This story is part of “How Does It Work?” — The Better India’s series that takes the everyday things you see, use and rely on, and uncovers the clever technology, design and sustainable thinking that makes them work better. From the objects in your home to the systems around you, we break down the ideas behind them in simple, easy-to-understand language. Because innovation isn’t always about futuristic machines or complicated technology. Sometimes, it’s hiding in the things we use every day.
Look at a satellite image of a cyclone, and you will see enormous bands of clouds spiralling over the ocean. What looks like a swirling mass of white clouds is actually a complex weather system, powered by warm seawater and shaped by winds moving thousands of metres above the Earth.
Predicting where this storm will travel, how strong it will become, and whether it will make landfall is no small feat. Yet, today, satellites and artificial intelligence (AI) are helping scientists make these forecasts faster and more accurately, giving communities valuable time to prepare.
In India, the India Meteorological Department (IMD) uses satellites such as INSAT-3D and INSAT-3DR to monitor cyclones. In 2025, satellite imagery helped track weather systems, including Cyclone Montha, which crossed the Andhra Pradesh coast as a severe cyclonic storm.
But how do machines in space and AI models on Earth work together to forecast a storm that has not yet reached the coast?
Step 1: Satellites keep an eye on the ocean
The first step is observation.
Geostationary weather satellites orbit approximately 36,000 km above the equator, moving at the same rate as the Earth’s rotation. This allows them to continuously observe the same broad region, capturing changes in cloud formations and atmospheric conditions.
India’s INSAT-3D and INSAT-3DR satellites monitor clouds, atmospheric moisture and temperatures to track developing storms. Photograph: (The Week)
India’s INSAT satellites use specialised sensors to capture visible-light and infrared images, as well as information about water vapour in the atmosphere. Unlike ordinary cameras, infrared sensors can measure temperatures even at night, helping scientists identify the cold, high cloud tops associated with powerful thunderstorms.
Scientists look for clues such as clusters of thunderstorms, curved cloud bands and the beginnings of a rotating system. Other satellite instruments measure ocean temperatures, rainfall and wind-related conditions, helping meteorologists understand whether the environment is becoming favourable for a cyclone.
Think of it as watching a storm’s earliest movements from above, even when it is far out at sea and beyond the view of coastal observers.
Step 2: AI learns to recognise weather patterns
Collecting data is only half the job. Scientists must also work out what it means for the storm’s future.
This is where AI comes in.
AI weather models are trained using decades of historical weather observations, atmospheric measurements and records of past storms. By studying these examples, they learn relationships between variables such as wind speed, air pressure, humidity and temperature.
For instance, a model may learn that certain combinations of atmospheric conditions and ocean temperatures have previously been associated with a storm strengthening or changing direction.
AI models analyse historical weather data to learn patterns in wind, pressure and temperature. Photograph: (CNET)
It is a little like learning to recognise a familiar pattern in a jigsaw puzzle. The AI compares the current conditions with patterns it has learnt, then estimates how the weather could evolve.
Google DeepMind’s WeatherNext Cyclones is one example. Its model combines global weather information with historical cyclone records to forecast a storm’s path, intensity and wind structure. In 2025, its forecasts helped the US National Hurricane Center anticipate Hurricane Melissa’s rapid intensification and landfall in Jamaica.
Step 3: The AI runs multiple possible futures
Here is where the technology becomes particularly useful.
Weather is a constantly changing system. A small difference in wind direction or atmospheric pressure can alter a cyclone’s eventual path. So, instead of treating one prediction as certain, advanced AI models can generate multiple possible scenarios.
This technique is called ensemble forecasting.
Imagine a cyclone moving towards the eastern coast of India. One forecast might show it turning north, another might suggest it moving towards the northwest, while others may predict different speeds or intensities.
Ensemble forecasting generates multiple possible storm tracks, helping meteorologists assess uncertainty. Photograph: (FOX Weather)
By comparing these possible tracks, meteorologists can identify areas that are more likely to be affected and understand where uncertainty remains.
WeatherNext Cyclones can generate 1,000 possible scenarios in less than a minute on specialised computing hardware. Its forecasts can extend up to 15 days ahead, although uncertainty generally increases the further into the future a prediction looks.
Step 4: Forecasters turn predictions into warnings
AI does not replace meteorologists. Instead, its predictions complement satellite observations, conventional weather models and expert analysis.
Forecasters assess the available evidence before issuing official warnings about a cyclone’s likely track, strength, rainfall and landfall. These warnings help authorities plan evacuations, fishermen return to shore, and emergency teams prepare shelters and supplies.
The goal is not to predict every movement perfectly. It is to reduce uncertainty early enough for people to act.
Early warnings help coastal communities prepare, fishermen return to shore and authorities organise evacuations. Photograph: (facebook/@TheMech Mind)
As satellites collect more detailed observations and AI models become better at interpreting them, cyclone forecasting continues to improve. Every additional hour of reliable warning can give vulnerable communities more time to protect homes, secure essential supplies and move to safety.
A cyclone may be impossible to control, but understanding its likely path is becoming easier. And that growing ability to see a storm coming is one of the most important ways technology can help save lives.
