Publications

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, Virolainen, Y.A., Timofeyev, Y.M., Polyakov, A.V. et al., Ground-Based FTIR Measurements of Atmospheric Nitric Acid at the NDACC, Izvestiya, Atmospheric and Oceanic Physics, 59, 167–173, https://doi.org/10.1134/S000143382302007X
Tags: FTIR, HNO3, Nitric Acid

2023, Virolainen, Y.A., Ionov, D.V. & Polyakov, A.V., Analysis of Long-Term Measurements of Tropospheric Ozone at the St. Petersburg State University Observational Site in Peterhof, Izvestiya, Atmospheric and Oceanic Physics, 59, 287–295, https://doi.org/10.1134/S000143382303009X
Tags: FTIR, Ozone

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, 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, Gruzdev, A.N., Elokhov A.S. , Changes in the column content and vertical distribution of NO2 according to the results of 30-year measurements at the Zvenigorod Scientific Station of the A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Izvestiya, Atmospheric and Oceanic Physics, 57 (1), 91–103, https://doi.org/10.31857/S0002351521010089
Tags: UVVis, NO2

2020, Stanevich, I., Jones, D. B. A., Strong, K., Parker, R. J., Boesch, H., Wunch, D., Notholt, J., Petri, C., Warneke, T., Sussmann, R., Schneider, M., Hase, F., Kivi, R., Deutscher, N. M., Velazco, V. A., Walker, K. A., and Deng, F, Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model, Geoscientific Model Development, 13, 3829–3862, https://doi.org/10.5194/gmd-13-3839-2020
Tags: FTIR, Model, CH4

2020, Polyakov, A., Y. Virolainen, A. Poberovskiy, M. Makarova and Y. Timofeyev, Atmospheric HCFC-22 total columns near St. Petersburg: stabilization with start of a decrease, International Journal of Remote Sensing, 41(11), 4365-4371, https://doi.org/10.1080/01431161.2020.1717668
Tags: FTIR, HCFC-22, Trends

2020, Sterckx, S., Ian Brown, Andreas Kääb, Maarten Krol, Rosemary Morrow, Pepijn Veefkind, K. Folkert Boersma, Martine De Mazière, Nigel Fox & Peter Thorne, Towards a European Cal/Val service for earth observation, International Journal of Remote Sensing, 41:12, 4496-4511, https://doi.org/10.1080/01431161.2020.1718240
Tags: FTIR, Validation

2019, Gruzdev, A.N., Accounting for serial correlation in a multiple linear regression problem on the example of analysis of the column NO2 content in the atmosphere, Izvestiya, Atmospheric and Oceanic Physics, 55, 65–72, https://doi.org/10.1134/S0001433819010043
Tags: NO2, UVVis