Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-6259-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6259-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
12 Aug 2022
Model description paper |
| 12 Aug 2022
| 12 Aug 2022
Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs
California Institute of Technology, Pasadena, California, USA
Yassine Tissaoui
New Jersey Institute of Technology, Newark, New Jersey, USA
Simone Marras
New Jersey Institute of Technology, Newark, New Jersey, USA
Zhaoyi Shen
California Institute of Technology, Pasadena, California, USA
Charles Kawczynski
California Institute of Technology, Pasadena, California, USA
Simon Byrne
California Institute of Technology, Pasadena, California, USA
Kiran Pamnany
California Institute of Technology, Pasadena, California, USA
Maciej Waruszewski
Naval Postgraduate School, Monterey, California, USA
Thomas H. Gibson
University of Illinois Urbana–Champaign, Urbana–Champaign, Illinois, USA
Jeremy E. Kozdon
Naval Postgraduate School, Monterey, California, USA
Valentin Churavy
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Lucas C. Wilcox
Naval Postgraduate School, Monterey, California, USA
Francis X. Giraldo
Naval Postgraduate School, Monterey, California, USA
Tapio Schneider
California Institute of Technology, Pasadena, California, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Related authors
No articles found.
Tapio Schneider, L. Ruby Leung, and Robert C. J. Wills
Atmos. Chem. Phys., 24, 7041–7062, https://doi.org/10.5194/acp-24-7041-2024, https://doi.org/10.5194/acp-24-7041-2024, 2024
Short summary
Short summary
Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.
Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, and Tapio Schneider
EGUsphere, https://doi.org/10.48550/arXiv.2404.14212, https://doi.org/10.48550/arXiv.2404.14212, 2024
Short summary
Short summary
Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models forecasting streamflow in river basins, redesigning them with the goal of integrating them into climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine learning based river models inside climate models.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
Short summary
Short summary
To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Z. Shen, J. Liu, L. W. Horowitz, D. K. Henze, S. Fan, Levy II H., D. L. Mauzerall, J.-T. Lin, and S. Tao
Atmos. Chem. Phys., 14, 6315–6327, https://doi.org/10.5194/acp-14-6315-2014, https://doi.org/10.5194/acp-14-6315-2014, 2014
Related subject area
Atmospheric sciences
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
Assessment of object-based indices to identify convective organization
The Global Forest Fire Emissions Prediction System version 1.0
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework
Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0
Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model
GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator
Preliminary evaluation of the effect of electro-coalescence with conducting sphere approximation on the formation of warm cumulus clouds using SCALE-SDM version 0.2.5–2.3.0
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Orbital-Radar v1.0.0: A tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
TAMS: a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems in simulated and satellite-derived datasets
Development of the adjoint of the unified tropospheric–stratospheric chemistry extension (UCX) in GEOS-Chem adjoint v36
New explicit formulae for the settling speed of prolate spheroids in the atmosphere: theoretical background and implementation in AerSett v2.0.2
ZJU-AERO V0.5: an Accurate and Efficient Radar Operator designed for CMA-GFS/MESO with the capability to simulate non-spherical hydrometeors
The Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations
Global variable-resolution simulations of extreme precipitation over Henan, China, in 2021 with MPAS-Atmosphere v7.3
The CHIMERE chemistry-transport model v2023r1
tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics
Simulation of marine stratocumulus using the super-droplet method: numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.1
Modeling of PAHs From Global to Regional Scales: Model Development and Investigation of Health Risks from 2013 to 2018 in China
WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model
Representing effects of surface heterogeneity in a multi-plume eddy diffusivity mass flux boundary layer parameterization
Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases
A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)
RoadSurf 1.1: open-source road weather model library
Calibrating and validating the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model: case studies in France and the United States
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
Short summary
Short summary
Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
Short summary
Short summary
Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
Short summary
Short summary
This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
Short summary
Short summary
In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
Short summary
Short summary
The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
Short summary
Short summary
The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
Short summary
Short summary
Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
Short summary
Short summary
Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
Short summary
Short summary
Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
Short summary
Short summary
Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Short summary
We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
Short summary
Short summary
Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
Short summary
Short summary
RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
Short summary
Short summary
We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
Short summary
Short summary
We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
Short summary
Short summary
Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
Short summary
Short summary
Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
Short summary
Short summary
AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
Short summary
Short summary
Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
Short summary
Short summary
Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-129, https://doi.org/10.5194/gmd-2024-129, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Orbital-radar is a Python tool transferring sub-orbital radar data (ground-based, airborne, and forward-simulated NWP) into synthetical space-borne cloud profiling radar data mimicking the platform characteristics, e.g. EarthCARE or CloudSat CPR. The novelty of orbital-radar is the simulation platform characteristic noise floors and errors. By this long time data sets can be transformed into synthetic observations for Cal/Valor sensitivity studies for new or future satellite missions.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
Short summary
Short summary
The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
Short summary
Short summary
In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
Short summary
Short summary
A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
Short summary
Short summary
The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
Short summary
Short summary
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
Short summary
Short summary
This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
Short summary
Short summary
Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
Short summary
Short summary
This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
Short summary
Short summary
Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
Short summary
Short summary
TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
Short summary
Short summary
Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
Short summary
Short summary
We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
Short summary
Short summary
A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
Short summary
Short summary
The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 2024
Short summary
Short summary
Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate in the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, https://doi.org/10.5194/gmd-17-5477-2024, 2024
Short summary
Short summary
In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
Short summary
Short summary
A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
Short summary
Short summary
Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
Short summary
Short summary
A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
Short summary
Short summary
We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1437, https://doi.org/10.5194/egusphere-2024-1437, 2024
Short summary
Short summary
We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can well reproduce the distribution of PAHs. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change of BaP is less than PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although "the Action Plan" has been implemented.
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
Short summary
Short summary
Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Nathan P. Arnold
Geosci. Model Dev., 17, 5041–5056, https://doi.org/10.5194/gmd-17-5041-2024, https://doi.org/10.5194/gmd-17-5041-2024, 2024
Short summary
Short summary
Earth system models often represent the land surface at smaller scales than the atmosphere, but surface–atmosphere coupling uses only aggregated surface properties. This study presents a method to allow heterogeneous surface properties to modify boundary layer updrafts. The method is tested in single column experiments. Updraft properties are found to reasonably covary with surface conditions, and simulated boundary layer variability is enhanced over more heterogeneous land surfaces.
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024, https://doi.org/10.5194/gmd-17-4983-2024, 2024
Short summary
Short summary
Nitrogen dioxide (NOx) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NOx emissions over Germany and illustrate the value of having such a method for fast projections.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-109, https://doi.org/10.5194/gmd-2024-109, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study updates CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosols (SOA) formation. Dust emission modifications make deflation areas more continuous, improving results in North America and the subarctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation, advance CESM's aerosol modelling results.
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024, https://doi.org/10.5194/gmd-17-4961-2024, 2024
Short summary
Short summary
Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
Short summary
Short summary
Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Virve Eveliina Karsisto
Geosci. Model Dev., 17, 4837–4853, https://doi.org/10.5194/gmd-17-4837-2024, https://doi.org/10.5194/gmd-17-4837-2024, 2024
Short summary
Short summary
RoadSurf is an open-source library that contains functions from the Finnish Meteorological Institute’s road weather model. The evaluation of the library shows that it is well suited for making road surface temperature forecasts. The evaluation was done by making forecasts for about 400 road weather stations in Finland with the library. Accurate forecasts help road authorities perform salting and plowing operations at the right time and keep roads safe for drivers.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
Short summary
Short summary
The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Cited articles
Abdi, D. S., Giraldo, F. X., Constantinescu, E., Lester III, C., Wilcox, L.,
and Warburton, T.: Acceleration of the Implicit-Explicit Non-Hydrostatic
Unified Model of the Atmosphere (NUMA) on Manycore Processors, Int.
J. High Perform. C., 33, 242–267,
https://doi.org/10.1177/1094342017732395, 2017a. a
Ahmad, N. and Lindeman, J.: Euler solutions using flux-based wave
decomposition, Int. J. Numer. Meth. Fl., 54,
47–72, https://doi.org/10.1002/fld.1392, 2007. a, b, c
Balaji, V.: Climbing down Charney's ladder: machine learning and the
post-Dennard era of computational climate science, Philos.
T. Roy. Soc. A, 379, 20200085, https://doi.org/10.1098/rsta.2020.0085, 2021. a
Bao, L., Klöfkorn, R., and Nair, R. D.: Horizontally Explicit and Vertically
Implicit (HEVI) Time Discretization Scheme for a Discontinuous Galerkin
Nonhydrostatic Model, Mon. Weather Rev., 143, 972–990,
https://doi.org/10.1175/MWR-D-14-00083.1, 2015. a
Bassi, F. and Rebay, S.: A high-order discontinuous Galerkin finite element
method solution of the 2d Euler equations, J. Comput. Phys.,
138, 251–285, https://doi.org/10.1006/jcph.1997.5454, 1997. a, b
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B.: Julia: A Fresh
Approach to Numerical Computing, SIAM Rev., 59, 65–98,
https://doi.org/10.1137/141000671, 2017. a
Bezanson, J., Chen, J., Chung, B., Karpinski, S., Shah, V. B., Vitek, J., and
Zoubritzky, L.: Julia: Dynamism and Performance Reconciled by Design, Proc.
ACM Program. Lang., 2, 1–23, https://doi.org/10.1145/3276490, 2018. a
Boyd, J. P.: The erfc-log filter and the asymptotics of the Euler and
Vandeven sequence accelerations, edited by: Ilin, A. V. and Scott, L. R., Proceedings
of the Third International Conference on Spectral and High Order Methods,
Houston Journal of Mathematics, 267–276, 1996. a
Brachet, M. E.: Direct simulation of three-dimensional turbulence in the
Taylor-Green vortex, Fluid Dyn. Res., 8, 1–8,
https://doi.org/10.1016/0169-5983(91)90026-f, 1991. a
Brachet, M. E., Meiron, D. I., Orszag, A., Nickel, B. G., Morf, R. H., and
Frisch, U.: Small-scale structure of the Taylor-Green vortex, J.
Fluid Mech., 130, 411–452, https://doi.org/10.1017/S0022112083001159, 1983. a, b
Canuto, V. M.: Compressible turbulence, Astrophys. J., 482, 827–851,
https://doi.org/10.1086/304175, 1997. a
Carpenter, M. H. and Kennedy, C. A.: Fourth-order 2N-storage Runge-Kutta
schemes, Tech. Rep. NASA TM-109112, National Aeronautics and Space
Administration, Langley Research Center, Hampton, VA, 1994. a
Chow, F. K. and Moin, P.: A further study of numerical errors in large-eddy
simulations, J. Comput. Phys., 184, 366–380,
https://doi.org/10.1016/S0021-9991(02)00020-7, 2003. a
Climate Modeling Alliance: ClimateMachine.jl (0.2.0), Zenodo [code], https://doi.org/10.5281/zenodo.5542395, 2020. a, b
Deardorff, J. W.: A numerical study of three-dimensional turbulent channel flow
at large Reynolds numbers, J. Fluid Mech., 41, 452–480,
https://doi.org/10.1017/S0022112070000691, 1970. a, b
Deardorff, J. W.: Three-dimensional numerical study of the height and mean
structure of a heated planetary boundary layer, Bound. Lay. Meteorol.,
7, 81–106, https://doi.org/10.1007/BF00224974, 1974. a
Deardorff, J. W.: Usefulness of liquid-water potential temperature in a
shallow-cloud model, J. Appl. Meteorol., 15, 98–102,
https://doi.org/10.1175/1520-0450(1976)015<0098:UOLWPT>2.0.CO;2, 1976. a
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a
three-dimensional model, Bound. Lay. Meteorol., 18, 495–527,
https://doi.org/10.1007/BF00119502, 1980. a, b, c
Deville, M. O., Fischer, P. F., and Mund, E. H.: High-order methods for
incompressible fluid flow, Cambridge University Press,
https://doi.org/10.1017/CBO9780511546792, 2002. a
Dipankar, A., Stevens, B., Heinze, R., Moseley, C., Zängl, G., Giorgetta, M.,
and Brdar, S.: Large eddy simulation using the general circulation model
ICON, J. Adv. Model. Earth Sy., 7, 963–986,
https://doi.org/10.1002/2015MS000431, 2015. a
Drikakis, D., Fureby, C., and Youngs, F.: Simulation of transition and
turbulence decay in the Taylor–Green vortex, J. Turbulence, 8,
1–12, https://doi.org/10.1080/14685240701250289, 2007. a
Durran, D. and Klemp, J.: A compressible model for the simulation of moist
mountain waves, Mon. Weather Rev., 111, 2341–2361,
https://doi.org/10.1175/1520-0493(1983)111<2341:ACMFTS>2.0.CO;2, 1983. a
Toro, E. F., Spruce, M., and Speares, W.: Restoration of the Contact
Surface in the HLL–Riemann Solver, Shock Waves, 4, 25–34,
https://doi.org/10.1007/BF01414629, 1994. a
Fuhrer, O., Osuna, C., Lapillonne, X., Gysi, T., Cumming, B., Bianco, M.,
Arteaga, A., and Schulthess, T. C.: Towards a performance portable,
architecture agnostic implementation strategy for weather and climate models,
Supercomput. Front. Inn., 1, 45–62,
https://doi.org/10.14529/jsfi140103, 2014. a
Fuhrer, O., Chadha, T., Hoefler, T., Kwasniewski, G., Lapillonne, X., Leutwyler, D., Lüthi, D., Osuna, C., Schär, C., Schulthess, T. C., and Vogt, H.: Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0, Geosci. Model Dev., 11, 1665–1681, https://doi.org/10.5194/gmd-11-1665-2018, 2018. a
Gal-Chen, T. and Somerville, R.: Numerical solution of the Navier-Stokes
equations with topography, J. Comput. Phys., 17, 276–310,
https://doi.org/10.1016/0021-9991(75)90054-6, 1975. a
Ghosal, S.: An Analysis of Numerical Errors in Large-Eddy Simulations of
Turbulence, J. Comput. Phys., 125, 187–206,
https://doi.org/10.1006/jcph.1996.0088, 1996. a
Giraldo, F. X.: An Introduction to Element-based Galerkin Methods on
Tensor-Product Bases: Analysis, Algorithms, and Applications, Springer,
https://doi.org/10.1007/978-3-030-55069-1, 2020. a
Giraldo, F. X. and Restelli, M.: A study of spectral element and
discontinuous Galerkin methods for the Navier-Stokes equations in
nonhydrostatic mesoscale atmospheric modeling: Equation sets and test
cases, J. Comput. Phys., 227, 3849–3877,
https://doi.org/10.1016/j.jcp.2007.12.009, 2008. a, b, c, d, e, f, g, h
Giraldo, F. X., Hesthaven, J. S., and Warburton, T.: Nodal high-order
discontinuous Galerkin methods for spherical shallow water equations, J.
Comput. Phys., 181, 499–525, https://doi.org/10.1006/jcph.2002.7139, 2002. a
Giraldo, F. X., Kelly, J. F., and Constantinescu, E. M.: Implicit-explicit
formulations of a three-dimensional nonhydrostatic unified model of the
atmosphere (NUMA), SIAM J. Sci. Comput., 35, B1162–B1194,
https://doi.org/10.1137/120876034, 2013. a
Harten, A.: High resolution schemes for hyperbolic conservation laws, J.
Comput. Phys., 49, 357–393, https://doi.org/10.1016/0021-9991(83)90136-5, 1983. a
Hesthaven, J. and Warburton, T.: Nodal discontinuous Galerkin method,
Algorithms, analysis and applications, Springer,
https://doi.org/10.1007/978-0-387-72067-8, 2008a. a
Hesthaven, J. S. and Warburton, T.: Nodal discontinuous Galerkin methods:
algorithms, analysis, and applications, vol. 54, Springer-Verlag New York
Inc, https://doi.org/10.1007/978-0-387-72067-8, 2008b. a, b
Holland, J. Z. and Rasmusson, E. M.: Measurements of the atmospheric mass,
energy, and momentum budgets over a 500-kilometer square of tropical ocean,
Mon. Weather Rev, 101, 44–57,
https://doi.org/10.1175/1520-0493(1973)101<0044:MOTAME>2.3.CO;2, 1973. a
Hunt, J. C. R., Wray, A., and Moin, P.: Eddies, stream, and convergence zones
in turbulent flows, Tech. Rep. CTR-S88, Center for Turbulence Research Report
CTR-S88, Stanford University, 1988. a
Jähn, M., Knoth, O., König, M., and Vogelsberg, U.: ASAM v2.7: a compressible atmospheric model with a Cartesian cut cell approach, Geosci. Model Dev., 8, 317–340, https://doi.org/10.5194/gmd-8-317-2015, 2015. a
Karniadakis, G. and Sherwin, S.: Spectral/hp element methods for CFD,
Oxford University Press, https://doi.org/10.1093/acprof:oso/9780198528692.001.0001,
1999. a
Kelly, J. F. and Giraldo, F. X.: Continuous and discontinuous Galerkin
methods for a scalable three-dimensional nonhydrostatic atmospheric model:
limited-area mode, J. Comput. Phys., 231, 7988–8008,
https://doi.org/10.1016/j.jcp.2012.04.042, 2012. a, b
Kennedy, C. A. and Carpenter, M. H.: Higher-order additive Runge–Kutta schemes
for ordinary differential equations, Appl. Numer. Math., 136,
183–205, https://doi.org/10.1016/j.apnum.2018.10.007, 2019. a
Kopriva, D. A.: Implementing spectral methods for partial differential
equations: Algorithms for scientists and engineers, Springer Science &
Business Media, https://doi.org/10.1007/978-90-481-2261-5, 2009. a
Kurowski, M., Grabowski, W. W., and Smolarkiewicz, P. K.: Anelastic and
compressible simulation of moist deep convection, J. Atmos.
Sci., 71, 3767–3787, https://doi.org/10.1175/JAS-D-14-0017.1, 2014. a
Light, D. and Durran, D.: Preserving Nonnegativity in Discontinuous Galerkin
Approximations to Scalar Transport via Truncation and Mass Aware Rescaling
(TMAR), Mon. Weather Rev., 144, 4771–4786,
https://doi.org/10.1175/MWR-D-16-0220.1, 2016. a
Lilly, D. K.: On the numerical simulation of buoyant convection, Tellus, 14,
148–172, https://doi.org/10.3402/tellusa.v14i2.9537, 1962. a, b
Lilly, D. K.: On the application of the eddy viscosity concept in the inertial
sub-range of turbulence, NCAR manuscript, 123, https://doi.org/10.5065/D67H1GGQ, 1966. a
Lin, W.-C. and McIntosh-Smith, S.: Comparing Julia to Performance Portable
Parallel Programming Models for HPC, in: 2021 International Workshop on
Performance Modeling, Benchmarking and Simulation of High Performance
Computer Systems (PMBS), 94–105, https://doi.org/10.1109/PMBS54543.2021.00016,
2021. a
Marras, S. and Giraldo, F. X.: A parameter-free dynamic alternative to
hyper-viscosity for coupled transport equations: application to the
simulation of 3D squall lines using spectral elements, J. Comput. Phys.,
283, 360–373, https://doi.org/10.1016/j.jcp.2014.11.046, 2015. a
Marras, S., Kelly, J. F., Giraldo, F. X., and Vázquez, M.:
Variational multiscale stabilization of high-order spectral elements for the
advection-diffusion equation, J. Comput. Phys., 231, 7187–7213, 2012. a
Marras, S., Kelly, J. F., Moragues, M., Müller, A., Kopera, M. A., Vázquez,
M., Giraldo, F. X., Houzeaux, G., and Jorba, O.: A Variational Multiscale
Stabilized Finite Element Method for the Solution of the Euler Equations
of Nonhydrostatic Stratified Flows, Arch. Comput. Methods Eng., 23, 673–722,
https://doi.org/10.1016/j.jcp.2012.10.056, 2015. a
Mason, P. J. and Callen, N. S.: On the magnitude of the subgrid-scale eddy
coefficient in large-eddy simulations of turbulent channel flow, J.
Fluid Mech., 162, 439–462, https://doi.org/10.1017/S0022112086002112, 1986. a
Matheou, G.: Numerical discretization and subgrid-scale model effects on
large-eddy simulations of a stable boundary layer, Q. J. Roy. Meteor. Soc.,
142, 3050–3062, 2016. a
Matheou, G. and Teixeira, J.: Sensitivity to Physical and Numerical Aspects of
Large-Eddy Simulation of Stratocumulus, Mon. Weather Rev., 147,
2621–2639, 2019. a
Matheou, G., Chung, D., Nuijens, L., Stevens, B., and Teixeira, J.: On the
fidelity of large-eddy simulation of shallow precipitating cumulus
convection, Mon. Weather. Rev., 139, 2918–2939, https://doi.org/10.1175/2011MWR3599.1,
2011. a
Mellado, J.: Cloud-Top Entrainment in Stratocumulus Clouds, Annu. Rev.
Fluid Mech., 49, 145–169, 2017. a
Mellado, J. P., Bretherton, C. S., Stevens, B., and Wyant, M. C.: DNS and
LES for Simulating Stratocumulus: Better Together, J. Adv.
Model. Earth Sy., 10, 1421–1438, https://doi.org/10.1029/2018MS001312, 2018. a
Moeng, C., McWilliams, J., Rotunno, R., Sullivan, P., and Weil, J.:
Investigating 2D modelling of atmospheric convection in the PBL, J. Atmos.
Sci., 61, 889–903, 2003. a
Moeng, C. H.: A Large-Eddy simulation model for the study of planetary
boundary-layer turbulence, J. Atmos. Sci., 41, 2052–2062, 1984. a
Moeng, C. H. and Wyngaard, J. C.: Spectral analysis of large-eddy simulations
of the convective boundary layer, J. Atmos. Sci., 45,
3573–3587, 1988. a
Müller, A., Kopera, M., Marras, S., Wilcox, L., Isaac, T., and Giraldo, F.:
Strong scaling for numerical weather prediction at petascale with the
atmospheric model NUMA, Int. J. High Perform. C., 33, 411–426, https://doi.org/10.1177/1094342018763966, 2018. a
Niegemann, J., Diehl, R., and Busch, K.: Efficient low-storage Runge–Kutta
schemes with optimized stability regions, J. Comput. Phys.,
231, 364–372, https://doi.org/10.1016/j.jcp.2011.09.003, 2012. a, b
Palmer, T.: Climate forecasting: build high-resolution global climate models,
Nature, 515, 338–339, https://doi.org/10.1038/515338a, 2014. a
Pressel, K. G., Kaul, C. M., Schneider, T., Tan, Z., and Mishra, S.: Large-eddy
simulation in an anelastic framework with closed water and entropy balances,
J. Adv. Model. Earth Sy., 7, 1425–1456,
https://doi.org/10.1002/2015MS000496, 2015. a, b, c, d
Pressel, K. G., Mishra, S., Schneider, T., Kaul, C. M., and Tan, Z.: Numerics
and Subgrid-Scale Modeling in Large Eddy Simulations of Stratocumulus Clouds,
J. Adv. Model. Earth Sy., 9, 1342–1365,
https://doi.org/10.1002/2016MS000778, 2017. a
Raymond, D. J.: Sources and sinks of entropy in the atmosphere, J.
Adv. Model. Earth Sy., 5, 755–763, 2013. a
Reddy, S., Tissaoui, Y., De Bragan ça Alves, F., Marras, S., and Giraldo,
F.: Comparison of Sub-Grid Scale Models for Large-Eddy Simulation Using a
High-Order Spectral Element Approximation of the Compressible Navier-Stokes
Equations at Low Mach Number, J. Comput. Phys.,
https://doi.org/10.13140/RG.2.2.17576.90885, in review, 2022. a
Roe, P.: Approximate Riemann Solvers, Parameter Vectors, and Difference
Schemes, J. Comput. Phys., 43, 357–372,
https://doi.org/10.1016/0021-9991(81)90128-5, 1981. a
Romps, D. M.: The dry-entropy budget of a moist atmosphere, J. Atmos. Sci., 65,
3779–3799, https://doi.org/10.1175/2008JAS2679.1, 2008. a, b, c, d
Rusanov, V.: Calculation of Interaction of Non–Steady Shock Waves with
obstacles, USSR Comp. Math. Math., 1,
267–279, https://doi.org/10.1016/0041-5553(62)90062-9, 1961. a
Savic-Jovcic, V. and Stevens, B.: The structure and mesoscale organization of
precipitating stratocumulus, J. Atmos. Sci., 65, 1587–1605,
https://doi.org/10.1175/2007JAS2456.1, 2008. a
Schalkwijk, J., Griffith, E., Post, H., and Jonker, H. J. J.: High performance
simulations of turbulent clouds on a desktop PC: Exploiting the GPU,
B. Am. Meteorol. Soc., 93, 307–314,
https://doi.org/10.1175/BAMS-D-11-00059.1, 2012. a, b
Schalkwijk, J., Jonker, H., Siebesma, A., and Bosveld, F.: A year-long
Large-Eddy Simulation of the weather over Cabauw: an overview, Mon.
Weather Rev., 143, 828–844, https://doi.org/10.1175/MWR-D-14-00293.1, 2015. a, b
Schär, C., Leuenberger, D., Fuhrer, O., Luthic, D., and Girard, C.:
A new terrain-following vertical coordinate formulation for atmospheric
prediction models, Mon. Weather Rev., 130, 2459–2480,
https://doi.org/10.1175/1520-0493(2002)130<2459:ANTFVC>2.0.CO;2, 2002. a, b, c
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di Girolamo, S.,
Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K.,
Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger,
M., Ubbiali, S., and Wernli, H.: Kilometer-Scale Climate Models: Prospects
and Challenges, B. Am. Meteorol. Soc., 101,
E567–E587, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020. a
Schneider, T., Kaul, C., and Pressel, K.: Possible climate transitions from
breakup of stratocumulus decks under greenhouse warming, Nat. Geosci.,
12, 163–167, https://doi.org/10.1038/s41561-019-0310-1, 2019. a
Shi, X., Chow, F. K., Street, R. L., and Bryan, G. H.: An Evaluation of LES
Turbulence Models for Scalar Mixing in the Stratocumulus-Capped Boundary
Layer, J. Atmos. Sci., 75, 1499–1507,
https://doi.org/10.1175/JAS-D-17-0392.1, 2018. a
Shu, C.-W. and Osher, S.: Efficient implementation of essentially
non-oscillatory shock-capturing schemes, J. Comput. Phys.,
77, 439–471, https://doi.org/10.1016/0021-9991(88)90177-5, 1988. a
Siebesma, A. P., Bretherton, C. S., Brown, A., Chlond, A., Cuxart, J.,
Duynkerke, P. G., Jiang, H., Khairoutdinov, M., Lewellen, D., Moeng, C.-H.,
Sanchez, E., Stevens, B., and Stevens, D. E.: A large eddy simulation intercomparison study of shallow cumulus
convection, J. Atmos. Sci., 60, 1201–1219,
https://doi.org/10.1175/1520-0469(2003)60<1201:ALESIS>2.0.CO;2, 2003. a, b, c, d, e
Smagorinsky, J.: General Circulation Experiments with the Primitive
Equations: I. The basic experiement, Mon. Weather Rev., 91, 99–164,
https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963. a, b
Smith, R.: Linear theory of stratified hydrostatic flow past an isolated
mountain, Tellus, 32, 348–364, https://doi.org/10.3402/tellusa.v32i4.10590, 1980. a
Smith, R. B.: The influence of mountains on the atmosphere, Adv.
Geophys., 21, 87–230, https://doi.org/10.1016/S0065-2687(08)60262-9,
1979. a
Stevens, B., Lenschow, D. H., Vali, G., Gerber, H., Bandy, A., Blomquist, B.,
Brenguier, J.-L., Bretherton, C. S., Burnet, F., Campos, T., Chai, S.,
Faloona, I., Friesen, D., Haimov, S., Laursen, K., Lilly, D. K., Loehrer, S. M., Malinowski, S. P.,
Morley, B., Petters, M. D., Rogers, D. C., Russell, L., Savic-Jovcic, V., Snider, J. R., Straub, D., Szumowski, M. J.,
Takagi, H., Thornton, D. C., Tschudi, M., Twohy, C., Wetzel, M., and van Zanten, M. C.: Dynamics and chemistry of marine
stratocumulus–DYCOMS-II, B. Am. Meteorol. Soc., 84, 579–593,
https://doi.org/10.1175/BAMS-84-5-579, 2003.
a
Stevens, B., Moeng, C.-H., Ackerman, A. S., Bretherton, C. S., Chlond, A., de
Roode, S., Edwards, J., Golaz, J.-C., Jiang, H., Khairoutdinov, M.,
Kirkpatrick, M. O., Lewellen, D. C., Lock, A., Müller, F., Stevens,
D. E., Whelan, E., and Zhu, P.: Evaluation of Large-Eddy Simulations via
Observations of Nocturnal Marine Stratocumulus, Mon. Weather Rev., 133,
1443–1462, https://doi.org/10.1175/MWR2930.1, 2005. a, b, c, d
Straka, J., Wilhelmson, R., Wicker, L., Anderson, J., and
Droegemeier, K.: Numerical solution of a nonlinear density current: a
benchmark solution and comparisons, Int. J. Numer.
Meth. Fl., 17, 1–22, https://doi.org/10.1002/fld.1650170103,
1993. a, b, c
Sullivan, P., McWilliams, J., and Moeng, C.: A subgrid-scale model for
large-eddy simulation of planetary boundary-layer flows, Bound.-Lay.
Meteorol., 71, 247–276, https://doi.org/10.1007/BF00713741, 1994. a
Tao, W.-K., Simpson, J., and McCumber, M.: An Ice-Water Saturation Adjustment,
Mon. Weather Rev., 117, 231–235, 1989. a
Vandeven, H.: Family of spectral filters for discontinuous problems, J.
Sci. Comput., 6, 159–192, https://doi.org/10.1007/BF01062118, 1991. a
Vreman, A.: An eddy-viscosity subgrid-scale model for turbulent shear flow:
algebraic theory and applications, Phys. Fluid., 16, 3670–3681,
https://doi.org/10.1063/1.1785131, 2004. a
Yamaguchi, T. and Feingold, G.: Technical note: Large-eddy simulation of cloudy
boundary layer with the Advanced Research WRF model, J. Adv. Model. Earth
Sy., 4, M09003, https://doi.org/10.1029/2012MS000164, 2012. a
Yu, M. L., Giraldo, F. X., Peng, M., and Wang, Z. J.: Localized Artificial
Viscosity Stabilization of Discontinuous Galerkin Methods for Nonhydrostatic
Mesoscale Atmospheric Modeling, Mon. Weather Rev., 143, 4823–4845,
https://doi.org/10.1175/MWR-D-15-0134.1, 2015. a
Short summary
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its...
