Articles | Volume 28, issue 24
https://doi.org/10.5194/hess-28-5401-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-5401-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating global precipitation fields by interpolating rain gauge observations using the local ensemble transform Kalman filter and reanalysis precipitation
Yuka Muto
CORRESPONDING AUTHOR
Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Shunji Kotsuki
CORRESPONDING AUTHOR
Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Institute for Advanced Academic Research, Chiba University, Chiba, Japan
Research Institute of Disaster Medicine, Chiba University, Chiba, Japan
Data Assimilation Research Team, RIKEN Center for Computational Science, Kobe, Japan
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Kenta Kurosawa, Atsushi Okazaki, Fumitoshi Kawasaki, and Shunji Kotsuki
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
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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.
Rikuto Nagai, Yang Bai, Masaki Ogura, Shunji Kotsuki, and Naoki Wakamiya
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
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Controlling chaotic systems is a key step toward weather control. The control simulation experiment (CSE) modifies weather systems using small perturbations, as shown in studies with the Lorenz-63 model. However, the effectiveness of CSE compared to other methods is unclear. This study evaluates CSE against model predictive control (MPC). Simulations reveal that MPC achieves higher success rates with less effort under certain conditions, linking control theory and atmospheric science.
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
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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).
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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.
Atsushi Okazaki, Diego Carrio, Quentin Dalaiden, Jarrah Harrison-Lofthouse, Shunji Kotsuki, and Kei Yoshimura
EGUsphere, https://doi.org/10.5194/egusphere-2025-1389, https://doi.org/10.5194/egusphere-2025-1389, 2025
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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 DA with estimated errors outperformed previous studies.
Takahito Mitsui, Shunji Kotsuki, Naoya Fujiwara, Atsushi Okazaki, and Keita Tokuda
EGUsphere, https://doi.org/10.5194/egusphere-2025-987, https://doi.org/10.5194/egusphere-2025-987, 2025
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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. Achieving up to 99.4 % success, it outperforms previous methods while keeping costs reasonable, offering a practical way to reduce disasters with limited control.
Shunji Kotsuki, Kenta Shiraishi, and Atsushi Okazaki
EGUsphere, https://doi.org/10.48550/arXiv.2407.17781, https://doi.org/10.48550/arXiv.2407.17781, 2024
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Artificial intelligence (AI) is playing a bigger role in weather forecasting, often competing with physical models. However, combining AI models with data assimilation, a process that improves weather forecasts by incorporating observation data, is still relatively unexplored. This study explored coupling ensemble data assimilation with an AI weather prediction model ClimaX, succeeded in employing weather forecasts stably by applying techniques conventionally used for physical models.
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
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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
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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
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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.
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.
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
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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.
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.
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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.
It is crucial to improve global precipitation estimates to understand water-related disasters...