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Carbon Monitoring Satellite (CarbonSat) is one of two candidate missions for ESA's Earth Explorer 8 (EE8) satellite to be launched around the end of this decade. The overarching objective of the CarbonSat mission is to improve our understanding of natural and anthropogenic sources and sinks of the two most important anthropogenic greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4). The unique feature of CarbonSat is its "GHG imaging capability", which is achieved via a combination of high spatial resolution (2 km×2 km) and good spatial coverage (wide swath and gap-free across- and along-track ground sampling). This capability enables global imaging of localized strong emission source, such as cities, power plants, methane seeps, landfills and volcanos, and likely enables better disentangling of natural and anthropogenic GHG sources and sinks. Source-sink information can be derived from the retrieved atmospheric column-averaged mole fractions of CO2 and CH4, i.e. XCO2 and XCH4, by inverse modelling. Using the most recent instrument and mission specification, an error analysis has been performed using the Bremen optimal EStimation DOAS (BESD/C) retrieval algorithm. We assess the retrieval performance for atmospheres containing aerosols and thin cirrus clouds, assuming that the retrieval forward model is able to describe adequately all relevant scattering properties of the atmosphere. To compute the errors for each single CarbonSat observation in a one-year period, we have developed an error parameterization scheme comprising six relevant input parameters: solar zenith angle, surface albedo in two bands, aerosol and cirrus optical depth, and cirrus altitude variations. Other errors, e.g. errors resulting from aerosol type variations, are partially quantified but not yet accounted for in the error parameterization. Using this approach, we have generated and analysed one year of simulated CarbonSat observations. Using this data set we estimate that systematic errors are for the overwhelming majority of cases (≈85 %) below 0.3 ppm for XCO2 (below 0.5 ppm for 99.5 %) and below 2 ppb for XCH4 (below 4 ppb for 99.3 %).We also show that the single-measurement precision is typically around 1.2 ppm for XCO2 and 7 ppb for XCH4 (1σ). The number of quality-filtered observations over cloud- and ice-free land surfaces is in the range of 33 to 47 million per month depending on season. Recently it has been shown that terrestrial vegetation chlorophyll fluorescence (VCF) emission needs to be considered for accurate XCO2 retrieval. We therefore retrieve VCF from clear Fraunhofer lines located around 755 nm and show that CarbonSat will provide valuable information on VCF. We estimate that the VCF single-measurement precision is approximately 0.3mWm-2 nm-1 sr-1 (1σ). [ABSTRACT FROM AUTHOR]

Carbon Monitoring Satellite (CarbonSat) is one of two candidate missions for ESA's Earth Explorer 8 (EE8) satellite -- the selected one to be launched around the end of this decade. The objective of the CarbonSat mission is to improve our understanding of natural and anthropogenic sources and sinks of the two most important anthropogenic greenhouse gases (GHG) carbon dioxide (CO2) and methane (CH4). The unique feature of CarbonSat is its "GHG imaging capability", which is achieved via a combination of high spatial resolution (2 km x 2 km) and good spatial coverage (wide swath and gap-free across- and along-track ground sampling). This capability enables global imaging of localized strong emission source such as cities, power plants, methane seeps, landfills and volcanos and better disentangling of natural and anthropogenic GHG sources and sinks. Source/sink information can be derived from the retrieved atmospheric column-averaged mole fractions of CO2 and CH4, i.e. XCO2 and XCH4, via inverse modeling. Using the most recent instrument and mission specification, an error analysis has been performed using the BESD/C retrieval algorithm. We focus on systematic errors due to aerosols and thin cirrus clouds, as this is the dominating error source especially with respect to XCO2 systematic errors. To compute the errors for each single CarbonSat observation in a one year time period, we have developed an error parameterization scheme based on six relevant input parameters: we consider solar zenith angle, surface albedo in two bands, aerosol and cirrus optical depth, and cirrus altitude variations but neglect, for example, aerosol type variations. Using this method we have generated and analyzed one year of simulated CarbonSat observations. Using this data set we estimate that scattering related systematic errors are mostly (approx. 85%) below 0.3 ppm for X CO2 (<0.5 ppm: 99.5%) and below 2 ppb for XCH 4 (<4 ppb: 99.3%). We also show that the single measurement precision is typically around 1.2 ppm for XCO 2and 7 ppb for XCH 4 (1-sigma). The number of quality filtered observations over cloud and ice free land surfaces is in the range 33-47million per month depending on month. Recently it has been shown that terrestrial Vegetation Chlorophyll Fluorescence (VCF) emission needs to be considered for accurate X CO2 retrieval. We therefore retrieve VCF from clear Fraunhofer lines located at 755 nm and show that CarbonSat will provide valuable information on VCF. The VCF single measurement precision is approximately 0.3mWm-2 nm-1 sr-1 (1-sigma). As a first application of the one year data set we assess the capability of CarbonSat to quantify the CO2 emissions of large cities using Berlin, the capital of Germany, as an example. We show that the precision of the inferred Berlin CO2 emissions as obtained from single CarbonSat overpasses is in the range 5-10MtCO2yr-1 (10-20%). We found that systematic errors could be on the same order depending on which assumptions are used with respect to observational and biogenic XCO2 modeling errors. [ABSTRACT FROM AUTHOR]

The validation of ozone profiles retrieved by satellite instruments through comparison with data from ground-based instruments is important to monitor the evolution of the satellite instrument, to assist algorithm development and to allow multi-mission trend analyses. In this study we compare ozone profiles derived from GOMOS night-time observations with measurements from lidar, microwave radiometer and balloon sonde. Collocated pairs are analysed for dependence on several geophysical and instrument observational parameters. Validation results are presented for the operational ESA level 2 data (GOMOS version 5.00) obtained during nearly seven years of observations and a comparison for a smaller dataset from the previous processor (version 4.02) is also included. The profiles obtained from dark limb measurements when the provided processing flag is properly considered match the ground-based measurements within ±2% over the altitude range 20 to 40 km. Outside this range, the pairs start to deviate more and there is a latitudinal dependence: in the polar region (higher amount of straylight contamination), differences are start to occur lower in the mesosphere than in the tropics, whereas for the lower part of the stratosphere the opposite happens: the profiles in the tropics reach less far down as the signal reduces faster because of the higher altitude at which the maximum ozone concentration is found compared to the mid and polar latitudes. Also the bias is shifting from mostly negative in the polar region to more positive in the tropics. Profiles measured under "twilight" conditions are often matching the ground-based measurements very well, but care has to be taken in all cases when dealing with "straylight" contaminated profiles. For the selection criteria applied here (data within 800 km, 3 degrees in equivalent latitude, 20 h (5 h above 50 km) and a relative ozone error in the GOMOS data of 20% or less), no dependence was found on stellar magnitude, star temperature, nor the azimuth angle of the line of sight. No evidence of a temporal trend was seen either in the bias or frequency of outliers, but a comparison applying less strict data selection criteria might show differently. [ABSTRACT FROM AUTHOR]

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) has been operating since March 2002 onboard of the ENVIronmental SATellite of the European Space Agency (ESA). The high resolution (0.035 cm-1 full width half maximum, unapodized) limb-emission measurements acquired by MIPAS in the first two years of operation have very good geographical and temporal coverage and have been re-processed by ESA with the most recent versions (4.61 and 4.62) of the inversion algorithms. The products of this processing chain are pressures at the tangent points and geolocated profiles of temperature and of the volume mixing ratios of six key atmospheric constituents: H2O, O3, HNO3, CH4, N2O and NO2. As for all the measurements made with innovative instruments and techniques, this data set requires a thorough validation. In this paper we present a geophysical validation of the temperature profiles derived from MIPAS measurements by the ESA retrieval algorithm. The validation is carried-out by comparing MIPAS temperature with correlative measurements made by radiosondes, lidars, in-situ and remote sensors operated either from the ground or stratospheric balloons. The results of the intercomparison indicate that the bias of the MIPAS profiles is generally smaller than 1 or 2K depending on altitude. Furthermore we find that, especially at the edges of the altitude range covered by the MIPAS scan, the random error estimated from the intercomparison is larger (typically by a factor of two to three) than the corresponding estimate derived on the basis of error propagation. In this work we also characterize the discrepancies between MIPAS temperature and the temperature fields resulting from the analyses of the European Centre for Medium-range Weather Forecasts (ECMWF). The bias and the standard deviation of these discrepancies are consistent with those obtained when comparing MIPAS to correlative measurements; however, in this case the detected bias has a peculiar behavior as a function of altitude. This behavior is very similar to that observed in previous studies and is suspected to be due to vertical oscillations in the ECMWF temperature. The current understanding is that, at least in the upper stratosphere (above ≈10 hPa), these oscillations are caused by a discrepancy between model biases and biases of assimilated radiances from primarily nadir sounders. [ABSTRACT FROM AUTHOR]

We discuss the quality of the two available SCIAMACHY limb ozone profile products. They were retrieved with the University of Bremen IFE's algorithm version 1.61 (hereafter IFE), and the official ESA offline algorithm (hereafter OL) versions 2.4 and 2.5. The ozone profiles were compared to a suite of correlative measurements from ground-based lidar and microwave, sondes, SAGE II and SAGE III (Stratospheric Aerosol and Gas Experiment). To correct for the expected Envisat pointing errors, which have not been corrected implicitly in either of the algorithms, we applied a constant altitude shift of -1.5 km to the SCIAMACHY ozone profiles. The IFE ozone profile data between 16 and 40 km are biased low by 3-6%. The average difference profiles show a characteristic zigzag shape with a wavelength of approximately 8 km. The standard deviation of the differences is typically 10% between 20 and 35 km. We show that more than 20% of the SCIAMACHY official ESA offline (OL) ozone profiles version 2.4 and 2.5 have unrealistic ozone values, most of these are north of 15° S. The remaining OL profiles compare well to correlative instruments above 24 km. Between 20 and 24 km, they underestimate ozone by 15±5%. [ABSTRACT FROM AUTHOR]