The challenge of Weather Forecasting: how Artificial Intelligence faces unpredictability

March is known for its unpredictable weather, often described as "crazy". This month marks the transition from winter to spring, with sunny days but also unexpected rain and temperature fluctuations. In this article, we seek to understand why these sudden changes occur and how artificial intelligence (AI) is trying to tackle the challenge of predicting them.
The change of season in March brings with it clmatic uncertainty. Cold air currents clash with warm air masses, making accurate weather forecasting difficult. Spring sunshine can give way to rain and snow.i
Weather forecasts are based on complex models that take into account variables such as atmospheric pressure, temperature, and wind direction. However, due to the chaotic nature of the climate, accurately predicting the weather, especially during seasonal changes like March, is very difficult.

Artificial intelligence is emerging as a valuable ally in the challenge of weather forecasting. Sophisticated algorithms can analyze large amounts of historical weather data, gather real-time information, and use predictive models to improve forecast accuracy. The ability of AI to adapt to complex models and learn from data makes it a powerful resource for addressing meteorological variability.

• Neural networks: artificial neural networks can be trained to recognize complex weather patterns and contribute to improving short-term forecast accuracy.
• Big data analysis: artificial intelligence can process huge series of meteorological data and identify correlations and patterns that cannot be found with traditional approaches.

Of course, these are not the only algorithms used, but they are certainly the ones that are best suited to handle large amounts of data and unknown patterns.

The weather forecasting market has grown significantly in recent years (just to give an idea of its size, it is around $2.5 billion) thanks to the evolution of technologies that provide the databases for forecasting analysis, such as DeepMC models that provide hour-by-hour temperature updates. Satellites are also evolving, collecting increasingly precise data, and companies like DeepMind and Earth Networks are developing increasingly advanced tools to visualize meteorological data seamlessly.
With these innovations, how long will it take to accurately tell us whether to go out with an umbrella or sunglasses (or both) even in March? The accumulation of data and "experience" of the models will increasingly help to have more accurate forecasts, but certainly not perfect; for a while longer, we will have to accept living with models capable of providing probabilistic results.