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

Wednesday, 9 January 2013
Exhibit Hall 3 (Austin Convention Center)
Jaime Daniels, NOAA, College Park, MD; and W. Bresky, S. Wanzong, and C. S. Velden

Handout (2.8 MB)

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.

Meteosat SEVERI imagery is serving as an important GOES-R ABI proxy data source for the development, testing, and validation of the GOES-R AMV algorithms given its similarities (spectral coverage, pixel resolution, and scanning rate) and performance (spectral noise, navigation/registration) to the future GOES-R ABI. The new GOES-R AMV algorithm is also being applied to the instrumentation on the current operational GOES series of satellites and is expected to replace the heritage AMV algorithm being used in NESDIS operations today. Plans at NOAA/NESDIS also include using the new GOES-R AMV algorithm to generate AMVs from the future VIIRS instrument on the NPP satellite.

Details of the GOES-R ABI AMV algorithm and the validation results will be presented and discussed.

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