Engineering topics
Publications and source records attributed to Christopher Yost.
NASA LaRC and research partners are analyzing geostationary satellite observations and products available at up to 30-sec frequency, in combination with ground-based and in situ datasets, to better understand and detect aviation and severe weather hazards.
The GOES-R series satellites are collecting observations of severe storms at unprecedented detail. Nearly every day, GOES-16/17 collects ABI visible and infrared (IR) imagery of storms at 1-minute or better resolution. When paired with GLM data, these ABI “Mesoscale Domain Sector” (MDS) observations allow us to infer processes that are occurring within storm updrafts and cloud tops via wind flows, temperature, visible texture, and lightning-generated radiances that can be detected using automated algorithms. Prior to the GOES-R series, GOES-8 to -15 observations were relatively coarse which limited their utility in severe storm forecasting after a storm became mature. GOES-R MDS data provides a new opportunity to determine how satellite-derived products could contribute to our understanding and detection of severe storms. This poster highlights recent research on severe storms supported by the NASA Weather and Atmospheric Dynamics Focus Area.
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Cloud property retrieval algorithms for passive satellite imagers are generally designed to take advantage of all the useful spectral information available for a particular satellite. This strategy optimizes accuracy and reduces misidentification and retrieval biases, particularly for modern satellites with many spectral channels. However, the application of dissimilar algorithms tailored for different satellite sensor scan present a problem within the climate data record (CDR). Algorithm inconsistencies can introduce artificial trends in the CDR that are tied to instrument changes rather than physical changes, especially when older satellites with limited spectral information are included. The NASA CERES (Clouds and the Earth’s Radiant Energy System) data record provides global cloud property retrievals across 23 years and more than 25 satellites. With the goal of producing a spatially and temporally continuous record of cloud properties, the CERES cloud working group has developed algorithms that use only 3 channels that are common to most geostationary satellite imagers: 0.65, 3.9, and 10.8 μm.