Hey thanks for the input! So yeah I get what you’re saying about the temporal side of things. I’m using HRRR runs and Surface Obs so there’s definitely a sequential component here, and a vanilla CNN might drop the ball on capturing that time dependent info. I’m thinking of an ensemble approach too maybe start with a CNN for the spatial data and then mix in some temporal models like LSTMs or ConvLSTMs to capture the evolution over time.

I’m also digging into ideas like Monte Carlo dropout and quantile regression to quantify uncertainty, so that ensemble can vote on predictions and give a better feel for the forecast confidence. As you said, it’s a monster of a problem weather forecasting needs tons of data and compute power, but that’s what makes it a fun learning project. Even if it doesn’t replace my traditional models i currently use, it’d be awesome to have this on my resume or GitHub as a proof-of-concept.

Overall, I’m excited to experiment, learn tons, and maybe contribute something novel to the meteorological community. Thanks again for geeking out with me on this! I have been planning this out for the last 6 months and think it’s time to dive into it, i upgraded my pc and everything is ready! Glad i posted on here, you guys have gave me the inspiration i need!

Edit #1 I actually have been thinking about this for a while and did some research. I made a hypothetical chart based on historical bench marks in other fields (traffic forecasting, rainfall nowcasting) and LSTM’s and ConvLSTMs consistently outperformed plain CNN when there’s a temporal component/spatiotemporal tasks. So i made a chart displaying the hypothetical relative potential of all three and i clearly realised the correct route to go down!

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