Date of Award
2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Atmospheric and Earth Science
Committee Chair
Udaysankar Nair
Committee Member
Sundar Christopher
Committee Member
Aaron Kaulfus
Research Advisor
Udaysankar Nair
Subject(s)
Energy budget (Geophysics), Solar radiation, Meteorology, Artificial intelligence
Abstract
Estimating heat and moisture exchange between the land and atmosphere has several important practical applications, including water resource management, air pollution forecasting, and atmospheric propagation modeling. Turnkey systems for measuring the surface energy budget typically cost between $5,000 and over $50,000. This study explores the use of artificial intelligence (AI) to predict sensible heat flux (SHF) and latent heat flux (LHF) using inexpensive surface meteorology data and downwelling solar radiation measurements as inputs. Observations from Amer- iFlux sites were used to train several AI models—Convolutional Neural Networks (CNNs), One Dimensional Transformers (1D Transformers), Artificial Neural Net- works (ANNs), and Long Short-Term Memory (LSTM) networks. Among these, the 1D Transformer model demonstrated the best performance, with an average root mean squared error (RMSE) of 46 W/m² and a correlation coefficient of 0.88 for SHF, and an RMSE of 48 W/m² with a correlation of 0.85 for LHF, establishing the feasibility of using AI to predict components of the surface energy budget using meteorological data and downwelling solar radiation as predictors.
Recommended Citation
Bonucchi, Joseph, "Utilization of artificial intelligence to predict surface energy budget" (2025). Theses. 772.
https://louis.uah.edu/uah-theses/772