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Homogenized total ozone data records from the European sensors GOME/ERS‐2, SCIAMACHY/Envisat, and GOME‐2/MetOp‐A
Author(s) -
Lerot C.,
Van Roozendael M.,
Spurr R.,
Loyola D.,
ColdeweyEgbers M.,
Kochenova S.,
Gent J.,
Koukouli M.,
Balis D.,
Lambert J.C.,
Granville J.,
Zehner C.
Publication year - 2014
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2013jd020831
Subject(s) - sciamachy , remote sensing , environmental science , atmospheric radiative transfer codes , tropospheric ozone , trace gas , radiative transfer , calibration , spectrometer , meteorology , troposphere , mathematics , physics , geography , optics , statistics
Within 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.