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The National Transportation Safety Board (NTSB) reports that weather conditions are the second most common cause of aviation accidents, causing 23% of all mishaps. Pilots need accurate information about wind speed, air temperature, air pressure, and visibility to decide when it is safe to fly. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) algorithm uses JPSS satellite data to help forecast aviation hazards.
Aviators have traditionally relied on 2D maps to make interpretations about clouds. The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the NOAA-20 and the NOAA/NASA Suomi National Polar-orbiting Partnership (Suomi NPP) satellites can create 3D models that show the size and shape of a cloud, where it’s located, and determine cloud is made up of liquid, ice, or a mixture. Yoo-Jeong Noh, a researcher at NOAA Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University, and her team have used JPSS data to create a new Cloud Base Height (CBH) algorithm that calculates cloud thickness and estimates cloud base height. This cloud cover information is important to pilots because it reveals information about parts of a cloud that traditional satellite methods cannot see. This gives users details that can be useful for pre-flight planning. The CIRA team also uses machine learning to generate cloud classifications from multi-layer clouds.
