13 citations · 24 across the 2 of their papers we have counts for
4 papers
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction
Montgomery Flora, Corey Potvin, Amy McGovern +1
Machine learning (ML) models are becoming increasingly common in the atmospheric science community with a wide range of applications. To enable users to understand what an ML model…
Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement
Montgomery Flora, Corey Potvin, Amy McGovern +1
With increasing interest in explaining machine learning (ML) models, the first part of this two-part study synthesizes recent research on methods for explaining global and local as…
Using Machine Learning to Calibrate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System
Montgomery Flora, Corey K. Potvin, Patrick S. Skinner +2
A primary goal of the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecaste…
Possible Implications of Self-Similarity for Tornadogenesis and Maintenance
Pavel Bělík, Brittany Dahl, Douglas Dokken +3
Self-similarity in tornadic and some non-tornadic supercell flows is studied and power laws relating various quantities in such flows are demonstrated. Magnitudes of the exponents…