Jeremy Mcgibbon

University of Washington Seattle | UW · Department of Atmospheric Sciences

Coarse-grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse-grained cloud fields from a fine-grid reference model are a natural target for a machine-learned parameterization. We machine-learn the coarsened-fine cloud properties as a function of coarse-grid model state in each grid cell of NOAA’s FV3GFS...

The use of machine learning (ML) for the online correction of coarse-resolution atmospheric models has proven effective in reducing biases in near-surface temperature and precipitation rate. However, this often introduces biases in the upper atmosphere and improvements are not always reliable across ML-corrective models trained with different rando...

Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's law driving forward rapid specialization of hardware architectures, building simulation codes on a low-level l...

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 tendencies, such that the climate model evolves more like a reference fine‐grid global storm‐resolving model (GSRM). Our past work demonstrating this approach was trained with short (40‐day)...

While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than the traditional physics-based models they rely on. Because ML-predicted corrections feed back into the climate model's base physics, the ML-corrected mo...

Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are approximated in GCMs via subgrid parameterizations, which contribute significantly to the uncertainty in GCM predi...

Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating is a primary driver of large and small-scale circulations throughout the global atmosphere, and clouds have important interactions with atmospheric radia...

Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse‐resolution global atmosphere model with real geography (a ∼200 km version of NOAA's FV3GFS) evolve more like a fine‐resolution model, at the scales resolved by both. This study extends that work for appl...

Earth system models are developed with a tight coupling to target hardware, often containing highly-specialized code predicated on processor characteristics. This coupling stems from using imperative languages that hard-code computation schedules and layout. In this work, we present a detailed account of optimizing the Finite Volume Cubed-Sphere (F...

Global atmospheric “storm‐resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse‐grid global climate models across a range of climates, reducing uncertainties in regional precipit...

Plain Language Summary After initialization from a realistic snapshot of the atmosphere, weather and climate models inevitably develop prediction errors compared to the real world. This decreases the usefulness of forecasts. These errors arise from the coarse resolution of the numerical models and from the uncertain treatment of small‐scale process...

Simulation software in geophysics is traditionally written in Fortran or C++ due to the stringent performance requirements these codes have to satisfy. As a result, researchers who use high-productivity languages for exploratory work often find these codes hard to understand, hard to modify, and hard to integrate with their analysis tools. fv3gfs-w...

Simulation software in geophysics is traditionally written in Fortran or C++ due to the stringent performance requirements these codes have to satisfy. As a result, these codes are often hard to understand, hard to modify and hard to interface with high-productivity languages used for exploratory work. fv3gfs-wrapper is an open-source Python-wrappe...

Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly resolve processes larger than 100 km and approximate any process occurring below this scale (e.g. thunderstorms) using so-called parametrizations. Machine learning could i...

This paper presents a process-oriented evaluation of precipitating stratocumulus and its transition to cumulus in version 1 of the Energy Exascale Earth System Model (E3SMv1) using comprehensive case-study observations from a field campaign of the Atmospheric Radiation Measurement program (ARM). The E3SMv1 single-column model (SCM) of the marine bo...

Flight data from the Cloud System Evolution over the Trades (CSET) campaign over the Pacific stratocumulus-to-cumulus transition are organized into 18 Lagrangian cases suitable for study and future modeling, made possible by the use of a track-and-resample flight strategy. Analysis of these cases shows that 2-day Lagrangian coherence of long-lived...

An artificial neural network is trained to reproduce thermodynamic tendencies and boundary layer properties from European Center for Medium‐Range Weather Forecasts Reanalysis 5th Generation high resolution realization reanalysis data over the summertime northeast Pacific stratocumulus to trade cumulus transition region. The network is trained progn...

An artificial neural network is trained to reproduce thermodynamic tendencies and boundary layer properties from ERA5 HIRES reanalysis data over the summertime Northeast Pacific stratocumulus to trade cumulus transition region. The network is trained prognostically using 7-day forecasts rather than using diagnosed instantaneous tendencies alone. Th...

sympl (System for Modelling Planets) and climt (Climate Modelling and Diagnostics Toolkit) are an attempt to rethink climate modelling frameworks from the ground up. The aim is to use expressive data structures available in the scientific Python ecosystem along with best practices in software design to allow scientists to easily and reliably combin...

The Cloud System Evolution in the Trades (CSET) study was designed to describe and explain the evolution of the boundary layer aerosol, cloud, and thermodynamic structures along trajectories within the North Pacific trade winds. The study centered on seven round trips of the National Science Foundation–National Center for Atmospheric Research (NSF–...

sympl (System for Modelling Planets) and climt (Climate Modelling and diagnostics Toolkit) represent an attempt to rethink climate modelling frameworks from the ground up. The aim is to use expressive data structures available in the scientific Python ecosystem along with best practices in software design to build models that are self-documenting,...

Marine boundary layer (MBL) aerosol particles affect the climate through their interaction with MBL clouds. Although both MBL clouds and aerosol particles have pronounced seasonal cycles, the factors controlling seasonal variability of MBL aerosol particle concentration are not well-constrained. In this paper an aerosol budget is constructed repres...

During the Marine ARM GPCI Investigation of Clouds (MAGIC) in Oct. 2011-Sept. 2012, a container ship making periodic cruises between Los Angeles, CA and Honolulu, HI was instrumented with surface meteorological, aerosol and radiation instruments, a cloud radar and ceilometer, and radiosondes. Here, large-eddy simulation (LES) is performed in a ship...

During the Marine ARM GPCI Investigation of Clouds (MAGIC) in Oct. 2011-Sept. 2012, a container ship making periodic cruises between Los Angeles, CA and Honolulu, HI was instrumented with surface meteorological, aerosol and radiation instruments, a cloud radar and ceilometer, and radiosondes. Here, large-eddy simulation (LES) is performed in a ship...

During the Marine ARM GPCI Investigation of Clouds (MAGIC) in Oct. 2011 - Sept. 2012, a container ship making periodic cruises between Los Angeles, CA and Honolulu, HI was instrumented with surface meteorological, aerosol and radiation instruments, a cloud radar and ceilometer, and radiosondes. Here, large-eddy simulation (LES) is performed in a sh...

The 2012-2013 MAGIC shipborne deployment of the ARM mobile facility sampled a broad range of subtropical marine stratocumulus (Sc), cumulus (Cu), and transition regimes during cruises between Long Beach, CA, and Hololulu, HI. Ship-following large-eddy simulations (LES) of selected cruise legs of 4-5 days are compared with a broad suite of observati...

Large-eddy simulations (LES) are presented of a transition from a well-mixed stratocumulus (Sc) capped boundary layer to a cumuliform boundary layer observed in Leg 15A of the MAGIC AMF deployment. The simulation uses a small doubly-periodic domain. A ship-following approach is used to take advantage of the continuous in-situ and cloud remote sensi...