Articles | Volume 22, issue 5
https://doi.org/10.5194/os-22-2915-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-22-2915-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Sep 2026
Research article |
| 25 Sep 2026
| 25 Sep 2026
LETKF-based Ocean Research Analysis version 2.0 for a quasi-global domain (LORA-QG): validation and intercomparison with eddy-permitting global ocean reanalysis datasets
RIKEN Center for Computational Science (R-CCS), Kobe, 650-0047, Japan
RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), Kobe, 650-0047, Japan
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan
Japan Marine Science Foundation, Mutsu, Japan
Takemasa Miyoshi
RIKEN Center for Computational Science (R-CCS), Kobe, 650-0047, Japan
RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), Kobe, 650-0047, Japan
Misako Kachi
Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), Tsukuba, Japan
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EGUsphere, https://doi.org/10.5194/egusphere-2026-2653, https://doi.org/10.5194/egusphere-2026-2653, 2026
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Modern environmental forecasting combines computer simulations with real-world observations. Most systems assume errors in these sources are independent, but in reality, they are occasionally linked. We developed a new method to estimate the hidden correlations. Using a simplified atmospheric model, we showed our method successfully identifies the correlations and significantly improves prediction accuracy. This work provides a practical path toward more reliable weather and ocean forecasts.
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We develop an ensemble-Kalman-filter-based regional ocean data assimilation system in which satellite and in situ observations are assimilated at a daily frequency. We find the best setting for dynamical balance and accuracy based on sensitivity experiments focused on how to inflate the ensemble spread and how to apply the analysis update to the model evolution. This study has a broader impact on more general data assimilation systems in which the initial shocks are a significant issue.
Kohei Cho, Misako Kachi, Rigen Shimada, and Josefino Comiso
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This study examines the minimum ensemble size for accurate geophysical forecasting using a method called the ensemble Kalman filter. We reformulate accuracy via observation noise-dependency to classify filter performance qualitatively. Through numerical experiments with a chaotic model, we link the minimum ensemble size for the accuracy to system's instability and propose an effective ensemble downsizing method that ensures both stability and accuracy.
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Nonlin. Processes Geophys., 33, 33–49, https://doi.org/10.5194/npg-33-33-2026, https://doi.org/10.5194/npg-33-33-2026, 2026
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This work extends the Mapping Particle Filter to account for local dependencies. Two localization methods are tested: a global particle flow with local kernels, and iterative local mappings based on correlation radius. Using a two-scale Lorenz-96 truth and a one-scale forecast model, experiments with full/partial and linear/nonlinear observations show Root Mean Square Error reductions using localized Gaussian mixture priors, achieving competitive performance against Gaussian filters.
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The accurate estimation of atmospheric state variables from radar observation in rapidly growing deep convection, which causes heavy thunderstorms, is a major challenge. This study examines the advantage of incorporating radar observation data with very high frequency such as 30 s compared with the conventional case of 5 min, from a theoretical perspective.
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Data-driven models (DDMs) learn from large datasets to make predictions, but data limitations affect reliability. Data assimilation (DA) improves accuracy by combining real-world observations with computational models. This research explores how DA enhances DDMs despite limited data. We propose an algorithm using DA to refine DDM training iteratively. This work has broad implications for fields like meteorology, engineering, and environmental science, where accurate prediction is critical.
Tim Trent, Marc Schröder, Shu-Peng Ho, Steffen Beirle, Ralf Bennartz, Eva Borbas, Christian Borger, Helene Brogniez, Xavier Calbet, Elisa Castelli, Gilbert P. Compo, Wesley Ebisuzaki, Ulrike Falk, Frank Fell, John Forsythe, Hans Hersbach, Misako Kachi, Shinya Kobayashi, Robert E. Kursinski, Diego Loyola, Zhengzao Luo, Johannes K. Nielsen, Enzo Papandrea, Laurence Picon, Rene Preusker, Anthony Reale, Lei Shi, Laura Slivinski, Joao Teixeira, Tom Vonder Haar, and Thomas Wagner
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Kenta Kurosawa, Shunji Kotsuki, and Takemasa Miyoshi
Nonlin. Processes Geophys., 30, 457–479, https://doi.org/10.5194/npg-30-457-2023, https://doi.org/10.5194/npg-30-457-2023, 2023
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Qiwen Sun, Takemasa Miyoshi, and Serge Richard
Nonlin. Processes Geophys., 30, 117–128, https://doi.org/10.5194/npg-30-117-2023, https://doi.org/10.5194/npg-30-117-2023, 2023
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Tobias Necker, David Hinger, Philipp Johannes Griewank, Takemasa Miyoshi, and Martin Weissmann
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Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Takemasa Miyoshi and Qiwen Sun
Nonlin. Processes Geophys., 29, 133–139, https://doi.org/10.5194/npg-29-133-2022, https://doi.org/10.5194/npg-29-133-2022, 2022
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The weather is chaotic and hard to predict, but the chaos implies an effective control where a small control signal grows rapidly to make a big difference. This study proposes a control simulation experiment where we apply a small signal to control
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Juan Ruiz, Guo-Yuan Lien, Keiichi Kondo, Shigenori Otsuka, and Takemasa Miyoshi
Nonlin. Processes Geophys., 28, 615–626, https://doi.org/10.5194/npg-28-615-2021, https://doi.org/10.5194/npg-28-615-2021, 2021
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Effective use of observations with numerical weather prediction models, also known as data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes pose a grand challenge because most data assimilation systems are based on linear processes and normal distribution errors. We investigate how, every 30 s, weather radar observations can help reduce the effect of nonlinear processes and nonnormal distributions.
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Short summary
We developed an eddy-permitting local ensemble Kalman filter (LETKF)-based Ocean Research Analysis (LORA) version 2.0 for a quasi-global domain (LORA-QG) covering the period from June 2002 onward. Validation shows that LORA-QG achieves accuracy comparable to existing global ocean reanalysis datasets. LORA-QG includes ensemble-based uncertainty estimates and individual terms of the heat and salinity budget equations, making it a valuable resource for ocean forecasting and process-based studies.
We developed an eddy-permitting local ensemble Kalman filter (LETKF)-based Ocean Research...
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