Supporting Situational Awareness of Aviation Users: Satellite Cloud Vertical Cross-sections Combining Multiple Weather Data Sources

Aviation forecasters and pilots make decisions for pre- and in-flight activities by monitoring the operating environment and conditions based on a variety of complex and diverse data sources. Although meteorological satellites have provided useful cloud observation data for decades, the use of satellite data in aviation has been limited to general 2D plain views of cloud images. Various satellite-based cloud products have been developed to provide more quantitative data. However, the information is often biased at/near cloud top due to the natural characteristics of conventional passive sensors onboard most operational satellites, and this has restricted the applications of satellite data in aviation. We have developed a Cloud Base algorithm using NASA A-Train satellite data as part of the NOAA Enterprise Cloud Algorithm Suite, which allows us to provide more vertically extended cloud layer information. In support of the NOAA JPSS Aviation Initiative effort to promote the use of satellite cloud products for aviation users, we introduced Cloud Vertical Cross-sections along flight routes to extend the benefit of satellite data into the vertical dimension. This experimental product is based on newly developed satellite-based 3D cloud data combining multiple NOAA satellite cloud products and supplementary data such as temperatures (from satellite-derived NUCAPS or numerical model data), terrain data, and PIREPs (icing and turbulence). Smoke data from the High-Resolution Rapid Refresh (HRRR) model was recently added for improved visibility information based on user feedback. All the data sets are interpolated into one 3D grid and displayed in a vertical cloud view. A web-based graphical visualization for custom cross-sections along user-selectable flight paths is available on our aviation website (aviation.cira.colostate.edu) for user evaluation. The work has been expanded beyond JPSS polar satellite data for Alaska to the entire CONUS with the addition of geostationary satellite data (currently GOES-16). We continue to obtain feedback from operational forecasters and pilots to improve the products and provide relevant training tools, focusing on user needs. Further refinements of science algorithms are ongoing for improved nighttime and multilayered cloud retrievals, exploring both traditional statistics and machine learning approaches. Leveraging these research efforts, we are also attempting to generate global 3D cloud fields by applying the data process to multiple satellite sensors into one global framework. This work will present current accomplishments and continuing efforts on scientific and user-engaged improvements.