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MAKING THE BLACK BOX MORE TRANSPARENT: Understanding the Physical Implications of Machine Learning

Bulletin of the American Meteorological Society, 2019-11, Vol.100 (11), p.2175-2200

2019

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Deep Learning for Spatially Explicit Prediction of Synoptic-Scale Fronts

Weather and forecasting, 2019-08, Vol.34 (4), p.1137-1160

2019

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A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability

Geophysical research letters, 2024-07, Vol.51 (13), p.n/a

2024

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A Hybrid Analog-Ensemble–Convolutional-Neural-Network Method for Postprocessing Precipitation Forecasts

Monthly weather review, 2022-06, Vol.150 (6), p.1495

2022

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SOLAR ENERGY PREDICTION: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems

Bulletin of the American Meteorological Society, 2015-08, Vol.96 (8), p.1388-1395

2015

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Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

Journal of advances in modeling earth systems, 2020-03, Vol.12 (3), p.n/a

2020

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USING ARTIFICIAL INTELLIGENCE TO IMPROVE REAL-TIME DECISION-MAKING FOR HIGH-IMPACT WEATHER

Bulletin of the American Meteorological Society, 2017-10, Vol.98 (10), p.2073-2090

2017

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Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms

Monthly weather review, 2019-08, Vol.147 (8), p.2827-2845

2019

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Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science

Environmental Data Science, 2022, Vol.1, Article e6

2022

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Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

Journal of advances in modeling earth systems, 2021-12, Vol.13 (12), p.e2021MS002474-n/a

2021

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Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Earth and space science (Hoboken, N.J.), 2024-07, Vol.11 (7), p.n/a

2024

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Deep-Learning-Based Gridded Downscaling of Surface Meteorological Variables in Complex Terrain. Part I: Daily Maximum and Minimum 2-m Temperature

Journal of applied meteorology and climatology, 2020-12, Vol.59 (12), p.2057-2073

2020

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Deep-Learning-Based Gridded Downscaling of Surface Meteorological Variables in Complex Terrain. Part II: Daily Precipitation

Journal of applied meteorology and climatology, 2020-12, Vol.59 (12), p.2075-2092

2020

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Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction

Monthly weather review, 2020-07, Vol.148 (7), p.2837-2861

2020

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A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate

Earth and space science (Hoboken, N.J.), 2021-09, Vol.8 (9), p.n/a

2021

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Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management

Remote sensing (Basel, Switzerland), 2023-07, Vol.15 (13), p.3372

2023

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Calibration of Machine Learning–Based Probabilistic Hail Predictions for Operational Forecasting

Weather and forecasting, 2020-02, Vol.35 (1), p.149-168

2020

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Evaluation of statistical learning configurations for gridded solar irradiance forecasting

Solar energy, 2017-07, Vol.150, p.383-393

2017

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Exploring NWS Forecasters’ Assessment of AI Guidance Trustworthiness

Weather and forecasting, 2024-08, Vol.39 (8), p.1219-1241

2024

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Tornadic Supercell Environments Analyzed Using Surface and Reanalysis Data: A Spatiotemporal Relational Data-Mining Approach

Journal of applied meteorology and climatology, 2012-12, Vol.51 (12), p.2203-2217

2012

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Identifying and Categorizing Bias in AI/ML for Earth Sciences

Bulletin of the American Meteorological Society, 2024-03, Vol.105 (3), p.E567-E583

2024

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NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

Bulletin of the American Meteorological Society, 2022-07, Vol.103 (7), p.E1658-E1668

2022

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Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles

Weather and forecasting, 2017-10, Vol.32 (5), p.1819-1840

2017

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Classification of Convective Areas Using Decision Trees

Journal of atmospheric and oceanic technology, 2009-07, Vol.26 (7), p.1341-1353

2009

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MAKING THE BLACK BOX MORE TRANSPARENT: Understanding the Physical Implications of Machine Learning

Deep Learning for Spatially Explicit Prediction of Synoptic-Scale Fronts

A Machine Learning‐Based Approach to Quantify ENSO Sources of Predictability

A Hybrid Analog-Ensemble–Convolutional-Neural-Network Method for Postprocessing Precipitation Forecasts

SOLAR ENERGY PREDICTION: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

USING ARTIFICIAL INTELLIGENCE TO IMPROVE REAL-TIME DECISION-MAKING FOR HIGH-IMPACT WEATHER

Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms

Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Deep-Learning-Based Gridded Downscaling of Surface Meteorological Variables in Complex Terrain. Part I: Daily Maximum and Minimum 2-m Temperature

Deep-Learning-Based Gridded Downscaling of Surface Meteorological Variables in Complex Terrain. Part II: Daily Precipitation

Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction

A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate

Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management

Calibration of Machine Learning–Based Probabilistic Hail Predictions for Operational Forecasting

Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences

Evaluation of statistical learning configurations for gridded solar irradiance forecasting

Exploring NWS Forecasters’ Assessment of AI Guidance Trustworthiness

Tornadic Supercell Environments Analyzed Using Surface and Reanalysis Data: A Spatiotemporal Relational Data-Mining Approach

Identifying and Categorizing Bias in AI/ML for Earth Sciences

NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles

Classification of Convective Areas Using Decision Trees

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