Articles | Volume 15, issue 12
https://doi.org/10.5194/acp-15-7039-2015
https://doi.org/10.5194/acp-15-7039-2015
Research article
 | 
30 Jun 2015
Research article |  | 30 Jun 2015

Balancing aggregation and smoothing errors in inverse models

A. J. Turner and D. J. Jacob

Related authors

High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2918,https://doi.org/10.5194/egusphere-2024-2918, 2024
Short summary
State-wide California 2020 Carbon Dioxide Budget Estimated with OCO-2 and OCO-3 satellite data
Matthew S. Johnson, Sofia D. Hamilton, Seongeun Jeong, Yuyan Cui, Dien Wu, Alex Turner, and Marc Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2152,https://doi.org/10.5194/egusphere-2024-2152, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
FootNet v1.0: Development of a machine learning emulator of atmospheric transport
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
EGUsphere, https://doi.org/10.31223/X5197G,https://doi.org/10.31223/X5197G, 2024
Short summary
A high-resolution satellite-based map of global methane emissions reveals missing wetland, fossil fuel, and monsoon sources
Xueying Yu, Dylan B. Millet, Daven K. Henze, Alexander J. Turner, Alba Lorente Delgado, A. Anthony Bloom, and Jianxiong Sheng
Atmos. Chem. Phys., 23, 3325–3346, https://doi.org/10.5194/acp-23-3325-2023,https://doi.org/10.5194/acp-23-3325-2023, 2023
Short summary
A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)
Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen
Biogeosciences, 19, 1777–1793, https://doi.org/10.5194/bg-19-1777-2022,https://doi.org/10.5194/bg-19-1777-2022, 2022
Short summary

Related subject area

Subject: Gases | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
A CO2–Δ14CO2 inversion setup for estimating European fossil CO2 emissions
Carlos Gómez-Ortiz, Guillaume Monteil, Sourish Basu, and Marko Scholze
Atmos. Chem. Phys., 25, 397–424, https://doi.org/10.5194/acp-25-397-2025,https://doi.org/10.5194/acp-25-397-2025, 2025
Short summary
Maximum ozone concentrations in the southwestern US and Texas: implications of the growing predominance of the background contribution
David D. Parrish, Ian C. Faloona, and Richard G. Derwent
Atmos. Chem. Phys., 25, 263–289, https://doi.org/10.5194/acp-25-263-2025,https://doi.org/10.5194/acp-25-263-2025, 2025
Short summary
Derivation of atmospheric reaction mechanisms for volatile organic compounds by the SAPRC mechanism generation system (MechGen)
William P. L. Carter, Jia Jiang, John J. Orlando, and Kelley C. Barsanti
Atmos. Chem. Phys., 25, 199–242, https://doi.org/10.5194/acp-25-199-2025,https://doi.org/10.5194/acp-25-199-2025, 2025
Short summary
Seasonal, regional, and vertical characteristics of high-carbon-monoxide plumes along with their associated ozone anomalies, as seen by IAGOS between 2002 and 2019
Thibaut Lebourgeois, Bastien Sauvage, Pawel Wolff, Béatrice Josse, Virginie Marécal, Yasmine Bennouna, Romain Blot, Damien Boulanger, Hannah Clark, Jean-Marc Cousin, Philippe Nedelec, and Valérie Thouret
Atmos. Chem. Phys., 24, 13975–14004, https://doi.org/10.5194/acp-24-13975-2024,https://doi.org/10.5194/acp-24-13975-2024, 2024
Short summary
The potential of drone observations to improve air quality predictions by 4D-Var
Hassnae Erraji, Philipp Franke, Astrid Lampert, Tobias Schuldt, Ralf Tillmann, Andreas Wahner, and Anne Caroline Lange
Atmos. Chem. Phys., 24, 13913–13934, https://doi.org/10.5194/acp-24-13913-2024,https://doi.org/10.5194/acp-24-13913-2024, 2024
Short summary

Cited articles

Bishop, C. M.: Pattern Recognition and Machine Learning, Springer, 1st Edn., New York, 2007.
Bocquet, M.: Towards optimal choices of control space representation for geophysical data assimilation, Mon. Weather Rev., 137, 2331–2348, https://doi.org/10.1175/2009MWR2789.1, 2009.
Bocquet, M. and Wu, L.: Bayesian design of control space for optimal assimilation of observations. II: Asymptotics solution, Q. J. Roy. Meteor. Soc., 137, 1357–1368, https://doi.org/10.1002/qj.841, 2011.
Bocquet, M., Wu, L., and Chevallier, F.: Bayesian design of control space for optimal assimilation of observations. Part I: Consistent multiscale formalism, Q. J. Roy. Meteor. Soc., 137, 1340–1356, https://doi.org/10.1002/qj.837, 2011.
Bousserez, N., Henze, D. K., Perkins, A., Bowman, K. W., Lee, M., Liu, J., Deng, F., and Jones, D. B. A.: Improved analysis-error covariance matrix for high-dimensional variational inversions: application to source estimation using a 3D atmospheric transport model, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.2495, online first, 2015.
Altmetrics
Final-revised paper
Preprint