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Comment by lucaspiller | Hacker News Reader

I've always wondered how meteorology works in terms of computer models. As someone knowing nothing, what's some good material to start learning how it works?

Now we have powerful computers and lots of data freely available (well maybe less now thanks to Trump), I've always dreamed of running an old computer model say from the 1980s at home.

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You can do better than the 1980s - the WRF (Weather Research and Forecasting) model [1] is up to date and will run on a PC (albeit not at a super high resolution). You can provide boundary conditions with freely available data from NOAA [2]

However, if you want to run the kind of forecasts that a weather center would do, you big issue would be getting input data. Some of the biggest advances in forecasting have come from improving the amount of satellite data being 'assimilated' into weather forecasts - look how much better the southern hemisphere became compared to the northern hemisphere during the early satellite era (1980-2000). Getting this data in a timely fashion is more of a challenge to do at home.

[1] - http://www2.mmm.ucar.edu/wrf/users/

[2] - https://nomads.ncdc.noaa.gov/

[3]Page 3 in - http://www.ecmwf.int/sites/default/files/elibrary/2012/14553...

On this topic you can now run WRF in Docker [1] [2], something that I have successfully done in the past on my MacBook Pro. I was even able to generate NCL [3] images from the output. However, I don't think you can truly take advantage of HPC capability via the Docker route if you have access to a supercomputer. Nevertheless, this makes the lives of graduate students the world over much easier since installing WRF is no easy task.

[1] https://www.ral.ucar.edu/projects/ncar-docker-wrf

[2] https://github.com/NCAR/container-wrf

[3] https://www.ncl.ucar.edu/

Also, I believe most weather forecasting makes good use of ensemble runs - take your initial data set from observations, introduce N sets of appropriately sized random variations in the initial data, and run N simulations. Then you look e.g. at the the spread of results, giving you information about the certainty of your prediction.

I suppose these perturbations would need to be extremely small, because of the butterfly effect.

The butterfly effect is overrated. There is always divergent behaviour in the long term, but most of the runs will come out very similar. In fact, it is a major problem to introduce enough variation into the parameters of the ensemble run to capture the natural variation which is observed by the data acquisition systems. The US systems use vector breeding and European systems tend to use the singular vector method, which as I understand it is a bit more effective.

http://journals.ametsoc.org/doi/abs/10.1175/2008MWR2498.1

I'm guessing they use something like Gaussian noise with a width approx. equal to the uncertainty in the initial conditions.

"Bred vectors" are an example: https://en.wikipedia.org/wiki/Bred_vector

Note that these vectors are generated not by just sampling the uncertainty in I.C.'s. Of course, this is because the space of I.C. perturbations is too high-dimensional to cover. In the method above, selection of perturbation direction is based on the adjustments implied when new data is sync'ed to the model.

There are other techniques.

Well honestly you have to start with the science & physics. The 'model' (call it numerical weather prediction) is just a bunch of physics coded up and plugged in together. For instance you have a cloud parameterization model that describes cloud formation & precipitation, which interacts with a land surface model, which interacts with the ocean model etc...

These models are just computer code translations of physical equations mainly. You get an equation to model how air (fluid) moves, code it up, set up a numerical process to integrate over a small time step. Depending on how many assumptions you want to make the equation can be relatively simple or highly complex.

In short, it's fuckin insane. This has some good stuff in it https://www.ral.ucar.edu/projects/armyrange/references/forec...

> In short, it's fuckin insane.

Just take a moment to consider the feedback loops: huge cloud cover blocks sunlight, so the ground temperature is cooler, so ground air pressure is lower, which probably acts like a sponge to pull in any nearby clouds, which would then increase chances of precipitation. I would imagine the time of day matters too, because cloud cover at night can trap in radiant heat... fucking insane.

I wonder if they put an open bounty on the problem, say for specific geographical regions: find a better prediction scheme for city X, win $Y

Don't worry, meteorologists are brutally competitive in their own way. They don't say anything, they just put up a chart comparing forecast accuracy.

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