LEXIE YANG: Novel approaches developed by scientists including Lexie Yang of ORNL scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.
SARAT SREEPATHI: Knowledge is pivotal to help societies wanting to make their communities more resilient and to determine the best strategies to mitigate the impacts of extreme climate events.
MELISSA DUMAS: Climate change impacts differ in cities versus rural areas. Melissa Allen Dumas's research looks at how weather and atmospheric conditions, people's behavior and activities and the presence of concrete and asphalt impact urban neighborhoods.
SCOTT PAINTER: New information about what's happening in rivers — and the microbially active zones adjacent to them — can lead to a clearer understanding of how water quality will be impacted by climate change, land use and population growth.
BANDANA KAR: Using novel data sets and computing systems, ORNL's Bandana Kar and colleagues simulate how climate change affects the safety and security of the country.
BUDHU BHADURI: During an invited presentation at AGU, Budhu Bhaduri will examine the challenge in processing large volumes of high-resolution earth observation and simulation data quickly into useful tools for those providing time-critical crisis response.
FORREST HOFFMAN: Computational earth system scientist Forrest Hoffman and his team make sophisticated climate models even smarter by "listening" to the earth and determining how environmental cycles and interactions may impact overall earth health.
DEEKSHA RASTOGI: Oak Ridge National Laboratory computational scientist Deeksha Rastogi partners with colleagues in high-performance computing to understand the human impacts of climate change.
MOET ASHFAQ: While exploring the impact of greenhouse gases in the air and atmospheric conditions at monsoon season, ORNL's Moet Ashfaq and colleagues used an unprecedented ensemble of climate model data for new insights of monsoon projections at regional scale.
Lexie Yang
Exploring Spatially Distributed Deep Learning Models for Global Gravitational Mapping
Novel approaches developed by scientists including Lexie Yang of ORNL scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days. Comprehensive image data is useful for stakeholders to make informed decisions. These new computational workflows use deep learning techniques to train and deploy models to better address location, environmental and time challenges when mapping structures. Yang's AGU presentation will home in on global gravitational mapping.
Session: Monday, Dec. 13
Related story: Modeling – Efficient infrastructure mapping
Sarat Sreepathi
Accelerating Earth System Predictability: Advances in High-Performance Computing, Numerical Modeling, Artificial Intelligence and Machine Learning I
Knowledge is pivotal to help societies wanting to make their communities more resilient and to determine the best strategies to mitigate the impacts of extreme climate events. Scientists such as Sarat Sreepathi and his team are using cutting-edge high-performance computing methods — machine learning and artificial intelligence — to address the challenge of predicting what earth systems might do next. In this AGU presentation, Sreepathi will walk through novel approaches toward achieving Earth System Predictability.
Session: Tuesday, Dec. 14
Related story: New exascale earth modeling system for energy
Melissa Allen Dumas
Optimal Resolution Urban Terrain Inputs to Microclimate Modeling for Local Climate Decision Support
Climate change impacts differ in cities versus rural areas. Melissa Allen Dumas's research looks at how weather and atmospheric conditions, people's behavior and activities, and the presence of concrete and asphalt impact urban neighborhoods. This information makes for smarter climate models that could help neighborhoods adapt and develop better climate change mitigation strategies. Dumas will lead an AGU presentation of her team's work.
Session: Tuesday, Dec. 14
Related story: Melissa Allen: The atmosphere’s the limit
Scott Painter
Toward More Mechanistic Representations of Biogeochemical Processes in RIver Networks: Implementation and Demonstration of a Multiscale Model
New information about what's happening in rivers — and the microbially active zones adjacent to them — can lead to a clearer understanding of how water quality will be impacted by climate change, land use and population growth. Scott Painter and colleagues at ORNL developed a new modeling capability that incorporates important biogeochemical processes in river networks, the focus of this AGU presentation.
Session: Wednesday, Dec. 15
Related story: Modeling – Predicting water quality
Bandana Kar
Earth Observations and Imagery Science to Assess and Forecast Risk and Resilience of Communities and Infrastructures Due to Climate Change I Oral
Using novel data sets and computing systems, ORNL's Bandana Kar and colleagues simulate how climate change affects the safety and security of the country. This research, which is the focus of her AGU presentation, can help policy and decision makers at federal, state and local levels quickly identify risk factors and develop real-world mitigation strategies.
Session: Thursday, Dec. 16
Related story: Researchers anticipate, help prevent national security consequences of climate crises
Budhu Bhaduri
GeoAI and Open Data for Time Critical Mission Support
During an invited presentation at AGU, Budhu Bhaduri will examine the challenge in processing large volumes of high-resolution Earth observation and simulation data quickly into useful tools for those providing time-critical crisis response. Bhaduri, director of ORNL's Geospatial Science and Human Security Division, will highlight progress and challenges of some of the emerging approaches, including machine learning/artificial intelligence and high-performance computing, illustrated with human dynamics, sustainable energy and urban infrastructure resiliency.
Session: Thursday, Dec. 16
Related story: ORNL-led team recognized for impactful sustainability research
Forrest Hoffman
Improving Earth System Predictability: New Mechanisms, Feedbacks and Approaches for Predicting Global Biogeochemical Cycles in Earth System Models
Computational Earth system scientist Forrest Hoffman and his team make sophisticated climate models even smarter by "listening" to the Earth and determining how environmental cycles and interactions may impact overall Earth health. His AGU presentation will explain how this can be applied to better predict future carbon dioxide levels in the atmosphere.
Session: Friday, Dec. 17
Related story: ORNL expertise supports latest IPCC report and efforts to understand, address climate change
Deeksha Rastogi
How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections?
Oak Ridge National Laboratory computational scientist Deeksha Rastogi partners with colleagues in high-performance computing to understand the human impacts of climate change. She uses Earth system modeling and scientific data analysis to project how climate change is likely to affect electricity demand, hydroelectric power generation, critical infrastructure and human health. Her AGU presentation will focus on techniques to improve these models, particularly related to future hydroclimate projections.
Session: Friday, Dec. 17
Related story: Deeksha Rastogi: Modeling climate extremes for community impact
Moet Ashfaq
Robust Late Twenty-First Century Shift in the Regional Monsoons in RegCM-CORDEX Simulations
While exploring the impact of greenhouse gases in the air and atmospheric conditions during and around monsoon season, ORNL's Moet Ashfaq and colleagues used an unprecedented ensemble of climate model data for new insights of monsoon projections at regional scale. Ashfaq's presentation at AGU will explain the robust shifts in regional monsoon modeling simulations.
Session: Friday, Dec. 17
Related story: Computing collaboration reveals global ripple effect of shifting monsoons
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