Articles | Volume 15, issue 5
https://doi.org/10.5194/amt-15-1145-2022
https://doi.org/10.5194/amt-15-1145-2022
Research article
 | 
07 Mar 2022
Research article |  | 07 Mar 2022

Truth and uncertainty. A critical discussion of the error concept versus the uncertainty concept

Thomas von Clarmann, Steven Compernolle, and Frank Hase

Related authors

Upper tropospheric pollutants observed by MIPAS: geographic and seasonal variations
Norbert Glatthor, Gabriele P. Stiller, Thomas von Clarmann, Bernd Funke, Sylvia Kellmann, and Andrea Linden
EGUsphere, https://doi.org/10.5194/egusphere-2024-1793,https://doi.org/10.5194/egusphere-2024-1793, 2024
Short summary
IMK–IAA MIPAS retrieval version 8: CH4 and N2O
Norbert Glatthor, Thomas von Clarmann, Bernd Funke, Maya García-Comas, Udo Grabowski, Michael Höpfner, Sylvia Kellmann, Michael Kiefer, Alexandra Laeng, Andrea Linden, Manuel López-Puertas, and Gabriele P. Stiller
Atmos. Meas. Tech., 17, 2849–2871, https://doi.org/10.5194/amt-17-2849-2024,https://doi.org/10.5194/amt-17-2849-2024, 2024
Short summary
Version 8 IMK/IAA MIPAS measurements of CFC-11, CFC-12, and HCFC-22
Gabriele P. Stiller, Thomas von Clarmann, Norbert Glatthor, Udo Grabowski, Sylvia Kellmann, Michael Kiefer, Alexandra Laeng, Andrea Linden, Bernd Funke, Maya García-Comas, and Manuel López-Puertas
Atmos. Meas. Tech., 17, 1759–1789, https://doi.org/10.5194/amt-17-1759-2024,https://doi.org/10.5194/amt-17-1759-2024, 2024
Short summary
MIPAS ozone retrieval version 8: middle-atmosphere measurements
Manuel López-Puertas, Maya García-Comas, Bernd Funke, Thomas von Clarmann, Norbert Glatthor, Udo Grabowski, Sylvia Kellmann, Michael Kiefer, Alexandra Laeng, Andrea Linden, and Gabriele P. Stiller
Atmos. Meas. Tech., 16, 5609–5645, https://doi.org/10.5194/amt-16-5609-2023,https://doi.org/10.5194/amt-16-5609-2023, 2023
Short summary
Version 8 IMK–IAA MIPAS temperatures from 12–15 µm spectra: Middle and Upper Atmosphere modes
Maya García-Comas, Bernd Funke, Manuel López-Puertas, Norbert Glatthor, Udo Grabowski, Sylvia Kellmann, Michael Kiefer, Andrea Linden, Belén Martínez-Mondéjar, Gabriele P. Stiller, and Thomas von Clarmann
Atmos. Meas. Tech., 16, 5357–5386, https://doi.org/10.5194/amt-16-5357-2023,https://doi.org/10.5194/amt-16-5357-2023, 2023
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations
Leon Kuhn, Steffen Beirle, Sergey Osipov, Andrea Pozzer, and Thomas Wagner
Atmos. Meas. Tech., 17, 6485–6516, https://doi.org/10.5194/amt-17-6485-2024,https://doi.org/10.5194/amt-17-6485-2024, 2024
Short summary
Improved convective cloud differential (CCD) tropospheric ozone from S5P-TROPOMI satellite data using local cloud fields
Swathi Maratt Satheesan, Kai-Uwe Eichmann, John P. Burrows, Mark Weber, Ryan Stauffer, Anne M. Thompson, and Debra Kollonige
Atmos. Meas. Tech., 17, 6459–6484, https://doi.org/10.5194/amt-17-6459-2024,https://doi.org/10.5194/amt-17-6459-2024, 2024
Short summary
Atmospheric propane (C3H8) column retrievals from ground-based FTIR observations in Xianghe, China
Minqiang Zhou, Pucai Wang, Bart Dils, Bavo Langerock, Geoff Toon, Christian Hermans, Weidong Nan, Qun Cheng, and Martine De Mazière
Atmos. Meas. Tech., 17, 6385–6396, https://doi.org/10.5194/amt-17-6385-2024,https://doi.org/10.5194/amt-17-6385-2024, 2024
Short summary
Can the remote sensing of combustion phase improve estimates of landscape fire smoke emission rate and composition?
Farrer Owsley-Brown, Martin J. Wooster, Mark J. Grosvenor, and Yanan Liu
Atmos. Meas. Tech., 17, 6247–6264, https://doi.org/10.5194/amt-17-6247-2024,https://doi.org/10.5194/amt-17-6247-2024, 2024
Short summary
Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS
Sora Seo, Pieter Valks, Ronny Lutz, Klaus-Peter Heue, Pascal Hedelt, Víctor Molina García, Diego Loyola, Hanlim Lee, and Jhoon Kim
Atmos. Meas. Tech., 17, 6163–6191, https://doi.org/10.5194/amt-17-6163-2024,https://doi.org/10.5194/amt-17-6163-2024, 2024
Short summary

Cited articles

Bayes, T.: An essay towards solving a problem in the doctrine of chances, Phil. Trans., 53, 370–418, https://doi.org/10.1098/rstl.1763.0053, 1763. a
Bich, W.: From Errors to Probability Density Functions. Evolution of the Concept of Measurement Uncertainty, IEEE T. Instrum. Meas., 61, 2153–2159, https://doi.org/10.1109/TIM.2012.2193696, 2012. a, b, c
Bridgman, P. W.: The Logic of Modern Physics, Macmillan, New York, OCoLC 609483443, 1927. a
Bureau International des Poids et Mésures: Report on the BIPM enquiry on error statements, Tech. Rep. BIPM-80/3, Bureau International des Poids et Mésures, F-92310, https://www.bipm.org/en/publications/rapports-bipm-pre-1990 (last access: 1 March 2022), 1980.​​​​​​​ a
Chang, H.: Operationalism, in: The Stanford Encyclopedia of Philosophy, edited by: Zalta, E. N., Metaphysics Research Lab, Stanford University, Winter 2019 edn., https://plato.stanford.edu/archives/win2019/entries/operationalism/ (last access: 28 February 2022), 2019. a
Download
Short summary
Contrary to the claims put forward in Evaluation of measurement data – Guide to the expression of uncertainty in measurement issued by the JCGM, the error concept and the uncertainty concept are the same. Arguments in favor of the contrary were found not to be compelling. Neither was any evidence presented that errors and uncertainties define a different relation between the measured and true values, nor is a Bayesian concept beyond the mere subjective probability referred to.