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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
https://doi.org/10.5194/amt-2018-347
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 23 Oct 2018

Research article | 23 Oct 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

A practical method to remove a priori information from lidar optimal estimation method retrievals

Ali Jalali1, Shannon Hicks-Jalali1, Robert J. Sica1,2, Alexander Haefele2,1, and Thomas von Clarmann3 Ali Jalali et al.
  • 1Department of Physics and Astronomy, The University of Western Ontario, London, Canada
  • 2Federal Office of Meteorology and Climatology, MeteoSwiss, Payerne, Switzerland
  • 3Forschungszentrum Karlsruhe, Institut fur Meteorologie und Klimaforschung, Karlsruhe, Germany

Abstract. Lidar retrievals of atmospheric temperature and water vapour mixing ratio profiles using the Optimal Estimation Method (OEM) typically use a retrieval grid whose number of points is larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their a priori, which can affect the results in the higher altitudes of the temperature and water vapour profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval’s averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by transforming the retrieval from a fine grid to a coarser grid such that the averaging kernel is close to unity at each grid point. In this case, setting the a priori term in the OEM retrieval equation to zero minimizes the effect of the a priori for the coarse grid retrieval. We demonstrate the improvements gained by this technique for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

Ali Jalali et al.
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Ali Jalali et al.
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Latest update: 13 Nov 2018
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Short summary
This paper builds upon the work in von Clarmann and Grabowski (AMT; 2007) concerning the a priori profile influence in the optimal estimation method applied to active remote sensing measurements, with examples given for lidar retrievals of temperature and water vapour mixing ratio. The optimal estimation method is a new technique for many active remote sensing researchers. This study gives insight into understanding the affect on retrievals of the a priori information.
This paper builds upon the work in von Clarmann and Grabowski (AMT; 2007) concerning the a...
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