Error Characterization of Atmospheric Motion Vectors Derived via a Nested Tracking Algorithm Developed for the GOES-R Advanced Baseline Imager (ABI)

A new Atmospheric Motion Vector (AMV) nested tracking algorithm has been developed for the Advanced Baseline Imager (ABI) to be flown on NOAA's future GOES-R satellite. The algorithm was designed to capture the dominant motion in each target scene from a family of local motion vectors derived for each target scene. Capturing this dominant motion is achieved through use of a two-dimensional clustering algorithm that segregates local displacements into clusters. The dominant motion is taken to be the average of the local displacements of points belonging to the largest cluster. This approach prevents excessive averaging of motion that may be occurring at multiple levels or at different scales that can lead to a slow speed bias and a poor quality AMV. A representative height is assigned to the dominant motion vector through exclusive use of cloud heights from pixels belonging to the largest cluster. This algorithm has been demonstrated to significantly improve the slow speed bias typically observed in AMVs derived from satellite imagery.

Meteosat SEVERI and GOES-N/O/P imagery are serving as important GOES-R ABI proxy data source for further development, testing, and validation of the GOES-R AMV winds and cloud property algorithms. Work is underway to also apply these new wind and cloud algorithms to NPP/VIIRS, MODIS, and AVHRR to generate winds operationally at NESDIS. Validation efforts aimed at characterizing the errors associated with the resulting satellite wind products have been ongoing. Available reference/ground truth wind measurements include: radiosondes, aircraft observations, and CALIPSO. Specific case studies are being performed for situations where the satellite wind product quality is poorest. Details of the GOES-R ABI AMV algorithm and the latest validation results will be presented and discussed.