Analysis of Surface-Energy Fluxes

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During the summers of 2022 and 2023,  I worked as a Research Intern at UFZ Center for Environmental Research in Leipzig, Germany. This research was conducted with remote sensing data with an aim to study land-atmosphere interactions and the energy-balance on Earth's surface. 

Given various sensible, latent, and ground heat fluxes on Earth's surface, we are able to derive its net radiation. The goal of this project was to determine how this energy balance is related to recorded air and soil temperature extremes. This gives us insight into various environmental processes such as evapotranspiration and soil-moisture feedback that are affected by these relations. As a result, I preprocessed, analyzed, and mapped this data to find correlations between the various fluxes and temperature extremes. 

My presentation and findings are linked below.

After the results of the previous year's research, I was tasked with creating a random forest regression model to derive latent heat flux from satellite and reanalysis data. After preprocessing and training the data, I used recursive feature elimination, random search, and other techniques during before cross validation to tune hyperparameters and determine important features of the model. 

The Github pertaining to this model, as well as the previous years' analysis, can be found here.