- Mila - Quebec AI Institute
Cloud dynamics are integral to forecasting and monitoring weather and climate processes. Due to a scarcity of high-quality datasets, limited research has been done to realistically model clouds. This proposal applies state-of-the art machine-learning techniques to address this shortage,using a real-life dataset,CloudCast.
Potential techniques, such as RNNs and CNNs paired with data augmentations are explored. Preliminary results show promise for the task of supervised video frame interpolation and video prediction. High performance is achieved with a supervised approach.
These video techniques demonstrate a potential to lower the cost for satellite capture, restoration, and calibration of errors in remote sensing data. Future work is proposed to develop more robust video predictions on this and other similar datasets. With these additions, climate scientists and other practitioners could successfully work at a higher frequency.