WRF VS fastGPT

WRF-ARW Modeling System is in the public domain and there is a fork called WRFg that can utilize GPU acceleration. The price of used Nvidia tesla GPUs could allow an enthusiast group to band together and thrive however that doesn't solve the sheer complexity of using the model and obtaining enough accurate data points to even run the model.

The NAM, RAP, and HRRR are all different smaller-scale, higher-resolution models for the US and North America based on the Weather Research and Forecasting model. These are due to eventually be replaced by the RRFS, based on the UFS Short-Range Weather Application.

example: https://github.com/wrf-model/WRF

Hi, I'm going to nitpick and say that most physics modeling software is not written in C. A whole bunch of physics programs are in Fortran (like weather models) .

For some context, WRF (https://github.com/wrf-model/WRF) the current state-of-the-art weather modeling system (also developed by NCAR) can only run on CPU. There have been efforts to run it on GPU (https://wrfg.net)... though it doesn't look like it's been kept up to date.

It seems like FastEddy mostly replaces WRF-LES, which is used for high-resolution localized modeling.

Most of the weather models run on supercomputers, and require a LOT of input data. Here’s one you can try if you’re so inclined. https://github.com/wrf-model/WRF

If anyone is curious, the WRF (Weather Research and Forecasting) model has a repository on GitHub.

Thanks! I know, LLM came much later after LLVM. But if you are interested in LLM that LFortran can compile, check out: https://github.com/certik/fastGPT/.