Results 31 to 40 of about 190 (113)

CloudResolvingClimateModeling/Simple-SP: v1.0

, 2021
Simple superparameterization example in Python using the DALES ...
Maria Chertova, Inti Pelupessy, Johanna H. Grönqvist, Daan Crommelin, Pier Siebesma, Fredrik Jansson, Gijs van den Oord   +6 more
core   +1 more source

Sensitivity of Coupled Tropical Pacific Model Biases to Convective Parameterization in CESM1

Journal of Advances in Modeling Earth Systems, 2018
Six month coupled hindcasts show the central equatorial Pacific cold tongue bias development in a GCM to be sensitive to the atmospheric convective parameterization employed.
M. D. Woelfle, S. Yu, C. S. Bretherton, M. S. Pritchard   +3 more
doaj   +1 more source

odel [PDF]

, 2016
This study evaluates several important statistics of daily rainfall based on frequency and amount distributions as simulated by a global climate model whose precipitation does not depend on convective parameterization - Super-Parameterized Community ...
Randall, David A, Branson, Mark D, Melissa A. Burt, Burt, Melissa A, Kooperman, Gabriel J, Michael S. Pritchard, David A. Randall, Mark D. Branson, Gabriel J. Kooperman, Pritchard, Michael S   +9 more
core   +1 more source

Long‐Lived Mesoscale Convective Systems of Superparameterized CAM and the Response of CAM [PDF]

Journal of Advances in Modeling Earth Systems, 2018
AbstractMesoscale organized convection is generally misrepresented in the large‐scale convective parameterizations used in contemporary climate models. This impacts extreme weather events (e.g., Madden‐Jullian Oscillation) and the general circulation driven by the significant amount of latent heat released from mesoscale organized convection.
Gino Chen, Ben P. Kirtman
openaire   +1 more source

Can Eulerian Eddy Diffusivity Be Inferred From Lagrangian Trajectories?

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract Lagrangian particle trajectories are widely used to characterize tracer dispersion and mixing driven by mesoscale currents (“eddies”), leading to estimates of eddy diffusivity that can in turn be used in non‐eddy‐resolving and eddy‐permitting ocean models.
Yueyang Lu, Igor Kamenkovich, Pavel Berloff   +2 more
wiley   +1 more source

Convection in a Parameterized and Superparameterized Model and Its Role in the Representation of the MJO [PDF]

Journal of the Atmospheric Sciences, 2009
Abstract The behavior of convection and the Madden–Julian oscillation (MJO) is compared in two simulations from the same global climate model but with two very different treatments of convection: one has a conventional parameterization of moist processes and the other replaces the parameterization with a two-dimensional cloud-resolving ...
Harry Hendon, Hongyan Zhu, Christian Jakob   +2 more
openaire   +1 more source

Exploring Ways to Reduce Biases in a Hybrid Global Climate Model With Machine‐Learned Moist Physics

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract In a previous study (Han et al., 2023, https://doi.org/10.1029/2022ms003508), we implemented a deep convolutional residual neural network for moist physics into the 3‐D real‐geography CAM5 and carried out a stable multi‐year integration successfully. However, the simulation has large temperature and moisture biases in high latitude troposphere
Yilun Han, Guang J. Zhang, Yong Wang
wiley   +1 more source

Evaluating Precipitation Features and Rainfall Characteristics in a Multi‐Scale Modeling Framework

Journal of Advances in Modeling Earth Systems, 2020
Cloud and precipitation systems are simulated with a multi‐scale modeling framework (MMF) and compared over the Tropics and Subtropics against the Tropical Rainfall Measuring Mission (TRMM) Radar‐defined Precipitation Features (RPFs) product.
Jiun‐Dar Chern, Wei‐Kuo Tao, Stephen E. Lang, Xiaowen Li, Toshihisa Matsui   +4 more
doaj   +1 more source

Beyond the Training Data: Confidence‐Guided Mixing of Parameterizations in a Hybrid AI‐Climate Model

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract Persistent systematic errors in Earth system models (ESMs) arise from difficulties in representing the full diversity of subgrid, multiscale atmospheric convection and turbulence. Machine learning (ML) parameterizations trained on short high‐resolution simulations show strong potential to reduce these errors.
Helge Heuer, Tom Beucler, Mierk Schwabe, Julien Savre, Manuel Schlund, Veronika Eyring   +5 more
wiley   +1 more source

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

, 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, TN, Aneesh C. Subramanian, Palmer, Timothy, Subramanian, AC, Tim N. Palmer, Subramanian, Aneesh   +5 more
core   +1 more source