Benjamin Scarino (SSAI)
Title: Senior Research Scientist
Technical Focus Area: Climate Science, Applied Science
Study Topics: Satellite instrument inter-calibration, cloud retrieval, satellite-based severe weather identification, land and sea surface temperature measurements, deep learning
Missions/Projects: CERES, LaRC Convective Weather Research
Ben Scarino has over twelve years of experience conducting research in the field of atmospheric science. He is a Senior Research Scientist with Science Systems and Applications, Inc. (SSAI), Hampton, VA, USA, supporting the Climate Science Branch of the NASA Langley Research Center Science Directorate. As part of his work for the Clouds and the Earth’s Radiant Energy System (CERES) Program, he supports the development of global multispectral cloud and surface property retrieval algorithms as well as satellite calibration/validation efforts. He is an expert in meteorology and has extensive experience with scientific analysis and data science. He is a specialist on inter-calibration techniques for Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites, as well as on determining corrections for instrument spectral band differences using hyperspectral detectors. Furthermore, he developed a bidirectional reflectance distribution function (BRDF) model used to quantify expected cloud reflectance to facilitate improved satellite-based anvil cloud detection, has worked on machine learning methods of predicting severe weather based on such anvil and overshooting cloud top observations along with numerical weather model reanalysis, and has specialized skill in using GEO and LEO satellite retrievals to derive anisotropy-corrected high-resolution skin temperature data sets.
- 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
- M.S., Meteorology, The Pennsylvania State University
- B.S., Meteorology, The Pennsylvania State University
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