112 citations · 295 across the 7 of their papers we have counts for
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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…
A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning
Randy J. Chase, David R. Harrison, Gary Lackmann +1
Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order…
Global Extreme Heat Forecasting Using Neural Weather Models
Ignacio Lopez-Gomez, Amy McGovern, Shreya Agrawal +1
Heat waves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disrup…
A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning
Randy J. Chase, David R. Harrison, Amanda Burke +2
Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unav…