Project:	Developing a Machine Learning Framework for Predicting Jacobians in Atmospheric Flux Inversions (JPL AO No. 16092)
Disciplines:	Mathematics, Geophysics
Mentor:	Sudhanshu Pandey, (JPL), Sudhanshu.Pandey@jpl.nasa.gov, Phone: (626) 379-6980
Background:	Accurately estimating greenhouse gas emissions, particularly carbon dioxide (CO₂) and methane (CH₄), is essential for understanding and mitigating climate change. Atmospheric flux inversion infers surface emissions from atmospheric concentration measurements by integrating observational data with transport models. Central to these models is the Jacobian matrix, which quantifies how emissions from various sources influence atmospheric concentrations. However, calculating Jacobians is computationally intensive, requiring extensive simulations for numerous emission sources and observation points. This complexity limits the scalability and efficiency of inversion models, especially with high-resolution data and large emission inventories. Recent advancements in machine learning offer promising solutions to accelerate these calculations. By training models to predict Jacobians, significant reductions in computational time can be achieved, allowing for more frequent and comprehensive emission estimations. Additionally, predictive Jacobians enhance air quality modeling by providing timely and accurate sensitivity information, improving pollutant dispersion and concentration forecasts. Leveraging machine learning not only makes large-scale atmospheric inversions more feasible but also supports more informed and timely climate policy decisions.
Description:	This internship involves developing a machine learning framework to predict Jacobian matrices used in atmospheric flux inversions. Utilizing atmospheric transport model simulations and related meteorological data, the Jacobians will be calculated and mapped to the input meteorological variables. The project aims to create accurate and efficient models that significantly reduce the computational time required for Jacobian calculations, enabling more frequent and comprehensive emission estimations.
References:	Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (3rd ed.). Wiley.
Student Requirements:	1. Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn). 2. Strong programming skills in Python. 3. Experience with atmospheric transport models (e.g., GEOS-Chem, WRF) is a plus. 4. Knowledge of numerical methods and matrix computations.
Location / Safety:	Project building and/or room locations: . Student will need special safety training: .
Programs:	This AO can be done under the following programs:   Program    Available To        SURF@JPL    Caltech students only  Click on a program name for program info and application requirements.