AI-Assisted Parameter Tuning Will Speed Development and Clarify Uncertainty in E3SM
Focal Area: This idea is best aligned with predictive modeling. It targets Earth System Model improvement and uncertainty quantification
54 ENVIRONMENTAL SCIENCES↗
SEARCH · Engineering Papers
Results for “Mathematics and Computing, Geosciences”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
AI-Automated Detection of Subgrid-scale Processes for Adaptivity Guidance
Focal Area(s) The proposed white paper focuses on focal areas 2 and 3, and intends to build a pipeline to automate the detection of unresolved processes based on model errors and assist subsequent modeling solutions.
54 ENVIRONMENTAL SCIENCES↗
AI-Based Approach for Advancing the Understanding of Spatiotemporal Drought Characteristics
Focal Area (3): Our proposal will focus on insight gleaned from observed and simulated complex data by using artificial intelligence (AI), big data analytics, and other advanced methods.
54 ENVIRONMENTAL SCIENCES↗
AI-Constrained Bottom-Up Ecohydrology and Improved Prediction of Seasonal, Interannual, and Decadal Flood and Drought Risks
Focal Areas: (2) Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system composed of a hierarchy of models (3)Insight gleaned from complex data (both observed & simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI
54 ENVIRONMENTAL SCIENCES↗
AI-Directed Adaptive Multifidelity Modeling of Water Availability and Quality at River Basin Scales
Focal Area: (2) Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models
54 ENVIRONMENTAL SCIENCES↗
AI-Driven Cross-Domain Knowledge Discoveryand Hypotheses Generation for Enhanced Earth System Predictability
Focal Area(s): 1. Data acquisition, multimodal data fusion and data-to-knowledge and knowledge-to-code transformations enabled by scaling AI and unsupervised deep learning. 2. Insights (cross-domain knowledge) extracted from complex data (both observational and simulated) using AI-driven approaches and large-scale data analytics.
54 ENVIRONMENTAL SCIENCES↗
AI-Driven Data Discovery to Improve Earth System Predictability
Focal Area(s): (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI.
54 ENVIRONMENTAL SCIENCES↗
An AI-Assisted Approach to Represent Human Influence on Surface and Subsurface Hydrology
Focal Area(s): (2) Predictive modeling through the use of AI techniques and AI-derived model components: This white paper focuses on using artificial intelligence (AI) to discover connections across processes and scales and to represent those processes in predictive models of anthropogenic impacts on the water cycle.
54 ENVIRONMENTAL SCIENCES↗
An AI-Enabled MODEX Framework for Improving Predictability of Subsurface Water Storage across Local and Continental Scales
Focal Area: (2) Predictive modeling through the use of AI techniques and AI-derived model components. (3) Insight gleaned from complex data using AI, big data analytics, and other advanced methods. We propose an AI-enabled model-experiment (MODEX) framework to improve the predictability of subsurface water storage (SWS) from local to conus scales in a changing environment by taking advantage of DOE’s observation and simulation capabilities, as well as to inform the model and the observation development.
54 ENVIRONMENTAL SCIENCES↗
Assessing Teleconnections-Induced Predictability of Regional Water Cycle on Seasonal to Decadal Timescales Using Machine Learning Approaches
Focal Area: (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: Predictive understanding of the regional water cycle based on teleconnections of climate modes of variability using AI methods.
54 ENVIRONMENTAL SCIENCES↗
Automated Custom Calibration for E3SM
Focal Area(s): Automated custom calibration for the Energy Exascale Earth System Model (E3SM) applies primarily to focal area 2: Predictive modeling through the use of AI techniques, and includes elements of focal area 3: Insight gleaned from complex data (both observed and simulated).
54 ENVIRONMENTAL SCIENCES↗
Automated Discovery of DOminaNt physics Informed Surrogates (ADDONIS) Framework for Improving Water Cycling Predictability
Focal Areas: The primary alignment of the proposed framework is with focal area 2, because it guides the construction of a hierarchy of predictive models generated from Statistical and AI/ ML techniques. There are connections to focal areas 1 and 3 because surrogates allow for coupled data assimilation techniques where the full model is too expensive (1) and the discovered balance laws can be used as an insightful analysis tool to validate the structure of models (3).
54 ENVIRONMENTAL SCIENCES↗
Automation is all you need: Faster Earth system models with AI/ML
Focal Area: Data acquisition and assimilation enabled by machine learning (ML), artificial intelligence (AI) and advanced methods. Science Challenge: Tropical cyclones can in- duce extreme water cycle events through dramatic precipitation and storm surge. More reliable models of intensity will translate into better prediction of the impact of extreme events in large scale Earth systems simulations. We demonstrate and describe AI/ML methodologies for rapid assimilation of new, in situ data products.
54 ENVIRONMENTAL SCIENCES↗
Black-Box Neural System Identification and Differentiable Programming to Improve Earth System Model Predictions February
Focal Area(s): Focal Area 2: AI for predictive modeling, including AI-derived model components, and AI-enabled applications including parameter optimization, data assimilation, and uncertainty quantification. Focal Area 1: AI-assisted data assimilation using numerical Earth system models. Science Challenge: Earth system models have structural biases that lead to uncertain predictions, and their complexity and expense makes it difficult constraining the models with data or improved physical understanding.
54 ENVIRONMENTAL SCIENCES↗
Bridging Multiscale Processes in Earth System Models with Physics-Guided Hierarchical Machine Learning
Focal Area(s): The focal area of this whitepaper is, “Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection).” The ideas and frameworks described herein are deemed site agnostic. As a use case, this group will initially focus on the coupling between land processes, surface/subsurface hydrological processes, coastal processes, and human activities in the U.S. Gulf states.
54 ENVIRONMENTAL SCIENCES↗
Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics
Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.
54 ENVIRONMENTAL SCIENCES↗
Characterization of Extreme Hydroclimate Events in Earth System Models using ML/AI
Focal Area(s): (1) We put forward the concepts of data assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization and unsupervised learning applied to downscale information within Earth System Models (ESMs). (2) We discuss predictive modeling through the use of AI techniques and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection) to improve the characterization of extreme hydroclimate events in ESMs. The AI-based models will run five-six order of magnitude times faster, yet will provide similar accuracy, allowing us to provide range bounds on uncertainty faster and thus enabling faster extreme event identification. (3) Further, we consider the insight gleaned from complex data (observed/simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI to improve the characterization of extreme hydroclimate events in ESMs.
54 ENVIRONMENTAL SCIENCES↗
Climate Intervention Assessment and Attribution
Focal Area(s): To ensure the US is positioned to scientifically attribute noncooperative climate interventions, we propose developing a foundational analytical framework for increased confidence in the assessment and attribution of observed changes in the climate. This framework will enable probabilistic attribution of prospective climate interventions. The target is to glean insight from complex Earth system data (Focal Area 3) by using AI to develop a prediction system comprised of a hierarchy of models (Focal Area 2).
54 ENVIRONMENTAL SCIENCES↗