Bio
Deeksha Rastogi is a Research Scientist in the Computational Urban Sciences Group within the Computational Science and Engineering Division at Oak Ridge National Laboratory since 2019. Before her current role, she worked as a Research Associate in the Computational Earth Sciences Group at ORNL during 2013-2019. She has 15 years of experience working in the field of atmospheric sciences. Her research focuses on understanding weather extremes, and associated impacts on natural and human systems at varying spatiotemporal scales. She employs high-performance computing and earth system modeling tools and scientific data analysis to achieve these objectives.
Deeksha has been actively involved in numerous projects supported by the U.S. Department of Energy, making significant contributions to the development of several high-resolution, physics-based datasets. She has published 30 peer-reviewed articles, 5 datasets, 6 technical reports, and 1 encyclopedia book chapter. She serves as the principal investigator for a DOE-funded project that aims to classify storm types in high-resolution datasets, facilitating storm-type-based precipitation frequency analysis. She also leads an ORNL Laboratory Directed Research and Development (LDRD) project that leverages cutting-edge artificial intelligence tools to support impact evaluations and establish a benchmark for future efforts in assessing the effects of weather extremes using big data and high-performance computing applications.
Deeksha earned her PhD in Energy Science and Engineering from the Bredesen Center at the University of Tennessee, Knoxville in 2019. She obtained her MS in Atmospheric Sciences from the University of Illinois Urbana Champaign in 2012 and her BS (B. Tech.) in Environmental Engineering from the Indian School of Mines, Dhanbad, India in 2010.
Deeksha received the Bredesen Center Fellowship from The University of Tennessee, Knoxville, TN from 2016 to 2019 and the Graduate Student Fellowship in the Advanced Study Program at the National Center for Atmospheric Research, Boulder, CO in 2019. Recognizing her contributions, she received the Graduate Student Researcher Award in the Science and Technology Category of the UT-Battelle Awards in 2019. Additionally, she was granted the ORNL LDRD Early Career Award in 2023.
Datasets
CMIP6-based Multi-model Hydroclimate Projection over the Conterminous US V1.1
CMIP6-based Multi-model Hydroclimate Projection over the Conterminous US
(https://hydrosource.ornl.gov/dataset/9505V3)
Thermodynamic Global Warming (TGW) Simulation Datasets
(https://data.msdlive.org/records/ksw6r-2xv06)
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every County in the Conterminous US
(https://doi.org/10.5281/zenodo.10420668)
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation