112 citations
- Colorado State UniversityUS9 papers
- Cooperative Institute for Research in Environmental SciencesUS5 papers
- NOAA National Severe Storms LaboratoryUS3 papers
- NSF National Center for Atmospheric ResearchUS3 papers
- University of Colorado BoulderUS3 papers
- University of OklahomaUS3 papers
- Cooperative Institute for Mesoscale Meteorological StudiesUS2 papers
- NOAA Global Systems Laboratory2 papers
- Albany State UniversityUS1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- École Centrale de LyonFR1 paper
- Fundación Ciencias Exactas y NaturalesAR1 paper
11 papers
Using machine learning to downscale coarse-resolution environmental variables for understanding the spatial frequency of convective storms
Hungjui Yu, Lander Ver Hoef, Kristen L. Rasmussen +1
Global climate models (GCMs), typically run at ~100-km resolution, capture large-scale environmental conditions but cannot resolve convection and cloud processes at kilometer scale…
HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales
Daniel Abdi, Isidora Jankov, Paul Madden +5
The High-Resolution Rapid Refresh (HRRR) model is a convection-allowing model used in operational weather forecasting across the contiguous United States (CONUS). To provide a comp…
Predicting Tropical Cyclone Track Forecast Errors using a Probabilistic Neural Network
M. A. Fernandez, Elizabeth A. Barnes, Randal J. Barnes +4
A new method for estimating tropical cyclone track uncertainty is presented and tested. This method uses a neural network to predict a bivariate normal distribution, which serves a…
WOMBAT v2.S: A Bayesian inversion framework for attributing global CO flux components from multiprocess data
Josh Jacobson, Michael Bertolacci, Andrew Zammit-Mangion +2
Contributions from photosynthesis and other natural components of the carbon cycle present the largest uncertainties in our understanding of carbon dioxide (CO) sources and sin…
Machine Learning Estimation of Maximum Vertical Velocity from Radar
Randy J. Chase, Amy McGovern, Cameron Homeyer +2
The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updr…
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…