Artificial intelligence (AI) is changing almost everything in our world. From helping people be more productive to streamlining business tasks to even meteorology, AI is finding a way to weave itself into our lives. We rely on weather forecasting to know not only what to expect on a daily basis but also if severe weather events might happen.
The role of AI in weather forecasting is still an evolving concept, but it’s significantly impacted how meteorologists collect and analyze data. The European Centre for Medium-Range Weather Forecasts (ECMWF) has launched a competition for meteorologists to develop AI-generated weather forecasts and analyze their performance between August 2025 and September 2026.
By harnessing computer science, meteorologists can improve decision-making and keep billions of people safe from dangerous weather events.
What is Artificial Intelligence (AI)?
AI is the simulation of human intelligence by computers or other machines. This technology uses different learning tools, like machine learning (ML), to learn about human behaviors, acquire information, and then draw conclusions. AI has many components, the two main ones being machine learning (ML) and deep learning forecasting.
Machine learning uses different algorithms that let computers learn and make informed decisions based on data. ML comes into weather forecasting by analyzing historical data. It can examine a large amount of data to identify weather patterns and, therefore, predict the weather.
A subset of ML is deep learning. It’s still a learning algorithm, but it incorporates many more layers, making information acquisition and data analysis deeper than ML alone. Deep learning can see complex relationships within the data to provide more accurate and data-driven predictions.
How AI Analyzes Weather Data
What makes AI so valuable in weather forecasting is its ability to process a large amount of data accurately and quickly. Weather prediction involves many data points that all come from different sources, so AI technology can process information from several points faster than a human can.
AI analyzes data from satellites, RADAR systems, and weather stations. It uses specific information, such as temperature and atmospheric conditions, to generate possible forecasts.
As the technology processes more information, it will develop over time to provide more accurate predictions based on the data. It will find the patterns and relationships between historical weather data for specific areas to provide a more reliable weather forecast for people.
Data Sources and Quality in AI Weather Forecasting
AI models use many of the same data sources as traditional weather forecasting methods, which include:
- Satellite imagery, including geostationary and polar orbiting, can be used to identify cloud cover.
- Ground-based RADAR is used for short-term, localized storm prediction.
- Weather stations capture data from one specific spot and can be used to harvest historical weather data.
- Ocean buoys gather water temperature data.
- Atmospheric soundings assist in predicting temperatures, humidity, wind speed, and atmospheric pressure.
Commercial aircraft data is a novel data source that is particularly helpful for wind speeds. These are all combined to generate forecast data that can assimilate thousands of weather variables. High-resolution forecasts require granular data, so the more sources that can be analyzed, the more accurate the data and decision-making will be.
While AI has many benefits over traditional models, especially when analyzing large datasets, there are still some technological challenges for AI models. These include:
- Noise: Unwanted or irrelevant information could enter the dataset.
- Gaps: Weather evolves quickly, so some data may not have been captured.
- Inconsistencies: Differences between observational data points can cause inaccurate forecasts.
- Biases: Some statistical models may rely too heavily on past data to determine future weather conditions.
To ensure accurate forecasts, meteorologists must analyze the raw data and compare it to the AI outputs, ensuring that nothing is missed.
Key Techniques in AI Weather Forecasting
Many things make AI prediction possible. Each one contributes to overall weather forecasting in its own way.
Machine Learning Models
There can’t be AI without machine learning. ML algorithms are detailed, and they can be broken down into three main categories:
- Supervised Learning: This process uses training data to teach the model about weather states. In weather forecasting, this ML type helps predict future conditions based on past weather data.
- Unsupervised Learning: This approach involves giving the algorithm weather data but without specific instructions. It typically uses a clustering technique. This learning is excellent for detecting anomalies in weather patterns.
- Reinforcement Learning: This technique requires people to train the algorithm with user feedback, which optimizes the weather models.
Data Assimilation
Data assimilation combines observations with model predictions to reduce uncertainty and increase the chances of accurate forecasts. This approach integrates real-time weather observations from different sources into numerical weather prediction models. It will continue to update and correct any data as new information comes in.
Computational Infrastructure
Weather forecasting involves very large datasets and real-time processing, which means it requires significant computational power.
These AI weather models would not be possible without hardware advancements, mainly more robust GPU. Google research teams also developed Tensor Processing Units (TPUs), which are custom-designed for AI products and can be used as a scalable resource in Google Cloud.
AI forecasting runs on complex models that require a lot of energy, which has led to concerns about energy consumption and its influence on the climate. However, companies like Google are working to reduce these impacts. The company’s most recent TPU, Trillium, uses 67% less energy than older models while still providing numerous benefits over traditional weather forecasting.
Ensemble Forecasting
Forecasting isn’t an exact science, but ensemble forecasting helps make predictions as accurate as possible. This is when multiple forecasts are generated, but they all use different models and initial weather conditions. Meteorologists use these forecasts to determine the probability of certain weather events.
Explainable AI (XAI)
Explainable AI (XAI) helps make AI predictions easy for humans to understand. XAI helps build trust and transparency with people so they can trust that the predictions are reliable. Humans use XAI to trace the path that leads to AI producing its results, so they can better convey what the AI-generated weather forecasts mean.
Applications of AI in Weather Forecasting
It’s impressive how meteorologists and weather agencies have been able to incorporate AI into their forecasting. They’ve introduced AI into weather forecasting in many different ways to help predict different weather events.
Nowcasting and Short-Term Forecasting
A common type of forecasting that meteorologists use AI for is short-term forecasting and nowcasting. Nowcasting is a very short-term weather prediction. Any prediction up to a few hours ahead of the current time is considered a nowcast.
AI is perfect for nowcasting because it can take high-resolution data from different sources, like satellites and RADARs, and analyze it quickly. This provides us with immediate weather conditions. These forecasts are typically helpful but are even more critical for severe event prediction in real time.
Nowcasting helps with early warning systems, especially for severe thunderstorms and tornadoes. This is vital in assisting people to prepare for bad weather and letting them know when to take cover.
Medium-Range Weather Forecasts and Long-Range Weather Forecasts
Medium-range forecasts are any predictions up to ten days from the reported time. These are great for planning your week, but human forecasters struggle with accuracy even ten days out.
Long-range weather forecasting is weather predictions weeks and months in advance. These are even harder to predict with accuracy because things can change often. AI is essential in helping analyze historical data and create more accurate medium-range forecasts for people.
These tools help predict seasonal weather by looking at patterns and analyzing atmospheric data for certain times of the year. AI is especially helpful because understanding long-range forecasts is vital for the agriculture, disaster management, and energy industries.
Extreme Weather Event Prediction
Predicting severe weather events is vital to protecting humans, animals, and businesses. Though certain events cannot be predicted with total accuracy, AI models still offer advantages over traditional models, particularly for more volatile and fast-moving conditions like tornadoes.
Tornadoes depend heavily on local conditions, such as wind, atmospheric pressure, and precipitation. AI forecasting can pinpoint these with greater accuracy, allowing people to escape from dangerous conditions.
Hurricanes or tropical cyclones are easier to see coming because the initial conditions that form them are better understood. Human forecasters can usually give a general area of their travel so that people can evacuate. However, hurricanes can still take surprising turns as they near landfall.
AI forecasting can provide more accurate predictions of when and where a storm will hit land. This keeps people safe and prevents unnecessary evacuations that disrupt people’s lives and commercial activities. The better a prediction is, the more people trust it and the more likely they are to take an evacuation notice seriously next time.
It’s hard to tell when other severe weather events, like heatwaves and floods, will occur, but AI can analyze the data over the last several years to see when they’re most likely to occur, so we can be better prepared. More accurate and improved forecasting from AI allows for safer preparation, mitigation strategies, and resource allocation.
Climate Change Modeling and Prediction
Beyond predicting weather forecasts, AI is helpful in climate modeling and prediction. It’s capable of analyzing climate data to better predict long-term climate change and the impact that it can have. AI can provide insight into potential changes by understanding how climate change can impact people.
AI technology can help us understand the impacts of climate change and strategize how to handle its effects, such as more severe weather. This way, individuals and governments can prepare for climate change and take precautions, such as fortifying dams or updating building codes to accommodate stronger storms.
Benefits and Challenges of AI in Weather Prediction
AI is beneficial in helping develop accurate weather forecasts and more. But even though this technology has a lot of things to appreciate, it’s not without its challenges.
Advantages
Some of the main advantages of an AI weather forecast system in combination with traditional forecasting models are:
- Improved understanding of complex weather patterns
- Increased accuracy of forecasts
- Enhanced prediction of severe weather
- More efficient and faster forecasts
Challenges
The technology is still relatively new in terms of weather forecasting, so using it to produce accurate forecasts quickly isn’t without challenges:
- AI needs high-quality and in-depth data to produce accurate results, and that’s not always available.
- Interpreting complex AI models can be difficult.
- Some AI models unknowingly reflect biases in the data, leading to skewed predictions.
Artificial intelligence is constantly evolving, so the future of AI in weather forecasting is bright. As the technology improves, we’ll continue to see more integration of AI with traditional numerical weather prediction models to give even better predictions.
Combining these prediction methods will only enhance predictions, especially as different AI models launch. We’ll see a considerable shift of physics-aware AI models in the future, combining traditional weather modeling and ML and leveraging high-performance computing and big data.
AI in weather forecasting isn’t going anywhere and will only continue to help us better understand different weather patterns.