Rebecca Gjini

Stratocumulus clouds are low level clouds with cloud decks that cover immense stretches of subtropical oceans and provide a net cooling effect on the planet. Stratocumulus clouds are an important area of study because clouds and aerosols are one of the largest sources of uncertainty when calculating Earth’s energy budget. My research explores the use of mathematics, computation, and inverse modeling to better understand stratocumulus clouds. Specifically, I work with two very different stratocumulus cloud models and connect the two using feature-based inversion. The first model is known as a large-eddy simulation (LES), a computationally expensive, 3D and time simulation that describes the state of the atmosphere. The second model is a phenomenological model based on predator-prey dynamics. In this scenario, the rain is the predator of clouds. To learn more about how I utilize inverse modeling to compare these two stratocumulus models, check out my SIAM News article below.

Check out my research:

Stratocumulus Clouds and Predator-prey Dynamics

Ensemble Kalman Inversion (EKI) is a derivative-free optimization method used to solve nonlinear least squares problems. The algorithm is designed to solve for the minimizer of a loss function over multiple optimization iterations. EKI relies on an ensemble to approximate derivative information using statistics from the ensemble. During each iteration, the ensemble members are updated using the derivative approximation and eventually the ensemble will collapse onto the minimizer of the loss function. To learn more about different iterative ensemble Kalman methods, check out this recent preprint.

Check out my research:

The Ensemble Kalman Inversion Race

Also known as algorithmic differentiation (AD), AD is a tool that is used to compute function derivatives in a methodological way. When a human programmer codes a function and its associated derivative, typically the derivative needs to be coded by hand. AD allows programmers to code up their original function and use the AD computer algorithm to solve for the derivative function. I implemented AD within a Python package called PyDDA (Pythonic Direct Data Assimilation) during my internship at the Argonne National Laboratory. PyDDA uses data from multiple weather radars to get 3D wind field velocities based on the 3D variational technique. I implemented AD within all the gradient functions of the package used to perform the cost function minimization. For more information on PyDDA and how AD is used within the package, check out the PyDDA documentation and github.

Check out PyDDA:

PyDDA documentation PyDDA github

Inverse problems are important scientific and mathematical problems because the solutions help us understand physical processes that are difficult to observe. The opposite of a forward problem, inverse problems are used to determine the factors that cause a set of observations to occur. One method of solving inverse problems is to use a Bayesian approach. The idea behind Bayesian inversion is to better understand model parameters (and their associated model output) in relation to data. In my research, I use various Bayesian inference methods, such as feature-based Bayesian inversion and Markov-chain Monte Carlo (MCMC). For more information on feature-based inversion and its applications to various numerical problems, below is a paper written by my advisor.

Check out this journal article:

Feature-based data assimilation in geophysics

The JAX Circulation Model (JCM) is an autodifferentiable, intermediate-complexity atmospheric model written in Python and JAX. JCM combines the dynamical core (Dinosaur) from Google's NeuralGCM with JAX implementations of atmospheric physics parameterizations. Many research directions can be explored with a differentiable climate model, including gradient-based calibration, data assimilation, and ML-enhanced climate modeling. For more information on JCM, below are links to the paper and repository!

Check out JCM:

JCM paper JCM repository