Articles | Volume 30, issue 1
https://doi.org/10.5194/npg-30-13-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-30-13-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jan 2023
Research article |
| 09 Jan 2023
| 09 Jan 2023
Guidance on how to improve vertical covariance localization based on a 1000-member ensemble
Institut für Meteorologie und Geophysik, Universität Wien, Vienna, Austria
David Hinger
Institut für Meteorologie und Geophysik, Universität Wien, Vienna, Austria
Philipp Johannes Griewank
Institut für Meteorologie und Geophysik, Universität Wien, Vienna, Austria
Takemasa Miyoshi
RIKEN Center for Computational Science, Kobe, Japan
Martin Weissmann
Institut für Meteorologie und Geophysik, Universität Wien, Vienna, Austria
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Kota Takeda and Takemasa Miyoshi
Nonlin. Processes Geophys., 33, 335–346, https://doi.org/10.5194/npg-33-335-2026, https://doi.org/10.5194/npg-33-335-2026, 2026
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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.
Yuki Kobayashi, Shun Ohishi, and Takemasa Miyoshi
EGUsphere, https://doi.org/10.5194/egusphere-2026-2653, https://doi.org/10.5194/egusphere-2026-2653, 2026
This preprint is open for discussion and under review for Nonlinear Processes in Geophysics (NPG).
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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.
Shun Ohishi, Takemasa Miyoshi, and Misako Kachi
EGUsphere, https://doi.org/10.5194/egusphere-2026-2277, https://doi.org/10.5194/egusphere-2026-2277, 2026
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We developed a new eddy-permitting local ensemble Kalman filter (LETKF)-based Ocean Research Analysis (LORA) version 2.0 for a quasi-global domain (LORA-QG) from June 2002. Validation results show that LORA-QG is the second-most accurate among the four quasi-global and global ocean analysis datasets and has sufficient accuracy for scientific and practical applications.
Bingying Shi, Philipp Griewank, Florian Meier, Jinzhong Min, and Martin Weissmann
EGUsphere, https://doi.org/10.5194/egusphere-2026-1463, https://doi.org/10.5194/egusphere-2026-1463, 2026
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The non-Gaussian distribution of first-guess departures can be mitigated by applying cloud-dependent error models. In this study, we evaluate three existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations instead of using the mean cloud effect of model and observations.
Maurus Borne, Peter Knippertz, Michael Rennie, and Martin Weissmann
Weather Clim. Dynam., 7, 489–505, https://doi.org/10.5194/wcd-7-489-2026, https://doi.org/10.5194/wcd-7-489-2026, 2026
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This study shows that Aeolus satellite wind lidar observations significantly improve wind forecasts and that these improvements lead to more accurate rainfall predictions, particularly at longer lead times and during winter seasons in the extratropics. The benefits are likely due to better representation of large-scale atmospheric features such as jet streams and Rossby waves, highlighting Aeolus's value for numerical weather prediction.
Juan M. Guerrieri, Manuel Pulido, Takemasa Miyoshi, Arata Amemiya, and Juan J. Ruiz
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.
Arata Amemiya and Takemasa Miyoshi
Nonlin. Processes Geophys., 33, 1–16, https://doi.org/10.5194/npg-33-1-2026, https://doi.org/10.5194/npg-33-1-2026, 2026
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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.
Michael Goodliff and Takemasa Miyoshi
EGUsphere, https://doi.org/10.5194/egusphere-2025-933, https://doi.org/10.5194/egusphere-2025-933, 2025
Preprint archived
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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.
Konstantin Krüger, Andreas Schäfler, Martin Weissmann, and George C. Craig
Weather Clim. Dynam., 5, 491–509, https://doi.org/10.5194/wcd-5-491-2024, https://doi.org/10.5194/wcd-5-491-2024, 2024
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Initial conditions of current numerical weather prediction models insufficiently represent the sharp vertical gradients across the midlatitude tropopause. Observation-space data assimilation output is used to study the influence of assimilated radiosondes on the tropopause. The radiosondes reduce systematic biases of the model background and sharpen temperature and wind gradients in the analysis. Tropopause sharpness is still underestimated in the analysis, which may impact weather forecasts.
Maurus Borne, Peter Knippertz, Martin Weissmann, Benjamin Witschas, Cyrille Flamant, Rosimar Rios-Berrios, and Peter Veals
Atmos. Meas. Tech., 17, 561–581, https://doi.org/10.5194/amt-17-561-2024, https://doi.org/10.5194/amt-17-561-2024, 2024
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This study assesses the quality of Aeolus wind measurements over the tropical Atlantic. The results identified the accuracy and precision of the Aeolus wind measurements and the potential source of errors. For instance, the study revealed atmospheric conditions that can deteriorate the measurement quality, such as weaker laser signal in cloudy or dusty conditions, and confirmed the presence of an orbital-dependant bias. These results can help to improve the Aeolus wind measurement algorithm.
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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This study aimed to enhance weather and hydrological forecasts by integrating soil moisture data into a global weather model. By assimilating atmospheric observations and soil moisture data, the accuracy of forecasts was improved, and certain biases were reduced. The method was found to be particularly beneficial in areas like the Sahel and equatorial Africa, where precipitation patterns vary seasonally. This new approach has the potential to improve the precision of weather predictions.
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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This paper is a follow-up of a work by Miyoshi and Sun which was published in NPG Letters in 2022. The control simulation experiment is applied to the Lorenz-96 model for avoiding extreme events. The results show that extreme events of this partially and imperfectly observed chaotic system can be avoided by applying pre-designed small perturbations. These investigations may be extended to more realistic numerical weather prediction models.
Anne Martin, Martin Weissmann, and Alexander Cress
Weather Clim. Dynam., 4, 249–264, https://doi.org/10.5194/wcd-4-249-2023, https://doi.org/10.5194/wcd-4-249-2023, 2023
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Global wind profiles from the Aeolus satellite mission are an important recent substitute for the Global Observing System, showing an overall positive impact on numerical weather prediction forecasts. This study highlights atmospheric dynamic phenomena constituting pathways for significant improvement of Aeolus for future studies, including large-scale tropical circulation systems and the interaction of tropical cyclones undergoing an extratropical transition with the midlatitude waveguide.
Shun Ohishi, Takemasa Miyoshi, and Misako Kachi
Geosci. Model Dev., 15, 9057–9073, https://doi.org/10.5194/gmd-15-9057-2022, https://doi.org/10.5194/gmd-15-9057-2022, 2022
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An adaptive observation error inflation (AOEI) method was proposed for atmospheric data assimilation to mitigate erroneous analysis updates caused by large observation-minus-forecast differences for satellite brightness temperature around clear- and cloudy-sky boundaries. This study implemented the AOEI with an ocean data assimilation system, leading to an improvement of analysis accuracy and dynamical balance around the frontal regions with large meridional temperature differences.
Konstantin Krüger, Andreas Schäfler, Martin Wirth, Martin Weissmann, and George C. Craig
Atmos. Chem. Phys., 22, 15559–15577, https://doi.org/10.5194/acp-22-15559-2022, https://doi.org/10.5194/acp-22-15559-2022, 2022
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A comprehensive data set of airborne lidar water vapour profiles is compared with ERA5 reanalyses for a robust characterization of the vertical structure of the mid-latitude lower-stratospheric moist bias. We confirm a moist bias of up to 55 % at 1.3 km altitude above the tropopause and uncover a decreasing bias beyond. Collocated O3 and H2O observations reveal a particularly strong bias in the mixing layer, indicating insufficiently modelled transport processes fostering the bias.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022, https://doi.org/10.5194/gmd-15-8325-2022, 2022
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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.
Shun Ohishi, Tsutomu Hihara, Hidenori Aiki, Joji Ishizaka, Yasumasa Miyazawa, Misako Kachi, and Takemasa Miyoshi
Geosci. Model Dev., 15, 8395–8410, https://doi.org/10.5194/gmd-15-8395-2022, https://doi.org/10.5194/gmd-15-8395-2022, 2022
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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.
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
naturein a computational simulation. Idealized experiments with a low-order chaotic system show successful results by small control signals of only 3 % of the observation error. This is the first step toward realistic weather simulations.
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.
Stefan Geiss, Leonhard Scheck, Alberto de Lozar, and Martin Weissmann
Atmos. Chem. Phys., 21, 12273–12290, https://doi.org/10.5194/acp-21-12273-2021, https://doi.org/10.5194/acp-21-12273-2021, 2021
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This study demonstrates the benefits of using both visible and infrared satellite channels to evaluate clouds in numerical weather prediction models. Combining these highly resolved observations provides significantly more and complementary information than using only infrared observations. The visible observations are particularly sensitive to subgrid water clouds, which are not well constrained by other observations.
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Short summary
This study investigates vertical localization based on a convection-permitting 1000-member ensemble simulation. We derive an empirical optimal localization (EOL) that minimizes sampling error in 40-member sub-sample correlations assuming 1000-member correlations as truth. The results will provide guidance for localization in convective-scale ensemble data assimilation systems.
This study investigates vertical localization based on a convection-permitting 1000-member...
