ampycloud: an algorithm to characterize cloud layers above aerodromes using ceilometer measurements
Abstract. Ceilometers are used routinely at aerodromes world-wide to measure the height and sky coverage fraction of cloud layers. This information, possibly combined with direct observations by human observers, contributes to the production of Meteorological Aerodrome Reports (METARs). Here, we present ampycloud, a new algorithm and associated Python package for automatic processing of ceilometer measurements, with the aim to characterize cloud layers above aerodromes. The ampycloud algorithm has been developed at the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss) as part of the AMAROC (AutoMETAR/AutoReport rOund the Clock) program, to fully automate the creation of METARs at Swiss civil aerodromes. ampycloud is designed to work with no (direct) human supervision. The algorithm consists of three distinct, sequential steps that rely on agglomerative clustering methods and Gaussian mixture models to identify distinct cloud layers. The robustness of the ampycloud algorithm stems from the first processing step, simple and reliable. It constrains the two subsequent processing steps that are more sensitive, but also better suited to handle complex cloud distributions. The software implementation of the ampycloud algorithm takes the form of an eponym, pip-installable Python package developed on Github. The code is made accessible to the general community as an open-source software under the terms of the 3-Clause BSD license.
ampycloud: example datasets https://zenodo.org/doi/10.5281/zenodo.10171151
Model code and software
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