Shawn Handler
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View article: A Machine Learning Explainability Tutorial for Atmospheric Sciences
A Machine Learning Explainability Tutorial for Atmospheric Sciences Open
With increasing interest in explaining machine learning (ML) models, this paper synthesizes many topics related to ML explainability. We distinguish explainability from interpretability, local from global explainability, and feature import…
View article: Weather-based ML Datasets
Weather-based ML Datasets Open
Included are three datasets from Handler et al. (2020), Flora et al. (2021), and Chase et al. (2022. 2023). Handler et al. (2020) is a 1-hr HRRR-based nowcasting dataset predicting frozen road surfaces. Flora et al. (2021) is a 0-150 min s…
View article: Weather-based ML Datasets
Weather-based ML Datasets Open
Included are three datasets and machine learning models from Handler et al. (2020), Flora et al. (2021), and Chase et al. (2022. 2023). Handler et al. (2020) is a 1-hr HRRR-based nowcasting dataset predicting frozen road surfaces. Flora et…
View article: Weather-based ML Datasets
Weather-based ML Datasets Open
Included are three datasets and machine learning models from Handler et al. (2020), Flora et al. (2021), and Chase et al. (2022. 2023). Handler et al. (2020) is a 1-hr HRRR-based nowcasting dataset predicting frozen road surfaces. Flora et…
View article: Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction Open
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 has learned, ML explainability has become a field of activ…
View article: Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement
Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement Open
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 aspects of ML models. This study distinguishes explainability…
View article: Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System
Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System Open
A primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0–3 h) severe weather forecasts. Postproc…
View article: Development of a Probabilistic Subfreezing Road Temperature Nowcast and Forecast Using Machine Learning
Development of a Probabilistic Subfreezing Road Temperature Nowcast and Forecast Using Machine Learning Open
In this study, a machine learning algorithm for generating a gridded CONUS-wide probabilistic road temperature forecast is presented. A random forest is used to tie a combination of HRRR model surface variables and information about the ge…
View article: Radar-Observed Bulk Microphysics of Midlatitude Leading-Line Trailing-Stratiform Mesoscale Convective Systems
Radar-Observed Bulk Microphysics of Midlatitude Leading-Line Trailing-Stratiform Mesoscale Convective Systems Open
In 2013, all NEXRAD WSR-88D units in the United States were upgraded to dual polarization. Dual polarization allows for the identification of precipitation particle shape, size, orientation, and concentration. In this study, dual-polarizat…