Primary Convener:
Christine Bassett, FedWriters

Convener:
Mark Olsen, NASA GSFC
Nachiketa Acharya, Pennsylvania State University
Johnna Infanti, Climate Prediction Center College Park

Early Career Convener:
Marybeth Arcodia, Colorado State University

Chair:
Christine Bassett, NOAA
Mark Olsen, NASA GSFC
Nachiketa Acharya, Pennsylvania State University
Johnna Infanti, Climate Prediction Center College Park
Marybeth Arcodia, Colorado State University

Timely and skillful subseasonal-to-seasonal (S2S) predictions are a critical tool for reducing economic risk and improving decision making across sectors such as energy, agriculture, and transportation. Additionally, industries rely on dependable S2S guidance to inform decisions on supply chains, logistics, and resource allocation. Artificial intelligence (AI) and machine learning (ML) are increasingly being explored to improve forecast skill by correcting systematic biases, enhancing model calibration, and sharpening representations of key drivers like the Madden-Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO). This session invites contributions that apply AI/ML to bias correction, hybrid modeling, and process diagnostics—especially where methods demonstrate operational potential or measurable gains in skill. We also welcome work integrating AI tools with traditional modeling frameworks to improve efficiency and decision support. We particularly encourage submissions that show how AI-driven innovations can strengthen model reliability, reduce costs, and support the delivery of actionable forecasts across public and private sectors.

Index Terms
1622 Earth system modeling
3238 Prediction

Neighborhoods:
3. Earth Covering