nikhil-shankar

Great questions.

1. The truth is we still have to investigate the the numerical stability of these models. Our GFT forecast rollouts are around 2 weeks (~60 steps) long and things are stable in in that range. We're working on longer-ranged forecasts internally.

2. The compute requirements are extremely favorable for ML methods. Our training costs are significantly cheaper than the fixed costs of the supercomputers that government agencies require and each forecast can be generated on 1 GPU over a few minutes instead of 1 supercomputer over a few hours.

3. There's a similar floating-point story in deep learning models with FP32, FP16, BF16 (and even lower these days)! An exciting area to explore

These are great resources, thank you. If you're open to it, we'd love to meet and chat about the energy space since we're newcomers to that arena. Shoot us an email at [email protected]

In the videos the true path is the dashed line and the government prediction is the solid line. Our prediction, from our GFT model, is the animation which plays in the background.

Yes, 100%! We'll still take a statistical/distributional approach to long-ranged climate behavior rather than trying to predict exact atmospheric states. Keep an eye out for more news on this

If there is sufficient data, we can train on it!

Hi, this looks really cool! Can we meet? Shoot us an email at [email protected]

We want to branch out to industries which are highly dependent on weather. That way we can integrate their data together with our core competency: the weather and climate. Some examples include the energy grid, agriculture, logistics, and defense.

You'd be surprised :D