Benjamin Scarino (NASA)
Title: Research Physical Scientist
Technical Focus Area: Climate Science, Applied Science
Study Topics: Imager Inter-calibration, Spectral Band Adjustment Factors, Land Surface Temperature Retrieval, Multilayer Cloud Retrieval, Severe Convection, Deep Learning.
Missions/Projects: CERES, SatCORPS
Email: benjamin.r.scarino@nasa.gov
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About:
Ben Scarino is a Research Scientist in the Climate Science Branch of the Science Directorate at the NASA Langley Research Center, where he is involved with the CERES Cloud Working Group, the CERES Imager and Geostationary Calibration Group, and the Satellite Severe Convection Research Group. In support of Earth Radiation Budget studies, Ben enhances global multispectral cloud property retrieval algorithms and develops satellite narrow-band imager calibration products. Specifically, he is an expert on stability monitoring techniques as applied to Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) space-based imagers, developing signal-correction algorithms for inter-instrument spectral band differences using hyperspectral detectors, and delivering absolute radiometric calibrations for visible and infrared channels using pseudo invariant Earth target and ray-matching approaches. Furthermore, Ben specializes in the improvement of anisotropy-corrected land surface temperature retrievals from satellites for use in data assimilation and Earth energy budget studies. He is also experienced in analyzing severe storm potential and developing climatologies using satellite-based identification of overshooting convective cloud tops. Ben has a strong interest in machine learning and has employed deep neural network approaches to various Earth remote sensing tasks, including cloud retrieval augmentation, imagery anomaly extraction, surface skin temperature estimation, severe hail prediction, and imager stability monitoring.
Publication Bibliography:
Publications:
- A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection, https://doi.org/10.5194/amt-13-5491-2020
- Deriving severe hail likelihood from satellite observations and model reanalysis parameters using a deep neural network https://doi.org/10.1175/AIES-D-22-0042.1
- A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection https://doi.org/10.5194/amt-13-5491-2020
- Evaluating the magnitude of VIIRS out-of-band response for varying Earth spectra https://doi.org/10.3390/rs12193267
- Global clear-sky surface skin temperature from multiple satellites using a single-channel algorithm with angular anisotropy corrections https://doi.org/10.5194/amt-10-351-2017
- A web-based tool for calculating spectral band difference adjustment factors derived from SCIAMACHY hyperspectral data https://doi.org/10.1109/TGRS.2015.2502904
- Retrieving clear-sky surface skin temperature for numerical weather prediction applications from geostationary satellite data https://doi.org/10.3390/rs5010342
Notable Awards:
- NASA Customer Performance Award (2019) for exceptional achievement in development and application of geostationary satellite calibration protocols for CERES
- NASA Group Achievement Awards:
- SEAC4RS Field Campaign (2015)
- The CERES Cloud Property Retrieval Subsystem (2014)
- MACPEX Field Campaign (2012)
- NASA Customer Performance Award (2011) for outstanding performance developing a cross-calibration technique for MODIS and SCIAMACHY radiances using simultaneous nadir overpass measurements
Related Websites:
Education/Professional Experience:
- M.S., Meteorology, The Pennsylvania State University
- B.S., Meteorology, The Pennsylvania State University
Hobbies/Interests:
- Woodworking, DIY