Weather forecasts have become much more accurate

Hannah Ritchie, writing at Our World in Data:

The first big change is that the data has improved. More extensive and higher-resolution observations can be used as inputs into the weather models. This is because we have more and better satellite data, and because land-based stations are covering many more areas around the globe, and at a higher density. The precision of these instruments has improved, too.

These observations are then fed into numerical prediction models to forecast the weather. That brings us to the next two developments. The computers on which these models are run have gotten much faster. Faster speeds are crucial: the Met Office now chunks the world into grids of smaller and smaller squares. While they once modeled the world in 90-kilometer-wide squares, they are now down to a grid of 1.5-kilometer squares. That means many more calculations need to be run to get this high-resolution map. The methods to turn the observations into model outputs have also improved. We’ve gone from very simple visions of the world to methods that can capture the complexity of these systems in detail.

As someone with two “armchair meterologists” in my life, I found this article fascinating. There’s so many extensions of this idea of more data capture and faster computing leading to increasingly accurate predictions at scale. One that comes to mind for me is crash detection in cars. It’s a stepping stone to the somewhat inevitable conclusion of full self-driving capability. The stones in-between are simply more data and better compute power. And oh, by-the-way, your vehicle will know if it’s going to rain and adjust for that too.