Image triplets of constant pressure level moisture analyses derived from geostationary satellite retrievals are used to calculate clear sky atmospheric motion vectors (AMV). This new AMV approach adapts the existing automated wind-tracking algorithm developed at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) and now employed operationally at NESDIS. The method allows the modified code to eliminate the often-imprecise vector height assignment algorithms, since the altitude of the moisture surfaces being tracked is provided by the retrieval output.

The retrieval moisture fields are provided from two sources: Real data from the current GOES sounder, and simulated data from the proposed hyperspectral sounder on GOES-R. Simulated hyperspectral satellite retrievals from the Geostationary Imaging Fourier Transform Spectrometer (GIFTS), and the Hyperspectral Environmental Suite (HES) are analyzed at 101 pressure levels. Levels that exhibit a strong water vapor signal and have some gradient are analyzed and converted to images for feature tracking. Winds can be derived in clear sky by tracking the advecting moisture features in an image triplet created from 3 successive analyses. As a result, vertical profiles of winds can be produced.

GOES East and West sounders provide real-time retrievals of temperature and moisture in cloud-free regions on an hourly basis. A single field of view retrieval with a nominal spatial resolution of 10 km is used in our experimentation. GOES sounder retrieved moisture is analyzed at 31 pressure levels, and these resulting fields are converted to imagery for feature tracking purposes in a similar manner described above.

The poster will describe the method and case studies processed to date. The achievements include a successful demonstration of the new approach concept. Early validation results and future directions for research will also be presented.