AMT paper Jalali et al. 2019

The following GRUAN-relevant paper was published at AMT: "A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals" by Jalali et al. (https://doi.org/10.5194/amt-12-3943-2019).

Title

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

Authors

Jalali, A., Hicks-Jalali, S., Sica, R. J., Haefele, A., and von Clarmann, T.

 

Published

by Atmospheric Measurement Techniques (AMT) at 2019-07-18

 

Abstract

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.

Citation

 

Download

Full article (PDF)

Comments to news

botMessage_toctoc_comments_9210