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, it seems like AI is everywhere. 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.
Machine Learning: The Power of Pattern Recognition
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.
Weather Forecast Models: A New Era in Efficiency
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.
Climate Simulation Models: A Different Story
Climate simulation models are a different story altogether. They require a physically consistent picture of the past, which is built by taking all available weather observations and filling out gaps with reanalysis. 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 spatial patterns through which they’ve changed in the past.
The Payoff: Efficiency and Accuracy
The payoff for these machine learning models is that they absolutely clean up on computational efficiency. ECMWF says a forecast run of the IFS uses about 1,000 times as much energy as a run of the AIFS and requires about 30 minutes versus three.
Conclusion
While AI is being used in weather and climate modeling, its impact may not be as revolutionary as claimed. Machine learning models excel at pattern recognition, but their limitations should be well understood. By separating fact from hype, we can better appreciate the potential of these new tools and work towards improving them.
Source: Original article