Publications

2024, Pardo Cantos, I., Mahieu, E., Chipperfield, M.P., Servais, C., Reimann, S., Vollmer, M.K., First HFC-134a retrievals from ground-based FTIR solar absorption spectra, comparison with TOMCAT model simulations, in-situ AGAGE observations, and ACE-FTS satellite data for the Jungfraujoch station, Journal of Quantitative Spectroscopy and Radiative Transfer, 318, 108938, https://doi.org/10.1016/j.jqsrt.2024.108938
Tags: CFC, FTIR, Model, Satellite, Validation

2023, Karagkiozidis, D., Koukouli M-E, Bais A, Balis D, Tzoumaka P., Assessment of the NO2 Spatio-Temporal Variability over Thessaloniki, Greece, Using MAX-DOAS Measurements and Comparison with S5P/TROPOMI Observations, Applied Sciences, 13(4):2641, https://doi.org/10.3390/app13042641
Tags: NO2, Satellite, UVVis

2023, Tu, Q., Hase, F., Chen, Z., Schneider, M., García, O., Khosrawi, F., Chen, S., Blumenstock, T., Liu, F., Qin, K., Cohen, J., He, Q., Lin, S., Jiang, H., and Fang, D., Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique, Atmospheric Measurement Techniques, 16, 2237–2262, https://doi.org/10.5194/amt-16-2237-2023
Tags: FTIR, NO2

2023, Poraicu, C., Müller, J.-F., Stavrakou, T., Fonteyn, D., Tack, F., Deutsch, F., Laffineur, Q., Van Malderen, R., and Veldeman, N., Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium, Geoscientific Model Development, 16, 479–508, https://doi.org/10.5194/gmd-16-479-2023
Tags: NO2, Satellite, Sonde

2023, Bruno, A.G., Harrison, J. J., Chipperfield, M. P., Moore, D. P., Pope, R. J., Wilson, C., Mahieu, E. and Notholt, J., Atmospheric distribution of HCN from satellite observations and 3-D model simulations, Atmospheric Chemistry and Physics, 23(8), 4849–4861, https://doi.org/10.5194/acp-23-4849-2023
Tags: FTIR, Model, Satellite

2022, Strahan, S.E., D. Smale, S. Solomon, G. Taha, M. R. Damon, S. D. Steenrod, N. Jones, B. Liley, R. Querel and J. Robinson, Unexpected Repartitioning of Stratospheric Inorganic Chlorine After the 2020 Australian Wildfires, Geophysical Research Letters, 49(14): e2022GL098290
Tags: Cl, Fire, Model

2022, Mariaccia, A., Keckhut P., Hauchecorne A., Claud C., Le Pichon A., Meftah M., Khaykin S., Assessment of ERA-5 Temperature Variability in the MiddleAtmosphere Using Rayleigh LiDAR Measurements between 2005 and 2020, Atmosphere, 13 (2), 242, http://doi.org/10.3390/atmos13020242
Tags: Lidar, Model, Temperature

2022, Koukouli, M.-E., Pseftogkas A, Karagkiozidis D, Skoulidou I, Drosoglou T, Balis D, Bais A, Melas D, Hatzianastassiou N., Air Quality in Two Northern Greek Cities Revealed by Their Tropospheric NO2 Levels, Atmosphere, 13(5):840, https://doi.org/10.3390/atmos13050840
Tags: NO2, UVVis

2022, Bahramvash Shams, S., V. P. Walden, J. W. Hannigan, W. J. Randel, I. V. Petropavlovskikh, A. H. Butler, and A. de la Cámara, Analyzing ozone variations and uncertainties at high latitudes during sudden stratospheric warming events using MERRA-2, Atmospheric Chemistry and Physics, 22.8, 5435–5458, https://doi.org/10.5194/acp-22-5435-2022
Tags: Model, Ozone

2022, Summa, D., F. Madonna, N. Franco, B. De Rosa, and P. Di Girolamo , Inter-comparison of atmospheric boundary layer (ABL) height estimates from different profiling sensors and models in the framework of HyMeX-SOP1, Atmospheric Measurement Techniques, 15, 4153–4170, https://doi.org/10.5194/amt-15-4153-2022
Tags: Lidar, Model