324 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Colorado State UniversityUS13 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
- University of MinnesotaUS2 papers
- Albany State UniversityUS1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- École Centrale de LyonFR1 paper
5 papers · 1 filter
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…
Indicator patterns of forced change learned by an artificial neural network
Elizabeth A. Barnes, Benjamin Toms, James W. Hurrell +3
Many problems in climate science require the identification of signals obscured by both the "noise" of internal climate variability and differences across models. Following previou…
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
Benjamin A. Toms, Elizabeth A. Barnes, Imme Ebert-Uphoff
Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks lea…