Random Sample Fitting Method to Determine the Planetary Boundary Layer Height Using Satellite-Based Lidar Backscatter Profiles

Abstract

The planetary boundary layer height (PBLH) is the atmospheric region closest to the earth’s surface and has important implications on weather forecasting, air quality, and climate research. However, lidar-based methods traditionally used to determine PBLH—such as the ideal profile fitting method (IPF), maximum gradient method, and wavelet covariance transform—are not only heavily influenced by cloud layers, but also rely heavily on a low signal-to-noise ratio (SNR). Therefore, a random sample fitting (RANSAF) method was proposed for PBLH detection based on combining the random sampling consensus and IPF methods. According to radiosonde measurements, the testing of simulated and satellite-based signals shows that the proposed RANSAF method can reduce the effects of the cloud layer and significantly fluctuating noise on lidar-based PBLH detection better than traditional algorithms. The low PBLH bias derived by the RANSAF method indicates that the improved algorithm has a superior performance in measuring PBLH under a low SNR or when a cloud layer exists where the traditional methods are mostly ineffective. The RANSAF method has the potential to determine regional PBLH on the basis of satellite-based lidar backscatter profiles.