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
15 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…
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…
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience
Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff
Convolutional neural networks (CNNs) have recently attracted great attention in geoscience due to their ability to capture non-linear system behavior and extract predictive spatiot…
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…
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset
Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes
Despite the increasingly successful application of neural networks to many problems in the geosciences, their complex and nonlinear structure makes the interpretation of their pred…
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…
Dual constant-flux energy cascades to both large scales and small scales
Annick Pouquet, Raffaele Marino, Pablo D. Mininni +1
In this paper, we present an overview of concepts and data concerning inverse cascades of excitation towards scales larger than the forcing scale in a variety of contexts, from two…
Variational Data Assimilation via Sparse Regularization
A. M. Ebtehaj, M. Zupanski, G. Lerman +1
This paper studies the role of sparse regularization in a properly chosen basis for variational data assimilation (VDA) problems. Specifically, it focuses on data assimilation of n…