Articles | Volume 9, issue 8
https://doi.org/10.5194/gmd-9-2893-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-2893-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
26 Aug 2016
Methods for assessment of models |
| 26 Aug 2016
EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06)
Sergey Skachko
CORRESPONDING AUTHOR
Royal Belgian Institute for Space Aeronomy, BIRA-IASB, Brussels, Belgium
Richard Ménard
Air Quality Research Division, Environment Canada, Dorval, Canada
Quentin Errera
Royal Belgian Institute for Space Aeronomy, BIRA-IASB, Brussels, Belgium
Yves Christophe
Royal Belgian Institute for Space Aeronomy, BIRA-IASB, Brussels, Belgium
Simon Chabrillat
Royal Belgian Institute for Space Aeronomy, BIRA-IASB, Brussels, Belgium
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- M2‐SCREAM: A Stratospheric Composition Reanalysis of Aura MLS Data With MERRA‐2 Transport K. Wargan et al. https://doi.org/10.1029/2022EA002632
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- Improving PM2.5 Forecasts in China Using an Initial Error Transport Model H. Wu et al. https://doi.org/10.1021/acs.est.0c01680
- Technical note: Reanalysis of Aura MLS chemical observations Q. Errera et al. https://doi.org/10.5194/acp-19-13647-2019
- Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part I: Physical Background and Coupled Modeling Aspects R. Ménard et al. https://doi.org/10.3390/atmos11020150
- Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. https://doi.org/10.3390/rs14020371
- NASA GEOS Composition Forecast Modeling System GEOS‐CF v1.0: Stratospheric Composition K. Knowland et al. https://doi.org/10.1029/2021MS002852
- Modelling stratospheric composition for the Copernicus Atmosphere Monitoring Service: multi-species evaluation of IFS-COMPO Cy49 S. Chabrillat et al. https://doi.org/10.5194/gmd-18-8973-2025
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- An improved air mass factor calculation for nitrogen dioxide measurements from the Global Ozone Monitoring Experiment-2 (GOME-2) S. Liu et al. https://doi.org/10.5194/amt-13-755-2020
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- Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1) J. Williams et al. https://doi.org/10.5194/gmd-15-4657-2022
27 citations as recorded by crossref.
- Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
- 基于卡尔曼平滑算法重建高时空分辨率星载IPDA激光雷达XCO2观测的方法研究 樊. Fan Zengchang et al. https://doi.org/10.3788/AOS241804
- Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. https://doi.org/10.1029/2020MS002180
- M2‐SCREAM: A Stratospheric Composition Reanalysis of Aura MLS Data With MERRA‐2 Transport K. Wargan et al. https://doi.org/10.1029/2022EA002632
- Data fusion in air pollution exposure assessment: Methods, applications, and future directions Y. Liu et al. https://doi.org/10.1080/10962247.2026.2660605
- Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis K. Miyazaki et al. https://doi.org/10.5194/acp-20-931-2020
- Stratospheric Fluorine as a Tracer of Circulation Changes: Comparison Between Infrared Remote‐Sensing Observations and Simulations With Five Modern Reanalyses M. Prignon et al. https://doi.org/10.1029/2021JD034995
- Harmonisation and diagnostics of MIPAS ESA CH4 and N2O profiles using data assimilation Q. Errera et al. https://doi.org/10.5194/amt-9-5895-2016
- Improved FTIR retrieval strategy for HCFC-22 (CHClF2), comparisons with in situ and satellite datasets with the support of models, and determination of its long-term trend above Jungfraujoch M. Prignon et al. https://doi.org/10.5194/acp-19-12309-2019
- The ALTIUS atmospheric limb sounder D. Fussen et al. https://doi.org/10.1016/j.jqsrt.2019.06.021
- Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions S. Li et al. https://doi.org/10.5194/gmd-16-4171-2023
- Estimation of CH4emission based on an advanced 4D-LETKF assimilation system J. Bisht et al. https://doi.org/10.5194/gmd-16-1823-2023
- Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses B. Dong et al. https://doi.org/10.5194/gmd-16-4233-2023
- Microwave radiometer observations of the ozone diurnal cycle and its short-term variability over Switzerland E. Sauvageat et al. https://doi.org/10.5194/acp-23-7321-2023
- Framework for the comparison of a priori and a posteriori error variance estimation and tuning schemes M. Sitwell & R. Ménard https://doi.org/10.1002/qj.3805
- Improving PM2.5 Forecasts in China Using an Initial Error Transport Model H. Wu et al. https://doi.org/10.1021/acs.est.0c01680
- Technical note: Reanalysis of Aura MLS chemical observations Q. Errera et al. https://doi.org/10.5194/acp-19-13647-2019
- Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part I: Physical Background and Coupled Modeling Aspects R. Ménard et al. https://doi.org/10.3390/atmos11020150
- Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part I: Design of the Assimilation System S. Voshtani et al. https://doi.org/10.3390/rs14020371
- NASA GEOS Composition Forecast Modeling System GEOS‐CF v1.0: Stratospheric Composition K. Knowland et al. https://doi.org/10.1029/2021MS002852
- Modelling stratospheric composition for the Copernicus Atmosphere Monitoring Service: multi-species evaluation of IFS-COMPO Cy49 S. Chabrillat et al. https://doi.org/10.5194/gmd-18-8973-2025
- Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ S. Voshtani et al. https://doi.org/10.3390/atmos14040758
- Quantifying uncertainties due to chemistry modelling – evaluation of tropospheric composition simulations in the CAMS model (cycle 43R1) V. Huijnen et al. https://doi.org/10.5194/gmd-12-1725-2019
- Quantitative comparison of variational and sequential data assimilation techniques for one-dimensional initial-value problems of ideal MHD J. Arnal & C. Groth https://doi.org/10.1016/j.compfluid.2024.106373
- An improved air mass factor calculation for nitrogen dioxide measurements from the Global Ozone Monitoring Experiment-2 (GOME-2) S. Liu et al. https://doi.org/10.5194/amt-13-755-2020
- On the capability of the future ALTIUS ultraviolet–visible–near-infrared limb sounder to constrain modelled stratospheric ozone Q. Errera et al. https://doi.org/10.5194/amt-14-4737-2021
- Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1) J. Williams et al. https://doi.org/10.5194/gmd-15-4657-2022
Saved (final revised paper)
Latest update: 15 Aug 2026
Short summary
In the present work, we performed a comparison of two broadly used data assimilation algorithms, 4D-Var and EnKF, applied to a state-of-the-art atmospheric chemistry transport model. The comparison is carried out using carefully calibrated error statistics. The paper discusses the advantages and disadvantages of each method applied to real-life conditions of a numerical atmospheric chemistry data assimilation.
In the present work, we performed a comparison of two broadly used data assimilation algorithms,...
