Results 61 to 70 of about 505 (141)

Physically Interpretable Emulation of a Moist Convecting Atmosphere With a Recurrent Neural Network

Geophysical Research Letters, Volume 52, Issue 17, 16 September 2025.
Abstract Data‐driven convective parameterization aims to accurately represent convective adjustments to large‐scale forcings in a computationally economic manner. While previous studies have demonstrated success using various model architectures, challenges persist in developing physically interpretable models and assessing generalizability and ...
Qiyu Song, Zhiming Kuang
wiley   +1 more source

Effects of Explicit Convection on Land Surface Air Temperature and Land‐Atmosphere Coupling in the Thermal Feedback Pathway

Journal of Advances in Modeling Earth Systems, 2018
Simulating and understanding continental temperature extremes is a critical issue in Earth System Modeling. Conventional general circulation models are impaired by imperfect cloud and boundary layer parameterization schemes with implications for ...
J. Sun, M. S. Pritchard
doaj   +1 more source

A perspective on climate model hierarchies [PDF]

, 2017
To understand Earth's climate, climate modelers employ a hierarchy of climate models spanning a wide spectrum of complexity and comprehensiveness.
Abbot, Abbot, Aditi Sheshadri, Alexeev, Anderson, Andrews, Arnold, Arnold, Becker, Bony, Bordoni, Bretherton, Bretherton, Budyko, Cess, Charney, Chiodo, Chou, Clement, Cronin, Daleu, Donner, Eady, Fields, Fraedrich, Frierson, Frierson, Gates, Gerber, Harrop, Harrop, Hassanzadeh, Held, Held, Held, Held, Held, Held, Held, Held, Hohenegger, Holton, Kang, Kennedy, Kuang, Manabe, McGuffie, Medeiros, Medeiros, Merlis, Merlis, Mitchell, Molteni, Moorthi, Mori, Nadir Jeevanjee, Neale, Neelin, North, O'Gorman, O'Gorman, O'Reilly, Pedram Hassanzadeh, Philips, Pithan, Polvani, Popke, Reed, Robinson, Romps, Rose, Satoh, Satoh, Schneider, Schneider, Seeley, Sellers, Silvers, Singh, Spencer Hill, Thompson, Tokioka, Tompkins, Voigt, Webb, Wing, Wing, Wyant, Zappa, Zhang, Zhang, Zhang, Zhang   +92 more
core   +2 more sources

A proof of concept for scale-adaptive parameterizations: the case of the Lorenz ’96 model [PDF]

, 2017
Constructing efficient and accurate parameterizations of sub-grid scale processes is a central area of interest in the numerical modelling of geophysical fluids.
Abramov, Abramov, Arakawa, Arakawa, Berner, Blender, Chekroun, Chekroun, Demaeyer, Eckmann, Franzke, Franzke, Gallavotti, Gallavotti, Ghil, Hallerberg, Holton, Imkeller, Kondrashov, Kravtsov, Li, Lorenz, Lucarini, Lucarini, Lucarini, Lucarini, Majda, Majda, Majda, Majda, McGuffie, Mori, Neumaier, Orrell, Palmer, Palmer, Park, Pavliotis, Peixoto, Plant, Pope, Ruelle, Ruelle, Sakradzija, Schneider, Trevisan, Trevisan, Wilks, Wouters, Wouters, Wouters, Wouters, Young, Zwanzig, Zwanzig   +54 more
core   +2 more sources

A Framework to Attribute Tropical Multiscale Precipitation Extremes to Rain Event Morphology in Deep Convective Systems

Journal of Geophysical Research: Atmospheres, Volume 130, Issue 12, 28 June 2025.
Abstract The different spatiotemporal scales used to calculate extreme precipitation intensities can lead to diverging interpretation when investigating their physical origin, impacts, and sensitivity to climate. Besides, the contribution of mesoscale convective systems (MCSs) to tropical precipitation extremes remains loosely quantified on various ...
M. Carenso, B. Fildier, R. Roca, T. Fiolleau   +3 more
wiley   +1 more source

Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction [PDF]

, 2017
Stochastic schemes to represent model uncertainty in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and ...
Palmer, Timothy, Subramanian, Aneesh
core   +2 more sources

Response of Global Mesoscale Convective Systems to Increased CO2 and Uniform SST Warming in a Global Storm‐Resolving Model

Earth's Future, Volume 13, Issue 6, June 2025.
Abstract Mesoscale convective systems (MCSs) are critical components of global energy and water cycles and significantly contribute to extreme weather events. However, projecting future MCS behavior remains challenging due to the limitations of regional models and the inadequate representation of MCSs in coarser climate models.
Wenhao Dong, Ming Zhao, Lucas Harris, Kai‐Yuan Cheng, Linjiong Zhou, V. Ramaswamy   +5 more
wiley   +1 more source

Reproducibility of Equatorial Kelvin Waves in a Superparameterized MIROC: 1. Implementation and Verification of Blockwise‐Coupled SP‐MIROC

Journal of Advances in Modeling Earth Systems
The potential scope of superparameterization (SP) was extended to higher resolutions of the global climate model (GCM) component by devising a technique called blockwise coupling.
K. Yamazaki, H. Miura
doaj   +1 more source

Large eddy simulation using the general circulation model ICON [PDF]

, 2015
ICON (ICOsahedral Nonhydrostatic) is a unified modeling system for global numerical weather prediction (NWP) and climate studies. Validation of its dynamical core against a test suite for numerical weather forecasting has been recently published by Zängl
Anurag Dipankar, Arakawa, Beljaars, Betts, Bjorn Stevens, Bony, Bryan, Chow, Christopher Moseley, Colella, Deardorff, Deardorff, Deardorff, Durran, Dutton, Garcia, Gassmann, Ghan, Ghosal, Grabowski, Grabowski, Günther Zängl, Hanley, Hinze, Khairoutdinov, Lilly, Louis, Marco Giorgetta, Maronga, Mason, Mason, Miller, Miura, Miura, Moeng, Moeng, Narcovich, Nicholls, Nitta, Ogura, Patankar, Raasch, Randall, Rieke Heinze, Sagaut, Satoh, Schmidt, Seifert, Siebesma, Siebesma, Slavko Brdar, Sommeria, Sommeria, Stevens, Stevens, Stevens, Stevens, Stevens, Stull, Sullivan, Wan, Willis, Wyngaard, Wyngaard, Xu, Xu, Zängl   +66 more
core   +3 more sources

Vertically Recurrent Neural Networks for Sub‐Grid Parameterization

Journal of Advances in Modeling Earth Systems, Volume 17, Issue 6, June 2025.
Abstract Machine learning has the potential to improve the physical realism and/or computational efficiency of parameterizations. A typical approach has been to feed concatenated vertical profiles to a dense neural network. However, feed‐forward networks lack the connections to propagate information sequentially through the vertical column.
P. Ukkonen, M. Chantry
wiley   +1 more source