arXiv subjects

Dhruv Balwada

Publications and source records attributed to Dhruv Balwada.

6 recordsLinked to original sources

arXiv2026

Wave-mean decomposition of scale-dependent kinetic energy from surface drifters

Separating waves and mean flows is a fundamental challenge in ocean dynamics. Lagrangian filtering of passive-tracer time series into high-frequency wave and low-frequency mean-flow components provides a practical route, as the relevant time scales are often cleanly split in the Lagrangian frame. Here we show that Lagrangian filtering can be applied to surface drifter observations, providing a powerful approach to quantify wave and mean-flow contributions to surface kinetic energy statistics. A key methodological choice is to implement the filtering in a generalized Lagrangian mean (GLM) framework, attributing filtered velocities to mean rather than particle trajectories; this produces more physically interpretable diagnostics. Using Gulf of Mexico drifter data, we compute second-order velocity structure functions (SF2s) for waves and mean flow components across spatial scales. With these filtered SF2s as a benchmark, we illustrate that Helmholtz decomposition of unfiltered SF2s alone should not be interpreted as a dynamical wave-mean decomposition. Applying Helmholtz decomposition to the filtered SF2s further illuminates seasonal dynamics. Mean-flow surface kinetic energy is rotationally dominated at scales larger than O(1) km, while at and below O(1) km, divergent and rotational contributions are approximately equipartitioned in both summer and winter, suggesting low-frequency divergent motions and possible associated vertical exchange. Winter mean flows are more active than summer mean flows over 500 m-10 km. Super-inertial motions are broadly consistent with linear waves. In winter, wave kinetic energy is concentrated at smaller spatial scales than in summer, possibly reflecting enhanced downscale transfer by stronger submesoscale mean flows.

physics.flu-dyn↗

arXiv2026

Estimating the Kinetic Energy Spectrum from the Second-Order Velocity Structure Function using a Regularized Fitting Approach

Ocean turbulence plays a key role in shaping large-scale circulation, heat uptake, and biogeochemical processes. The kinetic energy (KE) wavenumber spectrum is a fundamental diagnostic, quantifying how KE is distributed across spatial scales. The second-order structure function -- computed from velocity differences between spatially separated observations -- provides a complementary measure, but unlike the KE spectrum, it reflects a non-local, weighted integral of KE over all scales. Analytic relationships link the two metrics, permitting forward and inverse transformations between them. However, recovering the KE spectrum from the structure function via the inverse relationship is highly sensitive to sampling limitations and numerical discretization errors. Here we propose a regularized approach in which the spectrum is assumed to consist of a finite number of segments with distinct slopes and amplitudes, and the inversion is formulated as an optimization problem. The approach is first validated in an idealized setting; for a number of idealized KE spectra with prescribed sets of spectral slopes and amplitudes, the corresponding structure functions are computed by numerically evaluating the forward relationship. These structure functions are then used to determine the underlying parameters using our proposed approach, which shows that we are able to perfectly recover the parameters and consequently the KE spectra. The method is further evaluated on high-resolution ocean model output, where it reconstructs the underlying spectra well even in the presence of noise. Finally, we apply the method to surface drifter observations (GLAD and LASER experiments). The results show that the framework enables estimation of the KE spectrum from sparse Lagrangian data, extending spectral diagnostics beyond gridded Eulerian measurements.

physics.ao-ph↗

arXiv2025

A Framework for Hybrid Physics-AI Coupled Ocean Models

Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Parameterizations often lead to inaccuracies in climate models, with significant biases in the physics of key climate phenomena. Advances in artificial intelligence (AI) are now directly enabling the learning of unresolved processes from data to improve the physics of climate simulations. Here, we introduce a flexible framework for developing and implementing physics- and scale-aware machine learning parameterizations within climate models. We focus on the ocean and sea-ice components of a state-of-the-art climate model by implementing a spectrum of data-driven parameterizations, ranging from complex deep learning models to more interpretable equation-based models. Our results showcase the viability of AI-driven parameterizations in operational models, advancing the capabilities of a new generation of hybrid simulations, and include prototypes of fully coupled atmosphere-ocean-sea-ice hybrid simulations. The tools developed are open source, accessible, and available to all.

physics.ao-ph↗

arXiv2025

A Data-Driven Approach for Parameterizing Submesoscale Vertical Buoyancy Fluxes in the Ocean Mixed Layer

Parameterizations of O(1-10)km submesoscale flows in General Circulation Models (GCMs) represent the effects of unresolved vertical buoyancy fluxes in the ocean mixed layer. These submesoscale flows interact non-linearly with mesoscale and boundary layer turbulence, and it is challenging to account for all the relevant processes in physics-based parameterizations. In this work, we present a data-driven approach for the submesoscale parameterization, that relies on a Convolutional Neural Network (CNN) trained to predict mixed layer vertical buoyancy fluxes as a function of relevant large-scale variables. The data used for training is given from 12 regions sampled from the global high-resolution MITgcm-LLC4320 simulation. When compared with the baseline of a submesoscale physics-based parameterization, the CNN demonstrates high offline skill across all regions, seasons, and filter scales tested in this study. During seasons when submesoscales are most active, which generally corresponds to winter and spring months, we find that the CNN prediction skill tends to be lower than in summer months. The CNN exhibits strong dependency on the mixed layer depth and on the large scale strain field, a variable closely related to frontogenesis, which is currently missing from the submesoscale parameterizations in GCMs.

physics.ao-ph↗

arXiv2024

Multi-scale decomposition of sea surface height snapshots using machine learning

Knowledge of ocean circulation is important for understanding and predicting weather and climate, and managing the blue economy. This circulation can be estimated through Sea Surface Height (SSH) observations, but requires decomposing the SSH into contributions from balanced and unbalanced motions (BMs and UBMs). This decomposition is particularly pertinent for the novel SWOT satellite, which measures SSH at an unprecedented spatial resolution. Specifically, the requirement, and the goal of this work, is to decompose instantaneous SSH into BMs and UBMs. While a few studies using deep learning (DL) approaches have shown promise in framing this decomposition as an image-to-image translation task, these models struggle to work well across a wide range of spatial scales and require extensive training data, which is scarce in this domain. These challenges are not unique to our task, and pervade many problems requiring multi-scale fidelity. We show that these challenges can be addressed by using zero-phase component analysis (ZCA) whitening and data augmentation; making this a viable option for SSH decomposition across scales.

physics.ao-ph↗

arXiv2022

Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonality

The Ocean's turbulent energy cycle has a paradox; large-scale eddies under the control of Earth's rotation primarily transfer kinetic energy (KE) to larger scales via an inverse cascade, while a transfer to smaller scales is needed to accomplish dissipation. It has been argued, using numerical simulations, that fronts, waves and other turbulent structures can produce a forward cascade of KE toward dissipation scales. However, this forward cascade and its coexistence with known inverse cascade were not confirmed in observations. Here we present the first evidence of a dual KE cascade in the Ocean by analyzing velocity measurements from surface drifters released in the Gulf of Mexico. Our results show that KE is injected at two dominant scales and transferred to both large and small scales, with the downscale flux dominating at scales smaller than ~1-10km. The cascade rates are modulated seasonally, with stronger KE injection and forward transfer during winter.

physics.ao-ph↗