Talks

Atmosphere and ocean dynamics are dictated by balanced flows, such as mesoscale eddies, but determining a precise balanced state remains challenging in the presence of its nonlinear coupling with the unbalanced flows, such as internal gravity waves. The spontaneous loss of balance, resulting in nonlinear internal wave generation, challenges the existence of an invariant balanced state from a mathematical perspective, and at the same time has physical implications for the energy cycle of the atmosphere and ocean.

In this talk, I will discuss the recent progress in deriving and quantifying the balanced state in geophysical flows from nonlinear flow decomposition as well as the comparison of balanced states from different mathematical approaches: higher order balance and optimal balance . This decomposition is applied to varied oceanic regimes in a suite of idealized models to quantify spontaneous wave generation and assess its role in the energy cycle relative to other mechanisms. To ...

The principle dynamical components in the large scale ocean (i.e. spatial scales larger than O(100m)) are dominated by mesoscale eddies, submesoscale currents and interia gravity waves comprising both storm-forced Near-Inertial Waves (NIWs) and tidally generated internal waves (TIWs).  While mesoscale eddies (length scales of 10s of kms), generated primarily through baroclinic instability are the principal reservoir of kinetic energy in the world oceans, submesoscale currents (O(1-10 km)) comprising mixed layer eddies and fronts interact strongly with mesoscales and are critical for  regulating biogeochemical and air-sea fluxes. Here we use the so-called coarse-graining approach to compute cross-scale energy fluxes that shed light on dynamical interactions between these three components and their impact on the upper ocean. By first decomposing the oceanic velocity field into rotational and divergent components on one hand (i.e. a Helmholtz decomposition) and separately using an eddy-IG...

Mesoscale Eddies are rotating structures found in the ocean with size of 50 to 500 km with a lifespan of 10 to 100 days. Eddies can be detected from Sea Surface Height (SSH) data. These circulations play a major role in transportation of heat, salt, carbon and nutrients throughout the ocean and are also associated with complex atmosphere-ocean interactions. That is why the detection of the eddies is extremely important to understand the big picture of the climate. Various, physics based methods were used for eddies detection but dependent upon the threshold values and human expertise which leads to the false detection. Therefore, advance techniques of artificial intelligence are needed to overcome these challenges. Various Deep Learning based works were done over the South China Sea and South Atlantic Ocean but Bay of Bengal is less explored. In this study we trained deep learning models, U-Net and Attention U-Net, for detecting the mesoscale ocean eddies over Bay of Bengal from daily ...