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Published October 14, 2009 | Published
Journal Article Open

Water vapour total columns from SCIAMACHY spectra in the 2.36 μm window

Abstract

The potential of the shortwave infrared channel of the atmospheric spectrometer SCIAMACHY on Envisat to provide accurate measurements of total atmospheric water vapour columns is explored. It is shown that good quality results can be obtained for cloud free scenes above the continents using the Iterative Maximum Likelihood Method. In addition to the standard cloud filter employed in this method, further cloud screening is obtained by comparing simultaneously retrieved methane columns with values expected from models. A novel method is used to correct for the scattering effects introduced in the spectra by the ice layer on the detector window. The retrieved water vapour total vertical columns for the period 2003–2007 are compared with spatially and temporally collocated values from the European Centre for Mid-Range Weather Forecast (ECMWF) data. The observed differences for individual measurements have standard deviations not higher than 0.3 g/cm^2 and an absolute mean value smaller than 0.01 g/cm^2 with some regional excursions. The use of recently published spectroscopic data for water vapour led to a significant improvement in the agreement of the retrieved water vapour total columns and the values derived from ECMWF data. This analysis thus supports the superior quality of the new spectroscopic information using atmospheric data.

Additional Information

© 2009 Author(s). This work is distributed under the Creative Commons Attribution 3.0 License. Received: 12 May 2009 – Published in Atmos. Meas. Tech. Discuss.: 18 June 2009; Revised: 22 September 2009 – Accepted: 28 September 2009 – Published: 14 October 2009. SCIAMACHY is a joint project of the German Space Agency DLR and the Dutch Space Agency NIVR with contribution of the Belgian Space Agency. We thank the Netherlands SCIAMACHY Data Center and ESA for providing data. The work performed is (partly) financed by NIVR. The authors thank Arjo Segers, Coen Schrijvers, and Olaf Tuinder (KNMI) for preparing the ECMWF data.

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August 22, 2023
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