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. Weather forecasting is something we rely on so we can not only know what to expect on a daily basis but also if extreme weather events were to happen.
The role of AI in weather forecasting is still an evolving concept, but it’s significantly impacted how meteorologists collect and analyze data.
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 develop conclusions. AI has many components, the main two 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 weather conditions.
A subset of ML is deep learning. It’s still a learning algorithm, but it incorporates many more layers, making the acquisition of information 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.
Key Techniques in AI Weather Prediction
There are many things that make AI weather prediction possible. Each one contributes to overall weather forecasting in its own way.
Machine Learning Algorithms
There can’t be AI weather predictions without machine learning. ML algorithms are detailed, and we can break them down into three main categories:
- Supervised Learning: An approach that involves training an algorithm on a labeled dataset. In weather forecasting, this ML type helps predict future weather conditions based on past weather data.
- Unsupervised Learning: This approach is where the algorithm is given weather data but doesn’t have specific instructions. It has to find all the relationships and patterns within the data, whether relevant or not. It typically uses a clustering technique. This learning is excellent for seeing anomalies in weather patterns.
- Reinforcement Learning: This is a technique that requires people to train the algorithm with user feedback and trail and error so it can improve its performance. Its good fro optimizing weather models and refining predictions.
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.
Data assimilation is vital for providing the most up-to-date weather. It helps update the prediction models as new data arrives.
Ensemble Forecasting
Weather prediction isn’t an exact science, but ensemble forecasting helps make predictions as accurate as possible. Ensemble forecasting is when multiple forecasts are generated, but they all use different models and initial weather conditions.
This weather forecasting approach is helpful in determining variability and uncertainty in weather predictions. Meteorologists use these ensemble 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 weather predictions are reliable. Humans use XAI to trace the path that leads to AI producing whatever result. It helps them understand how it came to its conclusion so they can better convey weather predictions.
Applications of AI in Weather Prediction
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 very short-term weather predictions. 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 tornados. 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 weather 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 help 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
Extreme weather events can pose huge threats, so accurately predicting different severe weather events is vital to people’s safety. While certain extreme weather events can’t be 100% predicted, like tornados, AI can still predict weather that offers the right conditions for tornadoes to form based on atmospheric and historical weather data.
Hurricanes or tropical cyclones are much easier to see coming. Human forecasters can see when they start to form and generally where they’ll be going, giving people ample time to prepare or evacuate if necessary.
This type of AI weather forecasting is vital because even though there are specific times of year when tropical cyclones occur, it’s hard to predict their path as the storms get closer to land.
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 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 extreme weather.
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
Many believe the advantages of AI weather forecasting greatly outweigh the technology’s challenges. 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
Working with AI weather models benefits meteorologists and everyone in many ways. 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.
The Future of AI in Weather Forecasting
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 more accurate 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.
