Research Computing Days 2023
Document Type
Student Presentation
Presentation Date
3-28-2023
College
College of Arts and Sciences
Department
Department of Geosciences
Faculty Mentor
Dr. Alejandro N. Flores
Abstract
The representation of soil moisture in Earth System Models, like the Community Earth System Model (CESM), is an essential facet in modeling the response of the Earth System to climate change. Since their inception, land models have grown to represent critical processes like carbon cycling, ecosystem dynamics, terrestrial hydrology, and agriculture. They serve as a lower boundary condition for atmospheric general circulation models. With increasing process representation, they are computationally expensive. Hydrologists and modelers use several parameterization schemes to describe the water and energy balance. However, this is regarded as computationally expensive. Alternative tools called emulators (e.g., machine learning and artificial intelligence) incorporated with the empirical orthogonal function analysis can represent soil moisture.
Recommended Citation
Silwimba, Kachinga, "Integrating Empirical Orthogonal Functions (EOFs) into Machine Learning Model" (2023). Research Computing Days 2023. 14.
https://scholarworks.boisestate.edu/rcd_2023/14