|
| carabiner wrote:
| Run this for mountains, ski resorts so we know exactly which
| areas/runs will be most windy.
| cryptoz wrote:
| Well this is the coolest thing I've seen in weather tech in a
| while. I wonder if they will pair this with additional city-level
| data collection - anemometers/mini weather stations on the sides
| of buildings, etc. I'm the barometer-data-from-phones guy running
| All Clear on Play Store US:
| https://play.google.com/store/apps/details?id=com.allclearwe...
|
| with an interest in hyperlocal weather forecasting.
|
| I didn't see much about forecast accuracy in this article, but,
| still, extremely cool. I do wonder how much accuracy is possible
| - you cannot necessarily know how trucks and other human-caused
| short-term atmosphere changes will affect the weather.
| billiam wrote:
| This seems a bit much to enable good pizza delivery. The funders
| seem more interested in urban airflows for bioweapons attacks or
| other slightly more critical problems.
| quocanh wrote:
| This might enable simulation but forecasting is a chaotic problem
| isn't it? It's essentially like trying to predict a stream of
| random numbers - even if they follow a certain attractor, it's
| still impossible to know exactly what form they'll take.
| zardo wrote:
| Forecasting is done, and it's done by creating ensembles of
| weather simulations.
|
| Weather is chaotic, which is why forecast accuracy rapidly
| drops off the further out in time you make predictions. But
| still, they can be accurate enough far enough out into the
| future to be extremely useful.
| dragontamer wrote:
| Partial differential equations over multiple dimensions is
| innately chaotic, with exponential error bounds (the longer any
| simulation goes, the exponentially bigger the errors get).
|
| But getting more-and-more accurate predictions is the goal of
| any weather modeler. If the exponential error bounds gives you
| currently 1-day worth of predictions before the models go to
| crap... maybe an improved algorithm (or 10x more compute power)
| can get you 2-days worth of predictions instead.
|
| Even if you don't get a major change (maybe going from 1 day
| worth of predictions to 1.1 days of predictions), you might be
| able to convert that into 1/10th the compute power needed (ex:
| lower the accuracy down to 1-day prediction but cut back
| dramatically on the compute-power needed to perform the
| simulation).
| tppiotrowski wrote:
| "CPUs excel at performing multiple tasks, including control,
| logic, and device-management operations, but their ability to
| perform fast arithmetic calculations is limited. GPUs are the
| opposite. Originally designed to render 3D video games, GPUs are
| capable of fewer tasks than CPUs, but they are specially designed
| to perform mathematical calculations very rapidly."
|
| I thought the advantage of the GPU is not speed but parallelism
| or are modern power saving processors also slow compared to GPUs
| on non-parallel tasks?
|
| Also, is it common to use Registered Trademarks (FastEddy) in a
| paper these days. I know a lot of universities try to
| commercialize research, is this the reason for the trademark?
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