Seasonal forecasting using the GenCast probabilistic machine learning model | JoVE Visualize

Area of Science:

Earth and Climate Science
Artificial Intelligence in Meteorology
Atmospheric Physics

Background:

Machine-learnt weather prediction (MLWP) models rival conventional numerical weather prediction (NWP) in medium-range forecasts.
Extending MLWP performance to longer timescales, crucial for seasonal forecasting, remains an area of active research.
Interactions with slower Earth system components are vital for seasonal climate prediction.

Purpose of the Study:

To evaluate the efficacy of the GenCast MLWP model for seasonal forecasting using prescribed and observed sea surface temperatures (SST).
To compare GenCast's seasonal forecasting performance against the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system, SEAS5.
To assess the potential of MLWP models for comprehensive seasonal forecasting, including coupled Earth system components.

Main Methods:

GenCast, a probabilistic MLWP model, was adapted for seasonal forecasting in two configurations: GenCast-Persisted (climatological SST anomalies) and GenCast-Forced (observed SSTs).
Forecasts were generated and compared with SEAS5, focusing on precipitation, 2-meter temperature, mean sea level pressure (MSLP), and the North Atlantic Oscillation (NAO) index.
Reliability diagrams were used to assess forecast skill relative to climatology and SEAS5.

Main Results:

GenCast-Persisted accurately captured precipitation patterns associated with El Niño and La Niña events, with GenCast-Forced correcting some erroneous patterns.
Ensemble-based uncertainty in precipitation forecasts from GenCast compared favorably with SEAS5.
While SEAS5 excelled in tropical 2-meter temperature and MSLP, GenCast-Persisted showed higher skill in higher latitudes and mountainous regions for MSLP, correlating better with the NAO index.

Conclusions:

GenCast-Forced demonstrated forecast reliability comparable to SEAS5, indicating the potential of MLWP for seasonal forecasting.
GenCast-Persisted showed limited skill relative to climatology, highlighting the importance of accurate sea surface temperature forcing for seasonal predictions.
These findings suggest that MLWP models, when coupled with other Earth system components, could significantly advance seasonal forecasting capabilities.