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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 4: AAAI-22 Technical Tracks 4

Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration

February 1, 2023

Authors

Sijie He

Department of Computer Science & Engineering, University of Minnesota, Twin Cities Department of Computer Science, University of Illinois Urbana-Champaign


Xinyan Li

Department of Computer Science & Engineering, University of Minnesota, Twin Cities


Laurie Trenary

Department of Atmospheric, Oceanic, and Earth Science, George Mason University


Benjamin A Cash

Department of Atmospheric, Oceanic, and Earth Science, George Mason University


Timothy DelSole

Department of Atmospheric, Oceanic, and Earth Science, George Mason University


Arindam Banerjee

Department of Computer Science, University of Illinois Urbana-Champaign


Proceedings:

No. 4: AAAI-22 Technical Tracks 4

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Domain(s) Of Application

Downloads:

Download PDF

Abstract:

Sub-seasonal forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to advance SSF. In this paper, for the first time, we perform a fine-grained comparison of a suite of modern ML models with start-of-the-art physics-based dynamical models from the Subseasonal Experiment (SubX) project for SSF in the western contiguous United States. Additionally, we explore mechanisms to enhance the ML models by using forecasts from dynamical models. Empirical results illustrate that, on average, ML models outperform dynamical models while the ML models tend to generate forecasts with conservative magnitude compared to the SubX models. Further, we illustrate that ML models make forecasting errors under extreme weather conditions, e.g., cold waves due to the polar vortex, highlighting the need for separate models for extreme events. Finally, we show that suitably incorporating dynamical model forecasts as inputs to ML models can substantially improve the forecasting performance of the ML models. The SSF dataset constructed for the work and code for the ML models are released along with the paper for the benefit of the artificial intelligence community.

DOI:

10.1609/aaai.v36i4.20372


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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