EGU24-15586, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Identifying Windows of Opportunity in Deep Learning Weather Models

Daniel Banciu, Jannik Thuemmel, and Bedartha Goswami
Daniel Banciu et al.
  • University of Tübingen, Germany, (

Deep learning-based weather prediction models have gained popularity in recent years and are effective in forecasting weather over short to medium time scales with models such as FourCastNet being competitive with Numerical Weather Prediction models. 
However, on longer timescales, the complexity and interplay of different weather and climate variables leads to increasingly inaccurate predictions. 

Large-scale climate phenomena, such as the active periods of the Madden-Julian Oscillation (MJO), are known to provide higher predictability for longer forecast times.
These so called Windows of Opportunity thus hold promise as strategic tools for enhancing S2S forecasts.

In this work, we evaluate the capability of FourCastNet to represent and utilize the presence of (active) MJO phases.
First, we analyze the correlation between the feature space of FourCastNet and different MJO indices.
We further conduct a comparative analysis of prediction accuracy within the South East Asia region during active and inactive MJO phases.

How to cite: Banciu, D., Thuemmel, J., and Goswami, B.: Identifying Windows of Opportunity in Deep Learning Weather Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15586,, 2024.