Technical note: Boundary layer height determination from lidar for improving air pollution episode modeling: development of new algorithm and evaluation

Abstract

Abstract. Predicting air pollution events in the low atmosphere over megacities requires a thorough understanding of the tropospheric dynamics and chemical processes, involving, notably, continuous and accurate determination of the boundary layer height (BLH). Through intensive observations experimented over Beijing (China) and an exhaustive evaluation of existing algorithms applied to the BLH determination, persistent critical limitations are noticed, in particular during polluted episodes. Basically, under weak thermal convection with high aerosol loading, none of the retrieval algorithms is able to fully capture the diurnal cycle of the BLH due to insufficient vertical mixing of pollutants in the boundary layer associated with the impact of gravity waves on the tropospheric structure. Consequently, a new approach based on gravity wave theory (the cubic root gradient method: CRGM) is developed to overcome such weakness and accurately reproduce the fluctuations of the BLH under various atmospheric pollution conditions. Comprehensive evaluation of CRGM highlights its high performance in determining BLH from lidar. In comparison with the existing retrieval algorithms, CRGM potentially reduces related computational uncertainties and errors from BLH determination (strong increase of correlation coefficient from 0.44 to 0.91 and significant decreases of the root mean square error from 643 to 142 m). Such a newly developed technique is undoubtedly expected to contribute to improving the accuracy of air quality modeling and forecasting systems.

Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China

“…If high-frequency meteorological data were available, the model performance would improve. Many techniques have been developed to determine the PBLH, for example, through radiosonde measurements, remote sensing, laboratory experiments, and model simulations [51]. PBLHs from these methods show significant differences for both the stable and convective boundary layer.…”

Identify the contribution of elevated industrial plume to ground air quality by optical and machine learning methods

“…LIDAR was developed by the National Institute for Environmental Studies (Japan). More details can be found therein [29,30]. In March 2019, we set up an auto-shoot camera fixed at the ISF site to catch the different types of HISP's shapes.…”

Intercomparison of Mixing Layer Heights from the National Weather Service Ceilometer Test Sites and Collocated Radiosondes

“…Its height, defined as the top of the lowest layer of the atmosphere where these exchanges between the surface and upper troposphere mix into it in less than 1 or 2 h (Luo et al 2014), is directly related to the intensity of ML turbulence (Liu and Liang 2010). Therefore, it can be used as a scalar to constrain ML parameterization methods in atmospheric models to help improve weather and climate predictions (Yang et al 2017). Unfortunately, a high-density network of ML height observations within the United States does not exist, even though remote sensing technologies, such as ceilometers and lidars, show feasibility to accomplish this task (Hicks et al 2015;Emeis et al 2008).…”