Publications

2023, Mariaccia, A., Keckhut P., Hauchecorne A., Khaykin S., Ratynski M., Co‐Located Wind and Temperature Observations at Mid‐Latitudes During Mesospheric Inversion Layer Events, Geophysical Research Letters, 50 (9), pp.e2022GL102683, http://doi.org/10.1029/2022gl102683
Tags: Lidar, Temperature, Wind

2023, Toon, G., et al., N2O Temporal Variability from the Middle Troposphere to the Middle Stratosphere Based on Airborne and Balloon-Borne Observations during the Period 1987–2018, Atmosphere, 14(3), 585, https://doi.org/10.3390/atmos14030585
Tags: FTIR, N2O

2023, Chane Ming, F., Hauchecorne A., Bellisario C., Simoneau P., Keckhut P., Trémoulu S., Listowski C., Berthet G., Jégou F., Khaykin S., Tidiga M. et al., Case Study of a Mesospheric Temperature Inversion over Maïdo Observatory through a Multi-Instrumental Observation, Remote Sensing, 15, 2045, http://doi.org/10.3390/rs15082045
Tags: Lidar, Temperature

2023, Wizenberg, T., K. Strong, D.B.A. Jones, E. Lutsch, E. Mahieu, B. Franco, and L. Clarisse, Exceptional wildfire enhancements of PAN, C2H4, CH3OH, and HCOOH over the Canadian high Arctic during August 2017, Journal of Geophysical Research: Atmospheres, 128, e2022JD038052, https://doi.org/10.1029/2022JD038052
Tags: C2H4, CH3OH, Fire, FTIR, HCOOH

2023, Farhani, G., Martucci, G., Roberts, T., Haefele, A., Sica, R.J., A Bayesian neural network approach for tropospheric temperature retrievals from a lidar instrument, International Journal of Remote Sensing, 44:5, 1611-1627, http://doi.org/10.1080/01431161.2023.2187723
Tags: Algorithm, Lidar, Temperature

2022, Ardalan, M., Keckhut P., Hauchecorne A., Wing R., Meftah M., Farhani G., Updated Climatology of Mesospheric Temperature Inversions Detected by Rayleigh Lidar above Observatoire de Haute Provence, France, Using a K-Mean Clustering Technique, Atmosphere, 13 (5), pp.814, https://doi.org/10.3390/atmos13050814
Tags: Lidar, Temperature

2022, Le Du, T., Keckhut P., Hauchecorne A., Simoneau P., Observation of Gravity Wave Vertical Propagation through a Mesospheric Inversion Layer, Atmosphere, 13 (7), pp.1003, https://doi.org/10.3390/atmos13071003
Tags: Lidar, Temperature

2022, Karagkiozidis, D., Friedrich, M. M., Beirle, S., Bais, A., Hendrick, F., Voudouri, K. A., Fountoulakis, I., Karanikolas, A., Tzoumaka, P., Van Roozendael, M., Balis, D., and Wagner, T., Retrieval of tropospheric aerosol, NO2, and HCHO vertical profiles from MAX-DOAS observations over Thessaloniki, Greece: intercomparison and validation of two inversion algorithms, Atmospheric Measurement Techniques, 15, 1269–1301, https://doi.org/10.5194/amt-15-1269-2022
Tags: Aerosol, Algorithm, CalVal, HCHO, NO2, UVVis

2022, Lutsch, E., D. Wunch, D. B. A. Jones, C. Clerbaux, J. W. Hannigan, T.-L. He, I. Ortega, S. Roche, K. Strong, and H. M. Worden, Can the data assimilation of CO from MOPITT or IASI constrain high-latitude wildfire emissions? A Case Study of the 2017 Canadian Wildfires, Earth and Space Science, p. 44, https://doi.org/10.1002/essoar.10510875.1
Tags: CO, Fire, 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