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.
<small>Source: Ars Technica</small>
