Search Results - "Perkins, Andre"

Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations by Bretherton, Christopher S., Henn, Brian, Kwa, Anna, Brenowitz, Noah D., Watt‐Meyer, Oliver, McGibbon, Jeremy, Perkins, W. Andre, Clark, Spencer K., Harris, Lucas

“…Global atmospheric “storm‐resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They…”
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Linear Inverse Modeling for Coupled Atmosphere‐Ocean Ensemble Climate Prediction by Perkins, W. Andre, Hakim, Greg

“…Paleoclimate data assimilation (PDA) experiments reconstruct climate fields by objectively blending information from climate models and proxy observations. Due…”
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Emulation of Cloud Microphysics in a Climate Model by Perkins, W. Andre, Brenowitz, Noah D., Bretherton, Christopher S., Nugent, Jacqueline M.

“…We present a machine learning based emulator of a microphysics scheme for condensation and precipitation processes (Zhao‐Carr) used operationally in a global…”
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Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle by Kwa, Anna, Clark, Spencer K., Henn, Brian, Brenowitz, Noah D., McGibbon, Jeremy, Watt‐Meyer, Oliver, Perkins, W. Andre, Harris, Lucas, Bretherton, Christopher S.

“…One approach to improving the accuracy of a coarse‐grid global climate model is to add machine‐learned (ML) state‐dependent corrections to the prognosed model…”
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Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation by Watt‐Meyer, Oliver, Brenowitz, Noah D., Clark, Spencer K., Henn, Brian, Kwa, Anna, McGibbon, Jeremy, Perkins, W. Andre, Harris, Lucas, Bretherton, Christopher S.

“…Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a…”
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Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations by Clark, Spencer K., Brenowitz, Noah D., Henn, Brian, Kwa, Anna, McGibbon, Jeremy, Perkins, W. Andre, Watt‐Meyer, Oliver, Bretherton, Christopher S., Harris, Lucas M.

“…Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse‐resolution…”
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fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model by McGibbon, Jeremy, Brenowitz, Noah D, Cheeseman, Mark, Clark, Spencer K, Dahm, Johann P. S, Davis, Eddie C, Elbert, Oliver D, George, Rhea C, Harris, Lucas M, Henn, Brian, Kwa, Anna, Perkins, W. Andre, Watt-Meyer, Oliver, Wicky, Tobias F, Bretherton, Christopher S, Fuhrer, Oliver

“…Simulation software in geophysics is traditionally written in Fortran or C++ due to the stringent performance requirements these codes have to satisfy. As a…”
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Pandemic Pupils: Covid-19 and the Impact on Student Paramedics by Perkins, Andre, Kelly, Sarah, Dumbleton, Hannah, Whitfield, Steve

“…COVID-19 is having a profound effect on student paramedics. This commentary aims to explore and discuss how student paramedics have been affected by COVID -19…”
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Uncharted Waters: The Effects of Covid-19 on Student Paramedics by Whitfield, Steve, Perkins, Andre, Kelly, Sarah, Dumbleton, Hannah

“…Introduction The effect of COVID-19 pandemic shutdowns on education has been discussed broadly in both the media and among academics, however its true effects…”
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Constraints on methane emissions in North America from future geostationary remote-sensing measurements by Bousserez, Nicolas, Henze, Daven K, Rooney, Brigitte, Perkins, Andre, Wecht, Kevin J, Turner, Alexander J, Natraj, Vijay, Worden, John R

“…The success of future geostationary (GEO) satellite observation missions depends on our ability to design instruments that address their key scientific…”
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Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations by Watt‐Meyer, Oliver, Brenowitz, Noah D., Clark, Spencer K., Henn, Brian, Kwa, Anna, McGibbon, Jeremy, Perkins, W. Andre, Bretherton, Christopher S.

“…Due to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to the observed…”
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Reconstructing Coupled Atmosphere-ocean Variability over the Last Millennium by Perkins, Walter Andre

“…Coupled interactions between oceans and the atmosphere are fundamental to low-frequency variability of the Earth System. While the instrumental record provides…”
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ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses by Watt-Meyer, Oliver, Henn, Brian, McGibbon, Jeremy, Clark, Spencer K, Kwa, Anna, Perkins, W. Andre, Wu, Elynn, Harris, Lucas, Bretherton, Christopher S

“…Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such…”
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Machine-learned climate model corrections from a global storm-resolving model by Kwa, Anna, Clark, Spencer K, Henn, Brian, Brenowitz, Noah D, McGibbon, Jeremy, Perkins, W. Andre, Watt-Meyer, Oliver, Harris, Lucas, Bretherton, Christopher S

“…Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is…”
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Emulating Fast Processes in Climate Models by Brenowitz, Noah D, Perkins, W. Andre, Nugent, Jacqueline M, Watt-Meyer, Oliver, Clark, Spencer K, Kwa, Anna, Henn, Brian, McGibbon, Jeremy, Bretherton, Christopher S

“…Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate…”
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Machine Learning Climate Model Dynamics: Offline versus Online Performance by Brenowitz, Noah D, Henn, Brian, McGibbon, Jeremy, Clark, Spencer K, Kwa, Anna, Perkins, W. Andre, Watt-Meyer, Oliver, Bretherton, Christopher S

“…Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate…”
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