A Review of Techniques for Diagnosing the Atmospheric Boundary Layer Height (ABLH) Using Aerosol Lidar Data

The height of the atmospheric boundary layer (ABLH) or the mixing layer height (MLH) is a key parameter characterizing the planetary boundary layer, and the accurate estimation of that is critically important for boundary layer related studies, which include air quality forecasts and numerical weath...

Full description

Saved in:
Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 11; no. 13; p. 1590
Main Authors Dang, Ruijun, Yang, Yi, Hu, Xiao-Ming, Wang, Zhiting, Zhang, Shuwen
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 04.07.2019
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:The height of the atmospheric boundary layer (ABLH) or the mixing layer height (MLH) is a key parameter characterizing the planetary boundary layer, and the accurate estimation of that is critically important for boundary layer related studies, which include air quality forecasts and numerical weather prediction. Aerosol lidar is a powerful remote sensing instrument frequently used to retrieve the ABLH through detecting the vertical distributions of aerosol concentration. Presently available methods for ABLH determination from aerosol lidar are summarized in this review, including a lot of classical methodologies as well as some improved versions of them. Some new recently developed methods applying advanced techniques such as image edge detection, as well as some new methods based on multi-wavelength lidar systems, are also summarized. Although a lot of techniques have been proposed and have already given reasonable results in several studies, it is impossible to recommend a technique which is suitable in all atmospheric scenarios. More accurate instantaneous ABLH from robust techniques is required, which can be used to estimate or improve the boundary layer parameterization in the numerical model, or maybe possible to be assimilated into the weather and environment models to improve the simulation or forecast of weather and air quality in the future.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11131590