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, Ondrej Tichy, Marit Svendby Otervik, Sabine Eckhardt, Yves Balkanski, and Didier Hauglustaine
Aerosol Research Discuss., https://doi.org/10.5194/ar-2024-22,https://doi.org/10.5194/ar-2024-22, 2024
Preprint under review for AR
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
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025,https://doi.org/10.5194/gmd-18-141-2025, 2025
Short summary
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025,https://doi.org/10.5194/gmd-18-117-2025, 2025
Short summary
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025,https://doi.org/10.5194/gmd-18-101-2025, 2025
Short summary
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025,https://doi.org/10.5194/gmd-18-1-2025, 2025
Short summary
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024,https://doi.org/10.5194/gmd-17-8885-2024, 2024
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.