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View article: Measuring Sharpness of AI-Generated Meteorological Imagery
Measuring Sharpness of AI-Generated Meteorological Imagery Open
AI-based algorithms are emerging in many meteorological applications that produce imagery as output, including for global weather forecasting models. However, the imagery produced by AI algorithms, especially by convolutional neural networ…
View article: Leveraging Coproduction to Bridge Research and Operations in Operational Meteorology
Leveraging Coproduction to Bridge Research and Operations in Operational Meteorology Open
The benefits of collaboration between the research and operational communities during the research-to-operations (R2O) process have long been documented in the scientific literature. Operational forecasters have a practiced, expert insight…
View article: The Observed Availability of Data and Code in Earth Science and Artificial Intelligence
The Observed Availability of Data and Code in Earth Science and Artificial Intelligence Open
As the use of artificial intelligence (AI) has grown exponentially across a wide variety of science applications, it has become clear that it is critical to share data and code to facilitate reproducibility and innovation. AMS recently ado…
View article: Gridded Severe Hail Nowcasting Using 3D U-Nets, Lightning Observations, and the Warn-on-Forecast System
Gridded Severe Hail Nowcasting Using 3D U-Nets, Lightning Observations, and the Warn-on-Forecast System Open
Hailstorms cause billions of dollars in damage across the United States each year. Part of this cost could be reduced by increasing warning lead times. To contribute to this effort, we developed a nowcasting machine learning model that use…
View article: Using Generative Artificial Intelligence Creatively in the Classroom and Research: Examples and Lessons Learned
Using Generative Artificial Intelligence Creatively in the Classroom and Research: Examples and Lessons Learned Open
Although generative artificial intelligence (AI) is not new, recent technological breakthroughs have transformed its capabilities across many domains. These changes necessitate new attention from educators and specialized training within t…
View article: The value of convergence research for developing trustworthy AI for weather, climate, and ocean hazards
The value of convergence research for developing trustworthy AI for weather, climate, and ocean hazards Open
Artificial Intelligence applications are rapidly expanding across weather, climate, and natural hazards. AI can be used to assist with forecasting weather and climate risks, including forecasting both the chance that a hazard will occur an…
View article: Machine Learning Estimation of Maximum Vertical Velocity from Radar
Machine Learning Estimation of Maximum Vertical Velocity from Radar Open
The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite imag…
View article: AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography
AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography Open
The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) focuses on creating trustworthy AI for a variety of environmental and Earth science phenomena. AI2ES includes leading experts from AI…
View article: Identifying and Categorizing Bias in AI/ML for Earth Sciences
Identifying and Categorizing Bias in AI/ML for Earth Sciences Open
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 an ethical and responsible manner to minimize bi…
View article: Developing trustworthy AI for weather and climate
Developing trustworthy AI for weather and climate Open
By improving the prediction, understanding, and communication of powerful events in the atmosphere and ocean, artificial intelligence can revolutionize how communities respond to climate change.
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: Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences
Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences Open
Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthe…
View article: Machine Learning Estimation of Maximum Vertical Velocity from Radar
Machine Learning Estimation of Maximum Vertical Velocity from Radar Open
The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite imag…
View article: Application of the Technology Readiness Levels Framework to Natural Resource Management Tools
Application of the Technology Readiness Levels Framework to Natural Resource Management Tools Open
Technology advancements in fisheries science can provide useful tools to support natural resource management and conservation. However, new technologies may also present challenges for decision makers due to the lack of a standardized proc…
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: Masthead
Masthead Open
SYSTEMS publishes research on the development and application of methods in Artificial Intelligence (AI), Machine Learning (ML), data science, and statistics that is relevant to meteorology, atmospheric science, hydrology, climate science,…
View article: Toward Operational Real-Time Identification of Frontal Boundaries Using Machine Learning
Toward Operational Real-Time Identification of Frontal Boundaries Using Machine Learning Open
We present and evaluate a deep learning first-guess front-identification system that identifies cold, warm, stationary, and occluded fronts. Frontal boundaries play a key role in the daily weather around the world. Human-drawn fronts provi…
View article: A Review of Machine Learning for Convective Weather
A Review of Machine Learning for Convective Weather Open
We present an overview of recent work on using artificial intelligence (AI)/machine learning (ML) techniques for forecasting convective weather and its associated hazards, including tornadoes, hail, wind, and lightning. These high-impact p…
View article: A Machine Learning Tutorial for Operational Meteorology. Part II: Neural Networks and Deep Learning
A Machine Learning Tutorial for Operational Meteorology. Part II: Neural Networks and Deep Learning Open
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. To fill the dearth of resources covering neural networks with a mete…
View article: Trustworthy Artificial Intelligence for Environmental Sciences: An Innovative Approach for Summer School
Trustworthy Artificial Intelligence for Environmental Sciences: An Innovative Approach for Summer School Open
Many of our generation’s most pressing environmental science problems are wicked problems, which means they cannot be cleanly isolated and solved with a single “correct” answer. The NSF AI Institute for Research on Trustworthy AI in Weathe…
View article: Ethical and Responsible Use of AI/ML in the Earth, Space, and Environmental Sciences
Ethical and Responsible Use of AI/ML in the Earth, Space, and Environmental Sciences Open
AGU supports 130,000 enthusiasts to experts worldwide in Earth and space sciences.Through broad and inclusive partnerships, AGU aims to advance discovery and solution science that accelerate knowledge and create solutions that are ethical,…
View article: Challenges and Opportunities in Numerical Weather Prediction
Challenges and Opportunities in Numerical Weather Prediction Open
© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Jerald A. Brotzge, jeral…
View article: Masthead
Masthead Open
SYSTEMS publishes research on the development and application of methods in Artificial Intelligence (AI), Machine Learning (ML), data science, and statistics that is relevant to meteorology, atmospheric science, hydrology, climate science,…
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: A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning
A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning Open
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 to fill the dearth of resources covering neural networks wi…
View article: Masthead
Masthead Open
SYSTEMS publishes research on the development and application of methods in Artificial Intelligence (AI), Machine Learning (ML), data science, and statistics that is relevant to meteorology, atmospheric science, hydrology, climate science,…