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
Atmos. Chem. Phys., 25, 5159–5174, https://doi.org/10.5194/acp-25-5159-2025,https://doi.org/10.5194/acp-25-5159-2025, 2025
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
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025,https://doi.org/10.5194/gmd-18-1661-2025, 2025
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
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)
Comparative ozone production sensitivity to NOx and VOCs in Quito, Ecuador, and Santiago, Chile
María Cazorla, Melissa Trujillo, Rodrigo Seguel, and Laura Gallardo
Atmos. Chem. Phys., 25, 7087–7109, https://doi.org/10.5194/acp-25-7087-2025,https://doi.org/10.5194/acp-25-7087-2025, 2025
Short summary
South Asia anthropogenic ammonia emission inversion through assimilating IASI observations
Ji Xia, Yi Zhou, Li Fang, Yingfei Qi, Dehao Li, Hong Liao, and Jianbing Jin
Atmos. Chem. Phys., 25, 7071–7086, https://doi.org/10.5194/acp-25-7071-2025,https://doi.org/10.5194/acp-25-7071-2025, 2025
Short summary
A new parameterization of photolysis rates for oxygenated volatile organic compounds (OVOCs)
Yuwen Peng, Bin Yuan, Sihang Wang, Xin Song, Zhe Peng, Wenjie Wang, Suxia Yang, Jipeng Qi, Xianjun He, Yibo Huangfu, Xiao-Bing Li, and Min Shao
Atmos. Chem. Phys., 25, 7037–7052, https://doi.org/10.5194/acp-25-7037-2025,https://doi.org/10.5194/acp-25-7037-2025, 2025
Short summary
Constraining the budget of NOx and volatile organic compounds at a remote tropical island using multi-platform observations and WRF-Chem model simulations
Catalina Poraicu, Jean-François Müller, Trissevgeni Stavrakou, Crist Amelynck, Bert W. D. Verreyken, Niels Schoon, Corinne Vigouroux, Nicolas Kumps, Jérôme Brioude, Pierre Tulet, and Camille Mouchel-Vallon
Atmos. Chem. Phys., 25, 6903–6941, https://doi.org/10.5194/acp-25-6903-2025,https://doi.org/10.5194/acp-25-6903-2025, 2025
Short summary
Multi-observational estimation of regional and sectoral emission contributions to the persistent high growth rate of atmospheric CH4 for 2020–2022
Yosuke Niwa, Yasunori Tohjima, Yukio Terao, Tazu Saeki, Akihiko Ito, Taku Umezawa, Kyohei Yamada, Motoki Sasakawa, Toshinobu Machida, Shin-Ichiro Nakaoka, Hideki Nara, Hiroshi Tanimoto, Hitoshi Mukai, Yukio Yoshida, Shinji Morimoto, Shinya Takatsuji, Kazuhiro Tsuboi, Yousuke Sawa, Hidekazu Matsueda, Kentaro Ishijima, Ryo Fujita, Daisuke Goto, Xin Lan, Kenneth Schuldt, Michal Heliasz, Tobias Biermann, Lukasz Chmura, Jarsolaw Necki, Irène Xueref-Remy, and Damiano Sferlazzo
Atmos. Chem. Phys., 25, 6757–6785, https://doi.org/10.5194/acp-25-6757-2025,https://doi.org/10.5194/acp-25-6757-2025, 2025
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.
Share
Altmetrics
Final-revised paper
Preprint