Results 21 to 30 of about 5,035 (244)

Comparing storm resolving models and climates via unsupervised machine learning

Scientific Reports, 2023
AbstractGlobal storm-resolving models (GSRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult to quantify objective differences in how GSRMs resolve complex atmospheric formations.
Mooers, Griffin, Pritchard, Mike, Beucler, Tom, Srivastava, Prakhar, Mangipudi, Harshini, Peng, Liran, Gentine, Pierre, Mandt, Stephan   +7 more
openaire   +9 more sources

Vertical motions in clouds from EarthCare satellite and a global storm-resolving modeling. [PDF]

Sci Rep
Roh W, Satoh M, Matsugishi S, Aoki S, Kubota T, Okamoto H.   +5 more
europepmc   +4 more sources

Climate sensitivity and relative humidity changes in global storm-resolving model simulations of climate change. [PDF]

Sci Adv
Merlis TM, Cheng KY, Guendelman I, Harris L, Bretherton CS, Bolot M, Zhou L, Kaltenbaugh A, Clark SK, Vecchi GA, Fueglistaler S.   +10 more
europepmc   +2 more sources

Assessing Precipitation Over the Amazon Basin as Simulated by a Storm‐Resolving Model

Journal of Geophysical Research: Atmospheres, 2022
Abstract In this study, we investigate whether a better representation of precipitation in the Amazon basin arises through an explicit representation of convection and whether it is related to the representation of organized systems. In addition to satellite data, we use ensemble simulations of the ICON‐NWP model at storm‐resolving (2.
Paccini, L., Stevens, B., Stevens, B.; 1 Max Planck Institute for Meteorology Hamburg Germany   +2 more
openaire   +3 more sources

Convection‐Permitting Simulations With the E3SM Global Atmosphere Model

Journal of Advances in Modeling Earth Systems, 2021
This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28,
P. M. Caldwell, C. R. Terai, B. Hillman, N. D. Keen, P. Bogenschutz, W. Lin, H. Beydoun, M. Taylor, L. Bertagna, A. M. Bradley, T. C. Clevenger, A. S. Donahue, C. Eldred, J. Foucar, J.‐C. Golaz, O. Guba, R. Jacob, J. Johnson, J. Krishna, W. Liu, K. Pressel, A. G. Salinger, B. Singh, A. Steyer, P. Ullrich, D. Wu, X. Yuan, J. Shpund, H.‐Y. Ma, C. S. Zender   +29 more
doaj   +1 more source

Machine-learned climate model corrections from a global storm-resolving model

CoRR, 2022
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 ...
Anna Kwa, Spencer K. Clark, Brian Henn, Noah D. Brenowitz, Jeremy McGibbon, W. Andre Perkins, Oliver Watt-Meyer, Lucas Harris, Christopher S. Bretherton   +8 more
openaire   +2 more sources

Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection

Journal of Advances in Modeling Earth Systems, 2023
Using 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.
Clayton Sanford, Anna Kwa, Oliver Watt‐Meyer, Spencer K. Clark, Noah Brenowitz, Jeremy McGibbon, Christopher Bretherton   +6 more
doaj   +1 more source

Toward Eliminating the Decades‐Old “Too Zonal and Too Equatorward” Storm‐Track Bias in Climate Models

Journal of Advances in Modeling Earth Systems, 2023
Generations of climate models exhibit biases in their representation of North Atlantic storm tracks, which tend to be too far near the equator and too zonal. Additionally, models have difficulties simulating explosive cyclone growth. These biases are one
Sebastian Schemm
doaj   +1 more source

Global System for Atmospheric Modeling: Model Description and Preliminary Results

Journal of Advances in Modeling Earth Systems, 2022
The extension of a cloud‐resolving model, the System for Atmospheric Modeling (SAM), to global domains is described. The resulting global model, gSAM, is formulated on a latitude‐longitude grid. It uses an anelastic dynamical core with a single reference
Marat F. Khairoutdinov, Peter N. Blossey, Christopher S. Bretherton   +2 more
doaj   +1 more source

The North Sea Andrea storm and numerical simulations [PDF]

Natural Hazards and Earth System Sciences, 2014
A coupling of a spectral wave model with a nonlinear phase-resolving model is used to reconstruct the evolution of wave statistics during a storm crossing the North Sea on 8–9 November 2007.
E. M. Bitner-Gregersen, L. Fernandez, J. M. Lefèvre, J. Monbaliu, A. Toffoli   +4 more
doaj   +1 more source