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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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doi:10.5194/amtd-5-775-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
18 Jan 2012
Review status
A revision of this discussion paper for further review has not been submitted.
New dynamic NNORSY ozone profile climatology
A. K. Kaifel1, M. Felder1, C. DeClercq2,*, and J.-C. Lambert2 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.


Citation: Kaifel, A. K., Felder, M., DeClercq, C., and Lambert, J.-C.: New dynamic NNORSY ozone profile climatology, Atmos. Meas. Tech. Discuss., 5, 775-812, doi:10.5194/amtd-5-775-2012, 2012.
A. K. Kaifel et al.
Interactive discussionStatus: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC C179: 'Very interesting ozone climatology, but please improve the validation and help the users.', Anonymous Referee #1, 08 Mar 2012 Printer-friendly Version 
 
RC C214: 'Review of "New Dynamic NNORSY ozone profile climatology" by Kaifel et al.', Anonymous Referee #2, 12 Mar 2012 Printer-friendly Version 
A. K. Kaifel et al.
A. K. Kaifel et al.

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