Recently added

A Data-Driven Probabilistic Network Approach to Assess Model Similarity in CMIP Ensembles

Graafland, Catharina Elisabeth; Brands, Swen; Gutiérrez, José Manuel (American Meteorological Society, 2024)

Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea

Kim, Junsu; Kim, Yeon-Hee; Bok, Hyejeong; Jang, Sungbin; Cho, Eunju; Kim, Seungbum (American Meteorological Society, 2024)

Superresolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network

White, Charles H.; Ebert-Uphoff, Imme; Haynes, John M.; Noh, Yoo-Jeong (American Meteorological Society, 2024)

Global Extreme Heat Forecasting Using Neural Weather Models

Lopez-Gomez, Ignacio; McGovern, Amy; Agrawal, Shreya; Hickey, Jason (American Meteorological Society, 2023)

AMS Publications Support for Open, Transparent, and Equitable Research

Schuster, Douglas; Friedman, Michael (American Meteorological Society, 2023)

Global Extreme Heat Forecasting Using Neural Weather Models

Lopez-Gomez, Ignacio; McGovern, Amy; Agrawal, Shreya; Hickey, Jason (American Meteorological Society, 2023)

Emulating the Adaptation of Wind Fields to Complex Terrain with Deep Learning

Le Toumelin, Louis; Gouttevin, Isabelle; Helbig, Nora; Galiez, Clovis; Roux, Mathis; Karbou, Fatima (American Meteorological Society, 2023)

A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions

Chen, Jiaxin; Ashton, Ian G. C.; Steele, Edward C. C.; Pillai, Ajit C. (American Meteorological Society, 2023)

Bias Correcting Climate Model Simulations Using Unpaired Image-to-Image Translation Networks

Fulton, D. James; Clarke, Ben J.; Hegerl, Gabriele C. (American Meteorological Society, 2023)

Photographic Visualization of Weather Forecasts with Generative Adversarial Networks

Sigg, Christian; Cavallaro, Flavia; Günther, Tobias; Oswald, Martin R. (American Meteorological Society, 2023)

Convective-Scale Assimilation of Cloud Cover from Photographs Using a Machine Learning Forward Operator

Reinhardt, Maria; Schoger, Sybille Y.; Kurzrock, Frederik; Potthast, Roland (American Meteorological Society, 2023)

Deep Learning–Based Parameter Transfer in Meteorological Data

Farokhmanesh, Fatemeh; Höhlein, Kevin; Westermann, Rüdiger (American Meteorological Society, 2023)

Physics-Informed Deep Neural Network for Backward-in-Time Prediction: Application to Rayleigh–Bénard Convection

Hammoud, Mohamad Abed El Rahman; Alwassel, Humam; Ghanem, Bernard; Knio, Omar; Hoteit, Ibrahim (American Meteorological Society, 2023)

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Dematties, Dario; Raut, Bhupendra A.; Park, Seongha; Jackson, Robert C.; Shahkarami, Sean; Kim, Yongho; Sankaran, Rajesh; Beckman, Pete; Collis, Scott M.; Ferrier, Nicola (American Meteorological Society, 2023)

Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications

Haynes, Katherine; Lagerquist, Ryan; McGraw, Marie; Musgrave, Kate; Ebert-Uphoff, Imme (American Meteorological Society, 2023)

A Hybrid Physics–AI Model to Improve Hydrological Forecasts

Duan, Yanan; Akula, Sathish; Kumar, Sanjiv; Lee, Wonjun; Khajehei, Sepideh (American Meteorological Society, 2023)

Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience

Mamalakis, Antonios; Barnes, Elizabeth A.; Ebert-Uphoff, Imme (American Meteorological Society, 2023)

Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States

Osborne, Andrew P.; Zhang, Jian; Simpson, Micheal J.; Howard, Kenneth W.; Cocks, Stephen B. (American Meteorological Society, 2023)

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Molina, Maria J.; Richter, Jadwiga H.; Glanville, Anne A.; Dagon, Katherine; Berner, Judith; Hu, Aixue; Meehl, Gerald A. (American Meteorological Society, 2023)

Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?

Qu, Yongquan; Shi, Xiaoming (American Meteorological Society, 2023)

YaBeSH Engineering and Technology Library

Archive

Artificial Intelligence for the Earth Systems: Recent submissions

A Data-Driven Probabilistic Network Approach to Assess Model Similarity in CMIP Ensembles

Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea

Superresolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network

Global Extreme Heat Forecasting Using Neural Weather Models

AMS Publications Support for Open, Transparent, and Equitable Research

Global Extreme Heat Forecasting Using Neural Weather Models

Emulating the Adaptation of Wind Fields to Complex Terrain with Deep Learning

A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions

Bias Correcting Climate Model Simulations Using Unpaired Image-to-Image Translation Networks

Photographic Visualization of Weather Forecasts with Generative Adversarial Networks

Convective-Scale Assimilation of Cloud Cover from Photographs Using a Machine Learning Forward Operator

Deep Learning–Based Parameter Transfer in Meteorological Data

Physics-Informed Deep Neural Network for Backward-in-Time Prediction: Application to Rayleigh–Bénard Convection

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications

A Hybrid Physics–AI Model to Improve Hydrological Forecasts

Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience

Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?

Artificial Intelligence for the Earth Systems: Recent submissions

A Data-Driven Probabilistic Network Approach to Assess Model Similarity in CMIP Ensembles ﻿

Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea ﻿

Superresolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network ﻿

Global Extreme Heat Forecasting Using Neural Weather Models ﻿

AMS Publications Support for Open, Transparent, and Equitable Research ﻿

Global Extreme Heat Forecasting Using Neural Weather Models ﻿

Emulating the Adaptation of Wind Fields to Complex Terrain with Deep Learning ﻿

A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions ﻿

Bias Correcting Climate Model Simulations Using Unpaired Image-to-Image Translation Networks ﻿

Photographic Visualization of Weather Forecasts with Generative Adversarial Networks ﻿

Convective-Scale Assimilation of Cloud Cover from Photographs Using a Machine Learning Forward Operator ﻿

Deep Learning–Based Parameter Transfer in Meteorological Data ﻿

Physics-Informed Deep Neural Network for Backward-in-Time Prediction: Application to Rayleigh–Bénard Convection ﻿

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis ﻿

Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications ﻿

A Hybrid Physics–AI Model to Improve Hydrological Forecasts ﻿

Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience ﻿

Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States ﻿

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis ﻿

Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models? ﻿

A Data-Driven Probabilistic Network Approach to Assess Model Similarity in CMIP Ensembles

Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea

Superresolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network

Global Extreme Heat Forecasting Using Neural Weather Models

AMS Publications Support for Open, Transparent, and Equitable Research

Global Extreme Heat Forecasting Using Neural Weather Models

Emulating the Adaptation of Wind Fields to Complex Terrain with Deep Learning

A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions

Bias Correcting Climate Model Simulations Using Unpaired Image-to-Image Translation Networks

Photographic Visualization of Weather Forecasts with Generative Adversarial Networks

Convective-Scale Assimilation of Cloud Cover from Photographs Using a Machine Learning Forward Operator

Deep Learning–Based Parameter Transfer in Meteorological Data

Physics-Informed Deep Neural Network for Backward-in-Time Prediction: Application to Rayleigh–Bénard Convection

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications

A Hybrid Physics–AI Model to Improve Hydrological Forecasts

Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience

Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?