Atmospheric Boundary Layer Height from Commercial Ceilometers: Optimization and Validation of the MIPA Algorithm

Abstract. This study investigates the applicability of the Morphological Image Processing Approach (MIPA) for estimating the Atmospheric Boundary Layer Height (ABLH) from ceilometer observations. Based on our previous work, in which MIPA was assessed and validated using High Power LiDAR observations, the present study extends its application for the first time to three commercial ceilometers (CHM15k, CL31, and CL51). The analysis is based on a measurement campaign conducted at the CIAO (CNR-IMIOT Atmospheric Observatory) during spring 2024. Three case studies covering different atmospheric conditions were selected to quantify the retrieval performance. A grid search was performed to optimize MIPA for each ceilometer, while the retrieved ABLH values were evaluated against a reference dataset obtained from temporally frequent radiosonde launches. The retrievals were also compared with the proprietary algorithms implemented by the instrument manufacturers. Results demonstrate that MIPA provides robust ABLH estimates for the considered ceilometers and generally outperforms the corresponding operational retrievals. The optimization showed that only a limited subset of processing parameters required instrument-specific tuning, confirming the robustness of the proposed methodology. Among the investigated instruments, the CHM15k achieved the best overall performance, yielding the lowest RMSE and standard error with respect to radiosonde-derived ABLH. These results highlight the influence of sensor characteristics, such as signal-to-noise ratio and near-range measurement quality, on retrieval accuracy. Widespread availability, low cost, and continuous operation, together with MIPA's low computational cost, make ceilometers an attractive complement to advanced LiDAR systems, enabling denser and more continuous near-real-time ABLH monitoring networks at regional and potentially continental scales.