William K. Gregory
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View article: SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators Open
Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other geophysical processes. This paradigm allows…
View article: Subseasonal Forecast Improvements from Sea Ice Concentration Data Assimilation in the Antarctic
Subseasonal Forecast Improvements from Sea Ice Concentration Data Assimilation in the Antarctic Open
This study evaluates the impact of sea ice concentration (SIC) data assimilation (DA) on subseasonal forecasts of Antarctic sea ice by comparing reforecast experiment suites initialized from two sets of initial conditions (ICs): one with S…
View article: Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning Open
We showcase a hybrid modeling framework which embeds machine learning (ML) inference into the GFDL SPEAR climate model, for online sea ice bias correction during a set of global fully-coupled 1-year retrospective forecasts. We compare two …
View article: Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison
Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison Open
This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting…
View article: Scalable interpolation of satellite altimetry data with probabilistic machine learning
Scalable interpolation of satellite altimetry data with probabilistic machine learning Open
In this work, we present a new open-source Python programming library for performing efficient interpolation of non-stationary satellite altimetry data, using scalable Gaussian Process (GP) techniques. We showcase the library, GPSat, by us…
View article: Improvements in September Arctic sea ice predictions via assimilation of summer CryoSat-2 sea ice thickness observations
Improvements in September Arctic sea ice predictions via assimilation of summer CryoSat-2 sea ice thickness observations Open
Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt-season sea ice thickness (SIT) observations. The first year-round SIT observations, retrieved from Cryo…
View article: Towards improving numerical sea ice predictions with data assimilation and machine learning
Towards improving numerical sea ice predictions with data assimilation and machine learning Open
In this presentation we highlight recent developments in the implementation of Machine Learning (ML) algorithms into the large-scale sea ice model, SIS2. Specifically, we show how a Convolutional Neural Network (CNN) can be used to systema…
View article: Machine Learning for Online Sea Ice Bias Correction Within Global Ice‐Ocean Simulations
Machine Learning for Online Sea Ice Bias Correction Within Global Ice‐Ocean Simulations Open
In this study, we perform online sea ice bias correction within a Geophysical Fluid Dynamics Laboratory global ice‐ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., …
View article: Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations
Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Open
Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt‐season sea ice thickness (SIT) observations. The first year‐round SIT observations, retrieved from Cryo…
View article: Machine learning for online sea ice bias correction within global ice-ocean simulations
Machine learning for online sea ice bias correction within global ice-ocean simulations Open
In this study we perform online sea ice bias correction within a GFDL global ice-ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023) for the purpose of predictin…
View article: Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments
Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments Open
Data assimilation is often viewed as a framework for correcting short‐term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short‐term corrections, or analysis increments, can clos…
View article: Improvements in September Arctic sea ice predictions via assimilation of summer CryoSat-2 sea ice thickness observations
Improvements in September Arctic sea ice predictions via assimilation of summer CryoSat-2 sea ice thickness observations Open
Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt-season sea ice thickness (SIT) observations. The first year-round SIT observations, retrieved from Cryo…
View article: Deep learning of systematic sea ice model errors from data assimilation increments
Deep learning of systematic sea ice model errors from data assimilation increments Open
Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, can clos…
View article: Deep learning of systematic sea ice model errors from data assimilation increments
Deep learning of systematic sea ice model errors from data assimilation increments Open
Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, closely …
View article: Fast interpolation of satellite altimetry data with probabilistic machine learning and GPU
Fast interpolation of satellite altimetry data with probabilistic machine learning and GPU Open
Recent work has demonstrated how Gaussian Process Regression (GPR) can be used to interpolate Pan-Arctic radar freeboard of sea ice as measured by satellites. Sea ice freeboard is crucial to measuring sea ice thickness, and thus sea ice vo…
View article: Synoptic Variability in Satellite Altimeter‐Derived Radar Freeboard of Arctic Sea Ice
Synoptic Variability in Satellite Altimeter‐Derived Radar Freeboard of Arctic Sea Ice Open
Satellite observations of sea ice freeboard are integral to the estimation of sea ice thickness. It is commonly assumed that radar pulses from satellite‐mounted Ku‐band altimeters penetrate through the snow and reflect from the snow‐ice in…
View article: MedDRA Labeling Groupings to Improve Safety Communication in Product Labels
MedDRA Labeling Groupings to Improve Safety Communication in Product Labels Open
The granularity and structure of the International Council for Harmonisation’s (ICH) Medical Dictionary for Regulatory Activities (MedDRA) are useful for precise coding of adverse events (AEs) for data analysis. In product labeling for hea…
View article: Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations Open
The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice f…
View article: Machine learning tools for pattern recognition in polar climate science
Machine learning tools for pattern recognition in polar climate science Open
<p>Over the past four decades, the inexorable growth in technology and subsequently the availability of Earth-observation and model data has been unprecedented. Hidden within these data are the fingerprints of the physical processes …
View article: Reply on RC2
Reply on RC2 Open
Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer…
View article: Reply on RC1
Reply on RC1 Open
Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer…
View article: Comment on tc-2021-387
Comment on tc-2021-387 Open
Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer…
View article: Comment on tc-2021-387
Comment on tc-2021-387 Open
Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the following summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer…
View article: Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations Open
The indirect effect of winter Arctic Oscillation (AO) events on the proceeding summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice …
View article: A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations
A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations Open
Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn provide information on sea ice forecasts, volume budgets, and productivity rates. Current spatio-…
View article: Reponse to Dr. Rachel Tilling (RC2)
Reponse to Dr. Rachel Tilling (RC2) Open
Abstract. Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn provide information on sea ice forecasts, volume budgets, and productivity rates. Curre…