3 ms·

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 resolutio

by aeroman 10y ago

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/ http://www2.mmm.ucar.edu/wrf/users/ [2] - https://nomads.ncdc.noaa.gov/ https://nomads.ncdc.noaa.gov/ [3]Page 3 in - http://www.ecmwf.int/sites/default/files/elibrary/2012/14553-background-and-history.pdf http://www.ecmwf.int/sites/default/files/elibrary/2012/14553...

semi-extrinsic 10y ago
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.
- deleted 10y ago
  [deleted]
- amelius 10y ago
  I suppose these perturbations would need to be extremely small, because of the butterfly effect.
  - semi-extrinsic 10y ago
    I'm guessing they use something like Gaussian noise with a width approx. equal to the uncertainty in the initial conditions.
    - mturmon 10y ago
      "Bred vectors" are an example: https://en.wikipedia.org/wiki/Bred_vector 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.
  - angry_octet 10y ago
    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 http://journals.ametsoc.org/doi/abs/10.1175/2008MWR2498.1
julienchastang 10y ago
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 https://www.ral.ucar.edu/projects/ncar-docker-wrf [2] https://github.com/NCAR/container-wrf https://github.com/NCAR/container-wrf [3] https://www.ncl.ucar.edu/ https://www.ncl.ucar.edu/

by aeroman 10y ago

semi-extrinsic 10y ago

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.

deleted 10y ago
[deleted]
amelius 10y ago
I suppose these perturbations would need to be extremely small, because of the butterfly effect.
- semi-extrinsic 10y ago
  I'm guessing they use something like Gaussian noise with a width approx. equal to the uncertainty in the initial conditions.
  - mturmon 10y ago
    "Bred vectors" are an example: https://en.wikipedia.org/wiki/Bred_vector 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.
- angry_octet 10y ago
  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 http://journals.ametsoc.org/doi/abs/10.1175/2008MWR2498.1

julienchastang 10y ago

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 https://www.ral.ucar.edu/projects/ncar-docker-wrf [2] https://github.com/NCAR/container-wrf https://github.com/NCAR/container-wrf [3] https://www.ncl.ucar.edu/ https://www.ncl.ucar.edu/