Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-1989-2025
https://doi.org/10.5194/gmd-18-1989-2025
Development and technical paper
 | 
27 Mar 2025
Development and technical paper |  | 27 Mar 2025

A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks

Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers

Related authors

A large-eddy simulation analysis of collective wind farm axial-induction set points in the presence of blockage
Théo Delvaux and Johan Meyers
Wind Energ. Sci., 10, 613–630, https://doi.org/10.5194/wes-10-613-2025,https://doi.org/10.5194/wes-10-613-2025, 2025
Short summary
Turbine- and farm-scale power losses in wind farms: an alternative to wake and farm blockage losses
Andrew Kirby, Takafumi Nishino, Luca Lanzilao, Thomas D. Dunstan, and Johan Meyers
Wind Energ. Sci., 10, 435–450, https://doi.org/10.5194/wes-10-435-2025,https://doi.org/10.5194/wes-10-435-2025, 2025
Short summary
Effect of blockage on wind turbine power and wake development
Olivier Ndindayino, Augustin Puel, and Johan Meyers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-6,https://doi.org/10.5194/wes-2025-6, 2025
Preprint under review for WES
Short summary
Source reconstruction via deposition measurements of an undeclared radiological atmospheric release
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
EGUsphere, https://doi.org/10.5194/egusphere-2024-4057,https://doi.org/10.5194/egusphere-2024-4057, 2025
Short summary
Dries Allaerts, 1989–2024
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024,https://doi.org/10.5194/wes-9-2171-2024, 2024
Short summary

Related subject area

Atmospheric sciences
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025,https://doi.org/10.5194/gmd-18-1965-2025, 2025
Short summary
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025,https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025,https://doi.org/10.5194/gmd-18-1879-2025, 2025
Short summary
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025,https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
NeuralMie (v1.0): an aerosol optics emulator
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025,https://doi.org/10.5194/gmd-18-1809-2025, 2025
Short summary

Cited articles

Abril-Pla, O., Andreani, V., Carroll, C., Dong, L., Fonnesbeck, C. J., Kochurov, M., Kumar, R., Lao, J., Luhmann, C. C., Martin, O. A., Osthege, M., Vieira, R., Wiecki, T., and Zinkov, R.: PyMC: a modern, and comprehensive probabilistic programming framework in Python, PeerJ Computer Science, 9, e1516, https://doi.org/10.7717/peerj-cs.1516, 2023. a
Agentschap Digitaal Vlaanderen: Orthofotomozaïek, grootschalig, winteropnamen, kleur, 2013–2015, Vlaanderen, https://www.vlaanderen.be/datavindplaats/catalogus/ orthofotomozaiek-grootschalig-winteropnamen-kleur-2013-2015-vlaanderen, (last access: 25 March 2025), 2016. a
Arahmane, H., Dumazert, J., Barat, E., Dautremer, T., Carrel, F., Dufour, N., and Michel, M.: Statistical approach for radioactivity detection: A brief review, J. Environ. Radioactiv., 272, 107358, https://doi.org/10.1016/j.jenvrad.2023.107358, 2024. a
Barnard, J., McCulloch, R., and Meng, X.-L.: Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage, Stat. Sinica, 10, 1281–1311, 2000. a, b
Bergan, T. D.: Radioactive fallout in Norway from atmospheric nuclear weapons tests, J. Environ. Radioactiv., 60, 189–208, https://doi.org/10.1016/S0265-931X(01)00103-5, 2002. a
Download
Short summary
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known anomalous event.
Share