Session 11A LSTM and Other Time Series Machine Learning Methods for Time Series Prediction

Wednesday, 31 January 2024: 1:45 PM-3:00 PM
345/346 (The Baltimore Convention Center)
Host: 23rd Conference on Artificial Intelligence for Environmental Science
Cochairs:
Ryan Lagerquist and Jebb Q. Stewart, ESRL, Boulder, CO

With increasing advancements in time series machine learning research and models, this session will highlight research using time series specific models, such as LSTMs, in a variety of earth science applications. This includes but is not limited to topics in weather, climate, aerosol, and energy.

Papers:
1:45 PM
11A.1
Enhancing Regional Weather Forecasts over Complex Terrain: A Hybrid Physics-Machine Learning Approach
Ming Fan, ORNL, Oak Ridge, TN; and W. Zhang, H. G. Kang, K. R. Birdwell, and K. J. Evans

2:00 PM
11A.2
Predicting Forecast Error of Numerical Weather Prediction Models using an LSTM
David Aaron Evans, University at Albany, SUNY, Albany, NY; and K. J. Sulia, N. P. Bassill, C. D. Thorncroft, L. Gaudet, Ph.D., and J. C. Rothenberger

2:15 PM
11A.3
Gap-Filling AOD Data Using Deep Learning Techniques in Satellite Imagery
Yi Wang, Science and Technology Corporation (STC), Columbia, MD; and M. R. Schoeberl, R. B. Esmaili, and J. Liu

2:30 PM
11A.4
A Fuel Moisture Model for WRF-SFIRE from HRRR and RAWS Data by a Physics-Initialized Recurrent Neural Network
Jan Mandel, Univ. of Colorado Denver, Denver, CO; and J. Hirschi, A. Kochanski, A. Farguell, D. V. V. Mallia, B. Shaddy, A. A. Oberai, K. A. Hilburn, and J. Haley

2:45 PM
11A.5
Comparison of Multivariate Time Series Prediction Techniques for Emulating Noah-LSM Soil Moisture Outputs.
Mitchell Dodson, MSFC, Huntsville, AL; University of Alabama in Huntsville, Huntsville, AL

Recorded Presentation
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
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