4 ms·
My silly question for the day: Could you use interpretability techniques on this model to figure out the easiest places to perturb conditions to steer towards p
by ctoth 3y ago
My silly question for the day: Could you use interpretability techniques on this model to figure out the easiest places to perturb conditions to steer towards particular weather in a particular location (cloud seeding, or whatever?)
Is this the first part of the weather control machinery from Star Trek? In order to control, one must first predict?
- counters 3y agoIt doesn't seem like MetNet outputs a complete 3D atmospheric state, just specific (and mostly surface-level) forecast predictands [1]. The analysis you're describing could be done with a traditional numerical weather prediction and data assimilation system (especially if that system implements 4DVar, since you'll already have the model adjoint available - well, technically the tangent linear, but still applicable here). [1]: https://arxiv.org/pdf/2306.06079.pdf https://arxiv.org/pdf/2306.06079.pdf
- loxias 3y agoI love this line of thought, I've also entertained it from time to time in other domains. (macroecon :D) To some extent, yes, but you'd need more energy than is practical. Weather is a chaotic system -- future behavior can be highly sensitive to local fluctuations.
- SomewhatLikely 3y agoThat input might be too far outside of the training conditions for it to believe the sensors. It would probably assume those sensors you perturbed are faulty and ignore them.