The weather predictions have experienced a remarkable evolution in recent decades thanks to the combination of new technologies, mathematical models and the incorporation of artificial intelligence (IA). While forecasts originally relied on direct observation of patterns in the sky and accumulated experience, digitalization and the massive handling of data have transformed the way we anticipate the weather.
Currently, the accuracy in forecasts It's essential not only for those who decide whether to go out with an umbrella, but also for sectors such as agriculture, land, sea, and air transportation, disaster prevention, and even public health. Mathematical models make it possible to predict the evolution of temperature, humidity, pressure, precipitation, and wind with greater reliability in different regions and time scales.
Artificial intelligence expands the capabilities of meteorology
The arrival of artificial intelligence represents a giant leap in weather forecasting. Organizations like Météo-France and companies like Google have implemented systems that learn from massive data sets to produce faster and more detailed predictionsFor example, platforms like Weather Lab Google DeepMind's can now anticipate the formation, trajectory, and intensity of cyclones, generating dozens of possible scenarios up to two weeks in advance.
These AI models They stand out for their speed and efficiency, as they require fewer computational resources than traditional ones. However, they depend heavily on the quality and variety of the training data. A key aspect is that, while they show enormous potential for anticipating major phenomena—such as cyclones or intense storms—they struggle to predict exceptional or previously unrecorded events. AI, by relying on past patterns, can struggle when unprecedented conditions arise in the atmosphere.
For this reason, many meteorological agencies They are betting on a hybrid approach, combining physical models (which use the laws of atmospheric physics) with artificial intelligence tools. This collaboration improves both the speed and accuracy of predictions, especially in climate change scenarios where previously unseen behaviors may emerge.
New stations and global networks to improve predictions
Along with the digital revolution, the expansion of meteorological station networks in strategic locations is helping to expand the coverage and quality of the data collected. One of the most recent advances is the installation of new stations in hard-to-reach areas, such as the Mount Aconcagua in Argentina, or Mount Everest. These facilities allow real-time data collection on parameters such as temperature, solar radiation, pressure, and wind, even at altitudes above 6.500 meters.
The information collected by these stations, often in collaboration with international organizations, is transmitted via satellite to global monitoring networks. This improves the understanding of local and global phenomena, such as glacier dynamics, ice mass responses to warming, and the evolution of water resources. Furthermore, the data feeds into predictive models and increases the safety of expeditions and mountain dwellers.
Initiatives such as the Wayra project They show how international cooperation, science, and technology come together to better anticipate extreme events and their impact on water supply and disaster management.
Limitations and challenges in modern weather forecasting
Despite the progress, Predicting the weather remains a challenge due to the chaotic nature of the atmosphereExperts from the State Meteorological Agency point out that it is difficult to offer reliable deterministic forecasts beyond 4-6 days, and probabilistic forecasts rarely exceed 15 days with any guarantee. In regions like Asturias, the weather can change considerably in a very short period of time, and it is prudent to prepare for any scenario.
Artificial intelligence models have proven to be accurate in common situations, but When faced with unprecedented phenomena, such as hurricanes of a category never before seen in training data, they can fail in their forecast. Therefore, human interpretation and the experience of meteorologists remain irreplaceable in identifying risks and adapting warnings to each specific situation.
Hybrid approaches are being developed that incorporate both the physical dynamics of the atmosphere and AI's ability to recognize patterns. The future points to even more robust models, capable of better anticipating extreme events and managing the uncertainty inherent in weather forecasting.
Applications and benefits of increasingly accurate forecasts
La improvement in weather forecasting It has direct effects on daily life and medium- and long-term planning. Agriculture depends on knowing when rain or frost will occur in order to plan harvests. Air and sea transport benefit from anticipating storms or strong winds. Emergency management is more efficient when hurricanes or floods can be predicted early enough to evacuate or protect infrastructure. Even public health benefits from anticipating episodes of extreme heat or pollution.
International collaboration, investment in technology, and the integration of ever-increasing data from satellites, ground stations, and advanced models mean that the margin of error in forecasts continues to narrow year after year. However, the unpredictable nature of the atmosphere and the challenge of extreme events require constant innovation and the integration of human knowledge with the capabilities of artificial intelligence.
In this way, both scientists and citizens have increasingly powerful tools to anticipate changes in the weather, better understand the climate, and adapt to future challenges.
