Geostationary Satellite Sounder Pathfinder Project for 4-D Atmospheric Thermodynamics and Winds
Description
A k-nearest neighbors technique, coupled with an advanced data assimilation system are applied to hyperspectral infrared and geostationary satellite imager data to provide high spatiotemporal resolution analyses of atmospheric thermodynamics, winds and clouds for severe weather forecasting, cloud process studies and other meteorological applications.
Detailed example
high spatiotemporal resolution analyses of atmospheric thermodynamics, winds and clouds for severe weather forecasting, cloud process studies and other meteorological applications.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
A k-nearest neighbors technique, coupled with an advanced data assimilation system are applied to hyperspectral infrared and geostationary satellite imagery data to provide high spatiotemporal resolution analyses of atmospheric thermodynamics, winds and clouds for severe weather forecasting, cloud process studies and other meteorological applications.
Controls / human review
ATO: Not reported; PIA: Not published