Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-5917-2020
https://doi.org/10.5194/gmd-13-5917-2020
Development and technical paper
 | 
01 Dec 2020
Development and technical paper |  | 01 Dec 2020

On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART

Ondřej Tichý, Lukáš Ulrych, Václav Šmídl, Nikolaos Evangeliou, and Andreas Stohl

Related authors

Unchanged PM2.5 levels over Europe during COVID-19 were buffered by ammonia
Nikolaos Evangeliou, Ondřej Tichý, Marit Svendby Otervik, Sabine Eckhardt, Yves Balkanski, and Didier A. Hauglustaine
Aerosol Research, 3, 155–174, https://doi.org/10.5194/ar-3-155-2025,https://doi.org/10.5194/ar-3-155-2025, 2025
Short summary
Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling
Ondřej Tichý, Sabine Eckhardt, Yves Balkanski, Didier Hauglustaine, and Nikolaos Evangeliou
Atmos. Chem. Phys., 23, 15235–15252, https://doi.org/10.5194/acp-23-15235-2023,https://doi.org/10.5194/acp-23-15235-2023, 2023
Short summary
Real-time measurement of radionuclide concentrations and its impact on inverse modeling of 106Ru release in the fall of 2017
Ondřej Tichý, Miroslav Hýža, Nikolaos Evangeliou, and Václav Šmídl
Atmos. Meas. Tech., 14, 803–818, https://doi.org/10.5194/amt-14-803-2021,https://doi.org/10.5194/amt-14-803-2021, 2021
Short summary
Bayesian inverse modeling and source location of an unintended 131I release in Europe in the fall of 2011
Ondřej Tichý, Václav Šmídl, Radek Hofman, Kateřina Šindelářová, Miroslav Hýža, and Andreas Stohl
Atmos. Chem. Phys., 17, 12677–12696, https://doi.org/10.5194/acp-17-12677-2017,https://doi.org/10.5194/acp-17-12677-2017, 2017
Short summary
LS-APC v1.0: a tuning-free method for the linear inverse problem and its application to source-term determination
Ondřej Tichý, Václav Šmídl, Radek Hofman, and Andreas Stohl
Geosci. Model Dev., 9, 4297–4311, https://doi.org/10.5194/gmd-9-4297-2016,https://doi.org/10.5194/gmd-9-4297-2016, 2016
Short summary

Related subject area

Atmospheric sciences
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025,https://doi.org/10.5194/gmd-18-3065-2025, 2025
Short summary
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025,https://doi.org/10.5194/gmd-18-2983-2025, 2025
Short summary
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025,https://doi.org/10.5194/gmd-18-2861-2025, 2025
Short summary
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025,https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025,https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary

Cited articles

Abagyan, A., Ilyin, L., Izrael, Y., Legasov, V., and Petrov, V.: The information on the Chernobyl accident and its consequences, prepared for IAEA, Sov. At. Energy, 61, 301–320, https://doi.org/10.1007/BF01122262, 1986. a
Berchet, A., Pison, I., Chevallier, F., Bousquet, P., Conil, S., Geever, M., Laurila, T., Lavrič, J., Lopez, M., Moncrieff, J., Necki, J., Ramonet, M., Schmidt, M., Steinbacher, M., and Tarniewicz, J.: Towards better error statistics for atmospheric inversions of methane surface fluxes, Atmos. Chem. Phys., 13, 7115–7132, https://doi.org/10.5194/acp-13-7115-2013, 2013. a
Bocquet, M.: Reconstruction of an atmospheric tracer source using the principle of maximum entropy. II: Applications, Q. J. Roy. Meteor. Soc., 131, 2209–2223, 2005. a
Bocquet, M.: High-resolution reconstruction of a tracer dispersion event: application to ETEX, Q. J. Roy. Meteor. Soc., 133, 1013–1026, 2007. a, b
Bossew, P., Gering, F., Petermann, E., Hamburger, T., Katzlberger, C., Hernandez-Ceballos, M., De Cort, M., Gorzkiewicz, K., Kierepko, R., and Mietelski, J.: An episode of Ru-106 in air over Europe, September–October 2017–Geographical distribution of inhalation dose over Europe, J. Environ. Radioactiv., 205, 79–92, 2019. a
Download
Short summary
We study the estimation of the temporal profile of an atmospheric release using formalization as a linear inverse problem. The problem is typically ill-posed, so all state-of-the-art methods need some form of regularization using additional information. We provide a sensitivity study on the prior source term and regularization parameters for the shape of the source term with a demonstration on the ETEX experimental release and the Cs-134 and Cs-137 dataset from the Chernobyl accident.
Share