William E. Chapman
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View article: Improving AI Weather Prediction Models Using Global Mass and Energy Conservation Schemes
Improving AI Weather Prediction Models Using Global Mass and Energy Conservation Schemes Open
Artificial Intelligence (AI) weather prediction (AIWP) models are powerful tools for medium‐range forecasts but often lack physical consistency, leading to outputs that violate conservation laws. This study introduces a set of novel physic…
View article: Investigating the Use of Terrain‐Following Coordinates in AI‐Driven Precipitation Forecasts
Investigating the Use of Terrain‐Following Coordinates in AI‐Driven Precipitation Forecasts Open
Artificial Intelligence (AI) weather prediction (AIWP) models often produce “blurry” precipitation forecasts. This study presents a novel solution to tackle this problem—integrating terrain‐following coordinates into AIWP models. Forecast …
View article: Implementation and validation of a supermodeling framework into Community Earth System Model version 2.1.5
Implementation and validation of a supermodeling framework into Community Earth System Model version 2.1.5 Open
Here we present a research framework for the first atmosphere-connected supermodel using state-of-the-art atmospheric models. The Community Atmosphere Model (CAM) versions 5 and 6 exchange information interactively while running, a process…
View article: Community Research Earth Digital Intelligence Twin: a scalable framework for AI-driven Earth System Modeling
Community Research Earth Digital Intelligence Twin: a scalable framework for AI-driven Earth System Modeling Open
Recent advancements in artificial intelligence (AI) numerical weather prediction (NWP) have transformed atmospheric modeling. AI NWP models outperform state-of-the-art conventional NWP models like the European Center for Medium Range Weath…
View article: Community Research Earth Digital Intelligence Twin: a scalable framework for AI-driven Earth System Modeling
Community Research Earth Digital Intelligence Twin: a scalable framework for AI-driven Earth System Modeling Open
Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional physics-based systems, such as the Integrated Forecast Sys…
View article: CAMulator: Fast Emulation of the Community Atmosphere Model
CAMulator: Fast Emulation of the Community Atmosphere Model Open
We introduce CAMulator version 1, an auto-regressive machine-learned (ML) emulator of the Community Atmosphere Model version 6 (CAM6) that simulates the next atmospheric state given the prescribed sea surface temperatures and incoming sola…
View article: Improving climate bias and variability via CNN-based state-dependent model-error corrections
Improving climate bias and variability via CNN-based state-dependent model-error corrections Open
The influence of structural errors in general circulation models (GCMs) — stemming from missing physics, imperfect parameterizations of subgrid-scale processes, limited resolution, and numerical inaccuracies — results in system…
View article: Benefits of online bias-correction versus postproessing methods
Benefits of online bias-correction versus postproessing methods Open
Recently, there has been pronoued interest in predictability on the subseasonal-to-seasonal (S2S) timescale. Skill at this forecast range isonly positive, if the lead-time dependent forecast bias is removed.Recently, Chapman and Berner, 20…
View article: Simplifying Earth System Projections: Mimicking ESM Results with a Diffusion Model
Simplifying Earth System Projections: Mimicking ESM Results with a Diffusion Model Open
Ensemble simulations using Earth System Models (ESMs) have historically been used to gain insights into future climate scenarios. However, they present notable disadvantages, particularly their long computing times and the high technical t…
View article: Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections
Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections Open
We develop an approach to correct biases in the atmospheric component of the Community Earth System Model using convolutional neural networks (CNNs) to create a corrective model parameterization for online bias reduction. By predicting sys…
View article: Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts
Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts Open
Artificial Intelligence (AI) weather prediction (AIWP) models often produce ``blurry'' precipitation forecasts. This study presents a novel solution to tackle this problem -- integrating terrain-following coordinates into AIWP models. Fore…
View article: Improving AI weather prediction models using global mass and energy conservation schemes
Improving AI weather prediction models using global mass and energy conservation schemes Open
Artificial Intelligence (AI) weather prediction (AIWP) models are powerful tools for medium-range forecasts but often lack physical consistency, leading to outputs that violate conservation laws. This study introduces a set of novel physic…
View article: Learning Machine Learning with Lorenz-96
Learning Machine Learning with Lorenz-96 Open
International audience
View article: Implementation and validation of a supermodelling framework into CESM version 2.1.5
Implementation and validation of a supermodelling framework into CESM version 2.1.5 Open
Here we present a framework for the first atmosphere-connected supermodel using state-of-the-art atmospheric models. The Community Atmosphere Model (CAM) versions 5 and 6 exchange information interactively while running, a process known as…
View article: Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications Open
Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learnin…
View article: Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods
Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods Open
Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they…
View article: Exploring the Relative Importance of the MJO and ENSO to North Pacific Subseasonal Predictability
Exploring the Relative Importance of the MJO and ENSO to North Pacific Subseasonal Predictability Open
Here we explore the relative contribution of the Madden‐Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO) to midlatitude subseasonal predictive skill of upper atmospheric circulation over the North Pacific, using an inherent…
View article: Exploring the Relative Contribution of the MJO and ENSO to Midlatitude Subseasonal Predictability
Exploring the Relative Contribution of the MJO and ENSO to Midlatitude Subseasonal Predictability Open
Here we explore the relative contribution of the Madden-Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO) to midlatitude subseasonal predictive skill of upper atmospheric circulation over the North Pacific, using an inherent…
View article: Enhancing Regional Climate Downscaling through Advances in Machine Learning
Enhancing Regional Climate Downscaling through Advances in Machine Learning Open
Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are us…
View article: Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications Open
Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates and can be decomposed physically, but both physics and machin…
View article: Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications" Open
The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather predict…
View article: Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
Datasets used in "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications" Open
The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather predict…
View article: Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model
Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model Open
We develop and compare model-error representation schemes derived from data assimilation increments and nudging tendencies in multi-decadal simulations of the community atmosphere model, version 6. Each scheme applies a bias correction dur…
View article: Supermodeling: Improving Predictions with an Ensemble of Interacting Models
Supermodeling: Improving Predictions with an Ensemble of Interacting Models Open
The modeling of weather and climate has been a success story. The skill of forecasts continues to improve and model biases continue to decrease. Combining the output of multiple models has further improved forecast skill and reduced biases…
View article: Deep Learning Forecast Uncertainty for Precipitation over the Western United States
Deep Learning Forecast Uncertainty for Precipitation over the Western United States Open
Reliably quantifying uncertainty in precipitation forecasts remains a critical challenge. This work examines the application of a deep learning (DL) architecture, Unet, for postprocessing deterministic numerical weather predictions of prec…