The weather and climate science AI revolution isn’t revolutionary - Ars Technica
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It feels like there’s no escaping AI right now, whether you’re trying to type a sentence without being interrupted by a digital “assistant” or struggling to find a new refrigerator that doesn’t require a Wi-Fi connection for some reason. You’d be forgiven for wondering if we’re in the midst of a quantum leap in tech or whether people are just hyping up a heap of slop.
So what should we make of the growing use of AI in weather and climate modeling?
The conversation didn’t get off to a great start earlier this year when a National Weather Service office posted a forecast map featuring nonexistent cities in Idaho with names like “Whata Bod” and “Orangeotild.” Thankfully, that was just an AI-generated image produced for social media, not the actual forecast model. Meteorologists and climate scientists are not yet being replaced by large language model prompt engineers.
But AI is being used in these fields through techniques that researchers have studied for years and whose strengths and weaknesses are well understood. And for good reason, those techniques differ between weather and climate simulation models.
ML, not LLM
In all these models, “AI” refers to machine learning. Without diving into the technical details of the many variations of machine learning, the idea is straightforward: using computers to identify patterns in data.
Fitting a straight trend line to data, known as linear regression, is a very simple way to identify a pattern. And we can do regressions with more complicated curves and equations as well. The power (and potential pitfall) of machine learning is that an algorithm can handle much higher levels of complexity, picking out relationships we would have a tough time putting a finger on manually.
Machine learning starts with training a model from scratch. The model is assigned some structure—like a neural network—giving us a number of knobs that can be independently tweaked to fine-tune the algorithm’s behavior. It is given a huge pile of example data, often with the answer attached, such as thousands of bird photos labeled by species. The model then iteratively determines the best set of knob values to connect the photo’s contents to the correct species.
Some limitations should be obvious. This algorithm won’t identify a species it wasn’t trained on or any subpopulations of species that differ too much from the example. The quality of the training data matters a lot, too. If we only use photos of chickadees in pine trees, the model could include pine needles in its definition of chickadee-ness.
Without a lot of extra work, we may not know how the model arrives at its answers. The internal mechanisms are pretty much a black box most of the time.
The upside is real, though. Machine learning algorithms often outperform our best human-crafted algorithms, at least in terms of computational efficiency, if not also accuracy. They just have to be used properly, or the limitations will show.
Cloud computing
For weather forecast models, the process isn’t too different from our bird identification example, but the models are trained on two sets of weather data obtained a short time apart.
Because they aren’t solving lots of physics equations in every location, these models run far more quickly than traditional weather models.
A number of companies, including Google, Nvidia, Huawei, and Microsoft, have developed initial models—sometimes in collaboration with independent academics—that could compare favorably to the forecast models we currently use. Once we began to understand where the models excel and struggle, some of the major weather forecast centers started developing their own.
The European Centre for Medium-Range Weather Forecasts (ECMWF) put its first machine-learning-based model into service in February 2025, running it alongside its long-standing Integrated Forecasting System (IFS) model.
The AIFS model is trained using a reanalysis—a dataset built by taking all available weather observations and filling out a physically consistent picture where we don’t have measurements. This critical tool greatly simplifies the machine learning task of predicting the next global snapshot (six hours ahead) based on previous snapshots.
Each snapshot contains information on temperature, air pressure, wind, water vapor, cloud cover, precipitation, solar radiation, and soil moisture. Instead of applying the physics connecting any of those things, the model simply distills the...