4 ms·
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
by 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 agoI suppose these perturbations would need to be extremely small, because of the butterfly effect.
- semi-extrinsic 10y agoI'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 agoThe 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