DOI: 10.5061/dryad.05qfttf67
title: Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences

description: 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.
description: Details for data creation and processing can be found in Apke, J.
 M., Y. Noh, and K. Bedka, 2022: Comparison of Optical Flow Derivation
 Techniques for Retrieving Tropospheric Winds from Satellite Image
 Sequences, J. Atmos. Ocea. Tech., DOI: 10.1175/JTECH-D-22-0057.1
 Table 3 contains root mean square vector differences used to
 benchmark Atmospheric Motion Vector retrieval algorithms. Netcdfs contain
 atmospheric wind profiles collected by the Doppler Aerosol Wind-Profiling
 (DAWN) lidar data over the Pacific Ocean during the Aeolus Calibration and
 Validation field experiment, along with wind profiles estimated by optical
 flow-based cloud-drift tracking algorithms derived from Geostationary
 Operational Environmental Satellite (GOES)-R series Advanced Baseline
 Imager (ABI) 0.64, 3.9, and 6.9 um data blended with Global Forecast
 System (GFS) numerical weather prediction derived winds.  In this case,
 the DAWN winds are inferred to be the true state of the atmospheric winds,
 and the optical flow algorithms are retrievals that can be done anywhere
 within the ABI field of view. Hence, lower root mean squared vector
 difference values imply an approach which better retrieves the truth. 
 Within the manuscript, three optical flow approaches are tested: a 5x5
 least squares target tracking method called "Patch Matching," a
 dense (every image pixel) method based on penalty function minimization
 called "Variational Optical Flow," and a quality controlled
 Variational Optical Flow algorithm called "VOF-QC."  For more
 details on how each algorithm retrieves winds from cloud and
 water-vapor-drifts in ABI imagery, see the manuscript above.
description: A python script which demonstrates how to read the
 files and reproduce Table 3 is provided:
 apke_etal_2022_table3.py <br> The
 script requires the numpy, netCDF4, glob, and datetime libraries.  Modify
 the "inputdir" field to the location of the netcdf files to run
 (default is the directory of the script "./").