Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences

This study introduces a validation technique for quantitative comparison
of algorithms which retrieve winds from passive detection of cloud- and
water vapor-drift motions, also known as Atmospheric Motion Vectors
(AMVs).  The technique leverages airborne wind-profiling lidar
data collected in tandem with 1-min refresh rate geostationary satellite
imagery.  AMVs derived with different approaches are used with
accompanying numerical weather prediction model data to estimate the full
profiles of lidar-sampled winds which enables ranking of feature tracking,
quality control, and height-assignment accuracy and encourages meso-scale,
multi-layer, multi-band wind retrieval solutions.  The technique
is used to compare the performance of two brightness motion, or “optical
flow,” retrieval algorithms used within AMVs, 1) Patch Matching (PM; used
within operational AMVs) and 2) an advanced Variational Optical Flow (VOF)
method enabled for most atmospheric motions by new-generation
imagers.  The VOF AMVs produce more accurate wind retrievals than
the PM method within the benchmark in all imager bands explored. 
It is further shown that image regions with low texture and
multi-layer-cloud scenes in visible and infrared bands are tracked
significantly better with the VOF approach, implying VOF produces
representative AMVs where PM typically breaks down.  It is also
demonstrated that VOF AMVs have reduced accuracy where the brightness
texture does not advect with the mean wind (e.g. gravity waves), where the
image temporal noise exceeds the natural variability, and when the
height-assignment is poor.  Finally, it is found that VOF AMVs
have improved performance when using fine-temporal refresh rate imagery,
such as 1-min versus 10-min data.