scholarly journals Marine Atmospheric Boundary Layer Height over the Eastern Pacific: Data Analysis and Model Evaluation

2004 ◽  
Vol 17 (21) ◽  
pp. 4159-4170 ◽  
Author(s):  
Xubin Zeng ◽  
Michael A. Brunke ◽  
Mingyu Zhou ◽  
Chris Fairall ◽  
Nicholas A. Bond ◽  
...  

Abstract The atmospheric boundary layer (ABL) height (h) is a crucial parameter for the treatment of the ABL in weather and climate models. About 1000 soundings from 11 cruises between 1995 and 2001 over the eastern Pacific have been analyzed to document the large meridional, zonal, seasonal, and interannual variations of h. In particular, its latitudinal distribution in August has three minima: near the equator, in the intertropical convergence zone (ITCZ), and over the subtropical stratus/stratocumulus region near the west coast of California and Mexico. The seasonal peak of h in the ITCZ zone (between 5.6° and 11.2°N) occurs in the spring (February or April), while it occurs in August between the equator and 5.6°N. Comparison of these data with the 10-yr monthly output of the Community Climate System Model (CCSM2) reveals that overall the model underestimates h, particularly north of 20°N in August and September. Directly applying the radiosonde data to the CCSM2 formulation for computing h shows that, at the original vertical resolution (with the lowest five layers below 2.1 km), the CCSM2 formulation would significantly underestimate h. In particular, the correlation coefficient between the computed and observed h values is only 0.06 for cloudy cases. If the model resolution were doubled below 2.1 km, however, the performance of the model formulation would be significantly improved with a correlation coefficient of 0.78 for cloudy cases.

2021 ◽  
Vol 14 (6) ◽  
pp. 4335-4353
Author(s):  
Thomas Rieutord ◽  
Sylvain Aubert ◽  
Tiago Machado

Abstract. The atmospheric boundary layer height (BLH) is a key parameter for many meteorological applications, including air quality forecasts. Several algorithms have been proposed to automatically estimate BLH from lidar backscatter profiles. However recent advances in computing have enabled new approaches using machine learning that are seemingly well suited to this problem. Machine learning can handle complex classification problems and can be trained by a human expert. This paper describes and compares two machine-learning methods, the K-means unsupervised algorithm and the AdaBoost supervised algorithm, to derive BLH from lidar backscatter profiles. The K-means for Atmospheric Boundary Layer (KABL) and AdaBoost for Atmospheric Boundary Layer (ADABL) algorithm codes used in this study are free and open source. Both methods were compared to reference BLHs derived from colocated radiosonde data over a 2-year period (2017–2018) at two Météo-France operational network sites (Trappes and Brest). A large discrepancy between the root-mean-square error (RMSE) and correlation with radiosondes was observed between the two sites. At the Trappes site, KABL and ADABL outperformed the manufacturer's algorithm, while the performance was clearly reversed at the Brest site. We conclude that ADABL is a promising algorithm (RMSE of 550 m at Trappes, 800 m for manufacturer) but has training issues that need to be resolved; KABL has a lower performance (RMSE of 800 m at Trappes) than ADABL but is much more versatile.


Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 908
Author(s):  
Silver Onyango ◽  
Simon K. Anguma ◽  
Geoffrey Andima ◽  
Beth Parks

The atmospheric boundary layer height is important for constraining air pollution and meteorological models. This study attempted to validate the MODIS-estimated atmospheric boundary layer height (ABLH), and variation in the ABLH in Uganda was evaluated. The ABLH was estimated from MODIS data using the mixing ratio profile gradient method and compared to the ABLH estimated from radiosonde data using three different methods. Unlike in studies in other regions of the world, correlations between ABLH estimated using MODIS and radiosonde data were weak, implying limited usefulness of MODIS data for determining ABLH. However, the diurnal variation in MODIS-derived ABLH and particulate matter (PM10) was consistent with the expected inverse relationship between PM10 mass concentration and ABLH, and the mean MODIS-derived ABLH values were significantly lower during wet seasons than dry seasons, as expected.


2014 ◽  
Vol 7 (1) ◽  
pp. 173-182 ◽  
Author(s):  
T. Luo ◽  
R. Yuan ◽  
Z. Wang

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.


2014 ◽  
Vol 52 (8) ◽  
pp. 4717-4728 ◽  
Author(s):  
Diego Lange ◽  
Jordi Tiana-Alsina ◽  
Umar Saeed ◽  
Sergio Tomas ◽  
Francesc Rocadenbosch

Sign in / Sign up

Export Citation Format

Share Document