Automatic detection of atmospheric boundary layer height using ceilometer backscatter data assisted by a boundary layer model

2011 ◽  
Vol 138 (664) ◽  
pp. 649-663 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Angelo Riccio ◽  
Luca Caporaso ◽  
Giovanni Bonafé ◽  
Gian Paolo Gobbi ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Automatic Detection ◽  
Layer Model ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Boundary Layer Model ◽  
Atmospheric Boundary Layer Height

scholarly journals Lidar-based remote sensing of atmospheric boundary layer height over land and ocean

2014 ◽  
Vol 7 (1) ◽  
pp. 173-182 ◽  
Author(s):  
T. Luo ◽  
R. Yuan ◽  
Z. Wang
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Marine Boundary Layer ◽  
Mixing Layer ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Lidar Measurements ◽  
Mixing Layer Height ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Boundary Layer Height

Abstract. Atmospheric boundary layer (ABL) processes are important in climate, weather and air quality. A better understanding of the structure and the behavior of the ABL is required for understanding and modeling of the chemistry and dynamics of the atmosphere on all scales. Based on the systematic variations of the ABL structures over different surfaces, different lidar-based methods were developed and evaluated to determine the boundary layer height and mixing layer height over land and ocean. With Atmospheric Radiation Measurement Program (ARM) Climate Research Facility (ACRF) micropulse lidar (MPL) and radiosonde measurements, diurnal and season cycles of atmospheric boundary layer depth and the ABL vertical structure over ocean and land are analyzed. The new methods are then applied to satellite lidar measurements. The aerosol-derived global marine boundary layer heights are evaluated with marine ABL stratiform cloud top heights and results show a good agreement between them.

Atmospheric Boundary Layer Height Monitoring Using a Kalman Filter and Backscatter Lidar Returns

2014 ◽  
Vol 52 (8) ◽  
pp. 4717-4728 ◽  
Author(s):  
Diego Lange ◽  
Jordi Tiana-Alsina ◽  
Umar Saeed ◽  
Sergio Tomas ◽  
Francesc Rocadenbosch
Keyword(s):  
Boundary Layer ◽  
Kalman Filter ◽  
Atmospheric Boundary Layer ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Atmospheric Boundary Layer Height

scholarly journals Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques

2021 ◽  
pp. 105962
Author(s):  
Gregori de Arruda Moreira ◽  
Guadalupe Sánchez-Hernández ◽  
Juan Luis Guerrero-Rascado ◽  
Alberto Cazorla ◽  
Lucas Alados-Arboledas
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Machine Learning Techniques ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Learning Techniques ◽  
Atmospheric Boundary Layer Height
Sign in / Sign up

Export Citation Format

Share Document