A machine learning explainability tutorial for atmospheric sciences
With increasing interest in explaining machine learning (ML) models, this paper synthesizes
many topics related to ML explainability. We distinguish explainability from interpretability …
many topics related to ML explainability. We distinguish explainability from interpretability …
Warn-on-forecast system: From vision to reality
PL Heinselman, PC Burke, LJ Wicker… - Weather and …, 2024 - journals.ametsoc.org
In 2009, advancements in NWP and computing power inspired a vision to advance
hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This …
hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This …
Explainable machine learning-based prediction for aerodynamic interference of a low-rise building on a high-rise building
B Yan, W Ding, Z Jin, L Zhang, L Wang, M Du… - Journal of Building …, 2024 - Elsevier
Interference effects between buildings may significantly change the wind pressure
distribution on building façades and cause severe safety problems. In this study, a two-stage …
distribution on building façades and cause severe safety problems. In this study, a two-stage …
Exploring NWS Forecasters' Assessment of AI Guidance Trustworthiness
As artificial intelligence (AI) methods are increasingly used to develop new guidance
intended for operational use by forecasters, it is critical to evaluate whether forecasters …
intended for operational use by forecasters, it is critical to evaluate whether forecasters …
Probabilistic Convective Initiation Nowcasting Using Himawari-8 AHI with Explainable Deep Learning Models
Convective initiation (CI) nowcasting is crucial for reducing loss of human life and property
caused by severe convective weather. A novel deep learning method based on the U-Net …
caused by severe convective weather. A novel deep learning method based on the U-Net …
Identifying and Categorizing Bias in AI/ML for Earth Sciences
Artificial intelligence (AI) can be used to improve performance across a wide range of Earth
system prediction tasks. As with any application of AI, it is important for AI to be developed in …
system prediction tasks. As with any application of AI, it is important for AI to be developed in …
Machine Learning Investigation of Downburst Prone Environments in Canada
Thunderstorms are recognized as one of the most disastrous weather threats in Canada
because of their power to cause substantial damage to human-made structures and even …
because of their power to cause substantial damage to human-made structures and even …
[HTML][HTML] Thunderstorm prediction during pre-tactical air-traffic-flow management using convolutional neural networks
Thunderstorms can be a large source of disruption for European air-traffic management
causing a chaotic state of operation within the airspace system. In current practice, air-traffic …
causing a chaotic state of operation within the airspace system. In current practice, air-traffic …
A User-Focused Approach to Evaluating Probabilistic and Categorical Forecasts
A user-focused verification approach for evaluating probability forecasts of binary outcomes
(also known as probabilistic classifiers) is demonstrated that is (i) based on proper scoring …
(also known as probabilistic classifiers) is demonstrated that is (i) based on proper scoring …
Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model
An ensemble postprocessing method is developed for the probabilistic prediction of severe
weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS) …
weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS) …