  As tropical cyclones become more intense due to climate change, the rise of
Al-based modelling provides a more affordable and accessible approach compared
to traditional methods based on mathematical models. This work leverages
generative diffusion models to forecast cyclone trajectories and precipitation
patterns by integrating satellite imaging, remote sensing, and atmospheric
data. It employs a cascaded approach that incorporates three main tasks:
forecasting, super-resolution, and precipitation modelling. The training
dataset includes 51 cyclones from six major tropical cyclone basins from
January 2019 - March 2023. Experiments demonstrate that the final forecasts
from the cascaded models show accurate predictions up to a 36-hour rollout,
with excellent Structural Similarity (SSIM) and Peak-Singal-To-Noise Ratio
(PSNR) values exceeding 0.5 and 20 dB, respectively, for all three tasks. The
36-hour forecasts can be produced in as little as 30 mins on a single Nvidia
A30/RTX 2080 Ti. This work also highlights the promising efficiency of Al
methods such as diffusion models for high-performance needs in weather
forecasting, such as tropical cyclone forecasting, while remaining
computationally affordable, making them ideal for highly vulnerable regions
with critical forecasting needs and financial limitations. Code accessible at
https://github.com/nathzi1505/forecast-diffmodels.
