Climate Under Control: How AI Predicts the Weather

Until recently, forecasters relied on vast amounts of data from meteorological satellites, ground stations, weather radars, and other sources (e.g., ocean buoys, radiosondes), which is received in real-time.

This is challenging due to the complexity and chaotic nature of our atmosphere itself, as well as the lack of precise information and imperfections in the models. But today, we stand on the threshold of a quiet revolution in meteorology — artificial intelligence is no longer just refining forecasts; it is fundamentally changing the very approach to weather prediction, enhancing accuracy and speeding up the process.

Algorithms Displacing Classical Models

Traditional models, such as the American GFS or the European IFS, are based on physical equations of atmospheric dynamics (this is a system of mathematical equations that describe the movement of air in the atmosphere and predict its future state, taking into account factors such as pressure, temperature, and the Coriolis force). They are grounded in fundamental laws of physics and are used not only for weather forecasting but also for climate study and understanding weather phenomena. Their calculation requires supercomputing power and takes entire hours. That is, scientists-meteorologists try to understand and simulate all physical processes in the atmosphere by writing them down as equations. The computer solves these equations to find out what will happen next.

AI works differently: it looks for patterns and correlations in giant arrays of historical data. It acts like an experienced observer who, by looking at trillions of "before" and "after" snapshots, recognizes interconnections. For instance, when the weather map looked a certain specific way, then after a certain number of hours it almost always became another specific way. Neural networks train on enormous arrays of historical weather data, identifying hidden patterns and connections that escape the notice of scientists working with classical methods.

FourCastNet from NVIDIA is one of the first models that proved AI could not just supplement but completely replace physical calculations. It forecasts key atmospheric variables with accuracy comparable to the best global models, but does so 45,000 times faster. Where a supercomputer requires 6 hours, FourCastNet produces a global forecast in half a second. This allows not just accelerating the process, but also running numerous simulations with different initial conditions, creating highly precise ensemble forecasts.

Technology, Speed, Accuracy

The main breakthrough of AI lies in forecasting extreme phenomena with high spatiotemporal detail. The technology operates based on two key architectures:

Convolutional Neural Networks (CNN): Analyze spatial data, for example, satellite images, identifying nascent cloud clusters that may turn into a thunderstorm front.

Recurrent Neural Networks (RNN) and their advanced versions (e.g., LSTM, Transformers):Process time series, predicting the evolution of a weather system — where it is moving and at what speed.

The Chinese model NowcastingNet, developed within the FengWu project, is capable of predicting the trajectory and intensity of a rainfall with a resolution of 1 km for a period from 30 minutes to 6 hours. The algorithm analyzes current radar and satellite data and "completes" the future development of events, showing exactly how the rain cloud will spread across city blocks. The accuracy of such short-term forecasts for megacities already exceeds 90%, which is unattainable for classical models.

Impact on Infrastructure and Insurance

The emergence of ultra-precise forecasts is changing entire industries, turning weather from a controlled force into a manageable, albeit natural, factor.

Urban Infrastructure: In Japan, in the city of Fujisawa, a system based on AI predicts peak loads on the storm sewer in advance. This allows for the remote opening of control gates and redirecting water flows, preventing flooding. Similar solutions are being tested in the Netherlands for managing locks and dams. In Singapore, where downpours cause sudden floods, the national water agency PUB uses an AI model combined with machine learning to forecast street flooding with up to 30 minutes lead time, automatically activating pumping stations and alerting drivers through applications.

Insurance: European insurance companies like Swiss Re and Allianz already use AI forecasts for parametric insurance. The policy triggers automatically when the model predicts and records wind speeds above 25 m/s or precipitation exceeding 50 mm/hour in a specific geolocation. This eliminates the need for expert damage assessment and accelerates due payouts from long months to literally a few hours. In India, this technology was adapted for small farmers by the American company Arbol, which uses satellite data and AI for automatic payouts during drought in a specific district, something previously impossible due to the high cost of on-site damage assessment.

Logistics and Energy: For example, air traffic controllers at Frankfurt Airport already receive wind forecasts at ground level with an accuracy of up to 500 meters, allowing for the optimization of runway usage. Energy companies in Germany use cloud cover and wind forecasts to balance grid load, knowing when solar power stations will sharply decrease or increase generation. In Denmark, the national operator Energinet uses AI forecasts to manage an energy system with a high share of wind power generation, achieving record levels of "green" energy integration. In the USA, the railway giant BNSF Railway uses AI forecasts of rail temperature and the probability of snow drifts for the preemptive dispatch of snow removal equipment to critical sections of transcontinental routes, ensuring the uninterrupted flow of freight.

The Superpower Race for Weather

The implementation of AI-meteorology has become a matter of national security and economic advantage. Thus, the European Centre for Medium-Range Weather Forecasts (ECMWF), the creator of the world's most accurate IFS model, is actively developing the AIFS (Artificial Intelligence Forecasting System) project. This is a hybrid system where AI is used to quickly create a first version of the forecast, which is then refined using the physical model. It is also known that Météo-France is testing a system based on transformers for forecasting sudden floods on the Côte d'Azur.

The China Meteorological Administration (CMA), in turn, has been using its own FengWu model for operational forecasting since 2023. Chinese scientists claim that in terms of predicting typhoon trajectories, their system has already surpassed European counterparts.

The American approach is characterized by close cooperation between government giants and the private technology sector, especially with Silicon Valley companies. In 2023, the National Oceanic and Atmospheric Administration (NOAA) officially announced a partnership with Google DeepMindand NVIDIA. Within the "Earth Futures AI" project, a hybrid model is being tested that combines data from the new GOES-R series of satellites with deep learning algorithms. The key goal is creating a "digital twin" of the Atlantic Ocean for predicting hurricane intensity. Classical models forecast trajectory well but often make mistakes regarding rapid intensification of storms.

The Japan Meteorological Agency has implemented AI for forecasting "rain bombs" — sudden extreme precipitation characteristic of the local climate. The system analyzes humidity, sea surface temperature, and wind currents, warning of the threat 30-60 minutes in advance.

Can We Trust AI During Cataclysms

Although AI models handle short-term forecasts and predictions of medium-strength extreme phenomena, their behavior during unprecedented cataclysms still requires refinement and adjustment. The problem lies in the data: neural networks are trained on the past, and if a situation arises for which there is no historical analogue, the model may fail.

Therefore, the future lies not in choosing between traditional physical models and AI, but in their symbiosis. For example, when forecasting a hurricane, the physical model can calculate the overall trajectory and intensity, while the AI algorithm, working thousands of times faster, will specify exactly which coastal areas will receive the highest storm surge and where the most destructive wind gusts will be.

In the near future, weather will cease to be an abstract phenomenon, becoming a highly detailed dataset embedded into a city's digital model. Climate risk management is transforming from reactive response into proactive design, where AI serves as that very "digital twin" of the atmosphere, allowing us to peer into the future with the precision of a city block and a single minute.