It looks interesting. It's different. It's clearly able to find patterns linking what was happening to what will happen in some ways better than our current phy

It looks interesting. It's different. It's clearly able to find patterns linking what was happening to what will happen in some ways better than our current physics-based modelling, which is really neat. That's because it has been trained on what the real world actually does, rather than on what our physics models (which are incomplete) say it should do. I think there's definitely a place for this system in our forecasting, and I think it'll sit alongside the current physics-based systems. Forecasters regularly look at what multiple different models say, to get a feel for the level of uncertainty, and they'll temper that with experience about what sort of circumstances certain models are better than others in, so this is another one to add to the list. It appears that this model is better at predicting certain extreme events, so it is likely that a forecaster will pay special attention to it for that in particular. The system does have some problems. As mentioned in the article, it is a black box, so we can't look at what it has worked out and see why it differs from our physics models. It doesn't build an internal physical model of the atmosphere, so it may not be able to forecast as far into the future as a physics based model. It also seems limited in scope - it makes one particular type of forecast quite well, but not others such as local forecasts (limited area higher resolution). What might be very interesting is to see if this system can be integrated into a physics-model-based forecasting system. At the moment, the local models get extra data from the global models, which helps them know what weather is going to blow in through the boundaries of the local model. If this system can improve the global model, then that might be able to help the local models, even if the system isn't good at doing local forecasts itself. Weather forecasting has for a long time been a mixture of methods, usually depending on the range of the forecast. If you want to know if it's going to rain in the next five minutes, looking out the window is more accurate than going to the forecast. Within the next few hours, a very simple model that just looks at the weather radar and the wind direction to predict where the rain will fall is more accurate than a physics model (but less accurate for the next five minutes than looking out the window) - that's called "nowcasting". So it may be that this new system can slot in somewhere in-between nowcasting and physics-based forecasting. I think it'll be a very interesting development over the next few years. I think it's particularly interesting that the system uses so little compute time, which implies to me that maybe it could be made even better with more resources dedicated to it. I'm not in this field of study any more, but I'll be watching the news.