Session 9A Improvements to Subseasonal-to-Seasonal (S2S) Predictions Using Novel Statistical and Artificial Intelligence/Machine Learning (AI/ML) Methods I

Wednesday, 31 January 2024: 8:30 AM-10:00 AM
345/346 (The Baltimore Convention Center)
Host: 23rd Conference on Artificial Intelligence for Environmental Science
Submitters:
Nachiketa Acharya and Johnna Infanti
Cochairs:
Marybeth Arcodia, University of Miami RSMAS, Atmospheric Science, Miami, FL; Maria J. Molina; Johnna Infanti, NOAA / NWS / NCEP / Climate Prediction Center, College Park, MD and Nachiketa Acharya

Subseasonal-to-Seasonal (S2S) forecasting (between two weeks and a season ahead) is a rapidly developing area of forecasting, with the potential to provide valuable information for the development of climate services. Although S2S climate predictions have a comparative lack of skill beyond two-week lead times, over the past decade there has been a substantial research effort to improve prediction skill via novel advanced statistical and Artificial Intelligence/Machine Learning (AI/ML) methods either in terms of post-processing of the dynamical model output or data-driven models based on teleconnections. Additionally, there is a strong interest in understanding predictability on S2S scales using eXplainable Artificial Intelligence (XAI) which could help to improve forecast skill.

This session welcomes all aspects of improving forecasting on S2S scales including advanced statistical and Artificial Intelligence/Machine Learning (AI/ML) based post-processing (bias correction, multi-model ensemble) of the dynamical model output, and ML models based on teleconnections (empirical/data driven). Abstracts that explore XAI for predictability are also encouraged.

Papers:
8:30 AM
9A.1
When Machine Learning Objectives Compete for Improved Subseasonal Bias Correction, Who Wins?
Maria J. Molina, University of Maryland, College Park, MD; and K. Dagon, J. Schreck, J. S. Perez-Carrasquilla, K. J. Mayer, N. Sobhani, D. J. Gagne II, Ph.D., I. Ebert-Uphoff, C. Metzler, and G. A. Meehl

8:45 AM
9A.2
Development of an Improved Week 3-4 Temperature Consolidation First Guess
Cory F. Baggett, CPC, College Park, MD; and E. Burrows, D. Barandiaran, E. LaJoie, D. C. Collins, M. Goss, J. Infanti, J. Hicks, E. Oswald, and J. Gottschalck

9:00 AM
9A.3
Sea Surface Salinity Provides Subseasonal Predictability for Forecasts of Opportunity of U.S. Summertime Precipitation
Marybeth Arcodia, Colorado State University, Fort Collins, CO; and E. A. Barnes, P. J. Durack, P. Keys, and J. Rocha

9:15 AM
9A.4
Applying an Inherently Interpretable Neural Network to Subseasonal-to-Seasonal Climate Prediction
Nicolas J Gordillo, Colorado State Univ., Fort Collins, CO; and E. A. Barnes

9:30 AM
9A.5
Utilizing Machine Learning for Constructing Probabilistic Subseasonal Precipitation Forecasts in California
Nachiketa Acharya, NOAA, State College, CO; and K. J. C. Hall

9:45 AM
9A.6
Leveraging Interpretable Machine Learning Methods for Subseasonal Precipitation Forecasts in Western United States
Agniv Sengupta, SIO, La Jolla, CA; and M. J. DeFlorio, I. Yang, Z. Yang, J. L. Bano Medina, B. Guan, and L. Delle Monache

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