Effect of Trigonometric Transformations on the Machine Learning Prediction and Quality Control of Air Temperature

Authors

Andrea Fenoglio [*], Air Force Institute of Technology
Torrey J. Wagner, Air Force Institute of TechnologyFollow
Paul Auclair, Air Force Institute of Technology
Brent T. Langhals, Air Force Institute of TechnologyFollow

Document Type

Conference Proceeding

Publication Date

2022

Abstract

Conducting effective quality control of weather observations in real time is vital to the 14th Weather Squadron’s mission of providing authoritative climate data. This study explored automated quality control of weather observations by applying multiple machine learning techniques to 43,487 surface weather observations from 5 years of data at a single location. Temperature predictors were evaluated using recursive feature elimination on linear regression and XGBoost algorithms, as well as using a neural network hyperparameter sweep. Modeling was repeated after calculating trigonometric transforms of temporal variables to give the models insight into the diurnal heating cycle of the Earth. All models developed in this study demonstrated better performance than a trivial model and demonstrated an acceptable

Comments

The authors declare this is a work of the U.S. Government and is not subject to copyright protections in the United States.

Conference location: Las Vegas, NV, July 25-28, 2022

Source Publication

World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE 2022)

Recommended Citation

Fenoglio, Andrea [*]; Wagner, Torrey J.; Auclair, Paul; and Langhals, Brent T., "Effect of Trigonometric Transformations on the Machine Learning Prediction and Quality Control of Air Temperature" (2022). Faculty Publications. 1408.
https://scholar.afit.edu/facpub/1408