11–20 of 140 posts
This year, the wild variance in hourly weather reports on my phone has really been something. I attributed it to likely budget cuts as a result of DOGE, but if those forecasts came from Google itself the whole time, all is clear now.
I find that unlikely, my forecasts for much of Europe and East Asia have been consistently accurate.
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 cl…
No need for anecdata! We have the data: https://ourworldindata.org/weather-forecasts tdlr: Weather forecasts have improved a lot
Is anyone aware of good sources of higher resolution models? Hourly resolution like this model provides doesn’t help much now that energy markets have moved to 15-min and 5-min resolution.
Different models have different strengths, though. Some are shorter range (72h) or longer range (1-3 weeks). Some are higher resolution for where you live (the size of an area which it assigns a forecast to, so your forecast is more local).
Some governments will have their own weather model for your country that is the most accurate for where you live. What I did for a long time was use Windy and use HDRPS (a Canadian short range model with a higher resolution in Canada so I have more accurate forecasts). Now I just use the government of Canada weather app.
I genuinely wonder what the weather Channel, iPhone/Android official weather apps, etc. use under the hood for global models. My gut says ECMWF (a European model with global coverage) mixed with a little magic.
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 cl…
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.
I find it interesting that they quantify the improvement on speed and number of forecast-ed scenarios but lack details on how it results in improved accuracy of the forecast per: ``` WeatherNext 2 can generate forecasts 8x faster and with resolution up to 1-hour. This breakthrough is enabled by a new model that can provide hundreds of possible scenarios. ``` As an end user, all I care is that there's one accurate for…
I find it interesting that they quantify the improvement on speed and number of forecast-ed scenarios but lack details on how it results in improved accuracy of the forecast per: ``` WeatherNext 2 can generate forecasts 8x faster and with resolution up to 1-hour. This breakthrough is enabled by a new model that can provide hundreds of possible scenarios. ``` As an end user, all I care is that there's one accurate for…
Sure, those big physics-based models are very computationally intensive (national weather bureaus run them on sizeable HPC clusters), but you only need to run them every few hours in a central location and then distribute the outputs online. It's not like every forecaster in a country needs to run a model, they just need online access to the outputs. Even if they could run the models themselves, they would still need the mountains of raw observation data that feeds the models (weather stations, satellite imagery, radars, wind profilers...). And these are usually distributed by... the national weather bureau of that country. So the weather bureau might as well do the number crunching as well and distribute that.
I find it interesting that they quantify the improvement on speed and number of forecast-ed scenarios but lack details on how it results in improved accuracy of the forecast per: ``` WeatherNext 2 can generate forecasts 8x faster and with resolution up to 1-hour. This breakthrough is enabled by a new model that can provide hundreds of possible scenarios. ``` As an end user, all I care is that there's one accurate for…
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 cl…
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…
The standard graph that most people look at to get an idea about today and tomorrow: https://www.yr.no/en/forecast/graph/1-72837/Norway/Oslo/Oslo...
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.
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 cl…
No need for anecdata! We have the data: https://ourworldindata.org/weather-forecasts tdlr: Weather forecasts have improved a lot
I find it interesting that they quantify the improvement on speed and number of forecast-ed scenarios but lack details on how it results in improved accuracy of the forecast per: ``` WeatherNext 2 can generate forecasts 8x faster and with resolution up to 1-hour. This breakthrough is enabled by a new model that can provide hundreds of possible scenarios. ``` As an end user, all I care is that there's one accurate for…
Developing an ensemble of possible scenarios has been the central insight of weather forecasting since the 1960s when Edward Lorenz discovered that tiny differences in initial conditions can grow exponentially (the "butterfly effect"). Since they could really do it in the 90s, all competitive forecasts are based on these ensemble models.
When you hear "a 70% chance of rain," it more or less means "there was rain in 70 of the 100 scenarios we ran."[0] There is no "single accurate forecast scenario."
[0] Acknowledging this dramatically oversimplifies the models and the location where the rain could occur.