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Lexie yang
Exploration of spatially distributed deep learning models for global gravitational mapping
New approaches developed by scientists, including ORNL’s Lexie Yang, analyze massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours rather than in hours. days. Comprehensive image data is useful for stakeholders to make informed decisions. These new IT 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 focus on global gravity mapping.
Session: Monday, December 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 essential to help societies that wish to make their communities more resilient and 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 meet the challenge of predicting what Earth systems might do next. In this AGU presentation, Sreepathi will present new approaches to achieve Earth system predictability.
Session: Tuesday, December 14
Related Story: New Exascale Earth Modeling System for Energy
Melissa Allen Dumas
Optimal resolution urban land entries for microclimate modeling for local climate decision support
The impacts of climate change differ in cities and in rural areas. Melissa Allen Dumas’ research focuses on the impact of weather and atmospheric conditions, people’s behavior and activities, and the presence of concrete and asphalt on urban neighborhoods. This information creates smarter climate models that could help neighborhoods adapt and develop better climate change mitigation strategies. Dumas will lead an AGU presentation of his team’s work.
Session: Tuesday, December 14
Related Story: Melissa Allen: The Atmosphere is the Limit
Painter Scott
Towards more mechanistic representations of biogeochemical processes in river networks: implementation and demonstration of a multiscale model
New information about what is happening in rivers – and the microbial areas adjacent to them – may provide a better understanding of how water quality will be affected by climate change, land use and population growth. Scott Painter and his colleagues at ORNL have developed a new modeling capability that integrates important biogeochemical processes in river systems, the subject of this AGU presentation.
Session: Wednesday, December 15
Related story: Modeling – Predicting water quality
Bandana Kar
Earth Observation and Imagery Science to assess and forecast the risks and resilience of communities and infrastructure due to climate change I Oral
Using new data sets and computer systems, Bandana Kar and his colleagues at ORNL simulate how climate change affects the safety and security of the country. This research, which is the focus of his AGU presentation, can help policymakers at federal, state and local levels quickly identify risk factors and develop real-world mitigation strategies.
Session: Thursday, December 16
Related article: Researchers Anticipate and Help Prevent National Security Consequences of Climate Crises
Budhu bhaduri
GeoAI and Open Data for assistance with urgent missions
In a guest presentation at AGU, Budhu Bhaduri will examine the challenge of rapidly processing large volumes of high-resolution Earth observation and simulation data into useful tools for those providing emergency crisis response. Bhaduri, director of the geospatial sciences and human security division at ORNL, will highlight the progress and challenges of some of the emerging approaches, including machine learning / artificial intelligence and high performance computing, pictured by human dynamics, sustainable energy and the resilience of urban infrastructures.
Session: Thursday, December 16
Related story: ORNL-led team recognized for impactful sustainability research
Forrest hoffman
Improving Earth System Predictability: New Mechanisms, Feedbacks, and Approaches to Predict Global Biogeochemical Cycles in Earth System Models
Earth system computer scientist Forrest Hoffman and his team make sophisticated climate models even smarter by “listening” to the Earth and determining how environmental cycles and interactions can impact the overall health of the Earth. His presentation at AGU will explain how this can be applied to better predict future levels of carbon dioxide in the atmosphere.
Session: Friday, December 17
Related Story: ORNL’s Expertise Supports Latest IPCC Report and Efforts to Understand and Address Climate Change
Deeksha Rastogi
How can the choice of downscaling techniques and baseline meteorological observations affect future hydroclimatic projections?
Deeksha Rastogi, a computer scientist at Oak Ridge National Laboratory, partners with colleagues in high performance computing to understand the human impacts of climate change. It 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. His presentation at the AGU will focus on techniques for improving these models, particularly in connection with future hydroclimatic projections.
Session: Friday, December 17
Related Story: Deeksha Rastogi: Modeling Climate Extremes for Community Impact
Moët Ashfaq
Robust late 21st century change in regional monsoons in RegCM-CORDEX simulations
While exploring the impact of greenhouse gases in the air and atmospheric conditions during and around the monsoon, ORNL’s Moet Ashfaq and his colleagues used an unprecedented set of climate model data for new information on regional monsoon projections. Ashfaq’s presentation at AGU will explain the important changes in regional monsoon modeling simulations.
Session: Friday, December 17
Related Story: Computer Collaboration Reveals Global Ripple Effect of Changing Monsoons
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