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

Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geo...

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Bibliographic Details
Published inAtmospheric measurement techniques Vol. 12; no. 7; pp. 3943 - 3961
Main Authors Jalali, Ali, Hicks-Jalali, Shannon, Sica, Robert J, Haefele, Alexander, von Clarmann, Thomas
Format Journal Article
LanguageEnglish
Published Katlenburg-Lindau Copernicus GmbH 18.07.2019
Copernicus Publications
Subjects
Adequacy
Analysis
Apertures
Atmospheric temperature
Geophysics
Information retrieval
Kernels
Lidar
Methods
Mixing ratio
Optical radar
Optimization
Profiles
Remote sensing
Removal
Scattering
Temperature
Temperature effects
Water temperature
Water vapor
Water vapor profiles
Water vapour
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Summary:Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor 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 repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method 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.
ISSN:1867-8548
1867-1381
1867-8548
DOI:10.5194/amt-12-3943-2019