Inversion of the planetary boundary layer height from lidar by combining UNet++ and coordinate attention mechanism

The Arctic plays a significant role in global climate, and the planetary boundary layer height (PBLH) is one of the important parameters for studying Arctic climate. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) atmospheric observatory is an important location for studying the Arctic. However, the weather at the NSA site is complicated and varied. Arctic Haze frequently appears in this region from late autumn to early summer, while low clouds are prone to occur in summer. Meanwhile, due to the consistently low temperatures on the Arctic surface, the frequency of stable boundary layer occurrence is much higher than that in mid-latitude regions. All of these will increase the difficulty of PBLH detection. To address these challenges, we propose a PBLH inversion method based on deep-learning called Coord-UNet++. This method is based on UNet++ and introduces coordinate attention mechanism which can gather features in both horizontal and vertical directions, so it can more effectively capture spatial information in images to cope with complex weather conditions. The training set for the algorithm comes from the micropulse lidar at the NSA site, and the PBLH is labeled by using the microwave radiation profiler at the same site. This algorithm can achieve accurate inversion of the PBLH in complex weather conditions such as cloudy, haze and aerosol layer interference, R 2 reaches 0.87, and it performs well in long-term inversion, with much higher stability and accuracy than traditional methods.