Downscaling Numerical Weather Models With GANs (My CI 2019 Paper)

I saw some people on Twitter recently talking about work that’s similar to stuff I’d done last year (~April 2019). For a moment, I felt annoyance that I wasn’t cited, but then that quote about standing on each other’s shoulders and not each others’ toes came to mind, so I decided to blog about the paper since my marketing clearly wasn’t good enough.

Paper link


I did this work while at Terrafuse, and the basic idea is to do super-resolution, but for the weather.

My first pass at it was to do the simplest possible thing and just pretend it’s an image problem.


Get some pics of weather and super-res them.

The interesting bit: Confusion about Results

Here’s a sample of the results: It goes ground truth vs GAN vs SR-CNN vs bicubic upsampling. There’s also a zoom-in of the red region.

So the GAN looks pretty good.

But it does worse on metrics like MSE and SSIM.

The GAN images have a sharpness to them that the other predictions do not have, so I looked at their power spectral density.

The fit is so close that plotting it basically overlaid 2 curves on each other and I wasted 2 days trying to debug what I thought was an issue in matplotlib.

Later experiments showed that GANs match spectral density and other statistics of the data very quickly, within 2 epochs. But to also get good image quality takes almost a 100. I had a few ideas about how to use this, that made it into another paper.

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