Stratospheric aerosol extinction profile retrievals from SCIAMACHY limb-scatter observations

This dissertation presents a method for retrieving stratospheric aerosol extinction profiles from a global satellite data set. Ten years of limb radiance measurements with the instrument SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard the European environmental research satellite Envisat provides the unique opportunity to derive a stratospheric aerosol extinction data set over a long time period (2002 2012) with a good global coverage on a daily basis. Stratospheric sulfate aerosols have a significant impact on climate but the determination is still subject to large uncertainties. To improve our understanding of climate relevant processes an accurate determination of stratospheric aerosol properties is crucial. Deriving stratospheric aerosol extinction from limb radiance spectra requires complicated radiative transfer calculations. An algorithm based on a color-index approach combining normalized limb radiance spectra at 470 and 750 nm is applied to retrieve aerosol extinction profiles between 12 and 35 km altitude. A Mie phase function for typical background aerosols is implemented. The resulting SCIAMACHY stratospheric aerosol data set can serve as a foundation for climatological interpretation with respect to natural variability and anthropogenic impact

Relative drifts and biases between six ozone limb satellite measurements from the last decade

As part of European Space Agency’s (ESA) climate change initiative, high vertical resolution ozone profiles from three instruments all aboard ESA’s Envisat (GOMOS, MIPAS, SCIAMACHY) and ESA’s third party missions (OSIRIS, SMR, ACE-FTS) are to be combined in order to create an essential climate variable data record for the last decade. A prerequisite before combining data is the examination of differences and drifts between the data sets. In this paper, we present a detailed analysis of ozone profile differences based on pairwise collocated measurements, including the evolution of the differences with time. Such a diagnosis is helpful to identify strengths and weaknesses of each data set that may vary in time and introduce uncertainties in long-term trend estimates. The analysis reveals that the relative drift between the sensors is not statistically significant for most pairs of instruments. The relative drift values can be used to estimate the added uncertainty in physical trends. The added drift uncertainty is estimated at about 3% decade$^{-1}$ (1σ). Larger differences and variability in the differences are found in the lowermost stratosphere (below 20 km) and in the mesosphere

Relative drifts and biases between six ozone limb satellite measurements from the last decade

As part of European Space Agency’s (ESA) climate change initiative, high vertical resolution ozone profiles from three instruments all aboard ESA’s Envisat (GOMOS, MIPAS, SCIAMACHY) and ESA’s third party missions (OSIRIS, SMR, ACE-FTS) are to be combined in order to create an essential climate variable data record for the last decade. A prerequisite before combining data is the examination of differences and drifts between the data sets. In this paper, we present a detailed analysis of ozone profile differences based on pairwise collocated measurements, including the evolution of the differences with time. Such a diagnosis is helpful to identify strengths and weaknesses of each data set that may vary in time and introduce uncertainties in long-term trend estimates. The analysis reveals that the relative drift between the sensors is not statistically significant for most pairs of instruments. The relative drift values can be used to estimate the added uncertainty in physical trends. The added drift uncertainty is estimated at about 3% decade$^{-1}$ (1σ). Larger differences and variability in the differences are found in the lowermost stratosphere (below 20 km) and in the mesosphere

Relative Drifts and Biases Between Six Ozone Limb Satellite Measurements From the Last Decade

As part of European Space Agency\u27s (ESA) climate change initiative, high vertical resolution ozone profiles from three instruments all aboard ESA\u27s Envisat (GOMOS, MIPAS, SCIAMACHY) and ESA\u27s third party missions (OSIRIS, SMR, ACE-FTS) are to be combined in order to create an essential climate variable data record for the last decade. A prerequisite before combining data is the examination of differences and drifts between the data sets. In this paper, we present a detailed analysis of ozone profile differences based on pairwise collocated measurements, including the evolution of the differences with time. Such a diagnosis is helpful to identify strengths and weaknesses of each data set that may vary in time and introduce uncertainties in long-term trend estimates. The analysis reveals that the relative drift between the sensors is not statistically significant for most pairs of instruments. The relative drift values can be used to estimate the added uncertainty in physical trends. The added drift uncertainty is estimated at about 3% decade-1 (1σ). Larger differences and variability in the differences are found in the lowermost stratosphere (below 20 km) and in the mesosphere

Improving cloud information over deserts from SCIAMACHY Oxygen A-band measurements

International audienceThe retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites is sensitive to light scattered by clouds. The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on the Envisat satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) is a fast and robust algorithm providing cloud information from the O2 A-band for cloud correction of ozone. FRESCO provides a consistent set of cloud products by retrieving simultaneously effective cloud fraction and cloud top pressure. The FRESCO retrieved values are compared with the SCIAMACHY Level 2 operational cloud fraction of OCRA (Optical Cloud Recognition Algorithm) but, also, with cloud information from HICRU (Heidelberg Iterative Cloud Retrieval Utilities), SACURA (SemiAnalytical CloUd Retrieval Algorithm) and the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument. The results correlate well, but FRESCO overestimates cloud fraction over deserts. Thus, to improve retrievals at these locations, the FRESCO surface albedo databases are decontaminated from the presence of desert dust aerosols. This is achieved by using the GOME Absorbing Aerosol Index. It is shown that this approach succeeds well in producing more accurate cloud information over the Sahara

The semianalytical cloud retrieval algorithm for SCIAMACHY II. The application to MERIS and SCIAMACHY data

International audienceThe SemiAnalytical CloUd Retrieval Algorithm (SACURA) is applied to the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) data. In particular, we derive simultaneously cloud optical thickness (COT) and cloud top height (CTH), using SCIAMACHY measurements in the visible (442 nm, COT) and in the oxygen A-band (755?775 nm, CTH). Some of the results obtained are compared with those derived from the Medium Resolution Imaging Spectrometer (MERIS), which has better spatial resolution and observes almost the same scene as SCIAMACHY. The same cloud algorithm is applied to both MERIS and SCIAMACHY data. In addition, we perform the vicarious calibration of SCIAMACHY at the wavelength 442 nm, using MERIS measurements at the same wavelength. Differences in the retrieved COT for the same cloud field obtained using MERIS and SCIAMACHY measurements are discussed

NO2 Limb Retrieval in the Upper Troposphere/ Lower Stratosphere Region

As reactive nitrogen amounts in the stratosphere increase, accurate measurements of these trace gases is of high importance. The SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartog raphY) instrument on ENVISAT (European Environmental Satellite) performs measurements in limb geometry since 2002, providing global coverage of NO2 retrieval results every six days. In this study, a novel approach to improve the sensitivity of SCIAMACHY NO2 limb retrieval results at the UTLS (Upper Troposphere/ Lower Stratosphere) altitude layer is described. Additionally, the current NO2 limb retrieval product is validated in detail and both methods are used for case studies at the North Atlantic region

Twelve years of global observations of formaldehyde in the troposphere using GOME and SCIAMACHY sensors

This work presents global tropospheric formaldehyde columns retrieved from near-UV radiance measurements performed by the GOME instrument onboard ERS-2 since 1995, and by SCIAMACHY, in operation on ENVISAT since the end of 2002. A special effort has been made to ensure the coherence and quality of the CH<sub>2</sub>O dataset covering the period 1996–2007. Optimised DOAS settings are proposed in order to reduce the impact of two important sources of error in the derivation of slant columns, namely, the polarisation anomaly affecting the SCIAMACHY spectra around 350 nm, and a major absorption band of the O<sub>4</sub> collision complex centred near 360 nm. The air mass factors are determined from scattering weights generated using radiative transfer calculations taking into account the cloud fraction, the cloud height and the ground albedo. Vertical profile shapes of CH<sub>2</sub>O are provided by the global CTM IMAGES based on an up-to-date representation of emissions, atmospheric transport and photochemistry. A comprehensive error analysis is presented. This includes errors on the slant columns retrieval and errors on the air mass factors which are mainly due to uncertainties in the a priori profile and in the cloud properties. The major features of the retrieved formaldehyde column distribution are discussed and compared with previous CH<sub>2</sub>O datasets over the major emission regions