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
4 papers · 1 filter
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…
A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science
Lander Ver Hoef, Henry Adams, Emily J. King +1
Topological data analysis (TDA) is a tool from data science and mathematics that is beginning to make waves in environmental science. In this work, we seek to provide an intuitive…
Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?
Ryan Lagerquist, Imme Ebert-Uphoff
In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise veri…