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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amtd-5-775-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amtd-5-775-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: research article 18 Jan 2012

Submitted as: research article | 18 Jan 2012

Review status
This discussion paper is a preprint. A revision of the manuscript for further review has not been submitted.

New dynamic NNORSY ozone profile climatology

A. K. Kaifel1, M. Felder1, C. DeClercq2,*, and J.-C. Lambert2 A. K. Kaifel et al.
  • 1Center for Solar Energy and Hydrogen Research (ZSW) Baden-Württemberg, Stuttgart, Germany
  • 2Belgian Institute for Space Aeronomy, Brussels, Belgium
  • *now at: Advanced Mechanical and Optical Systems, Liège, Belgium

Abstract. Climatological ozone profile data are widely used as a-priori information for total ozone using DOAS type retrievals as well as for ozone profile retrieval using optimal estimation, for data assimilation or evaluation of 3-D chemistry-transport models and a lot of other applications in atmospheric sciences and remote sensing. For most applications it is important that the climatology represents not only long term mean values but also the links between ozone and dynamic input parameters. These dynamic input parameters should be easily accessible from auxiliary datasets or easily measureable, and obviously should have a high correlation with ozone. For ozone profile these parameters are mainly total ozone column and temperature profile data. This was the outcome of a user consultation carried out in the framework of developing a new, dynamic ozone profile climatology.

The new ozone profile climatology is based on the Neural Network Ozone Retrieval System (NNORSY) widely used for ozone profile retrieval from UV and IR satellite sounder data. NNORSY allows implicit modelling of any non-linear correspondence between input parameters (predictors) and ozone profile target vector. This paper presents the approach, setup and validation of a new family of ozone profile climatologies with static as well as dynamic input parameters (total ozone and temperature profile). The neural network training relies on ozone profile measurement data of well known quality provided by ground based (ozonesondes) and satellite based (SAGE II, HALOE, and POAM-III) measurements over the years 1995–2007. In total, four different combinations (modes) for input parameters (date, geolocation, total ozone column and temperature profile) are available.

The geophysical validation spans from pole to pole using independent ozonesonde, lidar and satellite data (ACE-FTS, AURA-MLS) for individual and time series comparisons as well as for analysing the vertical and meridian structure of different modes of the NNORSY ozone profile climatology. The NNORSY ozone profile climatology is available to the community as a comprehensive software library.

A. K. Kaifel et al.
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Interactive discussion
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
A. K. Kaifel et al.
A. K. Kaifel et al.
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