Abstract. This paper introduces the recent European AerosolResearch Lidar Network (EARLINET) quality-assurance efforts at instrument level. Within two dedicated campaigns and five single-site intercomparison activities, 21 EAR-LINET systems from 18 EARLINET stations were intercompared between 2009 and 2013. A comprehensive strategy for campaign setup and data evaluation has been established. Eleven systems from nine EARLINET stations participated in the EARLINET Lidar Intercomparison 2009 (EARLI09). In this campaign, three reference systems were qualified which served as traveling standards thereafter. EARLINET systems from nine other stations have been compared against these reference systems since 2009. We present and discuss comparisons at signal and at product level from all campaigns for more than 100 individual measurement channels at the wavelengths of 355, 387, 532, and 607 nm. It is shown that in most cases, a very good agreement of the compared systems with the respective reference is obtained. Mean signal deviations in predefined height ranges are typically below ±2 %. Particle backscatter and extinction coefficients agree within ±2 × 10 −4 km −1 sr −1 and ± 0.01 km −1 , respectively, in most cases. For systems or channels that showed larger discrepancies, an in-depth analysis of deficiencies was performed and technical solutions and upgrades were proposed and realized. The intercomparisons have reinforced confidence in the EARLINET data quality and allowed us to draw conclusions on necessary system improvements for some instruments and to identify major challenges that need to be tackled in the future.

A solution based on a Kalman filter to trace the evolution of the atmospheric boundary layer (ABL) sensed by a ground-based elastic-backscatter tropospheric lidar is presented. An erf-like profile is used to model the mixing-layer top and the entrainment-zone thickness. The extended Kalman filter (EKF) enables to retrieve and track the ABL parameters based on simplified statistics of the ABL dynamics and of the observation noise present in the lidar signal. This adaptive feature permits to analyze atmospheric scenes with low signal-to-noise ratios (SNRs) without the need to resort to long-time averages or range-smoothing techniques, as well as to pave the way for future automated detection solutions. First, EKF results based on oversimplified synthetic and experimental lidar profiles are presented and compared with classic ABL estimation quantifiers for a case study with different SNR scenarios.Index Terms-Adaptive kalman filtering, laser radar, remote sensing, signal processing.

This study presents, for the first time ever, occulting signals of the Global Navigation Satellite Systems (GNSSs) acquired at two polarizations from a Low Earth Orbiter, and it shows that they sense heavy precipitation. The data sets are obtained from early stages of the Radio Occultation and Heavy Precipitation experiment aboard the PAZ satellite, launched in February 2018 and activated in May 2018. Preliminary calibration algorithms are applied to remove other systematic effects, and the resulting vertical profiles of polarimetric phase shift observations are compared to precipitation information from other missions. The analysis of the data shows consistency between Radio Occultation and Heavy Precipitation experiment aboard the PAZ satellite polarimetric phase shift measurements and presence of hydrometeors, with strong signatures from heavy precipitation. The polarimetric measurements also capture vertical features consistent with the vertical structures of precipitation.