Atmospheric boundary-layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds

The use of Automatic Lidars and Ceilometers (ALC) is increasingly extended beyond monitoring cloud base height to the study of atmospheric boundary layer (ABL) dynamics. Therefore, long-term sensor network observations require robust algorithms to automatically detect the mixed layer height (Z_ML). Here, a novel automatic algorithm CABAM (Characterising the Atmospheric Boundary layer based on ALC Measurements) is presented. CABAM is the first non-proprietary mixed layer height algorithm specifically designed for the commonly deployed Vaisala CL31 ceilometer. The method tracks Z_ML, takes into account precipitation, classifies the ABL based on cloud cover and cloud type, and determines the relation between Z_ML and cloud base height. CABAM relies solely on ALC measurements. Results perform well against independent reference (AMDAR: Aircraft Meteorological Data Relay) measurements and supervised Z_ML detection. AMDAR-derived temperature inversion heights allow Z_ML evaluation throughout the day. Very good agreement is found in the afternoon when the mixed layer height extends over the full ABL. However, during night or the morning transition the temperature inversion is more likely associated with the top of the residual layer. From comparison with SYNOP reports, the ABL classification scheme generally correctly distinguishes between convective and stratiform boundary-layer clouds, with slightly better performance during daytime. Applied to 6 years of ALC observations in central London, Kotthaus and Grimmond (), a companion paper, demonstrate CABAM results are valuable to characterise the urban boundary layer over London, United Kingdom, where clouds of various types are frequent.