Publications
- 180 results
- Tag: HNO3
- Tag: Temperature
- Tag: XCO2
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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, Trieu, T.T.N., I. Morino, O. Uchino, Y. Tsutsumi, T. Sakai, T. Nagai, A. Yamazaki, H. Okumura, K. Arai, K. Shiomi, D.F. Pollard, B. Liley , Influences of aerosols and thin cirrus clouds on GOSAT XCO2 and XCH4 using Total Carbon Column Observing Network, sky radiometer, and lidar data, International Journal of Remote Sensing, 43:5, 1770-1799, https://doi.org/10.1080/01431161.2022.2038395
Tags: Aerosol, Clouds, FTIR, Lidar, Satellite, UVVis, XCH4, XCO2
2021, Dogniaux, M., C. Crevoisier, R. Armante, V. Capelle, T. Delahaye, V. Cassé, M. De Mazière, N. M. Deutscher, D.G. Feist, O.E. Garcia, D.W.T. Griffith, F. Hase, L.T. Iraci, R. Kivi, I. Morino, J. Notholt, D.F. Pollard, C.M. Roehl, K. Shiomi, K. Strong, Y. Té, V.A. Velazco, and T. Warneke, The Adaptable 4A Inversion (5AI): description and first XCO2 retrievals from Orbiting Carbon Observatory-2 (OCO-2) observations, Atmospheric Measurement Techniques, 14, 4689–4706, https://doi.org/10.5194/amt-14-4689-2021
Tags: FTIR, Satellite, XCO2
2021, Marlton, G., et al., Using a network of temperature lidars to identify temperature biases in the upper stratosphere in ECMWF reanalyses, Atmospheric Chemistry and Physics, 21(8), 6079–6092, https://doi.org/10.5194/acp-21-6079-2021
Tags: Lidar, Model, Temperature
2021, Wing, R., S. Godin-Beekmann, W. Steinbrecht, T.J. Mcgee, J.T. Sullivan, S. Khaykin, G. Sumnicht, and L. Twigg, Evaluation of the new DWD ozone and temperature lidar during the Hohenpeißenberg Ozone Profiling Study (HOPS) and comparison of results with previous NDACC campaigns, Atmospheric Measurement Techniques, 14(5), 3773-3794, https://doi.org/10.5194/amt-14-3773-2021
Tags: Lidar, Ozone, Temperature, Validation
2021, Noel, S., et al., XCO2 retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm, Atmospheric Measurement Techniques, 14, 3837–3869, https://doi.org/10.5194/amt-14-3837-2021
Tags: FTIR, XCO2
2021, Yu, P., Sean M. Davis, Owen B. Toon, Robert W. Portmann, Charles G. Bardeen, John E. Barnes, Hagen Telg, Christopher Maloney and Karen H. Rosenlof, Persistent Stratospheric Warming Due to 2019–2020 Australian Wildfire Smoke, Geophysical Research Letters, 48, 7, https://doi.org/10.1029/2021GL092609
Tags: Lidar, Fire, Temperature
2021, Martucci, G., Navas-Guzmán, F., Renaud, L., Romanens, G., Gamage, S. M., Hervo, M., Jeannet, P., and Haefele, A., Validation of pure rotational Raman temperature data from the Raman Lidar for Meteorological Observations (RALMO) at Payerne, Atmospheric Measurement Techniques, 14, 1333–1353, https://doi.org/10.5194/amt-14-1333-2021
Tags: Lidar, Temperature
2021, Klanner, L., K. Höveler, D. Khordakova, M. Perfahl, C. Rolf, T. Trickl, H. Vogelmann, A powerful lidar system capable of one-hour measurements of water vapour in the troposphere and the lower stratosphere as well as the temperature in the upper stratosphere and mesosphere, Atmospheric Measurement Techniques, 14, 531–555, https://doi.org/10.5194/amt-14-531-2021
Tags: Lidar, Temperature, H2O
2021, David, L., et al., XCO2 estimates from the OCO-2 measurements using a neural network approach, Atmospheric Measurement Techniques, 14, 117–132, https://doi.org/10.5194/amt-14-117-2021
Tags: FTIR, XCO2