Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
18 Nov 2022
Development and technical paper |
| 18 Nov 2022
| 18 Nov 2022
A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
Shunji Kotsuki
CORRESPONDING AUTHOR
RIKEN Center for Computational Science, Kobe, Japan
Center for Environmental Remote Sensing, Chiba University, Chiba,
Japan
PRESTO, Japan Science and Technology Agency, Chiba, Japan
Takemasa Miyoshi
CORRESPONDING AUTHOR
RIKEN Center for Computational Science, Kobe, Japan
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program,
Kobe, Japan
RIKEN Cluster for Pioneering Research, Kobe, Japan
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, Maryland, USA
Keiichi Kondo
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, Japan
RIKEN Center for Computational Science, Kobe, Japan
Roland Potthast
Deutscher Wetterdienst, Offenbach, Germany
Applied Mathematics, University of Reading, Reading, UK
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A tropical cyclone is a circular air movement that emerges over warm waters of the tropical ocean and its movement is guided by complex interactions between the ocean and the atmosphere. To better understand this complexity, we adopted ideas and techniques from biology and bioinformatics, to have a fresh look at this matter. This led to the creation of "MeteoScape," a tool that calculates the probability of paths for tropical cyclones can take and visualize them in an understandable way.
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Extreme weather poses serious risks, making prevention crucial. Using the Lorenz 96 model as a testbed, we propose a bottom-up approach to mitigate extreme events via local interventions guided by multi-scenario ensemble forecasts. Unlike control-theoretic methods, our approach selects the best control scenario from available options. It achieves a high success rate of 99.4% while maintaining reasonable costs, offering a practical strategy to reduce extremes under limited control.
Atsushi Okazaki, Diego S. Carrió, Quentin Dalaiden, Jarrah Harrison-Lofthouse, Shunji Kotsuki, and Kei Yoshimura
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Data assimilation (DA) has been used to reconstruct paleoclimate fields. DA integrates model simulations and climate proxies based on their error sizes. Consequently, error information is vital for DA to function optimally. This study estimated observation errors using "innovation statistics" and demonstrated that DA with estimated errors outperformed previous studies.
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Geosci. Model Dev., 18, 7215–7225, https://doi.org/10.5194/gmd-18-7215-2025, https://doi.org/10.5194/gmd-18-7215-2025, 2025
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Yusuke Hiraga, Jacqueline Muthoni Mbugua, Shunji Kotsuki, Yoshiharu Suzuki, Shu-Hua Chen, Atsushi Hamada, Kazuaki Yasunaga, and Takuya Funatomi
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This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
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Can cloud seeding be a strategy to mitigate localized heavy rainfall disasters? Our numerical experiments showed that injecting large amounts of ice nuclei into convective clouds inhibited the growth of individual ice crystals to sizes sufficient for rainfall, thereby reducing rainfall in the worst-hit area and shifting it downstream. This shows promise for using cloud seeding to lessen rain-related disasters, although further studies are needed to confirm its broader effectiveness.
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Nonlin. Processes Geophys., 32, 293–307, https://doi.org/10.5194/npg-32-293-2025, https://doi.org/10.5194/npg-32-293-2025, 2025
Short summary
Short summary
We propose ensemble-based model predictive control (EnMPC), a novel method that improves the control of complex systems like the atmosphere by integrating control theory with data assimilation. Unlike traditional methods, which are computationally expensive, EnMPC uses ensemble simulations to efficiently handle uncertainties and optimize solutions. This approach reduces computational cost while maintaining accuracy, making it a promising step toward real-world applications in dynamic system control.
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Nonlin. Processes Geophys., 32, 281–292, https://doi.org/10.5194/npg-32-281-2025, https://doi.org/10.5194/npg-32-281-2025, 2025
Short summary
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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 (RMSE) reductions using localized Gaussian mixture priors, achieving competitive performance against Gaussian filters.
Wolfgang A. Müller, Stephan Lorenz, Trang V. Pham, Andrea Schneidereit, Renate Brokopf, Victor Brovkin, Nils Brüggemann, Fatemeh Chegini, Dietmar Dommenget, Kristina Fröhlich, Barbara Früh, Veronika Gayler, Helmuth Haak, Stefan Hagemann, Moritz Hanke, Tatiana Ilyina, Johann Jungclaus, Martin Köhler, Peter Korn, Luis Kornblüh, Clarissa Kroll, Julian Krüger, Karel Castro-Morales, Ulrike Niemeier, Holger Pohlmann, Iuliia Polkova, Roland Potthast, Thomas Riddick, Manuel Schlund, Tobias Stacke, Roland Wirth, Dakuan Yu, and Jochem Marotzke
EGUsphere, https://doi.org/10.5194/egusphere-2025-2473, https://doi.org/10.5194/egusphere-2025-2473, 2025
Short summary
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ICON XPP is a newly developed Earth System model configuration based on the ICON modeling framework. It merges accomplishments from the recent operational numerical weather prediction model with well-established climate components for the ocean, land and ocean-biogeochemistry. ICON XPP reaches typical targets of a coupled climate simulation, and is able to run long integrations and large-ensemble experiments, making it suitable for climate predictions and projections, and for climate research.
Fumitoshi Kawasaki, Atsushi Okazaki, Kenta Kurosawa, Tadashi Tsuyuki, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-1785, https://doi.org/10.5194/egusphere-2025-1785, 2025
Short summary
Short summary
A major challenge in weather control aimed at mitigating extreme weather events is identifying effective control inputs under limited computational resources. This study proposes a novel control framework called model predictive control with foreseeing horizon, designed to efficiently control chaotic dynamical systems. Using a 40-variable chaotic dynamical model, the proposed method successfully mitigated extreme events and reduced computational cost compared to the conventional approach.
Pascal Oettli, Keita Tokuda, Yusuke Imoto, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-1458, https://doi.org/10.5194/egusphere-2025-1458, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
A tropical cyclone is a circular air movement that emerges over warm waters of the tropical ocean and its movement is guided by complex interactions between the ocean and the atmosphere. To better understand this complexity, we adopted ideas and techniques from biology and bioinformatics, to have a fresh look at this matter. This led to the creation of "MeteoScape," a tool that calculates the probability of paths for tropical cyclones can take and visualize them in an understandable way.
Michael Goodliff and Takemasa Miyoshi
EGUsphere, https://doi.org/10.5194/egusphere-2025-933, https://doi.org/10.5194/egusphere-2025-933, 2025
Short summary
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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.
Yuka Muto and Shunji Kotsuki
Hydrol. Earth Syst. Sci., 28, 5401–5417, https://doi.org/10.5194/hess-28-5401-2024, https://doi.org/10.5194/hess-28-5401-2024, 2024
Short summary
Short summary
It is crucial to improve global precipitation estimates to understand water-related disasters and water resources. This study proposes a new methodology to interpolate global precipitation fields from ground rain gauge observations using ensemble data assimilation and the precipitation of a numerical weather prediction model. Our estimates agree better with independent rain gauge observations than existing precipitation estimates, especially in mountainous or rain-gauge-sparse regions.
Fumitoshi Kawasaki and Shunji Kotsuki
Nonlin. Processes Geophys., 31, 319–333, https://doi.org/10.5194/npg-31-319-2024, https://doi.org/10.5194/npg-31-319-2024, 2024
Short summary
Short summary
Recently, scientists have been looking into ways to control the weather to lead to a desirable direction for mitigating weather-induced disasters caused by torrential rainfall and typhoons. This study proposes using the model predictive control (MPC), an advanced control method, to control a chaotic system. Through numerical experiments using a low-dimensional chaotic system, we demonstrate that the system can be successfully controlled with shorter forecasts compared to previous studies.
Toshiyuki Ohtsuka, Atsushi Okazaki, Masaki Ogura, and Shunji Kotsuki
EGUsphere, https://doi.org/10.48550/arXiv.2405.19546, https://doi.org/10.48550/arXiv.2405.19546, 2024
Preprint withdrawn
Short summary
Short summary
We utilize weather forecasts in the reverse direction and determine how much we should change the temperature or humidity of the atmosphere at a certain time to change the future rainfall as desired. Even though a weather phenomenon is complicated, we can superimpose the effects of small changes in the atmosphere and find suitable small changes to realize desirable rainfall by solving an optimization problem. We examine this idea on a realistic weather simulator and show it is promising.
Shunji Kotsuki, Fumitoshi Kawasaki, and Masanao Ohashi
Nonlin. Processes Geophys., 31, 237–245, https://doi.org/10.5194/npg-31-237-2024, https://doi.org/10.5194/npg-31-237-2024, 2024
Short summary
Short summary
In Earth science, data assimilation plays an important role in integrating real-world observations with numerical simulations for improving subsequent predictions. To overcome the time-consuming computations of conventional data assimilation methods, this paper proposes using quantum annealing machines. Using the D-Wave quantum annealer, the proposed method found solutions with comparable accuracy to conventional approaches and significantly reduced computational time.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
Short summary
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
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
Short summary
Short summary
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.
Mao Ouyang, Keita Tokuda, and Shunji Kotsuki
Nonlin. Processes Geophys., 30, 183–193, https://doi.org/10.5194/npg-30-183-2023, https://doi.org/10.5194/npg-30-183-2023, 2023
Short summary
Short summary
This research found that weather control would change the chaotic behavior of an atmospheric model. We proposed to introduce chaos theory in the weather control. Experimental results demonstrated that the proposed approach reduced the manipulations, including the control times and magnitudes, which throw light on the weather control in a real atmospheric model.
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
Short summary
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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.
Tobias Necker, David Hinger, Philipp Johannes Griewank, Takemasa Miyoshi, and Martin Weissmann
Nonlin. Processes Geophys., 30, 13–29, https://doi.org/10.5194/npg-30-13-2023, https://doi.org/10.5194/npg-30-13-2023, 2023
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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.
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.
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.
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Short summary
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.
Data assimilation plays an important part in numerical weather prediction (NWP) in terms of...
