Articles | Volume 30, issue 4
https://doi.org/10.5194/npg-30-457-2023
© 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-30-457-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Oct 2023
Research article |
| 23 Oct 2023
| 23 Oct 2023
Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model
Data Assimilation Research Team, RIKEN Center for Computational Science, Kobe, Japan
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Shunji Kotsuki
CORRESPONDING AUTHOR
Data Assimilation Research Team, RIKEN Center for Computational Science, Kobe, Japan
Center for Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
PRESTO, Japan Science and Technology Agency, Chiba, Japan
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan
Prediction Science Laboratory, RIKEN Cluster for Pioneering Research, Kobe, Japan
Takemasa Miyoshi
Data Assimilation Research Team, RIKEN Center for Computational Science, Kobe, Japan
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan
Prediction Science Laboratory, RIKEN Cluster for Pioneering Research, Kobe, Japan
Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
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Arata Amemiya and Takemasa Miyoshi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2543, https://doi.org/10.5194/egusphere-2025-2543, 2025
This preprint is open for discussion and under review for Nonlinear Processes in Geophysics (NPG).
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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 seconds compared with the conventional case of 5 minutes, from a theoretical perspective.
Juan Martin Guerrieri, Manuel Arturo Pulido, Takemasa Miyoshi, Arata Amemiya, and Juan José Ruiz
EGUsphere, https://doi.org/10.5194/egusphere-2025-2420, https://doi.org/10.5194/egusphere-2025-2420, 2025
This preprint is open for discussion and under review for Nonlinear Processes in Geophysics (NPG).
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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.
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
This preprint is open for discussion and under review for Nonlinear Processes in Geophysics (NPG).
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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.
Michael Goodliff and Takemasa Miyoshi
EGUsphere, https://doi.org/10.5194/egusphere-2025-933, https://doi.org/10.5194/egusphere-2025-933, 2025
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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.
Kenta Kurosawa, Atsushi Okazaki, Fumitoshi Kawasaki, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-595, https://doi.org/10.5194/egusphere-2025-595, 2025
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We propose Ensemble Model Predictive Control (EnMPC), a novel method that improves control of complex systems like weather 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.
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
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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.
Rikuto Nagai, Yang Bai, Masaki Ogura, Shunji Kotsuki, and Naoki Wakamiya
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2024-26, https://doi.org/10.5194/npg-2024-26, 2024
Revised manuscript accepted for NPG
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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.
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.
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.
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.
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.
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.
Keiichi Kondo and Takemasa Miyoshi
Nonlin. Processes Geophys., 26, 211–225, https://doi.org/10.5194/npg-26-211-2019, https://doi.org/10.5194/npg-26-211-2019, 2019
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This study investigates non-Gaussian statistics of the data from a 10240-member ensemble Kalman filter. The large ensemble size can resolve the detailed structures of the probability density functions (PDFs) and indicates that the non-Gaussian PDF is caused by multimodality and outliers. While the outliers appear randomly, large multimodality corresponds well with large analysis error, mainly in the tropical regions and storm track regions where highly nonlinear processes appear frequently.
Atsushi Okazaki, Takumi Honda, Shunji Kotsuki, Moeka Yamaji, Takuji Kubota, Riko Oki, Toshio Iguchi, and Takemasa Miyoshi
Atmos. Meas. Tech., 12, 3985–3996, https://doi.org/10.5194/amt-12-3985-2019, https://doi.org/10.5194/amt-12-3985-2019, 2019
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The JAXA is surveying the feasibility of a potential satellite mission equipped with a precipitation radar on a geostationary orbit, as a successor of the GPM Core Observatory. We investigate what kind of observation data will be available from the radar using simulation techniques. Although the quality of the observation depends on the radar specifications and the position of precipitation systems, the results demonstrate that it would be possible to obtain three-dimensional precipitation data.
Guo-Yuan Lien, Daisuke Hotta, Eugenia Kalnay, Takemasa Miyoshi, and Tse-Chun Chen
Nonlin. Processes Geophys., 25, 129–143, https://doi.org/10.5194/npg-25-129-2018, https://doi.org/10.5194/npg-25-129-2018, 2018
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The ensemble forecast sensitivity to observation (EFSO) method can efficiently clarify under what conditions observations are beneficial or detrimental for assimilation. Based on EFSO, an offline assimilation method is proposed to accelerate the development of data selection strategies for new observing systems. The usefulness of this method is demonstrated with the assimilation of global satellite precipitation data.
Hazuki Arakida, Takemasa Miyoshi, Takeshi Ise, Shin-ichiro Shima, and Shunji Kotsuki
Nonlin. Processes Geophys., 24, 553–567, https://doi.org/10.5194/npg-24-553-2017, https://doi.org/10.5194/npg-24-553-2017, 2017
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This is the first study assimilating the satellite-based leaf area index observations every 4 days into a numerical model simulating the growth and death of individual plants. The newly developed data assimilation system successfully reduced the uncertainties of the model parameters related to phenology and carbon dynamics. It also provides better estimates of the present vegetation structure which can be used as the initial states for the simulation of the future vegetation change.
Stephen G. Penny and Takemasa Miyoshi
Nonlin. Processes Geophys., 23, 391–405, https://doi.org/10.5194/npg-23-391-2016, https://doi.org/10.5194/npg-23-391-2016, 2016
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Particle filters in their basic form have been shown to be unusable for large geophysical systems because the number of required particles grows exponentially with the size of the system. We have applied the ideas of localized analyses at each model grid point and use ensemble weight smoothing to blend each local analysis with its neighbors. This new local particle filter (LPF) makes large geophysical applications tractable for particle filters and is competitive with a popular EnKF alternative.
Hisashi Yashiro, Koji Terasaki, Takemasa Miyoshi, and Hirofumi Tomita
Geosci. Model Dev., 9, 2293–2300, https://doi.org/10.5194/gmd-9-2293-2016, https://doi.org/10.5194/gmd-9-2293-2016, 2016
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We propose the design and implementation of an ensemble data assimilation framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file I/O and multi-node communication. With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework promises drastic reduction of total execution time.
X. Han, X. Li, G. He, P. Kumbhar, C. Montzka, S. Kollet, T. Miyoshi, R. Rosolem, Y. Zhang, H. Vereecken, and H.-J. H. Franssen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-8-7395-2015, https://doi.org/10.5194/gmdd-8-7395-2015, 2015
Revised manuscript not accepted
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DasPy is a ready to use open source parallel multivariate land data assimilation framework with joint state and parameter estimation using Local Ensemble Transform Kalman Filter. The Community Land Model (4.5) was integrated as model operator. The Community Microwave Emission Modelling platform, COsmic-ray Soil Moisture Interaction Code and the Two-Source Formulation were integrated as observation operators for the multivariate assimilation of soil moisture and soil temperature, respectively.
S. G. Penny, E. Kalnay, J. A. Carton, B. R. Hunt, K. Ide, T. Miyoshi, and G. A. Chepurin
Nonlin. Processes Geophys., 20, 1031–1046, https://doi.org/10.5194/npg-20-1031-2013, https://doi.org/10.5194/npg-20-1031-2013, 2013
Related subject area
Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
Bridging Data Assimilation and Control: Ensemble Model Predictive Control for High-Dimensional Nonlinear Systems
Evaluation of Effectiveness of Intervention Strategy in Control Simulation Experiment through Comparison with Model Predictive Control
Explaining the high skill of Reservoir Computing methods in El Niño prediction
A comparison of two nonlinear data assimilation methods
On Process-Oriented Conditional Targeted Covariance Inflation (TCI) for 3D-Volume Radar Data Assimilation
Leading the Lorenz 63 system toward the prescribed regime by model predictive control coupled with data assimilation
Quantum data assimilation: a new approach to solving data assimilation on quantum annealers
Computation of covariant lyapunov vectors using data assimilation
Reducing manipulations in a control simulation experiment based on instability vectors with the Lorenz-63 model
Control simulation experiments of extreme events with the Lorenz-96 model
A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe
Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis
Control simulation experiment with Lorenz's butterfly attractor
Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction
A study of capturing Atlantic meridional overturning circulation (AMOC) regime transition through observation-constrained model parameters
Fast hybrid tempered ensemble transform filter formulation for Bayesian elliptical problems via Sinkhorn approximation
Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales
Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty
Generalization properties of feed-forward neural networks trained on Lorenz systems
Kenta Kurosawa, Atsushi Okazaki, Fumitoshi Kawasaki, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-595, https://doi.org/10.5194/egusphere-2025-595, 2025
Short summary
Short summary
We propose Ensemble Model Predictive Control (EnMPC), a novel method that improves control of complex systems like weather 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. Discuss., https://doi.org/10.5194/npg-2024-26, https://doi.org/10.5194/npg-2024-26, 2024
Revised manuscript accepted for NPG
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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.
Francesco Guardamagna, Claudia Wieners, and Henk Dijkstra
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2024-24, https://doi.org/10.5194/npg-2024-24, 2024
Revised manuscript accepted for NPG
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Artificial intelligence (AI) has recently shown promising results in ENSO (El Niño Southern Oscillation) forecasting, outperforming traditional models. Yet, AI models deliver accurate predictions without showing the underlying mechanisms. Our study examines a specific AI model, the Reservoir Computer (RC). Our results show that the RC is less sensitive to initial perturbations than the traditional Zebiak and Cane (ZC) model. This reduced sensitivity can explain the RC's superior skills.
Vivian A. Montiforte, Hans E. Ngodock, and Innocent Souopgui
Nonlin. Processes Geophys., 31, 463–476, https://doi.org/10.5194/npg-31-463-2024, https://doi.org/10.5194/npg-31-463-2024, 2024
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Advanced data assimilation methods are complex and computationally expensive. We compare two simpler methods, diffusive back-and-forth nudging and concave–convex nonlinearity, which account for change over time with the potential of providing accurate results with a reduced computational cost. We evaluate the accuracy of the two methods by implementing them within simple chaotic models. We conclude that the length and frequency of observations impact which method is better suited for a problem.
Klaus Vobig, Roland Potthast, and Klaus Stephan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2876, https://doi.org/10.5194/egusphere-2024-2876, 2024
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We present a novel approach to targeted covariance inflation (TCI) which aims to improve the assimilation of 3D radar reflectivity and, eventually, the short-term forecast of reflectivity and precipitation.
Using an operational numerical weather prediction framework, our numerical results show that TCI makes the system accurately generate new reflectivity cells and significantly improves the fractional skill score of forecasts over lead times of up to six hours by up to 10 %.
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.
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.
Shashank Kumar Roy and Amit Apte
EGUsphere, https://doi.org/10.5194/egusphere-2023-2168, https://doi.org/10.5194/egusphere-2023-2168, 2023
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We focus on the computation of Lyapunov vectors for dynamical systems using partial and noisy observations using data assimilation. We study how the errors in the approximated LVs are implicitly affected by the errors in the estimated trajectory. Our results reveal the difference in sensitivity of the individual vectors in low and high dimensions. Additionally, we find that the subspaces defined by the LVs computed from the approximate trajectory are less sensitive than the individual vectors.
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.
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
Short summary
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.
Clara Deser and Adam S. Phillips
Nonlin. Processes Geophys., 30, 63–84, https://doi.org/10.5194/npg-30-63-2023, https://doi.org/10.5194/npg-30-63-2023, 2023
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Past and future climate change at regional scales is a result of both human influences and natural (internal) variability. Here, we provide an overview of recent advances in climate modeling and physical understanding that has led to new insights into their respective roles, illustrated with original results for the European climate. Our findings highlight the confounding role of internal variability in attribution, climate model evaluation, and accuracy of future projections.
Dikraa Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas
Nonlin. Processes Geophys., 29, 329–344, https://doi.org/10.5194/npg-29-329-2022, https://doi.org/10.5194/npg-29-329-2022, 2022
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This study introduces a well-known use of hybrid methods in data assimilation (DA) algorithms that has not yet been explored for precipitation analyses. Our approach combined an ensemble-based DA approach with an existing deterministically based DA. Both DA scheme families have desirable aspects that can be leveraged if combined. The DA hybrid method showed better precipitation analyses in regions with a low rate of assimilated surface observations, which is typically the case in winter.
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.
Zhao Liu, Shaoqing Zhang, Yang Shen, Yuping Guan, and Xiong Deng
Nonlin. Processes Geophys., 28, 481–500, https://doi.org/10.5194/npg-28-481-2021, https://doi.org/10.5194/npg-28-481-2021, 2021
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A general methodology is introduced to capture regime transitions of the Atlantic meridional overturning circulation (AMOC). The assimilation models with different parameters simulate different paths for the AMOC to switch between equilibrium states. Constraining model parameters with observations can significantly mitigate the model deviations, thus capturing AMOC regime transitions. This simple model study serves as a guideline for improving coupled general circulation models.
Sangeetika Ruchi, Svetlana Dubinkina, and Jana de Wiljes
Nonlin. Processes Geophys., 28, 23–41, https://doi.org/10.5194/npg-28-23-2021, https://doi.org/10.5194/npg-28-23-2021, 2021
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To infer information of an unknown quantity that helps to understand an associated system better and to predict future outcomes, observations and a physical model that connects the data points to the unknown parameter are typically used as information sources. Yet this problem is often very challenging due to the fact that the unknown is generally high dimensional, the data are sparse and the model can be non-linear. We propose a novel approach to address these challenges.
Michiel Van Ginderachter, Daan Degrauwe, Stéphane Vannitsem, and Piet Termonia
Nonlin. Processes Geophys., 27, 187–207, https://doi.org/10.5194/npg-27-187-2020, https://doi.org/10.5194/npg-27-187-2020, 2020
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A generic methodology is developed to estimate the model error and simulate the model uncertainty related to a specific physical process. The method estimates the model error by comparing two different representations of the physical process in otherwise identical models. The found model error can then be used to perturb the model and simulate the model uncertainty. When applying this methodology to deep convection an improvement in the probabilistic skill of the ensemble forecast is found.
Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper
Nonlin. Processes Geophys., 27, 209–234, https://doi.org/10.5194/npg-27-209-2020, https://doi.org/10.5194/npg-27-209-2020, 2020
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Geophysical flows span a broader range of temporal and spatial scales than can be resolved numerically. One way to alleviate the ensuing numerical errors is to combine simulations with measurements, taking account of the accuracies of these two sources of information. Here we quantify the distribution of numerical simulation errors without relying on high-resolution numerical simulations. Specifically, small-scale random vortices are added to simulations while conserving energy or circulation.
Sebastian Scher and Gabriele Messori
Nonlin. Processes Geophys., 26, 381–399, https://doi.org/10.5194/npg-26-381-2019, https://doi.org/10.5194/npg-26-381-2019, 2019
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Neural networks are a technique that is widely used to predict the time evolution of physical systems. For this the past evolution of the system is shown to the neural network – it is
trained– and then can be used to predict the evolution in the future. We show some limitations in this approach for certain systems that are important to consider when using neural networks for climate- and weather-related applications.
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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.
This study aimed to enhance weather and hydrological forecasts by integrating soil moisture data...
