It feels like real weather AI|Forecast|whatever_you_want_to_call_it is still far, far away. Maybe it's just the consumer aspect of weather apps but I don't feel as if I get any more accurate data now than I did back when my parents turned to the daily weather channel for the forecast. Still a lot of clear days when rain was predicted or the even more dreaded torrential downpour when it was supposed to be sunny and clear.
Obviously all I have is anecdata for what I'm mentioning here but from a consumer perspective I don't feel like these model enhancements are really making average folks feel as if weather is any more understood than it was decades ago.
I've found this to be more related to poor representation of the data than inaccurate data.
For example on Apple's Weather app, a "rainy" day means a high chance of rain at any point during the day. If it's 80% chance of rain at 5am and sunny the rest of the day– that counts as rainy. You can see an hourly report for more info, and generally this is pretty accurate. You have to learn how to find the right data, know your local area, and interpret it yourself.
Then you have to consider what effects this has on your plans and it gets more complicated. Finding a window to walk the dog, choosing a day to go sailing, or determining conditions for backcountry skiing all have different requirements and resources. What I'd like AI to do is know my own interests and highlight what the forecast means for me.
In Norway people are extremely weather-focused, and the national weather service delivers quite advanced graphics for people to understand what is going on.
The live weather radar which shows where it is raining right now and prediction/history for rain +/- 90 minutes. This is accurate enough that you can use it to time your walk from the office to the subway and avoid getting wet:
https://www.yr.no/en/map/radar/1-72837/Norway/Oslo/Oslo/Oslo
Then you have more specialised forecasts of course. Dew point, feels like temperature, UV, pollution, avalanche risks, statistics, sea conditions, tides, ... People tend to geek out quite heavily on these.
In my experience, these forecasts are really good 5-7 days out, and then degrade in reliability (as you would expect from predictions of chaotic systems). The apps that show you a rain cloud and a percentage number are always terrible in my experience for some reason, even if the origin of the data is the same. I'm not sure why that might be.
> I don't feel as if I get any more accurate data now than I did back when my parents turned to the daily weather channel for the forecast.
The accuracy improvement is provable. A four-day forecast today is as accurate as a one-day forecast 30 years ago. And this is supremely impressive, because the difficulty of predicting the weather grows exponentially, not linearly, with time.
You are welcome to your feelings - and to be fair, I'm not sure that our understanding of the weather has improved as much as our computational power to extend predictions has.
You're 100% correct, but there's a subtlety in what the commenter is talking about.
Yes, _in aggregate_, forecasts are objectively, quantifiably better in 2025 than they were in 2005 let alone 1985. But any given, specific forecast may have unique and egregious failure modes. Look no further than the GFS' complete inability to lock on to the forecast track for Hurricane Melissa a month ago. This is dramatically compounded when you look at mesoscale forecast, where higher spatial resolution is a liability that leads to double-penalty errors (e.g. setting up a mesoscale snow squall band just slightly south of where it actually develops).
And keep in mind that the benchmarks shared from this model product are evaluating an ensemble mean, which further confounds things. Even if the ensemble mean is well-calibrated and accurate, there can be critical spread from the ensemble members themselves.
The forecasts are being actively improved, it's just not an overnight step change.
For example, I have just added rainbow.ai short term precipitation forecast into https://weathergraph.app, and it's the best short term forecast I have ever used - based on radar data + AI prediction based on wind etc.
It sounds simple, but there is surprising complexity even just getting (in fast predicting) the 'ground truth' from the radar data, as each radar is noisy, is updated at a different time, might not work at a time ... so even the "current precipitation according to radars" is not a reading, but a result of ML model.
Obviously all I have is anecdata for what I'm mentioning here but from a consumer perspective I don't feel like these model enhancements are really making average folks feel as if weather is any more understood than it was decades ago.