospheric NO2 columns formed by consistent agreement among SCIAMACHY, MIPAS and HIRDLS limb records (all of which agree to within 0.25 × 1015 molecules cm−2 or better than 10%) allows us to draw attention to relative errors in other data sets, e.g., (1) WACCM overestimates NO2 densities in the extratropical lower stratosphere, particularly in the springtime and over northern latitudes by up to 35% relative to limb observations, and (2) there are remarkable discrepancies between stratospheric NO2 column estimates from limb and nadir techniques, with a characteristic seasonally and latitudinally dependent pattern. We find that SCIAMACHY nadir and OMI stratospheric columns show overall biases of −0.5 × 1015 molecules cm−2 (−20%) and +0.6 × 1015 molecules cm−2 (+20%) relative to limb observations, respectively. It is argued that additive biases in nadir stratospheric columns are not expected to affect tropospheric retrievals significantly, and that they can be attributed to errors in the total slant column density, related either to algorithmic or instrumental effects. In order to obtain accurate and long-term time series of stratospheric NO2, an effort towards the harmonization of currently used differential optical absorption spectroscopy (DOAS) approaches to nadir retrievals becomes essential, as well as their agreement to limb and ground-based observations, particularly now that limb techniques are giving way to nadir observations as the next generation of climate and air quality monitoring instruments pushes forth.

The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; retrieval algorithm products with measurements from the TCCON

Abstract. Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO2, all algorithms reach the precision requirements for inverse modelling (&lt; 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding &lt; 0.5 ppm threshold. For XCH4, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the &lt; 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH4 precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the &lt; 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively.

An analysis of the global spatial variability of column-averaged CO2 from SCIAMACHY and its implications for CO2 sources and sinks

Satellite observations of carbon dioxide (CO2) are important because of their potential for improving the scientific understanding of global carbon cycle processes and budgets. We present an analysis of the column-averaged dry air mole fractions of CO2 (denoted XCO2) of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) retrievals, which were derived from a satellite instrument with relatively long-term records (2003–2009) and with measurements sensitive to the near surface. The spatial-temporal distributions of remotely sensed XCO2 have significant spatial heterogeneity with about 6–8% variations (367–397 ppm) during 2003–2009, challenging the traditional view that the spatial heterogeneity of atmospheric CO2 is not significant enough (<4%) to have any large effect on terrestrial ecosystem carbon cycles. By comparison with surface measurements from the NOAA Earth System Research Laboratory (ESRL) GLOBALVIEW network, significant statistical relationships between XCO2 and surface CO2 were found for major ecosystems, with the exception of tropical forest. In addition, when compared with a simulated terrestrial carbon uptake from the Integrated Biosphere Simulator (IBIS) and the Emissions Database for Global Atmospheric Research (EDGAR) carbon emission inventory, the latitudinal gradient of XCO2 seasonal amplitude was influenced by the combined effect of terrestrial carbon uptake, carbon emission, and atmospheric transport, suggesting no direct implications for terrestrial carbon sinks. From the investigation of the growth rate of XCO2 we found that the increase of CO2 concentration was dominated by temperature in the northern hemisphere (20–90°N) and by precipitation in the southern hemisphere (20–90°S), with the major contribution to global average occurring in the northern hemisphere. These findings indicated that the satellite measurements of atmospheric CO2 improve not only the estimations of atmospheric inversion, but also the understanding of the terrestrial ecosystem carbon dynamics and its feedback to atmospheric CO2.

Observed atmospheric total column ozone distribution from SCIAMACHY over Peninsular Malaysia

The increase in atmospheric ozone has received great attention because it degrades air quality and brings hazard to human health and ecosystems. The aim of this study was to assess the seasonal variations of ozone concentrations in Peninsular Malaysia from January 2003 to December 2009 using Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Level-2 data of total column ozone WFMD version 1.0 with spatial resolution 1° × 1.25° were acquired through SCIAMACHY. Analysis for trend of five selected sites exhibit strong seasonal variation in atmospheric ozone concentrations, where there is a significant difference between northeast monsoon and southwest monsoon. The highest ozone values occurred over industrial and congested urban zones (280.97 DU) on August at Bayan Lepas. The lowest ozone values were observed during northeast monsoon on December at Subang (233.08 DU). In addition, the local meteorological factors also bring an impact on the atmospheric ozone. During northeast monsoon, with the higher rate of precipitation, higher relative humidity, low temperature, and less sunlight hours let to the lowest ozone concentrations. Inversely, the highest ozone concentrations observed during southwest monsoon, with the low precipitation rate, lower relative humidity, higher temperature, and more sunlight hours. Back trajectories analysis is carried out, in order to trace the path of the air parcels with high ozone concentration event, suggesting cluster of trajectory (from southwest of the study area) caused by the anthropogenic sources associated with biogenic emissions from large tropical forests, which can make important contribution to regional and global pollution.

Estimation of the lifetime of nitrogen oxides over India using SCIAMACHY observations

The NOx lifetime is needed to determine NOx source strength and allow for source apportionment studies. Satellite observations of tropospheric column NO2 may be used to estimate the NOx lifetime by analysing tropospheric NO2 decay in the outflow region. In this study, we used Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) observations of tropospheric NO2 to quantify the lifetime of NOx, ‘’, over the Indian region. We analysed a tropospheric NO2 plume that originated over the city of Mumbai and its downwind evolution over the Arabian Sea as a function of distance (decay curve). The observed pattern of decay curve is fitted with the exponential decay fit model, which gives an e-folding distance of 107 km. The fit result yields a mean lifetime of NOx of about = 10.2 h based on SCIAMACHY observations. In addition, we quantified using CHIMERE chemical mechanisms and compared the result with the satellite-based estimation. The estimated for India, based on CHIMERE simulation, is about 11.9 h, comparable to the calculated by satellite observations.

Homogenized total ozone data records from the European sensors GOME/ERS‐2, SCIAMACHY/Envisat, and GOME‐2/MetOp‐A

AbstractWithin the European Space Agency's Climate Change Initiative, total ozone column records from GOME (Global Ozone Monitoring Experiment), SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY), and GOME‐2 have been reprocessed with GODFIT version 3 (GOME‐type Direct FITting). This algorithm is based on the direct fitting of reflectances simulated in the Huggins bands to the observations. We report on new developments in the algorithm from the version implemented in the operational GOME Data Processor v5. The a priori ozone profile database TOMSv8 is now combined with a recently compiled OMI/MLS tropospheric ozone climatology to improve the representativeness of a priori information. The Ring procedure that corrects simulated radiances for the rotational Raman inelastic scattering signature has been improved using a revised semi‐empirical expression. Correction factors are also applied to the simulated spectra to account for atmospheric polarization. In addition, the computational performance has been significantly enhanced through the implementation of new radiative transfer tools based on principal component analysis of the optical properties. Furthermore, a soft‐calibration scheme for measured reflectances and based on selected Brewer measurements has been developed in order to reduce the impact of level‐1 errors. This soft‐calibration corrects not only for possible biases in backscattered reflectances, but also for artificial spectral features interfering with the ozone signature. Intersensor comparisons and ground‐based validation indicate that these ozone data sets are of unprecedented quality, with stability better than 1% per decade, a precision of 1.7%, and systematic uncertainties less than 3.6% over a wide range of atmospheric states.

Improved volcanic ash detection based on a hybrid reverse absorption technique

A noble volcanic ash (VA) detection method based on a hybrid reverse absorption technique was successfully applied in the analysis of major volcanic eruptions that occurred in Russia, Iceland, Chile, Italy, and Japan by using the MODerate-resolution Imaging Spectroradiometer (MODIS) observation data. Sensitivity studies using radiative-transfer simulations by using various environmental parameters such as ash loadings, sizes, layer heights, and surface emissions, revealed that VA effects on brightness temperatures (BT) can reach up to 40K. The advantage of the hybrid algorithm is its ability to detect distinct VA pixels during the day and night from satellite observations. The results showed that the hybrid algorithm can minimize the false detection of VA pixels, while well-known reverse absorption methods show abundant false VA pixels over bright surfaces and cloud formations. Further, the time-and-space distribution of the VA pixels is in good agreement with the data pertaining to operational aerosol products obtained from the scanning imaging absorption spectrometer for atmospheric cartography (SCIAMACHY) instrument on board ESA's Envisat and the cloud-aerosol Lidar and infrared pathfinder satellite observations (CALIPSO). This novel algorithm is expected to provide a fine spatial and temporal resolution of VA monitoring from high spectral or geostationary satellite observation data.

On the consistency between global and regional methane emissions inferred from SCIAMACHY, TANSO-FTS, IASI and surface measurements

Abstract. Satellite retrievals of methane weighted atmospheric columns are assimilated within a Bayesian inversion system to infer the global and regional methane emissions and sinks for the period August 2009 to July 2010. Inversions are independently computed from three different space-borne observing systems and one surface observing system under several hypotheses for prior-flux and observation errors. Posterior methane emissions are compared and evaluated against surface mole fraction observations via a chemistry-transport model. Apart from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY), the simulations agree fairly well with the surface mole fractions. The most consistent configurations of this study using TANSO-FTS (Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer), IASI (Infrared Atmospheric Sounding Interferometer) or surface measurements induce posterior methane global emissions of, respectively, 565 ± 21 Tg yr−1, 549 ± 36 Tg yr−1 and 538 ± 15 Tg yr−1 over the one-year period August 2009–July 2010. This consistency between the satellite retrievals (apart from SCIAMACHY) and independent surface measurements is promising for future improvement of CH4 emission estimates by atmospheric inversions.