Nighttime Contrail Characterization from Multisource Lidar and Meteorological Observations

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Authors: Florian Mandija, Philippe Keckhut, Dunya Alraddawi, Abdanour Irbah, Alain Sarkissian, S. Khaykin, Frédéric Peyrin, J.-L. BARAY
Cited by: 2
References: 62

Abstract

The present study provides a comprehensive nighttime contrail characterization combining Raman lidar, ADS-B flight data, and ECMWF ERA5 reanalysis over southern France. Observations of different case studies of contrail formation and development throughout their lifetimes provide valuable insights into the contrails’ morphological, microphysical, and optical properties, persistence, and dispersion. We present a multisource methodology to detect and characterize nighttime aircraft contrails over the Observatory of Haute-Provence (OHP) in France. The determination of contrail signatures was performed by applying sensitivity analyses by spatiotemporal thresholding and clustering for contrail detection. Optimizing the thresholds permits the improvement of contrail detection and the reduction of unnecessary noise. The optimal combination of these thresholds, which best reduces false positives and negatives, was SR = 2.1, time = 7.2 min, and altitude = 0.3 km. Subsequent merging of the spots produces persistent contrail signatures at altitudes of 8.7–10.3 km, with thicknesses of 0.1–1.1 km, widths of 2–2.8 km, and optical depths of 0.05–0.40. Contrail optical depth correlates significantly with geometrical thickness and width, which highlights the interplay between contrail morphology and ambient thermodynamic conditions. Our methodology demonstrates the value of combining lidar and flight data for contrail characterization using lidar measurements, flight data, and meteorological information.

Fields of Study & Subjects

Atmospheric aerosols and clouds Meteorological Phenomena and Simulations Spectroscopy and Laser Applications

Publication details

Journal / source
Remote Sensing
Publisher
MDPI AG
Volume
18
Issue
2
Pages
210–210
Publication date
8 January 2026
Language
EN