5 ms·

Predicting only two hours ahead doesn't seem that impressive or helpful to me in itself; major weather-related decisions usually need more time. Still, interest

Predicting only two hours ahead doesn't seem that impressive or helpful to me in itself; major weather-related decisions usually need more time. Still, interesting stuff! A few family members and I usually get little headaches hours before rain if it's preceded by a dip in air pressure, although I've never measured the accuracy or utility of this.

mewpmewp2 5y ago
One obvious thing that came to mind immediately is something like racing, for example F1. Knowing what tyres to put on is a make or break there. If DeepMind truly is better than anything else there, I'd imagine F1 to be one of the first to start using it.
IshKebab 5y ago
Predicting 2 hours ahead is one of the most useful weather forecasting tasks. 2 hours is enough time to prepare for rain in loads of situations. And sure the title is a bit ridiculous. Anyone can predict incoming rainfall. The question is can it predict it accurately. The abstract says: > we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods Probably a bit better than your headaches.
tonyedgecombe 5y ago
I think farmers would like to know what is happening during the next hour or two during harvest.
ddek 5y ago
That's really interesting about the headaches. It makes sense. The model is actually useful for google. Road traffic is closely linked to weather, with some routes worse impacted than others. If you predict the weather, you can predict changes in congestion patterns caused by the rain, so you can predict journey times better. Most journeys people are using google maps for are probably in the 30min-2hr range. It's also simply interesting because predicting the progress of frontal rainfall is not something we're good at. We can apply conventional extrapolation, but this only considers the direction of the weather, not at all the changing saturation of the clouds.
- counters 5y ago
  Actually, predicting the progress of frontal rainfall is by far the easiest nowcasting problem. The squall lines that develop along cold fronts more-or-less move in a straight line at a constant speed. They're easy to isolate and track in sequences of imagery using well-developed image processing and segmentation techniques. Their total motion is grossly constrained by large-scale kinematics in the atmosphere. And over short time periods (0-3 hours) individual features/cells embedded in a line are more-or-less persistent. With conventional extrapolation (e.g. DarkSky) you can nail the timing of rain at your location down to about 1-2 minutes by looking at a few sequential radar images, give or take a minute or two if the the edge of the precipitation is a bit more diffuse.

mewpmewp2 5y ago

One obvious thing that came to mind immediately is something like racing, for example F1. Knowing what tyres to put on is a make or break there. If DeepMind truly is better than anything else there, I'd imagine F1 to be one of the first to start using it.

IshKebab 5y ago

Predicting 2 hours ahead is one of the most useful weather forecasting tasks. 2 hours is enough time to prepare for rain in loads of situations. And sure the title is a bit ridiculous. Anyone can predict incoming rainfall. The question is can it predict it accurately. The abstract says: > we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods Probably a bit better than your headaches.

tonyedgecombe 5y ago

I think farmers would like to know what is happening during the next hour or two during harvest.

ddek 5y ago

That's really interesting about the headaches. It makes sense. The model is actually useful for google. Road traffic is closely linked to weather, with some routes worse impacted than others. If you predict the weather, you can predict changes in congestion patterns caused by the rain, so you can predict journey times better. Most journeys people are using google maps for are probably in the 30min-2hr range. It's also simply interesting because predicting the progress of frontal rainfall is not something we're good at. We can apply conventional extrapolation, but this only considers the direction of the weather, not at all the changing saturation of the clouds.

counters 5y ago
Actually, predicting the progress of frontal rainfall is by far the easiest nowcasting problem. The squall lines that develop along cold fronts more-or-less move in a straight line at a constant speed. They're easy to isolate and track in sequences of imagery using well-developed image processing and segmentation techniques. Their total motion is grossly constrained by large-scale kinematics in the atmosphere. And over short time periods (0-3 hours) individual features/cells embedded in a line are more-or-less persistent. With conventional extrapolation (e.g. DarkSky) you can nail the timing of rain at your location down to about 1-2 minutes by looking at a few sequential radar images, give or take a minute or two if the the edge of the precipitation is a bit more diffuse.