How it works
Cracking the code of subseasonal-to-seasonal (S2S) weather predictability presents a completely unique challenge compared to forecasts of up to two weeks. A fundamentally different approach is required
Traditional NWP models predict for up to two weeks by taking the current state of the atmosphere — with parameters like temperature, humidity, pressure, wind speed, and wind direction — and using equations to simulate how these conditions will evolve over time.
The further traditional NWP models are trying to predict into the future, the poorer the forecast quality becomes.
The precision of their predictions begins to blur due to the sheer chaos of atmospheric interactions. This happens due to the growth of random small-scale disturbances, affecting larger and larger – finally – synoptic-scale weather systems. The fascinating chaotic chain of events is affectionately referred to as ‘the butterfly effect’.
In order to solve this challenge, we need to step away from using models which are primarily based on physical equations that drive simulations.
Beyond Weather's AI-model relies on recognizing patterns and correlations in historical data and using those to predict future conditions. It's a fundamentally different approach, focusing more on empirical relationships rather than physical laws.
The growth in climate data and artificial intelligence has paved the way to solve this problem. Artificial intelligence allows us to systematically search for and discover connections in our climate system that can boost predictive power on a longer timescale. By learning from observations from more than 70 years of climate data and predicting from observations directly, we can circumvent the issues of numerical weather predictions.
Traditional NWP models for weather prediction often fall short, largely due to their inability to fully grasp the significance of climate system interplays, known as teleconnections. These teleconnections aren't just trivia; they are critical catalysts driving our weather patterns over extended periods.
Take, for instance, the well-known phenomenon known as ENSO—El Niño/La Niña oscillation. It’s a climate event occurring in the Pacific Ocean’s equatorial region. Here, the water’s temperature either warms (El Niño), cools (La Niña), or holds steady. And this has an astounding ripple effect on global weather patterns. Certain areas become more humid, while others experience drier conditions. Traditional NWP models, with their grid-based system, often fail to fully grasp the impact of these phenomena.
AI, specifically machine learning, identifies complex, nonlinear relationships in data that might be missed by traditional statistical methods. Our AI-driven model recognizes patterns and learns from vast amounts of data, making it ideal for handling the complexities of climate data. The model’s ultimate goal is to pinpoint those regions that play a pivotal role in shaping specific outcomes, such as winter temperatures in a place like the Netherlands.