Articles | Volume 20, issue 6
https://doi.org/10.5194/npg-20-1031-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/npg-20-1031-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Nov 2013
Research article |
| 26 Nov 2013
The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model
S. G. Penny
Applied Mathematics and Scientific Computation, University of Maryland, College Park, Maryland, USA
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
National Centers for Environmental Prediction (NCEP), NOAA Center for Weather and Climate Prediction, College Park, Maryland, USA
E. Kalnay
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA
J. A. Carton
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
B. R. Hunt
Department of Mathematics, College Park, Maryland, USA
Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA
Applied Mathematics and Scientific Computation, University of Maryland, College Park, Maryland, USA
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA
Center for Scientific Computation and Mathematical Modeling, University of Maryland, College Park, Maryland, USA
T. Miyoshi
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
RIKEN Advanced Institute for Computational Science, Kobe, Japan
G. A. Chepurin
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
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Cited
28 citations as recorded by crossref.
- The Hybrid Local Ensemble Transform Kalman Filter S. Penny https://doi.org/10.1175/MWR-D-13-00131.1
- GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction A. Molod et al. https://doi.org/10.1029/2019JD031767
- Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea S. Sanikommu et al. https://doi.org/10.1029/2019JC015611
- Estimating Ocean Observation Impacts on Coupled Atmosphere‐Ocean Models Using Ensemble Forecast Sensitivity to Observation (EFSO) C. Chang et al. https://doi.org/10.1029/2023GL103154
- Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning E. Kalnay et al. https://doi.org/10.5194/npg-30-217-2023
- Mathematical foundations of hybrid data assimilation from a synchronization perspective S. Penny https://doi.org/10.1063/1.5001819
- Assimilation of Tropical Cyclone Track and Wind Radius Data with an Ensemble Kalman Filter M. Kunii https://doi.org/10.1175/WAF-D-14-00088.1
- Impact of the TAO/TRITON Array on Reanalyses and Predictions of the 2015 El Niño E. Hackert et al. https://doi.org/10.1029/2023JC020039
- A local particle filter for high-dimensional geophysical systems S. Penny & T. Miyoshi https://doi.org/10.5194/npg-23-391-2016
- Optimal Kalman-like filter for a class of nonlinear stochastic systems S. Kong et al. https://doi.org/10.1016/j.joes.2022.03.002
- Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System E. Massoud et al. https://doi.org/10.5194/esd-14-147-2023
- Signatures of aerosol-cloud interactions in GiOcean: a coupled global reanalysis with two-moment cloud microphysics C. Song et al. https://doi.org/10.5194/acp-25-15567-2025
- Ensemble Kalman Filtering with One-Step-Ahead Smoothing N. Raboudi et al. https://doi.org/10.1175/MWR-D-17-0175.1
- Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation B. Baduru et al. https://doi.org/10.1016/j.ocemod.2019.101470
- On Oceanic Initial State Errors in the Ensemble Data Assimilation for a Coupled General Circulation Model Y. Chen et al. https://doi.org/10.1029/2022MS003106
- A modified iterative ensemble Kalman filter data assimilation method B. Xu et al. https://doi.org/10.1088/1755-1315/81/1/012197
- An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0 S. Ohishi et al. https://doi.org/10.5194/gmd-15-8395-2022
- Satellite Sea Surface Salinity Observations Impact on El Niño/Southern Oscillation Predictions: Case Studies From the NASA GEOS Seasonal Forecast System E. Hackert et al. https://doi.org/10.1029/2019JC015788
- LORA: a local ensemble transform Kalman filter-based ocean research analysis S. Ohishi et al. https://doi.org/10.1007/s10236-023-01541-3
- How Efficient Is Model-to-Model Data Assimilation at Mitigating Atmospheric Forcing Errors in a Regional Ocean Model? G. Shapiro & M. Salim https://doi.org/10.3390/jmse11050935
- An ensemble Kalman filter-based ocean data assimilation system improved by adaptive observation error inflation (AOEI) S. Ohishi et al. https://doi.org/10.5194/gmd-15-9057-2022
- Partial-convolution-implemented generative adversarial network for global oceanic data assimilation Y. Ham et al. https://doi.org/10.1038/s42256-024-00867-x
- Using Observations near the Poles in the AFES-LETKF Data Assimilation System A. Yamazaki et al. https://doi.org/10.2151/sola.2017-008
- An Investigation of Ocean Model Uncertainties Through Ensemble Forecast Experiments in the Southwest Atlantic Ocean L. Lima et al. https://doi.org/10.1029/2018JC013919
- Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability Y. Sawada et al. https://doi.org/10.1029/2018JD029643
- Tracer and observationally derived constraints on diapycnal diffusivities in an ocean state estimate D. Trossman et al. https://doi.org/10.5194/os-18-729-2022
- A Hybrid Global Ocean Data Assimilation System at NCEP S. Penny et al. https://doi.org/10.1175/MWR-D-14-00376.1
- Beyond Inflation: Backscatter Parameterizations to Address the Variability Deficit in Global Ocean Data Assimilation K. Boden et al. https://doi.org/10.3390/jmse14141273
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