Supplementary data for: Nowcasting 3D cloud fields using forward warping optical flow

Large- to global-domain short-term prediction of clouds (0-3 hours), or
cloud nowcasting, remains relevant to civilian and military applications
ranging from solar energy production to intelligence gathering. 
Despite the capabilities of contemporary numerical weather prediction
models, nowcasting methods based on near real-time observations (i.e.
satellite imagery) hold operational value due to their relative
computational efficiency and accuracy for short-term
applications.  A commonly used nowcasting approach involves using
two or more images to retrieve the apparent motions of features, or
optical flow, which can be used to extrapolate the future location of
those features.  However, such approaches generally assume that
the optical flow field remains unchanged with respect to time which is
challenging to apply to piecewise cloud fields from satellite
imagery.  Here, we propose a method to nowcast clouds that adapts
a computer vision technique for image interpolation, commonly referred to
as warping, to account for temporal changes to optical flow fields derived
from infrared satellite imagery. We evaluate the proposed method for 991
randomly selected regional cases from 2024 and perform a detailed analysis
on three specific cases. Applying a dense (every image pixel) optical flow
retrieval technique to full-disk GOES infrared imagery, we demonstrate
that forward warping of the optical flow field when coupled with simple
occlusion reasoning, improves skill in cloud nowcasting.