M. A. Khodkar, Athanasios C. Antoulas, Pedram Hassanzadeh
We show the skills of a data-driven low-dimensional linear model in predicting the spatio-temporal evolution of turbulent Rayleigh-Bénard convection. The model is based on dynamic mode decomposition with delay-embedding, which provides a data-driven finite-dimensional approximation to the system's Koopman operator. The model is built using vector-valued observables from direct numerical simulations, and can provide accurate predictions. Similar high prediction skills are found for the Kuramoto-Sivashinsky equation in the strongly-chaotic regimes.
M. A. Khodkar, Pedram Hassanzadeh
A data-driven, model-free framework is introduced for calculating Reduced-Order Models (ROMs) capable of accurately predicting time-mean responses to external forcings, or forcings needed for specified responses, e.g., for control, in fully turbulent flows. The framework is based on using the Fluctuation-Dissipation Theorem (FDT) in the space of a limited number of modes obtained from Dynamic Mode Decomposition (DMD). Using the DMD modes as the basis functions, rather than the commonly used Proper Orthogonal Decomposition (POD) modes, resolves a previously identified problem in applying FDT to high-dimensional, non-normal turbulent flows. Employing this DMD-enhanced FDT method (FDT$_\mathrm{DMD}$), a 1D linear ROM with horizontally averaged temperature as state vector, is calculated for a 3D Rayleigh-Bénard convection system at the Rayleigh number of $10^6$ using data obtained from Direct Numerical Simulation (DNS). The calculated ROM performs well in various tests for this turbulent flow, suggesting FDT$_\mathrm{DMD}$ as a promising method for developing ROMs for high-dimensional, turbulent systems.
Philip Marcus, Suyang Pei, Chung-Hsiang Jiang, Joseph Barranco, Pedram Hassanzadeh, Daniel Lecoanet
Oct 29, 2014·astro-ph.SR·PDF There is considerable interest in hydrodynamic instabilities in dead zones of protoplanetary disks as a mechanism for driving angular momentum transport and as a source of particle-trapping vortices to mix chondrules and incubate planetesimal formation. We present simulations with a pseudo-spectral anelastic code and with the compressible code Athena, showing that stably stratified flows in a shearing, rotating box are violently unstable and produce space-filling, sustained turbulence dominated by large vortices with Rossby numbers of order 0.2-0.3. This Zombie Vortex Instability (ZVI) is observed in both codes and is triggered by Kolmogorov turbulence with Mach numbers less than 0.01. It is a common view that if a given constant density flow is stable, then stable vertical stratification should make the flow even more stable. Yet, we show that sufficient vertical stratification can be unstable to ZVI. ZVI is robust and requires no special tuning of boundary conditions, or initial radial entropy or vortensity gradients (though we have studied ZVI only in the limit of infinite cooling time). The resolution of this paradox is that stable stratification allows for a new avenue to instability: baroclinic critical layers. ZVI has not been seen in previous studies of flows in rotating, shearing boxes because those calculations frequently lacked vertical density stratification and/or sufficient numerical resolution. Although we do not expect appreciable angular momentum transport from ZVI in the small domains in this study, we hypothesize that ZVI in larger domains with compressible equations may lead to angular transport via spiral density waves.
Mani Mahdinia, Pedram Hassanzadeh, Philip S. Marcus, Chung-Hsiang Jiang
The linear stability of three-dimensional (3D) vortices in rotating, stratified flows has been studied by analyzing the non-hydrostatic inviscid Boussinesq equations. We have focused on a widely-used model of geophysical and astrophysical vortices, which assumes an axisymmetric Gaussian structure for pressure anomalies in the horizontal and vertical directions. For a range of Rossby number ($-0.5 < Ro < 0.5$) and Burger number ($0.02 < Bu < 2.3$) relevant to observed long-lived vortices, the growth rate and spatial structure of the most unstable eigenmodes have been numerically calculated and presented as a function of $Ro-Bu$. We have found neutrally-stable vortices only over a small region of the $Ro-Bu$ parameter space: cyclones with $Ro \sim 0.02-0.05$ and $Bu \sim 0.85-0.95$. However, we have also found that anticyclones in general have slower growth rates compared to cyclones. In particular, the growth rate of the most unstable eigenmode for anticyclones in a large region of the parameter space (e.g., $Ro<0$ and $0.5 \lesssim Bu \lesssim 1.3$) is slower than $50$ turn-around times of the vortex (which often corresponds to several years for ocean eddies). For cyclones, the region with such slow growth rates is confined to $0<Ro<0.1$ and $0.5 \lesssim Bu \lesssim 1.3$. While most calculations have been done for $f/\bar{N}=0.1$ (where $f$ and $\bar{N}$ are the Coriolis and background Brunt-Väisälä frequencies), we have numerically verified and explained analytically, using non-dimensionalized equations, the insensitivity of the results to reducing $f/\bar{N}$ to the more ocean-relevant value of $0.01$. The results of this paper provide a steppingstone to study the more complicated problems of the stability of geophysical (e.g., those in the atmospheres of giant planets) and astrophysical vortices (in accretion disks).
Pedram Hassanzadeh, Zhiming Kuang
A linear response function (LRF) determines the mean-response of a nonlinear climate system to weak imposed forcings, and an eddy flux matrix (EFM) determines the eddy momentum and heat flux responses to mean-flow changes. Neither LRF nor EFM can be calculated from first principles due the lack of a complete theory for turbulent eddies. Here the LRF and EFM for an idealized dry atmosphere are computed by applying numerous localized weak forcings, one at a time, to a GCM with Held-Suarez physics and calculating the mean-responses. The LRF and EFM for zonally-averaged responses are then constructed using these forcings and responses through matrix inversion. Tests demonstrate that LRF and EFM are fairly accurate. Spectral analysis of the LRF shows that the most excitable dynamical mode, the neutral vector, strongly resembles the model's Annular Mode. The framework described here can be employed to compute the LRF/EFM for zonally-asymmetric responses and more complex GCMs. The potential applications of the LRF/EFM constructed here are i) forcing a specified mean-flow for hypothesis-testing, ii) isolating/quantifying the eddy-feedbacks in complex eddy-mean flow interaction problems, and iii) evaluating/improving more generally-applicable methods currently used to construct LRFs or diagnose eddy-feedbacks in comprehensive GCMs or observations. As an example for iii, in Part 2, the LRF is also computed using the fluctuation-dissipation theorem (FDT), and the previously-calculated LRF is exploited to investigate why FDT performs poorly in some cases. It is shown that dimension-reduction using leading EOFs, which is commonly used to construct LRFs from the FDT, can significantly degrade the accuracy due to the non-normality of the operator.
M. A. Khodkar, Pedram Hassanzadeh, Saleh Nabi, Piyush Grover
A One-Dimensional (1D) Reduced-Order Model (ROM) has been developed for a 3D Rayleigh-Bénard convection system in the turbulent regime with Rayleigh number $\mathrm{Ra}=10^6$. The state vector of the 1D ROM is horizontally averaged temperature. Using the Green's Function (GRF) method, which involves applying many localized, weak forcings to the system one at a time and calculating the responses using long-time averaged Direct Numerical Simulations (DNS), the system's Linear Response Function (LRF) has been computed. Another matrix, called the Eddy Flux Matrix (EFM), that relates changes in the divergence of vertical eddy heat fluxes to changes in the state vector, has also been calculated. Using various tests, it is shown that the LRF and EFM can accurately predict the time-mean responses of temperature and eddy heat flux to external forcings, and that the LRF can well predict the forcing needed to change the mean flow in a specified way (inverse problem). The non-normality of the LRF is discussed and its eigen/singular vectors are compared with the leading Proper Orthogonal Decomposition (POD) modes of the DNS data. Furthermore, it is shown that if the LRF and EFM are simply scaled by the square-root of Rayleigh number, they perform equally well for flows at other $\mathrm{Ra}$, at least in the investigated range of $ 5 \times 10^5 \le \mathrm{Ra} \le 1.25 \times 10^6$. The GRF method can be applied to develop 1D or 3D ROMs for any turbulent flow, and the calculated LRF and EFM can help with better analyzing and controlling the nonlinear system.
Ashesh Chattopadhyay, Adam Subel, Pedram Hassanzadeh
To make weather/climate modeling computationally affordable, small-scale processes are usually represented in terms of the large-scale, explicitly-resolved processes using physics-based or semi-empirical parameterization schemes. Another approach, computationally more demanding but often more accurate, is super-parameterization (SP), which involves integrating the equations of small-scale processes on high-resolution grids embedded within the low-resolution grids of large-scale processes. Recently, studies have used machine learning (ML) to develop data-driven parameterization (DD-P) schemes. Here, we propose a new approach, data-driven SP (DD-SP), in which the equations of the small-scale processes are integrated data-drivenly using ML methods such as recurrent neural networks. Employing multi-scale Lorenz 96 systems as testbed, we compare the cost and accuracy (in terms of both short-term prediction and long-term statistics) of parameterized low-resolution (LR), SP, DD-P, and DD-SP models. We show that with the same computational cost, DD-SP substantially outperforms LR, and is better than DD-P, particularly when scale separation is lacking. DD-SP is much cheaper than SP, yet its accuracy is the same in reproducing long-term statistics and often comparable in short-term forecasting. We also investigate generalization, finding that when models trained on data from one system are applied to a system with different forcing (e.g., more chaotic), the models often do not generalize, particularly when the short-term prediction accuracy is examined. But we show that transfer-learning, which involves re-training the data-driven model with a small amount of data from the new system, significantly improves generalization. Potential applications of DD-SP and transfer-learning in climate/weather modeling and the expected challenges are discussed.
Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh
Models of many engineering and natural systems are imperfect. The discrepancy between the mathematical representations of a true physical system and its imperfect model is called the model error. These model errors can lead to substantial differences between the numerical solutions of the model and the state of the system, particularly in those involving nonlinear, multi-scale phenomena. Thus, there is increasing interest in reducing model errors, particularly by leveraging the rapidly growing observational data to understand their physics and sources. Here, we introduce a framework named MEDIDA: Model Error Discovery with Interpretability and Data Assimilation. MEDIDA only requires a working numerical solver of the model and a small number of noise-free or noisy sporadic observations of the system. In MEDIDA, first the model error is estimated from differences between the observed states and model-predicted states (the latter are obtained from a number of one-time-step numerical integrations from the previous observed states). If observations are noisy, a data assimilation (DA) technique such as ensemble Kalman filter (EnKF) is employed to provide the analysis state of the system, which is then used to estimate the model error. Finally, an equation-discovery technique, here the relevance vector machine (RVM), a sparsity-promoting Bayesian method, is used to identify an interpretable, parsimonious, and closed-form representation of the model error. Using the chaotic Kuramoto-Sivashinsky (KS) system as the test case, we demonstrate the excellent performance of MEDIDA in discovering different types of structural/parametric model errors, representing different types of missing physics, using noise-free and noisy observations.
Ashesh Chattopadhyay, Pedram Hassanzadeh, Devika Subramanian
In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory (RNN-LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale ($X$), intermediate ($Y$), and fast/small-scale ($Z$) processes. For training or testing, only $X$ is available; $Y$ and $Z$ are never known or used. We show that RC-ESN substantially outperforms ANN and RNN-LSTM for short-term prediction, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps, equivalent to several Lyapunov timescales. The RNN-LSTM and ANN show some prediction skills as well; RNN-LSTM bests ANN. Furthermore, even after losing the trajectory, data predicted by RC-ESN and RNN-LSTM have probability density functions (PDFs) that closely match the true PDF, even at the tails. The PDF of the data predicted using ANN, however, deviates from the true PDF. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems such as weather/climate are discussed.
Pedram Hassanzadeh, Zhiming Kuang
A linear response function (LRF) relates the mean-response of a nonlinear system to weak external forcings and vice versa. Even for simple models of the general circulation, such as the dry dynamical core, the LRF cannot be calculated from first principles due to the lack of a complete theory for eddy-mean flow feedbacks. According to the Fluctuation-Dissipation Theorem (FDT), the LRF can be calculated using only the covariance and lag-covariance matrices of the unforced system. However, efforts in calculating the LRFs for GCMs using FDT have produced mixed results, and the reason(s) behind the poor performance of the FDT remains unclear. In Part 1 of this study, the LRF of an idealized GCM, the dry dynamical core with Held-Suarez physics, is accurately calculated using Green's functions. In this paper (Part 2), the LRF of the same model is computed using FDT, which is found to perform poorly for some of the test cases. The accurate LRF of Part 1 is used with a linear stochastic equation to show that dimension-reduction by projecting the data onto leading EOFs, which is commonly used for FDT, can alone be a significant source of error. Simplified equations and examples of 2 x 2 matrices are then used to demonstrate that this error arises because of the non-normality of the operator. These results suggest that errors caused by dimension-reduction are a major, if not the main, contributor to the poor performance of the LRF calculated using FDT, and that further investigations of dimension-reduction strategies with a focus on non-normality are needed.
Adam Subel, Ashesh Chattopadhyay, Yifei Guan, Pedram Hassanzadeh
Developing data-driven subgrid-scale (SGS) models for large eddy simulations (LES) has received substantial attention recently. Despite some success, particularly in a priori (offline) tests, challenges have been identified that include numerical instabilities in a posteriori (online) tests and generalization (i.e., extrapolation) of trained data-driven SGS models, for example to higher Reynolds numbers. Here, using the stochastically forced Burgers turbulence as the test-bed, we show that deep neural networks trained using properly pre-conditioned (augmented) data yield stable and accurate a posteriori LES models. Furthermore, we show that transfer learning enables accurate/stable generalization to a flow with 10x higher Reynolds number.
Karan Jakhar, Yifei Guan, Pedram Hassanzadeh
By combining AI and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation (DNS) data. Large-eddy simulation (LES) with this closure is accurate and stable, reproducing DNS statistics including those of extremes. We also show that the new closure could be derived from a 4th-order truncated Taylor expansion. Prior analytical and AI-based work only found the 2nd-order expansion, which led to unstable LES. The additional terms emerge only when inter-scale energy transfer is considered alongside standard reconstruction criterion in the sparse-equation discovery.
Pedram Hassanzadeh, Zhiming Kuang
The linear response function (LRF) of an idealized GCM, the dry dynamical core with Held-Suarez physics, is used to accurately compute how eddy momentum and heat fluxes change in response to the zonal wind and temperature anomalies of the annular mode at the low-frequency limit. Using these results and knowing the parameterizations of surface friction and thermal radiation in Held-Suarez physics, the contribution of each physical process (meridional and vertical eddy fluxes, surface friction, thermal radiation, and meridional advection) to the annular mode dynamics is quantified. Examining the quasi-geostrophic potential vorticity balance, it is shown that the eddy feedback is positive and increases the persistence of the annular mode by a factor of more than two. Furthermore, how eddy fluxes change in response to only the barotropic component of the annular mode, i.e., vertically averaged zonal wind (and no temperature) anomaly, is also calculated similarly. The response of eddy fluxes to the barotropic-only component of the annular mode is found to be drastically different from the response to the full (barotropic+baroclinic) annular mode anomaly. In the former, the barotropic governor effect significantly suppresses the eddy generation leading to a negative eddy feedback that decreases the persistence of the annular mode by nearly a factor of three. These results suggest that the baroclinic component of the annular mode anomaly, i.e., the increased low-level baroclinicity, is essential for the persistence of the annular mode, consistent with the baroclinic mechanism but not the barotropic mechanism proposed in the previous studies.
Ashesh Chattopadhyay, Pedram Hassanzadeh, Saba Pasha
Convolutional neural networks (CNNs) can potentially provide powerful tools for classifying and identifying patterns in climate and environmental data. However, because of the inherent complexities of such data, which are often spatio-temporal, chaotic, and non-stationary, the CNN algorithms must be designed/evaluated for each specific dataset and application. Yet to start, CNN, a supervised technique, requires a large labeled dataset. Labeling demands (human) expert time, which combined with the limited number of relevant examples in this area, can discourage using CNNs for new problems. To address these challenges, here we (1) Propose an effective auto-labeling strategy based on using an unsupervised clustering algorithm and evaluating the performance of CNNs in re-identifying these clusters; (2) Use this approach to label thousands of daily large-scale weather patterns over North America in the outputs of a fully-coupled climate model and show the capabilities of CNNs in re-identifying the 4 clustered regimes. The deep CNN trained with $1000$ samples or more per cluster has an accuracy of $90\%$ or better. Accuracy scales monotonically but nonlinearly with the size of the training set, e.g. reaching $94\%$ with $3000$ training samples per cluster. Effects of architecture and hyperparameters on the performance of CNNs are examined and discussed.
Pedram Hassanzadeh, Gregory P. Chini, Charles R. Doering
The calculus of variations is employed to find steady divergence-free velocity fields that maximize transport of a tracer between two parallel walls held at fixed concentration for one of two constraints on flow strength: a fixed value of the kinetic energy or a fixed value of the enstrophy. The optimizing flows consist of an array of (convection) cells of a particular aspect ratio Gamma. We solve the nonlinear Euler-Lagrange equations analytically for weak flows and numerically (and via matched asymptotic analysis in the fixed energy case) for strong flows. We report the results in terms of the Nusselt number Nu, a dimensionless measure of the tracer transport, as a function of the Peclet number Pe, a dimensionless measure of the energy or enstrophy of the flow. For both constraints the maximum transport Nu_{MAX}(Pe) is realized in cells of decreasing aspect ratio Gamma_{opt}(Pe) as Pe increases. For the fixed energy problem, Nu_{MAX} \sim Pe and Gamma_{opt} \sim Pe^{-1/2}, while for the fixed enstrophy scenario, Nu_{MAX} \sim Pe^{10/17} and Gamma_{opt} \sim Pe^{-0.36}. We also interpret our results in the context of certain buoyancy-driven Rayleigh-Benard convection problems that satisfy one of the two intensity constraints, enabling us to investigate how the transport scalings compare with upper bounds on Nu expressed as a function of the Rayleigh number \Ra. For steady convection in porous media, corresponding to the fixed energy problem, we find Nu_{MAX} \sim \Ra and Gamma_{opt} \sim Ra^{-1/2}$, while for steady convection in a pure fluid layer between free-slip isothermal walls, corresponding to fixed enstrophy transport, Nu_{MAX} \sim Ra^{5/12} and Gamma_{opt} \sim Ra^{-1/4}.
Philip S. Marcus, Suyang Pei, Chung-Hsiang Jiang, Pedram Hassanzadeh
Mar 18, 2013·astro-ph.EP·PDF A previously unknown instability creates space-filling lattices of 3D vortices in linearly-stable, rotating, stratified shear flows. The instability starts from an easily-excited critical layer. The layer intensifies by drawing energy from the background shear and rolls-up into vortices that excite new critical layers and vortices. The vortices self-similarly replicate to create lattices of turbulent vortices. The vortices persist for all time. This self-replication occurs in stratified Couette flows and in the dead zones of protoplanetary disks where it can de-stabilize Keplerian flows.
Pedram Hassanzadeh, Philip S. Marcus, Patrice Le Gal
We derive a relationship for the vortex aspect ratio $α$ (vertical half-thickness over horizontal length scale) for steady and slowly evolving vortices in rotating stratified fluids, as a function of the Brunt-Vaisala frequencies within the vortex $N_c$ and in the background fluid outside the vortex $\bar{N}$, the Coriolis parameter $f$, and the Rossby number $Ro$ of the vortex: $α^2 = Ro(1+Ro) f^2/(N_c^2-\bar{N}^2)$. This relation is valid for cyclones and anticyclones in either the cyclostrophic or geostrophic regimes; it works with vortices in Boussinesq fluids or ideal gases, and the background density gradient need not be uniform. Our relation for $α$ has many consequences for equilibrium vortices in rotating stratified flows. For example, cyclones must have $N_c^2 > \bar{N}^2$; weak anticyclones (with $|Ro| < 1$) must have $N_c^2 < \bar{N}^2; and strong anticyclones must have $N_c^2 > \bar{N}^2$. We verify our relation for $α$ with numerical simulations of the three-dimensional Boussinesq equations for a wide variety of vortices, including: vortices that are initially in (dissipationless) equilibrium and then evolve due to an imposed weak viscous dissipation or density radiation; anticyclones created by the geostrophic adjustment of a patch of locally mixed density; cyclones created by fluid suction from a small localised region; vortices created from the remnants of the violent breakups of columnar vortices; and weakly non-axisymmetric vortices. The values of the aspect ratios of our numerically-computed vortices validate our relationship for $α$, and generally they differ significantly from the values obtained from the much-cited conjecture that $α= f/\bar{N}$ in quasi-geostrophic vortices.
Ding Ma, Pedram Hassanzadeh, Zhiming Kuang
A linear response function (LRF) that relates the temporal tendency of zonal mean temperature and zonal wind to their anomalies and external forcing is used to accurately quantify the strength of the eddy-jet feedback associated with the annular mode in an idealized GCM. Following a simple feedback model, the results confirm the presence of a positive eddy-jet feedback in the annular mode dynamics, with a feedback strength of 0.137 day$^{-1}$ in the idealized GCM. Statistical methods proposed by earlier studies to quantify the feedback strength are evaluated against results from the LRF. It is argued that the mean-state-independent eddy forcing reduces the accuracy of these statistical methods because of the quasi-oscillatory nature of the eddy forcing. A new method is proposed to approximate the feedback strength as the regression coefficient of low-pass filtered eddy forcing onto low-pass filtered annular mode index, which converges to the value produced by the LRF when timescales longer than 200 days are used for the low-pass filtering. Applying the new low-pass filtering method to the reanalysis data, the feedback strength in the Southern annular mode is found to be 0.121 day$^{-1}$, which is presented as an improvement over previous estimates. This work also highlights the importance of using sub-daily data in the analysis by showing the significant contribution of medium-scale waves of periods less than 2 days to the annular mode dynamics, which was under-appreciated in most of previous research. The present study provides a framework to quantify the eddy-jet feedback strength in models and reanalysis data.
Mohammad Amin Khodkar, Pedram Hassanzadeh, Athanasios Antoulas
We introduce a data-driven method and shows its skills for spatiotemporal prediction of high-dimensional chaotic dynamics and turbulence. The method is based on a finite-dimensional approximation of the Koopman operator where the observables are vector-valued and delay-embedded, and the nonlinearities are treated as external forcings. The predictive capabilities of the method are demonstrated for well-known prototypes of chaos such as the Kuramoto-Sivashinsky equation and Lorenz-96 system, for which the data-driven predictions are accurate for several Lyapunov timescales. Similar performance is seen for two-dimensional lid-driven cavity flows at high Reynolds numbers.
Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander
There are different strategies for training neural networks (NNs) as subgrid-scale parameterizations. Here, we use a 1D model of the quasi-biennial oscillation (QBO) and gravity wave (GW) parameterizations as testbeds. A 12-layer convolutional NN that predicts GW forcings for given wind profiles, when trained offline in a big-data regime (100-years), produces realistic QBOs once coupled to the 1D model. In contrast, offline training of this NN in a small-data regime (18-months) yields unrealistic QBOs. However, online re-training of just two layers of this NN using ensemble Kalman inversion and only time-averaged QBO statistics leads to parameterizations that yield realistic QBOs. Fourier analysis of these three NNs' kernels suggests why/how re-training works and reveals that these NNs primarily learn low-pass, high-pass, and a combination of band-pass filters, consistent with the importance of both local and non-local dynamics in GW propagation/dissipation. These findings/strategies apply to data-driven parameterizations of other climate processes generally.