Severe Weather Forecasting via the Sole Use of Satellite Data

Resumo

The forecasting of (severe) weather/climate systems using satellite telemetry and Machine Learning (ML) is generally held back by the size and availability of the pertaining datasets. This research outlines a newly devised pipeline for the automated construction of concise datasets designed to convert computationally expensive raw data from a netCDF4 database into a simpler format, with the end goal of future use in severe weather forecasting via the sole use of satellite data as an alternative to more conventional, expensive and localized means. By representing components of the dataset as int8 RGB(A) values of PNG images, data can be spatially related in a concise, consistent and visualizable manner that significantly reduces dataset size relative to the size of the raw dataset. This method is used on Atmospheric Motion Vectors (AMVs) derived from multispectral satellite telemetry via Optical flow Code for Tracking, Atmospheric motion vector, and Nowcasting Experiments (OCTANE) in the construction of a dataset capable of use in prediction of future movements of clouds.