Improvements to Subseasonal-to Seasonal and Seasonal-to-Decadal (S2S/S2D) Predictions using Novel Statistical, Machine and Deep Learning Methods Poster Session

Monday, 13 January 2025: 3:00 PM-4:30 PM

Hall C (New Orleans Ernest N. Morial Convention Center)

Host: 24th Conference on Artificial Intelligence for Environmental Science

Papers:

131

The Use of Observations in Learning Optimal Masks for California Temperature Prediction

McKenna Lee Eichenauer, Purdue University, Westfield, IN; Purdue University, West Lafayette, IN; and M. Fernandez, J. Landsberg, and E. A. Barnes

132

Subseasonal-to-Seasonal Analog Forecasting Using Machine Learning and Uncertainty Quantification

Jacob Landsberg, Colorado State Univ., Fort Collins, CO; and E. A. Barnes, M. Fernandez, and M. Newman

133

Probabilistic Subseasonal-to-Seasonal Prediction of Precipitation in E3SM

Jolan von Plutzner, Colorado State University, Fort Collins, CO

134

"Am I Confused or Is This Confusing?": Deep Uncertainty Quantification of North Pacific SST Variability

Devin M McAfee, Colorado State University, Fort Collins, CO; and E. A. Barnes

135

Predicting and Interpreting Atmospheric Blocking Using Convolutional Neural Networks and Explainable-AI Techniques

William Stevens, Purdue University, West Lafayette, IN; and Y. Hu, A. N. Nhu, H. Peng, Y. Zhang, and L. Wang

Integrating Chaotic Dynamics and Machine Learning for Medium-term Drought Prediction

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