Publications (3)0 Total impact
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Article: GOME-2 NO2 and the NOAA Air Quality Program Air Quality Forecast :
Energy Policy. 01/2007; -
Chapter: Chapter 7: From Radiation Fields to Atmospheric Concentrations - Retrieval of Geophysical Parameters
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ABSTRACT: Satellite-based atmospheric remote sensing aims at deriving the properties of trace gases, aerosols and clouds, as well as surface parameters from the measured top-of-atmosphere spectral radiance and reflectance. This requires, besides high quality spectra, an accurate modelling of the radiative transfer of solar radiation through the atmosphere to the sensor (forward model) and methods to derive the constituent properties from the measured top-of-atmosphere spectra (inversion methods). Many trace gases have structured absorption spectra in the UV-VIS spectral range serving as the starting point for determining their abundance by applying Differential Optical Absorption Spectroscopy (DOAS) or similar methods. In the UV-VIS-NIR and SWIR spectral regions the solar radiation is strongly scattered by clouds and aerosols. Therefore the presence of clouds and aerosol particles and their properties can also be inferred from the outgoing radiance measured by space-based instruments. Contrary to the forward model, the inversion methods allow to derive characteristics of the atmospheric state based on the measured quantities. A common product of the inversion of satellite measurements in limb, nadir or occultation geometry are total columns or height-resolved profiles of trace gas concentrations and aerosol parameters. Retrieving trace gas amounts in the troposphere constitutes a specific challenge. SCIAMACHY’s unique limb/nadir matching capability provides access to tropospheric columns by combining total columns obtained from nadir geometry with simultaneously measured stratospheric columns obtained from limb geometry.ISBN: 978-90-481-9895-5 -
Article: Evaluation of GOME ozone profiles from nine different algorithms
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ABSTRACT: An evaluation is made of ozone profiles retrieved from measurements of the nadir-viewing Global Ozone Monitoring Experiment (GOME) instrument. Currently four different approaches are used to retrieve ozone profile information from GOME measurements, which differ in the use of external information and a priori constraints. In total nine different algorithms will be evaluated exploiting the Optimal Estimation (Royal Netherlands Meteorological Institute, Rutherford Appleton Laboratory, University of Bremen, National Oceanic and Atmospheric Administration, Smithsonian Astrophysical Observatory), Phillips-Tikhonov Regularization (Space Research Organization Netherlands), Neural Network (Center for Solar Energy and Hydrogen Research, Tor Vergata University), and Data Assimilation (German Aerospace Center) approaches. Analysis tools are used to interpret data sets that provide averaging kernels. In the interpretation of these data, the focus is on the vertical resolution, the indicative altitude of the retrieved value, and the fraction of a priori information. The evaluation is completed with a comparison of the results to lidar data from the NDSC (Network for Detection of Stratospheric Change) stations in Andoya (Norway), Observatoire Haute Provence (France), Mauna Loa (USA), Lauder (New Zealand) and Dumont d’Urville (Antarctic) for the years 1997–1999. In total the comparison involves nearly 1000 ozone profiles, and allows the analysis of GOME data measured in different global regions and hence observational circumstances. The main conclusion of this paper is that unambiguous information on the ozone profile can at best be retrieved in the altitude range 15–48 km with a vertical resolution of 10 to 15 km, precision of 5–10%, and a bias up to 5% or 20% depending on the success of recalibration of the input spectra. The sensitivity of retrievals to ozone at lower altitudes varies from scheme to scheme and includes significant influence from a priori assumptions.Journal of Geophysical Research. 111(D21).
