Articles | Volume 7, issue 4
Geosci. Model Dev., 7, 1451–1465, 2014
https://doi.org/10.5194/gmd-7-1451-2014
Geosci. Model Dev., 7, 1451–1465, 2014
https://doi.org/10.5194/gmd-7-1451-2014

Methods for assessment of models 16 Jul 2014

Methods for assessment of models | 16 Jul 2014

Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

S. Skachko et al.

Related authors

Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1)
Sergey Skachko, Mark Buehner, Stéphane Laroche, Ervig Lapalme, Gregory Smith, François Roy, Dorina Surcel-Colan, Jean-Marc Bélanger, and Louis Garand
Geosci. Model Dev., 12, 5097–5112, https://doi.org/10.5194/gmd-12-5097-2019,https://doi.org/10.5194/gmd-12-5097-2019, 2019
Short summary
Technical note: Reanalysis of Aura MLS chemical observations
Quentin Errera, Simon Chabrillat, Yves Christophe, Jonas Debosscher, Daan Hubert, William Lahoz, Michelle L. Santee, Masato Shiotani, Sergey Skachko, Thomas von Clarmann, and Kaley Walker
Atmos. Chem. Phys., 19, 13647–13679, https://doi.org/10.5194/acp-19-13647-2019,https://doi.org/10.5194/acp-19-13647-2019, 2019
Short summary
Harmonisation and diagnostics of MIPAS ESA CH4 and N2O profiles using data assimilation
Quentin Errera, Simone Ceccherini, Yves Christophe, Simon Chabrillat, Michaela I. Hegglin, Alyn Lambert, Richard Ménard, Piera Raspollini, Sergey Skachko, Michiel van Weele, and Kaley A. Walker
Atmos. Meas. Tech., 9, 5895–5909, https://doi.org/10.5194/amt-9-5895-2016,https://doi.org/10.5194/amt-9-5895-2016, 2016
Short summary
EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06)
Sergey Skachko, Richard Ménard, Quentin Errera, Yves Christophe, and Simon Chabrillat
Geosci. Model Dev., 9, 2893–2908, https://doi.org/10.5194/gmd-9-2893-2016,https://doi.org/10.5194/gmd-9-2893-2016, 2016
Short summary

Related subject area

Atmospheric sciences
The Vertical City Weather Generator (VCWG v1.3.2)
Mohsen Moradi, Benjamin Dyer, Amir Nazem, Manoj K. Nambiar, M. Rafsan Nahian, Bruno Bueno, Chris Mackey, Saeran Vasanthakumar, Negin Nazarian, E. Scott Krayenhoff, Leslie K. Norford, and Amir A. Aliabadi
Geosci. Model Dev., 14, 961–984, https://doi.org/10.5194/gmd-14-961-2021,https://doi.org/10.5194/gmd-14-961-2021, 2021
Short summary
A zero-dimensional view of atmospheric degradation of levoglucosan (LEVCHEM_v1) using numerical chamber simulations
Loredana G. Suciu, Robert J. Griffin, and Caroline A. Masiello
Geosci. Model Dev., 14, 907–921, https://doi.org/10.5194/gmd-14-907-2021,https://doi.org/10.5194/gmd-14-907-2021, 2021
Short summary
The Nonhydrostatic ICosahedral Atmospheric Model for CMIP6 HighResMIP simulations (NICAM16-S): experimental design, model description, and impacts of model updates
Chihiro Kodama, Tomoki Ohno, Tatsuya Seiki, Hisashi Yashiro, Akira T. Noda, Masuo Nakano, Yohei Yamada, Woosub Roh, Masaki Satoh, Tomoko Nitta, Daisuke Goto, Hiroaki Miura, Tomoe Nasuno, Tomoki Miyakawa, Ying-Wen Chen, and Masato Sugi
Geosci. Model Dev., 14, 795–820, https://doi.org/10.5194/gmd-14-795-2021,https://doi.org/10.5194/gmd-14-795-2021, 2021
Short summary
Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1
Jingyu Wang, Jiwen Fan, Robert A. Houze Jr., Stella R. Brodzik, Kai Zhang, Guang J. Zhang, and Po-Lun Ma
Geosci. Model Dev., 14, 719–734, https://doi.org/10.5194/gmd-14-719-2021,https://doi.org/10.5194/gmd-14-719-2021, 2021
Short summary
Development of WRF/CUACE v1.0 model and its preliminary application in simulating air quality in China
Lei Zhang, Sunling Gong, Tianliang Zhao, Chunhong Zhou, Yuesi Wang, Jiawei Li, Dongsheng Ji, Jianjun He, Hongli Liu, Ke Gui, Xiaomei Guo, Jinhui Gao, Yunpeng Shan, Hong Wang, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 14, 703–718, https://doi.org/10.5194/gmd-14-703-2021,https://doi.org/10.5194/gmd-14-703-2021, 2021
Short summary

Cited articles

Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61, 72–83, 2009.
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances, Q. J. Roy. Meteorol. Soc., 134, 1951–1970, 2008.
Buehner, M., Houtekamer, P. L., Charette, C., Mitchell, H. L., and He, B.: Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments, Mon. Weather Rev., 138, 1550–1566, 2010a.
Buehner, M., Houtekamer, P. L., Charette, C., Mitchell, H. L., and He, B.: Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part II: One-Month Experiments with Real Observations, Mon. Weather Rev., 138, 1567–1586, 2010b.
Constantinescu, E. M., Sandu, A., Chai, T., and Carmichael, G. R.: Ensemble-based chemical data assimilation. I: General approach, Q. J. Roy. Meteorol. Soc., 133, 1229–1243, 2007a.
Download