Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Journal Home About Current Issue All Issues

Reducing the error of lidar determination of the aerosol concentration during the probing of a weakly turbid atmosphere

N. A. Sanotskaya

Not Accessible

Your library or personal account may give you access

Abstract

Because the characteristics of the atmospheric aerosol need to be determined at significant distances from the lidar, methods are considered for interpreting weak backscattering signals. It is established that the error of determining the aerosol concentration can be substantially reduced by using an effective statistical-processing algorithm and weighting factors.

© 2015 Optical Society of America

PDF Article
More Like This
Quasi-analytical determination of noise-induced error limits in lidar retrieval of aerosol backscatter coefficient by the elastic, two-component algorithm

Michaël Sicard, Adolfo Comerón, Francisco Rocadenbosch, Alejandro Rodríguez, and Constantino Muñoz
Appl. Opt. 48(2) 176-182 (2009)

Methodology for error analysis and simulation of lidar aerosol measurements

Philip B. Russell, Thomas J. Swissler, and M. Patrick McCormick
Appl. Opt. 18(22) 3783-3797 (1979)

Effects of auxiliary atmospheric state parameters on the aerosol optical properties retrieval errors of high-spectral-resolution lidar

Yupeng Zhang, Dong Liu, Zhuofan Zheng, Zhengkuan Liu, DeYun Hu, Bing Qi, Chong Liu, Lei Bi, Kejun Zhang, Chunao Wen, Lingying Jiang, Yuling Liu, Ju Ke, and Zhongming Zang
Appl. Opt. 57(10) 2627-2637 (2018)

View More...

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters
Select Topics Cancel
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use