arXiv subjects

Ryo Onishi

Publications and source records attributed to Ryo Onishi.

18 recordsLinked to original sources

arXiv2026

Finite-Time Relaxation of Inertial Particle Clustering in Non-Equilibrium Turbulence

Inertial particles in turbulence form clusters, which strongly affect particle collisions and transport properties. Clustering models based on statistically stationary turbulence implicitly assume the instantaneous-equilibrium approximation when applied to time-varying non-equilibrium turbulence. However, the validity of this approximation remains unclear. In this study, the temporal response of inertial particle clustering in non-equilibrium turbulence was investigated using direct numerical simulation of homogeneous isotropic turbulence with unsteady forcing. Periodic responses of the flow and clustering intensity were evaluated by varying the forcing period. The flow showed non-equilibrium scaling for all forcing periods. The relationship between instantaneous energy dissipation rate and clustering intensity showed hysteresis exceeding statistically stationary fluctuations when the forcing period exceeded several large-eddy turnover times. For the particles with the largest inertia, clustering intensity took values of 0.80 and 1.56 times the reference value at the same instantaneous energy dissipation rate. This shows that the instantaneous-equilibrium approximation is not appropriate under such conditions. A linear relaxation model was constructed from transient responses, in which clustering intensity approaches the instantaneous-equilibrium value with a finite relaxation time. The relaxation time scaling was identified as $τ_g = 1.0 T_\mathrm{e}(t)\,\mathrm{St}(t)^{0.40}$, where $T_\mathrm{e}(t)$ and $\mathrm{St}(t)$ are the instantaneous large-eddy turnover time and Stokes number. The model reduced the maximum relative error from 49% to 10% for the particles with the largest inertia and from 76% to 22% in an independent validation case. These results demonstrate that finite-time relaxation improves prediction accuracy for clustering intensity in non-equilibrium turbulence.

physics.flu-dyn↗

arXiv2026

Fractal-based variable drag model for porous-media tree representations

Accurate representation of trees is essential for predictive urban micrometeorological simulations, but explicitly resolving detailed tree geometry is computationally prohibitive. Trees are therefore commonly represented as porous media, often with a constant drag coefficient even when spatially heterogeneous area-density distributions are introduced. This limits transferability across inflow conditions and can increase sensitivity to grid resolution, particularly in the grayscale regime where a tree is represented by only a few computational cells. We propose a fractal-based variable-drag model for porous-media tree representations, in which the drag coefficient is prescribed cell-wise as $C_D=C_D(n_{\mathrm{eff}},Re_{\mathrm{eff}})$. Here, $n_{\mathrm{eff}}$ is the cell-effective branching order representing unresolved local morphological complexity, and $Re_{\mathrm{eff}}$ is the cell-effective Reynolds number representing the local flow regime. The model is assessed using steady Reynolds-averaged Navier--Stokes simulations of a porous fractal tree over systematic sweeps of grid resolution and inflow velocity. Model performance is evaluated primarily using aerodynamic porosity, which measures the bulk momentum attenuation induced by the tree. The proposed model produces a plausible aerodynamic response and improves robustness to grid resolution compared with constant-$C_D$ models. It also captures the variation of bulk drag across inflow conditions without empirical retuning. Notably, this whole-tree response is recovered through local cell-wise quantities, $n_{\mathrm{eff}}$ and $Re_{\mathrm{eff}}$. These results demonstrate that morphology- and flow-dependent drag provides a practical route to improving porous-media tree modeling.

physics.flu-dyn↗

arXiv2026

Uncertainty-Aware Spatiotemporal Super-Resolution Data Assimilation with Diffusion Models

Data assimilation (DA) improves prediction of chaotic systems by combining model forecasts with sparse, noisy observations. Many DA methods are inherently probabilistic, but accurate probabilistic DA is often computationally expensive because it requires repeated high-resolution (HR) forecasts and large ensembles. In this study, we develop DiffSRDA, a probabilistic spatiotemporal super-resolution data assimilation framework based on denoising diffusion models, and evaluate it on an idealized barotropic ocean jet instability testbed. DiffSRDA is trained offline to generate short HR analysis windows conditioned on (i) a time series of low-resolution (LR) forecast frames and (ii) sparse HR observations. Repeated reverse diffusion sampling then produces an ensemble of HR analyses, providing both point estimates and uncertainty information. Despite relying only on low-cost LR forecasts, DiffSRDA achieves reconstruction quality close to that of an Ensemble Kalman Filter (EnKF) driven by HR forecasts, while improving over deterministic CNN-based SRDA baselines. The sampled ensemble also yields physically meaningful uncertainty patterns, with spread concentrated in dynamically active regions similarly to EnKF. A key practical result is that accurate base DiffSRDA cycling does not require long reverse chains: most of the full-chain accuracy is retained with only a few reverse steps, making diffusion-based SRDA practical for repeated cycling. Finally, by exploiting the score-based structure of diffusion sampling, we demonstrate training-free observation-consistency guidance for deployment-time sensor-layout shifts, enabling improved use of changed observation configurations without retraining. Overall, diffusion models provide a practical, uncertainty-aware, and computationally efficient approach for spatiotemporal SRDA in chaotic fluid flows.

physics.flu-dyn↗

arXiv2026

LCS.jl: A High-Performance, Multi-Platform Computational Model in Julia for Turbulent Particle-Laden Flows

Multiphase turbulent flow phenomena are observed not only in industrial devices but also in environmental flows, and direct numerical simulation (DNS) plays a key role in their investigation. Many numerical models have been developed; nevertheless, few models are highly optimized for GPU platforms, which represent the current mainstream in high-performance computing (HPC). In this study, we developed LCS.jl (Lagrangian Cloud Simulator in Julia), a single-source and multi-platform multiphase turbulence simulation model implemented in Julia language and KernelAbstractions.jl. Validation results confirmed that the present fluid and particle statistics agree well with those obtained in prior studies. A GPU-native particle communication algorithm based on prefix-scan reduced the particle communication cost from approximately 78% (CPU-delegated) to 10% of total execution time. LCS.jl achieved computational performance equivalent to the Fortran implementation in many-processes computations. For GPUs, strong scaling efficiency was maintained above 85% (up to 256 GPUs) and weak scaling efficiency above 90% (up to 216 GPUs) on TSUBAME4.0 (a GPU supercomputer at the Institute of Science Tokyo). LCS.jl achieved a maximum speedup of 18.0x on GPUs over CPUs. A trial heterogeneous execution achieved a 72% reduction in execution time compared to the CPU-only configuration even in configurations where the GPU was not the primary compute device. These results demonstrate that LCS.jl is a multiphase turbulence simulation platform that achieves both portability and scalability across a variety of computational resource configurations.

physics.flu-dyn↗

arXiv2026

Direct Lagrangian tracking simulation of droplet growth in vertically-developing turbulent cloud

We developed a new explicit cloud microphysical model, based on direct numerical simulation (DNS) with Lagrangian particle tracking. The model employs a vertically-elongated quasi-1D computational domain extending from the ground to the cloud top to explicitly capture the vertical structure of clouds. This allows us to simulate the all warm-cloud microphysical processes, including activation, condensation growth, collision-coalescence growth, and sedimentation. A homogeneous isotropic turbulence field is incorporated into this domain to explicitly resolve the turbulent wind fluctuations. Cloud microphysics simulations with and without turbulent wind fluctuations were performed to clarify the impact of turbulence on droplet growth. We obtained new insights into the altitude- and time-dependent microphysical statistics, which cannot be obtained through conventional DNS researches for a cubic box domain with periodic boundaries. The comparison have shown that turbulence promoted the collision-coalescence growth of droplets. During the early developing stage, where the updraft was present, turbulence promoted the collisions between droplets with similar sizes (autoconversions) in the middle layer of the cloud. In later stage, relatively large droplets produced by autoconversions actively collected smaller droplets (accretions) in the middle and lower layers. The onset of precipitation at the ground occurred earlier and the first raindrop at the ground was larger in turbulence case than that in non-turbulence case.

physics.ao-ph↗

arXiv2025

Probabilistic Super-Resolution for Urban Micrometeorology via a Schrödinger Bridge

This study employs a neural network that represents the solution to a Schrödinger bridge problem to perform super-resolution of 2-m temperature in an urban area. Schrödinger bridges generally describe transformations between two data distributions based on diffusion processes. We use a specific Schrödinger-bridge model (SM) that directly transforms low-resolution data into high-resolution data, unlike denoising diffusion probabilistic models (simply, diffusion models; DMs) that generate high-resolution data from Gaussian noise. Low-resolution and high-resolution data were obtained from separate numerical simulations with a physics-based model under common initial and boundary conditions. Compared with a DM, the SM attains comparable accuracy at one-fifth the computational cost, requiring 50 neural-network evaluations per datum for the DM and only 10 for the SM. Furthermore, high-resolution samples generated by the SM exhibit larger variance, implying superior uncertainty quantification relative to the DM. Owing to the reduced computational cost of the SM, our results suggest the feasibility of real-time ensemble micrometeorological prediction using SM-based super-resolution.

physics.ao-ph↗

arXiv2025

Zero-Shot Super-Resolution from Unstructured Data Using a Transformer-Based Neural Operator for Urban Micrometeorology

This study demonstrates that a transformer-based neural operator (TNO) can perform zero-shot super-resolution of two-dimensional temperature fields near the ground in urban areas. During training, super-resolution is performed from a horizontal resolution of 100 m to 20 m, while during testing, it is performed from 100 m to a finer resolution of 5 m. This setting is referred to as zero-shot, since no data with the target 5 m resolution are included in the training dataset. The 20 m and 5 m resolution data were independently obtained by dynamically downscaling the 100 m data using a physics-based micrometeorology model that resolves buildings. Compared to a convolutional neural network, the TNO more accurately reproduces temperature distributions at 5 m resolution and reduces test errors by approximately 33%. Furthermore, the TNO successfully performs zero-shot super-resolution even when trained with unstructured data, in which grid points are randomly arranged. These results suggest that the TNO recognizes building shapes independently of grid point locations and adaptively infers the temperature fields induced by buildings.

physics.ao-ph↗

arXiv2025

Unsupervised Super-Resolution Data Assimilation Using Conditional Variational Autoencoders with Estimating Background Covariances via Super-Resolution

This study proposes a theory of unsupervised super-resolution data assimilation (SRDA) using conditional variational autoencoders (CVAEs). We derive an evidence lower bound for unsupervised learning, showing that our theory is an extension of a traditional data assimilation (DA) method, namely the three-dimensional variational (3D-Var) formalism. In contrast to 3D-Var, our theory exploits the non-locality of super-resolution (SR) to learn background covariances without explicitly imposing them for assimilating distant observations. For linear SR, SR operators serve as background error covariance matrices,whereas for nonlinear SR, error backpropagation through SR neural networks induces covariance structures in inference. SRDA can naturally be realized with CVAEs because the loss function for CVAEs is generally an evidence lower bound. By incorporating the SR neural network into the CVAE, the encoder estimates the high-resolution (HR) analysis from HR observations and low-resolution forecasts. The decoder acts as the observation operator by reconstructing the HR observations from the estimated HR analysis. The effectiveness of SRDA was evaluated through numerical experiments using an idealized barotropic ocean jet system. Compared to inference with an ensemble Kalman filter, SRDA demonstrated superior accuracy in HR inference. SRDA was also computationally efficient because it does not require HR numerical integration or ensemble calculations. The findings of this study provide a theoretical basis for integrating SR and DA, which will stimulate further research in this direction.

physics.ao-ph↗

arXiv2025

Two-Stage Super-Resolution Simulation Method of Three-Dimensional Street-Scale Atmospheric Flows for Real-Time Urban Micrometeorology Prediction

A two-stage super-resolution simulation method is proposed for street-scale air temperature and wind velocity, which considerably reduces computation time while maintaining accuracy. The first stage employs a convolutional neural network (CNN) to correct large-scale flows above buildings in the input low-resolution simulation results. The second stage uses another CNN to reconstruct small-scale flows between buildings from the output of the first stage, resulting in high-resolution inferences. The CNNs are trained using high-resolution simulation data for the second stage and their coarse-grained version for the first stage as the ground truth, where the high-resolution simulations are conducted independently of the low-resolution simulations used as input. This learning approach separates the spatial scales of inference in each stage. The effectiveness of the proposed method was evaluated using micrometeorological simulations in an actual urban area around Tokyo Station in Japan. The super-resolution simulation successfully inferred high-resolution atmospheric flows, reducing errors by approximately 50% compared to the low-resolution simulations. Furthermore, the two-stage approach enabled localized high-resolution inferences, reducing GPU memory usage to as low as 12% during training. The total wall-clock time for 60-min predictions was reduced to 6.83 min, which was 3.32% of the high-resolution simulation time.

physics.ao-ph↗

arXiv2023

Spatio-Temporal Super-Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks with Domain Generalization

Deep learning has recently gained attention in the atmospheric and oceanic sciences for its potential to improve the accuracy of numerical simulations or to reduce computational costs. Super-resolution is one such technique for high-resolution inference from low-resolution data. This paper proposes a new scheme, called four-dimensional super-resolution data assimilation (4D-SRDA). This framework calculates the time evolution of a system from low-resolution simulations using a physics-based model, while a trained neural network simultaneously performs data assimilation and spatio-temporal super-resolution. The use of low-resolution simulations without ensemble members reduces the computational cost of obtaining inferences at high spatio-temporal resolution. In 4D-SRDA, physics-based simulations and neural-network inferences are performed alternately, possibly causing a domain shift, i.e., a statistical difference between the training and test data, especially in offline training. Domain shifts can reduce the accuracy of inference. To mitigate this risk, we developed super-resolution mixup (SR-mixup)--a data augmentation method for domain generalization. SR-mixup creates a linear combination of randomly sampled inputs, resulting in synthetic data with a different distribution from the original data. The proposed methods were validated using an idealized barotropic ocean jet with supervised learning. The results suggest that the combination of 4D-SRDA and SR-mixup is effective for robust inference cycles. This study highlights the potential of super-resolution and domain-generalization techniques, in the field of data assimilation, especially for the integration of physics-based and data-driven models.

physics.ao-ph↗

arXiv2023

Effects of surface tension reduction on wind-wave growth and air-water scalar transfer

Effect of surface tension reduction on wind-wave growth is investigated using direct numerical simulation (DNS) of air-water two-phase turbulent flow. The incompressible Navier-Stokes equations for air and water sides are solved using an arbitrary Lagrangian-Eulerian method with boundary-fitted moving grids. The wave growth of finite-amplitude and non-breaking gravity--capillary waves, whose wavelength is less than 0.07 m, is simulated for two cases of different surface tensions under a low--wind speed condition of several meters per second. The results show that significant wave height for the smaller surface tension case increases faster than that for the larger surface tension case. Energy fluxes for gravity and capillary wave scales reveal that, when the surface tension is reduced, the energy transfer from the significant gravity waves to capillary waves decreases and the significant waves accumulate more energy supplied by wind. This results in faster wave growth for the smaller surface tension case. Effect on the scalar transfer across the air-water interface is also investigated. The results show that the scalar transfer coefficient on the water side decreases due to the surface tension reduction. The decrease is caused by suppression of turbulence in the water side. In order to support the conjecture, the surface tension effect is compared with laboratory experiments in a small wind-wave tank.

physics.flu-dyn↗

arXiv2023

Super-Resolution of Three-Dimensional Temperature and Velocity for Building-Resolving Urban Micrometeorology Using Physics-Guided Convolutional Neural Networks with Image Inpainting Techniques

Atmospheric simulations for urban cities can be computationally intensive because of the need for high spatial resolution, such as a few meters, to accurately represent buildings and streets. Deep learning has recently gained attention across various physical sciences for its potential to reduce computational cost. Super-resolution is one such technique that enhances the resolution of data. This paper proposes a convolutional neural network (CNN) that super-resolves instantaneous snapshots of three-dimensional air temperature and wind velocity fields for urban micrometeorology. This super-resolution process requires not only an increase in spatial resolution but also the restoration of missing data caused by the difference in the building shapes that depend on the resolution. The proposed CNN incorporates gated convolution, which is an image inpainting technique that infers missing pixels. The CNN performance has been verified via supervised learning utilizing building-resolving micrometeorological simulations around Tokyo Station in Japan. The CNN successfully reconstructed the temperature and velocity fields around the high-resolution buildings, despite the missing data at lower altitudes due to the coarseness of the low-resolution buildings. This result implies that near-surface flows can be inferred from flows above buildings. This hypothesis was assessed via numerical experiments where all input values below a certain height were made missing. This research suggests the possibility that building-resolving micrometeorological simulations become more practical for urban cities with the aid of neural networks that enhance computational efficiency.

physics.ao-ph↗

arXiv2022

Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks

This paper investigates the super-resolution (SR) of velocity fields in two-dimensional fluids from the viewpoint of rotational equivariance. SR refers to techniques that estimate high-resolution images from those in low resolution and has lately been applied in fluid mechanics. The rotational equivariance of SR models is defined as the property in which the super-resolved velocity field is rotated according to a rotation of the input, which leads to the inference covariant to the orientation of fluid systems. Generally, the covariance in physics is related to symmetries. To clarify a relationship to symmetries, the rotational consistency of datasets for SR is newly introduced as the invariance of pairs of low- and high-resolution velocity fields with respect to rotation. This consistency is sufficient and necessary for SR models to acquire rotational equivariance from large datasets with supervised learning. Such a large dataset is not required when rotational equivariance is imposed on SR models through weight sharing of convolution kernels as prior knowledge. Even if a fluid system has rotational symmetry, this symmetry may not carry over to a velocity dataset, which is not rotationally consistent. This inconsistency can occur when the rotation does not commute with the generation of low-resolution velocity fields. These theoretical suggestions are supported by the results from numerical experiments, where two existing convolutional neural networks (CNNs) are converted into rotationally equivariant CNNs and the inferences of the four CNNs are compared after the supervised training.

physics.flu-dyn↗

arXiv2021

Super-Resolution of Near-Surface Temperature Utilizing Physical Quantities for Real-Time Prediction of Urban Micrometeorology

The present paper proposes a super-resolution (SR) model based on a convolutional neural network and applies it to the near-surface temperature in urban areas. The SR model incorporates a skip connection, a channel attention mechanism, and separated feature extractors for the inputs of temperature, building height, downward shortwave radiation, and horizontal velocity. We train the SR model with sets of low-resolution (LR) and high-resolution (HR) images from building-resolving large-eddy simulations (LESs) in a city, where the horizontal resolutions of LR and HR are 20 and 5 m, respectively. The generalization capability of the SR model is confirmed with LESs in another city. The estimated HR temperature fields are more accurate than those of the bicubic interpolation and image SR model that takes only the temperature as its input. Except for the temperature input, the building height is the most important to reconstruct the HR temperature and enables the SR model to reduce errors in temperature near building boundaries. The SR model considers the appropriate boundary for each building from its height information. The analysis of attention weights indicates that the importance of the building height increases as the downward shortwave radiation becomes larger. The contrast between sun and shade is strengthened with the increase in solar radiation, which may affect the temperature distribution. The short inference time suggests the potential of the proposed SR model to facilitate a real-time HR prediction in metropolitan areas by combining it with an LR building-resolving LES model.

physics.ao-ph↗

arXiv2019

Storm modulation is feasible through a strategic use of air conditioners

Storm trainings, consisting of line-shaped aggregates of cumulonimbi, bring persistent local heavy rains, often causing devastating floods and landslides. Weather control techniques could in theory help prevent such disasters, but so far successful weather control has been limited to local rain initiation or the diffusion of local clouds. No successful strategies have been proposed for the control of mesoscale storms. Here we show that a strategic use of consumer air conditioners, which can typically remove about 1kg of moisture from the air per hour when run in dehumidification mode, and which are installed in large numbers in big cities, can modulate a storm downstream. We numerically reproduced a storm training that affected the Hiroshima metropolis in Japan in 2014, and conducted experiments to test the sensitivity of the storm to the initial moisture field near the surface. We propose an empirically-derived formula for a control efficiency parameter, which can be used to estimate the impact of moisture removal on the rainfall accumulation. It reveals that removal of half a kiloton of moisture, which could be achieved within half an hour in a city with a population of one million since more than one air conditioner is installed per capita in Japan, could lead to a significant reduction of the total rainfall accumulation over a 100 km2 area of heavy rain during the storm event. Conversely, our results indicate that some summertime storms occurring inside or near a metropolis could be strengthened by the excess moisture discharged from large numbers of air conditioners used for cooling rooms. We anticipate our results, which reveal that human activity can have a significant impact on storms, will be a starting point for considering the coupling of weather and the economy, and will contribute to the development of a sustainable society.

physics.ao-ph↗

arXiv2019

WaveRange: Wavelet-based data compression for three-dimensional numerical simulations on regular grids

A wavelet-based method for compression of three-dimensional simulation data is presented and its software framework is described. It uses wavelet decomposition and subsequent range coding with quantization suitable for floating-point data. The effectiveness of this method is demonstrated by applying it to example numerical tests, ranging from idealized configurations to realistic global-scale simulations.

physics.comp-ph↗

arXiv2019

Super-Resolution Simulation for Real-Time Prediction of Urban Micrometeorology

We propose a super-resolution (SR) simulation system that consists of a physics-based meteorological simulation and an SR method based on a deep convolutional neural network (CNN). The CNN is trained using pairs of high-resolution (HR) and low-resolution (LR) images created from meteorological simulation results for different resolutions so that it can map LR simulation images to HR ones. The proposed SR simulation system, which performs LR simulations, can provide HR prediction results in much shorter operating cycles than those required for corresponding HR simulation prediction system. We apply the SR simulation system to urban micrometeorology, which is strongly affected by buildings and human activity. Urban micrometeorology simulations that need to resolve urban buildings are computationally costly and thus cannot be used for operational real-time predictions even when run on supercomputers. We performed HR micrometeorology simulations on a supercomputer to obtain datasets for training the CNN in the SR method. It is shown that the proposed SR method can be used with a spatial scaling factor of 4 and that it outperforms conventional interpolation methods by a large margin. It is also shown that the proposed SR simulation system has the potential to be used for operational urban micrometeorology predictions.

physics.ao-ph↗

arXiv2018

Large-scale intermittency and rare events boosted at dimensional crossover in anisotropic turbulence

Understanding rare events in turbulence provides a basis for the science of extreme weather, for which the atmosphere is modeled by Navier-Stokes equations (NSEs). In solutions of NSEs for isotropic fluids, various quantities, such as fluid velocities, roughly follow Gaussian distributions, where extreme events are prominent only in small-scale quantities associated with the dissipation-dominating length scale or anomalous scaling regime. Using numerical simulations, this study reveals another universal promotion mechanism at much larger scales if three-dimensional fluids accompany strong two-dimensional anisotropies, as is the case in the atmosphere. The dimensional crossover between two and three dimensions generates prominent fat-tailed non-Gaussian distributions with intermittency accompanied by colossal chain-like structures with densely populated self-organized vortices (serpentinely organized vortices (SOV)). The promotion is caused by a sudden increase of the available phase space at the crossover length scale. Since the discovered intermittency can involve much larger energies than those in the conventional intermittency in small spatial scales, it governs extreme events and chaotic unpredictability in the synoptic weather system.

physics.flu-dyn↗