arXiv subjects

Yohei Sawada

Publications and source records attributed to Yohei Sawada.

14 recordsLinked to original sources

arXiv2026

Regional Chaos Synchronization towards controlling high-dimensional open environmental systems

Although controlling chaos can be regarded as a form of chaos synchronization, conventional chaos synchronization does not provide a practical framework for controlling high-dimensional open chaotic systems, such as weather. In high-dimensional open environmental systems, it is simply impossible to make the difference between drive and response systems asymptotically approach zero over the entire domain. Instead of conventional global chaos synchronization, here I define regional chaos synchronization, in which state variables within only a small target region are synchronized with a target trajectory using actuators located in the target region. Theoretical considerations and numerical experiments show that the performance of regional chaos synchronization is substantially degraded by the injection of errors from exterior regions that the controller is not intended to modify. Even under the same actuator specifications, the performances of regional chaos synchronization differ among trajectories, which is not the case of conventional global chaos synchronization. An appropriate target trajectory differs substantially from the natural trajectory only in the target region. This choice of target trajectories can minimize the injection of errors from exterior regions and thereby enable successful regional chaos synchronization. The implications of this new framework for realistic control problems such as weather control are discussed.

physics.geo-ph↗

arXiv2026

Data-driven sequential analysis of tipping in high-dimensional complex systems

Abrupt transitions ("tipping") in nonlinear dynamical systems are often accompanied by changes in the geometry of the attracting set, but quantifying such changes from partial and noisy observations in high-dimensional systems remains challenging. We address this problem with a sequential diagnostic framework, Data Assimilation-High dimensional Attractor's Structural Complexity (DA-HASC). First, this method reconstructs system's high-dimensional state using data assimilation from limited and noisy observations. Second, we quantify a structural complexity of the high-dimensional system dynamics from the reconstructed state by manifold learning. Third, we capture underlying changes in the system by splitting the reconstructed timeseries into sliding windows and analyzing the changes in the temporally local attractor's structural complexity. The structural information is provided as graph Laplacian and measured by Von Neumann entropy in this framework. We evaluate DA-HASC on both synthetic and real-world datasets and demonstrate that it can detect tipping under high-dimensionality and imperfect system knowledge. We further discuss how this framework behaves across different tipping mechanisms.

physics.geo-ph↗

arXiv2025

Data-driven Exploration of Tropical Cyclone's Controllability

Although the chaotic nature of the atmosphere may enable efficient control of tropical cyclones (TCs) via small-scale perturbations, few studies have proposed data-driven optimization methods to identify such perturbations. Here, we apply the recently proposed Ensemble Kalman Control (EnKC) to a TC simulation. We show that EnKC finds small-scale perturbations that mitigate TC. An EnKC-estimated reduction in surface water vapor, located approximately 250km from the TC center, suppresses convective activity and latent heat release in the eye wall, leading to a reduction of TC intensity. To advance the discovery of feasible TC mitigation strategies, we discuss the potential of this data-driven method for leveraging chaos, as well as its remaining challenges.

physics.geo-ph↗

arXiv2025

Multi-Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins

Despite the necessity for accurate flood prediction, many regions lack sufficient river discharge observations. Although numerous models for daily river discharge prediction exist, achieving high accuracy, interpretability, and efficiency under data-scarce conditions remains a major challenge. We address this with a novel method, HYdrological Prediction with multi-model Ensemble and Reservoir computing (HYPER). Our approach applies Bayesian model averaging (BMA) to 47 "uncalibrated" catchment-based conceptual hydrological models. A reservoir computing (RC) model, a type of machine learning model, is then trained via linear regression to correct BMA output errors, a non-iterative process ensuring computational efficiency. For ungauged basins, we infer the required BMA and RC weights by mapping them to catchment attributes from gauged basins, creating a generalizable framework. Evaluated on 87 Japanese basins, in a data-rich scenario, HYPER (median Nash Sutcliffe Efficiency, NSE, of 0.59) performed comparably to a benchmark LSTM (NSE 0.64) but required only 3 % of its computational time. In a data-scarce scenario (where only ~20 % of basins are gauged), HYPER maintained robust performance (NSE 0.51) by leveraging the physical structure of the ensemble. In contrast, the LSTM's performance degraded substantially (NSE -0.61) due to data insufficiency. These results demonstrate that calibrating individual conceptual hydrological models is unnecessary when using a sufficiently large ensemble that is assembled and combined with machine-learning-based bias correction. HYPER provides a robust, efficient, and generalizable solution for discharge prediction, particularly in ungauged basins. By eliminating basin-specific calibration, HYPER offers a scalable, interpretable framework for accurate hydrological prediction in diverse data-scarce regions.

cs.LG↗

arXiv2025

Blended parameterization in an atmospheric model: Improving severestorm ensemble prediction by considering uncertainties in model physics

Physics parameterizations are often needed for numerical weather prediction (NWP) of precipitation forecast. This is mainly because the resolutions of most computational atmospheric models are not fine enough to explicitly resolve sub-grid scale processes associated with precipitation systems. The various options in each physical parameterization scheme introduce model physics uncertainty, leading to variations in simulated precipitation due to differing representations of physical processes. We aim to quantify and reduce uncertainties in severe storm prediction arising from selecting physics parameterization schemes. In this study, we introduced a method called "blended parameterization" in an atmospheric model. This method parameterizes the selection of physical parameterization schemes using weighting parameters. This approach reduces the model selection problem to the optimization of these weighting parameters. The Markov Chain Monte Carlo (MCMC) method was used to estimate the posterior probability distribution of the weighting parameters. The large computational cost of MCMC was resolved by a surrogate model that efficiently mimics the relationship between weighting parameters and likelihood. Subsequently, the weighting parameters were sampled from the posterior distribution, followed by conducting an ensemble simulation to predict rainfall in Vietnam. Our optimized "blended parameterized" ensemble prediction system outperformed a conventional physical ensemble prediction system similar to that operationally used in Vietnam.

physics.geo-ph↗

arXiv2024

Predictability of climate tipping focusing on the internal variability of the Earth system

Prediction of climate tipping is challenging due to the lack of recent observation of actual climate tipping. Despite many previous efforts to accurately predict the existence and timing of climate tippings under specific climate scenarios, the predictability of climate tipping, the necessary conditions under which climate tipping can be predicted, has yet to be explored. In this study, the predictability of climate tipping is analyzed by Observation System Simulation Experiment (OSSE), in which the value of observation for prediction is assessed through the idealized experiment of data assimilation, using a simplified dynamic vegetation model and an Atlantic Meridional Overturning Circulation (AMOC) two box model. We find that the ratio of internal variability to observation error, or signal-to-noise ratio, should be large enough to accurately predict climate tippings. When observation can accurately resolve the internal variability of the system, assimilating these observations into process-based models can effectively improve the skill of predicting climate tippings. Our quantitative estimation of required observation accuracy to predict climate tipping implies that the existing observation network is not always sufficient to accurately project climate tipping.

physics.ao-ph↗

arXiv2024

Quest for an efficient mathematical and computational method to explore optimal extreme weather modification

It is a grand challenge to find a feasible weather modification method to mitigate the impact of extreme weather events such as tropical cyclones. Previous works have proposed potentially effective actuators and assessed their capabilities to achieve weather modification objectives through numerical simulations. However, few studies have explored efficient mathematical and computational methods to inversely determine optimal actuators from specific modification goals. Here I demonstrate the utility of the ensemble Kalman filter (EnKF)-based control method, referred to as ensemble Kalman control (EnKC). The series of numerical experiments with the Lorenz 96 model indicates that EnKC efficiently identifies local, small, and intermittent control perturbations that can mitigate extreme events. The existing techniques of EnKF, such as background error covariance localization and observation error covariance inflation, can improve the sparsity and efficiency of the control. This work paves the way toward the real-world applications of EnKC to explore the controllability of extreme atmospheric events.

stat.AP↗

arXiv2024

Ensemble Kalman filter meets model predictive control in chaotic systems

Although data assimilation originates from control theory, the relationship between modern data assimilation methods in geoscience and model predictive control has not been extensively explored. In the present paper, I discuss that the modern data assimilation methods in geoscience and model predictive control essentially minimize the similar quadratic cost functions. Inspired by this similarity, I propose a new ensemble Kalman filter (EnKF)-based method for controlling spatio-temporally chaotic systems, which can be applied to high-dimensional and nonlinear Earth systems. In this method, the reference vector, which serves as the control target, is assimilated into the state space as a pseudo-observation by ensemble Kalman smoother to obtain the appropriate perturbation to be added to a system. A proof-of-concept experiment using the Lorenz 63 model is presented. The system is constrained in one wing of the butterfly attractor without tipping to the other side by reasonably small control perturbations which are comparable with previous works.

physics.geo-ph↗

arXiv2023

An efficient and robust estimation of spatio-temporally distributed parameters in dynamic models by an ensemble Kalman filter

The accuracy of Earth system models is compromised by unknown and/or unresolved dynamics, making the quantification of systematic model errors essential. While a model parameter estimation, which allows parameters to change spatio-temporally, shows promise in quantifying and mitigating systematic model errors, the estimation of the spatio-temporally distributed model parameters has been practically challenging. Here we present an efficient and practical method to estimate time-varying parameters in high-dimensional spaces. In our proposed method, Hybrid Offline and Online Parameter Estimation with ensemble Kalman filtering (HOOPE-EnKF), model parameters estimated by EnKF are constrained by results of offline batch optimization, in which the posterior distribution of model parameters is obtained by comparing simulated and observed climatological variables. HOOPE-EnKF outperforms the original EnKF in synthetic experiments using a two-scale Lorenz96 model and a simple global general circulation model. One advantage of HOOPE-EnKF over traditional EnKFs is that its performance is not greatly affected by inflation factors for model parameters, thus eliminating the need for extensive tuning of inflation factors. We thoroughly discuss the potential of HOOPE-EnKF as a practical method for improving parameterizations of process-based models and prediction in real-world applications such as numerical weather prediction.

stat.ME↗

arXiv2022

An efficient estimation of time-varying parameters of dynamic models by combining offline batch optimization and online data assimilation

It is crucially important to estimate unknown parameters in earth system models by integrating observation and numerical simulation. For many applications in earth system sciences, an optimization method which allows parameters to temporally change is required. In the present paper, an efficient and practical method to estimate the time-varying parameters of relatively low dimensional models is presented. In the newly proposed method, called Hybrid Offline Online Parameter Estimation with Particle Filtering (HOOPE-PF), an inflation method to maintain the spread of ensemble members in a sampling-importance-resampling particle filter is improved using a non-parametric posterior probabilistic distribution of time-invariant parameters obtained by comparing simulated and observed climatology. The HOOPE-PF outperforms the original sampling-importance-resampling particle filter in synthetic experiments with toy models and a real-data experiment with a conceptual hydrological model especially when the ensemble size is small. The advantage of HOOPE-PF is that its performance is not greatly affected by the size of perturbation to be added to ensemble members to maintain their spread while it is critically important to get the optimal performance in the original particle filter. Since HOOPE-PF is the extension of the existing particle filter which has been extensively applied to many earth system models such as land, ecosystem, hydrology, and paleoclimate reconstruction, the HOOPE-PF can be applied to improve the simulation of these earth system models by considering time-varying model parameters.

physics.geo-ph↗

arXiv2021

Socio-meteorology: flood prediction, social preparedness, and cry wolf effects

To improve the efficiency of flood early warning systems (FEWS), it is important to understand the interactions between natural and social systems. The high level of trust in authorities and experts is necessary to improve the likeliness of individuals to take preparedness actions responding to warnings. Despite a lot of efforts to develop the dynamic model of human and water in socio-hydrology, no socio-hydrological models explicitly simulate social collective trust in FEWS. Here we develop the stylized model to simulate the interactions of flood, social collective memory, social collective trust in FEWS, and preparedness actions responding to warnings by extending the existing socio-hydrological model. We realistically simulate the cry wolf effect, in which many false alarms undermine the credibility of the early warning systems and make it difficult to induce preparedness actions. We found (1) considering the dynamics of social collective trust in FEWS is more important in the technological society with infrequent flood events than in the green society with frequent flood events; (2) as the natural scientific skill to predict flood events is improved, the efficiency of FEWS gets more sensitive to the behavior of social collective trust, so that forecasters need to determine their warning threshold by considering the social aspects.

physics.geo-ph↗

arXiv2021

Ecohydrological land reanalysis

The accurate estimation of terrestrial water and vegetation is a grand challenge in hydrometeorology. Many previous studies developed land data assimilation systems (LDASs) and provided global-scale land surface datasets by integrating numerical simulation and satellite data. However, vegetation dynamics has not been explicitly solved in these land reanalysis datasets. Here we present the newly developed land reanalysis dataset, ECoHydrological Land reAnalysis (ECHLA). ECHLA is generated by sequentially assimilating C- and X- band microwave brightness temperature satellite observations into a land surface model which can explicitly simulate the dynamic evolution of vegetation biomass. The ECHLA dataset provides semi-global soil moisture from surface to 1.95m depth, Leaf Area Index (LAI), and vegetation water content and is available from 2003 to 2010 and from 2013 to 2019. We assess the performance of ECHLA to estimate soil moisture and vegetation dynamics by comparing the ECHLA dataset with independent satellite and in-situ observation data. We found that our sequential update by data assimilation substantially improves the skill to reproduce the seasonal cycle of vegetation. Data assimilation also contributes to improving the skill to simulate soil moisture mainly in the shallow soil layers (0-0.15m depth). The ECHLA dataset will be publicly available and expected to contribute to understanding terrestrial ecohydrological cycles and water-related natural disasters such as drought.

physics.geo-ph↗

arXiv2020

Combining Ensemble Kalman Filter and Reservoir Computing to predict spatio-temporal chaotic systems from imperfect observations and models

Prediction of spatio-temporal chaotic systems is important in various fields, such as Numerical Weather Prediction (NWP). While data assimilation methods have been applied in NWP, machine learning techniques, such as Reservoir Computing (RC), are recently recognized as promising tools to predict spatio-temporal chaotic systems. However, the sensitivity of the skill of the machine learning based prediction to the imperfectness of observations is unclear. In this study, we evaluate the skill of RC with noisy and sparsely distributed observations. We intensively compare the performances of RC and Local Ensemble Transform Kalman Filter (LETKF) by applying them to the prediction of the Lorenz 96 system. Although RC can successfully predict the Lorenz 96 system if the system is perfectly observed, we find that RC is vulnerable to observation sparsity compared with LETKF. To overcome this limitation of RC, we propose to combine LETKF and RC. In our proposed method, the system is predicted by RC that learned the analysis time series estimated by LETKF. Our proposed method can successfully predict the Lorenz 96 system using noisy and sparsely distributed observations. Most importantly, our method can predict better than LETKF when the process-based model is imperfect.

cs.LG↗

arXiv2020

Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model

The performance of land surface models (LSMs) significantly affects the understanding of atmospheric and related processes. Many of the LSMs' soil and vegetation parameters were unknown so that it is crucially important to efficiently optimize them. Here I present a globally applicable and computationally efficient method for parameter optimization and uncertainty assessment of the LSM by combining Markov Chain Monte Carlo (MCMC) with machine learning. First, I performed the long-term (decadal scales) ensemble simulation of the LSM, in which each ensemble member has different parameters' values, and calculated the gap between simulation and observation, or the cost function, for each ensemble member. Second, I developed the statistical machine learning based surrogate model, which is computationally cheap but accurately mimics the relationship between parameters and the cost function, by applying the Gaussian process regression to learn the model simulation. Third, we applied MCMC by repeatedly driving the surrogate model to get the posterior probabilistic distribution of parameters. Using satellite passive microwave brightness temperature observations, both synthetic and real-data experiments in the Sahel region of west Africa were performed to optimize unknown soil and vegetation parameters of the LSM. The primary findings are (1) the proposed method is 50,000 times as fast as the direct application of MCMC to the full LSM; (2) the skill of the LSM to simulate both soil moisture and vegetation dynamics can be improved; (3) I successfully quantify the characteristics of equifinality by obtaining the full non-parametric probabilistic distribution of parameters.

stat.AP↗