210 citations · 356 across the 8 of their papers we have counts for
8 papers
Developing an Offshore Machine Learning Surface Layer Scheme
Susan Dettling, Sue Ellen Haupt, Thomas Brummet +3
Turbulent fluxes between the surface and the atmosphere are typically parameterized using empirically fit relationships. Here we test machine learning techniques for fitting the re…
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
John S. Schreck, David John Gagne, Charlie Becker +13
Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates an…
Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management
John S. Schreck, William Petzke, Pedro A. Jimenez +5
Monitoring the fuel moisture content (FMC) of vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations with numer…
Mimicking non-ideal instrument behavior for hologram processing using neural style translation
John S. Schreck, Matthew Hayman, Gabrielle Gantos +2
Holographic cloud probes provide unprecedented information on cloud particle density, size and position. Each laser shot captures particles within a large volume, where images can…
Neural network processing of holographic images
John S. Schreck, Gabrielle Gantos, Matthew Hayman +2
HOLODEC, an airborne cloud particle imager, captures holographic images of a fixed volume of cloud to characterize the types and sizes of cloud particles, such as water droplets an…
The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences
Amy McGovern, Imme Ebert-Uphoff, David John Gagne +1
Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion…
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
Eric Zelikman, Sharon Zhou, Jeremy Irvin +7
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-…
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
David John Gagne, Hannah M. Christensen, Aneesh C. Subramanian +1
Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some ex…