NESDIS' Atmospheric Motion Vector (AMV) Nested Tracking Algorithm: Exploring its Performance

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 has been 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.

GOES-N/O/P, Meteosat SEVERI, and NPP/VIIRS imagery are serving as GOES-R ABI proxy data sources for the continued development, testing, and validation of the GOES-R AMV algorithms. This talk will focus on the performance of the nested tracking algorithm as supported by comparisons to a variety of reference/ground truth wind observations and case study analyses. This talk will also touch briefly on the performance and impact of the nested tracking AMVs within the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Finally, this talk will highlight some of the enhancements that have been made to the algorithm and discuss areas of future work.