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GPUs open the potential to forecast urban weather for drones and air taxis

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Re: GPUs open the potential to forecast urban weather for drones and air taxis

#21

Post by diamondlovesyou »

twofornone wrote: post #17
labster wrote: post #16

Earlier quoted context omitted.

I’ve been out of the field for a decade now, but I’m not surprised WRF is still the dominant model. So how different is ARW from the reference implementation? Model physics are not quite real physics anyway, and your initial conditions have some garbage and low sig figs anyway. Is the error propagation measurable — does it make long term forecasts significantly worse?

I'm not a domain expert but I would expect weather patterns to be chaotic and thus even small perturbations (errors) can lead to significant divergence.

I'm a level above an amatuer in this, having studied data assimilation in college (I was a Math major). Numerical errors are a given, errors in general too, actually, especially in the measurements used to train the hidden parameters (it is very similar to supervised ML). Data assimilation is a collection of techniques used to tackle that issue: 3dvar, 4dvar, kalman filter, extended kalman filter, ensemble kalman filter, particle filters, and so many others are used to find the most likely (minimum energy etc) hidden state (the mean and covariance) from a given set of measurements+associated covariances and then that resulting hidden state is used to run the model "forward" (generally in time). AIUI, DA was specifically developed for weather modeling. It is definitely true that weather modeling is a heavy employer in anycase. This is on top of the methods used to solve PDEs ie so that they are forward/backward stable etc.

The errors in the weather forecast are not the result of chaos: they are the result of the errors in the measurements (recorded in the observation covariance) and the sparsity of the measurements themselves vs the size of Earth, for example, and limitations in model resolution (consider a FEM grid over the entire surface of Earth). The effect of chaos just compounds these errors near bifurcations around fixed points.

Perturbations are not used in the way you think; think of Taylor series approximations around specific points of interest.

Anyway, I work on compilers/auto-vectorization now (lol), so I'll defer to The Expert, if such person wants to chime in.

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#22

Post by sampo »

wongarsu wrote: post #11
tppiotrowski wrote: post #8

“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…

It's useful to think of GPUs as CPUs with low core frequency but incredibly wide SIMD instructions. They are very good at doing the exact same instructions in parallel (apply this formula to 4 million pixels), they are fairly bad at anything that includes branches or loops (doesn't fit the SIMD model), or random memory access (only so much memory bandwidth to go around between all the cores). That makes GPUs generall…

> They are very good at doing the exact same instructions in parallel (apply this formula to 4 million pixels)

In numerical fluid mechanics, you rather want to apply the same formula to the nearest 27 (3x3x3) or 125 (5x5x5) pixels in the 3-dimensional array. And then store the result to another 3-dimensional array.

Or maybe for calculating a value in a 3d array A, you need to apply a formula that looks at the nearest 27 values in both array A itself and also in another 3d array B. Maybe also C.

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#23

Post by RNCTX »

spijdar wrote: post #20
choeger wrote: post #18

But why? What do I do with a forecast about wind speeds in urban areas? Let's be generous and say the forecast will be able to tell me one hour in advance about wind speed and direction on every street in my neighbourhood. Now I order a drone-delivered pizza. What does the drone do with this knowledge? It still needs to get the Pizza from A to B, ASAP.

Drones/sUAVs tend to be pretty sensitive to wind and the weather in general. Being able to know, as you put it, the wind speed and direction on every street means being able to potentially avoid significant slow downs and/or running out of battery from having to use more power to compensate for the wind. Of course that's if you can predict the weather with good enough temporal and spatial resolution to be useful. But…

IFR manned flight requires enough fuel to fly to your alternate destination airport + 45 minutes.

Considering the idea of flying a drone with something valuable on it is a theft / vandalism / collision nuisance already that will probably never be used outside of a few test markets, I find it highly unlikely that "running out of power" will be a concern. Assuming hypothetically that they were allowed to operate on a larger scale than they are today which, again, is a dubious assumption to begin with, they will have similar reserve requirements that are easy to calculate based on observed wind conditions at a nearby airport.

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#24

Post by labster »

diamondlovesyou wrote: post #21
twofornone wrote: post #17

Earlier quoted context omitted.

I'm not a domain expert but I would expect weather patterns to be chaotic and thus even small perturbations (errors) can lead to significant divergence.

I'm a level above an amatuer in this, having studied data assimilation in college (I was a Math major). Numerical errors are a given, errors in general too, actually, especially in the measurements used to train the hidden parameters (it is very similar to supervised ML). Data assimilation is a collection of techniques used to tackle that issue: 3dvar, 4dvar, kalman filter, extended kalman filter, ensemble kalman fil…

I was an atmospheric scientist, but I never got in too deep in the modeling — if I had, I’d have actually received the PhD lol. But yes, data assimilation very important in weather models. You know what’s going on out in the oceans where instruments are sparse, because land measurements are advected (blown) over the ocean. You still need to assimilate to correct to IRL physics.

The data itself is noisy. Bad readings aren’t uncommon. Common hygrometers (humidity sensors) have hysteresis, wind is turbulent, radiosondes stop transmitting in midair. Some data is really weird, like GPS occultation data, which gives temperature mixed with humidity along a 200km long cylinder. Suffice to say that while higher order approximations in modeling have helped, DA is super important because measurements are both sparse and flaky.

But that’s why I asked: numerical errors are typically dwarfed by measurement errors. So it shouldn’t be worse than a member of an ensemble model, right?

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#25

Post by amelius »

mwambua wrote: post #9

For some context, WRF ( https://github.com/wrf-model/WRF ), the current state-of-the-art weather modeling system (also developed by NCAR) can (to my limited knowledge) only run on CPU. There have been efforts to run it on GPU ( https://wrfg.net )... though it doesn't look like it's been kept up to date. It seems like FastEddy mostly replaces WRF-LES, which is used for high-resolution localized modeling.

What's at the heart of it? Is it solving large sparse linear equations?

If so, wouldn't it be relatively simple to swap-in a CUDA implementation of this solver?

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#26

Post by jcun4128 »

side note: I think it'll be cool/crazy to see some kind of city-level planning that models like cars moving on the ground in real time, and things (drones) flying in the air, 3D buildings/space they take up... that would be a cool system to setup. I thought Airbus was doing something like that for their Vahana since it isn't piloted.

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#27

Post by heavenlyblue »

datameta wrote: post #14
zardo wrote: post #6

Earlier quoted context omitted.

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.

A few years ago I heard it said that a 5 day forecast has the accuracy of a 48 hour forecast from two decades ago.

It does seem to me like a lot of that accuracy comes from real-time metric collection from all of the stations around the world (And there are many more stations, too).

For example, in London iPhone gives you almost real-time "current weather" state. Stuff like "it's raining now and it's going to stop raining in the next 5 minutes". You can only do that via collecting the radar data in real-time and correlating it with GPS. Not so much prediction.

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#28

Post by seoaeu »

tppiotrowski wrote: post #8

“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…

The architecture of GPUs is less exotic than people would have you believe. A given graphics card will have a couple dozen relatively slow cores (arranged in a NUMA hierarchy) each with 10 logical threads and 32 or 64-wide SIMD. The 10 logical threads/core enables many concurrent memory operations to be in flight at the same time, while the wide SIMD enables massive parallelism.

There's some cleverness in the programming model however: the code the programmer writes is executed on a single SIMD lane so 32 or 64 copies of it can be run in lockstep. In total, to keep every lane of every logical thread of every core busy requires thousands of concurrent threads.

(There is also some special purpose hardware for graphics related tasks, but that is less relevant to GPGPU workloads)

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#29

Post by A-Train »

jcun4128 wrote: post #26

side note: I think it'll be cool/crazy to see some kind of city-level planning that models like cars moving on the ground in real time, and things (drones) flying in the air, 3D buildings/space they take up... that would be a cool system to setup. I thought Airbus was doing something like that for their Vahana since it isn't piloted.

For traffic simulation there is https://github.com/a-b-street/abstreet which is very impressive project.

Re: GPUs open the potential to forecast urban weather for drones and air taxis

#30

Post by jcun4128 »

A-Train wrote: post #29
jcun4128 wrote: post #26

side note: I think it'll be cool/crazy to see some kind of city-level planning that models like cars moving on the ground in real time, and things (drones) flying in the air, 3D buildings/space they take up... that would be a cool system to setup. I thought Airbus was doing something like that for their Vahana since it isn't piloted.

For traffic simulation there is https://github.com/a-b-street/abstreet which is very impressive project.

That looks cool, OSM/(can be) browser based looks like. Wonder if the paths for the roads are drawn automatically. Anyway that's something 3D is just an extruded layer/box I suppose.
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