Detection, variations and intercomparison of the planetary boundary layer depth from radiosonde, lidar and infrared spectrometer

• A new method for detecting planetary boundary layers is introduced.
• The method is valid for use with micropulse lidar, radiosonde and infrared spectrometer.
• PBLs spanning eight years are obtained and compared among different input data.
• Diurnal and seasonal variations of PBL are presented over the SGP of US.
• Strengths and limitations are revealed.

The depth of the planetary boundary layer (PBL) and its temporal evolution have important effects on weather, air quality and climate. While there are methods to detect the PBL depth from atmospheric profiles, few can be applied to different types of measurements and cope with changing atmospheric conditions. Many require supporting information from other instruments. In this study, two common methods for PBL depth detection (wavelet covariance and iterative curve-fitting) are combined, modified and applied to long-term time series of radiosonde profiles, micropulse lidar (MPL) measured backscatter and atmospheric emitted radiance interferometer (AERI) data collected at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. Intercomparison among the three PBL retrieval products shows the robustness of the algorithm. The comparisons were made for different times of day, four seasons, and variable sky conditions. While considerable uncertainties exist in PBL detection using all three types of measurements, the agreement among the PBL products is promising under certain conditions, and the different measurements have complementary advantages. The best agreement in the seasonal cycle occurs in winter, and the best agreement in the diurnal cycle when the boundary-layer regime is mature and changes slowly. PBL depths from instruments with higher temporal resolution (MPL and AERI) are of comparable accuracy to radiosonde-derived PBL depths; AERI excels for shallow PBLs, MPL for cloudy conditions. The new continuous PBL data set can be used to improve model parameterizations of PBL and our understanding of atmospheric transport of pollutants which affect clouds, air quality and human health.