3D atmospheric wind is a key missing observable in NASA's Earth Observation System (EOS) and presents large initial condition uncertainties in data assimilation systems. Atmospheric motion vectors (AMVs), which track cloud/water vapor movement and assign a height, provide key passively sensed initial condition information for numerical weather prediction. However, data produced by current operational AMV algorithms are sparsely available and contain high uncertainties in the vertical direction. Comparison with radiosonde profiles indicates that AMV height assignment errors comprise 70% of uncertainty. Our work aims to fill EOS gaps by learning a mapping between high-temporal resolution GOES-16/17 geostationary satellites operated by NOAA/NASA and radiosonde observations. We use our WindFlow model that applies deep learning based optical flow techniques to track clouds and water vapor in sequences of geostationary images to produce direction and speed data. WindFlow provides an efficient approach to generate dense and accurate wind vectors. Outputs of WindFlow, paired with thermal infrared imagery, provides information content to directly predict wind speed, humidity, and temperature at varying pressure levels. This proposal aims to generate a 3D atmospheric product from geostationary satellites validated against radiosondes and lidar observations.
Applications of 3D atmospheric winds are numerous throughout NASA Earth science research and development. Assimilation of our data with systems operated at the NASA's Global Modeling and Assimilation Office (GMAO) has the potential to improve analysis and forecast products, including short-term and sub-seasonal. Dense atmospheric winds will also have implications to wildfire monitoring and subsequent air quality issues.
Commercially the developed technology has applications to aviation, renewable energy, finance/insurance, and forecasting. In aviation, surface level and atmospheric winds have great economic value to airlines in terms of safety and potential operating cost savings. Renewable energy markets are largely powered by weather conditions and must be estimated for stable operation of the power grid.