Articles | Volume 15, issue 13
https://doi.org/10.5194/gmd-15-5309-2022
© 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-15-5309-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
13 Jul 2022
Development and technical paper |
| 13 Jul 2022
| 13 Jul 2022
uDALES 1.0: a large-eddy simulation model for urban environments
Ivo Suter
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
Tom Grylls
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Birgit S. Sützl
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Chair of Building Physics, ETH, Zürich, Switzerland
Sam O. Owens
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Chris E. Wilson
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Maarten van Reeuwijk
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Related authors
Dominik Brunner, Ivo Suter, Leonie Bernet, Lionel Constantin, Stuart K. Grange, Pascal Rubli, Junwei Li, Jia Chen, Alessandro Bigi, and Lukas Emmenegger
EGUsphere, https://doi.org/10.5194/egusphere-2025-640, https://doi.org/10.5194/egusphere-2025-640, 2025
Short summary
Short summary
In order to support the city of Zurich in tracking its path to net-zero greenhouse gas emissions planned to be reached by 2040, a CO2 emission monitoring system was established. The system combines a dense network of CO2 sensors with a high-resolution atmospheric transport model GRAMM/GRAL. This study presents the setup of the model together with its numerous inputs and evaluates its performance in comparison with the observations from the CO2 sensor network.
Dominik Brunner, Ivo Suter, Leonie Bernet, Lionel Constantin, Stuart K. Grange, Pascal Rubli, Junwei Li, Jia Chen, Alessandro Bigi, and Lukas Emmenegger
EGUsphere, https://doi.org/10.5194/egusphere-2025-640, https://doi.org/10.5194/egusphere-2025-640, 2025
Short summary
Short summary
In order to support the city of Zurich in tracking its path to net-zero greenhouse gas emissions planned to be reached by 2040, a CO2 emission monitoring system was established. The system combines a dense network of CO2 sensors with a high-resolution atmospheric transport model GRAMM/GRAL. This study presents the setup of the model together with its numerous inputs and evaluates its performance in comparison with the observations from the CO2 sensor network.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
Short summary
Short summary
The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
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.
Related subject area
Atmospheric sciences
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Chempath 1.0: an open-source pathway analysis program for photochemical models
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Atmospheric moisture tracking with WAM2layers v3
A new set of indicators for model evaluation complementing FAIRMODE's modelling quality objective (MQO)
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Development of a High-Resolution Coupled SHiELD-MOM6 Model. Part I – Model Overview, Coupling Technique, and Validation in a Regional Setup
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
Short summary
Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025, https://doi.org/10.5194/gmd-18-4417-2025, 2025
Short summary
Short summary
A numerical model that simulates the measurement processes behind the ground-based radars used to detect volcanic ash clouds is introduced. Using weather radars to detect volcanic clouds is not ideal, as fine ash particles are smaller than raindrops and remain undetected. We evaluate the performance of weather radars to study ash clouds and to identify optimal frequencies that balance the trade-off between a higher return signal and the higher path attenuation that comes at these higher frequencies.
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025, https://doi.org/10.5194/gmd-18-4433-2025, 2025
Short summary
Short summary
Photochemical models describe how the composition of the atmosphere changes due to chemical reactions, transport, and other processes. These models are useful for studying the composition of the Earth's and other planets' atmospheres. Understanding the results of these models can be difficult. Here, we build on previous work to develop open-source code that can identify the reaction chains (pathways) that produce the results of these models, facilitating the understanding of these results.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025, https://doi.org/10.5194/gmd-18-4353-2025, 2025
Short summary
Short summary
Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line And Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, this model is valuable for airglow research and astronomical observatories.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
Geosci. Model Dev., 18, 4335–4352, https://doi.org/10.5194/gmd-18-4335-2025, https://doi.org/10.5194/gmd-18-4335-2025, 2025
Short summary
Short summary
We introduce a new version of WAM2layers (Water Accounting Model – 2 layers), a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data had become a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent, reliable, and easier to maintain.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025, https://doi.org/10.5194/gmd-18-4231-2025, 2025
Short summary
Short summary
We assess relevance and utility indicators by evaluating nine Copernicus Atmospheric Monitoring Service models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and winter–summer gradients reveal issues. O3 evaluation shows that seasonal gradients are useful. Overall, the indicators reveal model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
Short summary
A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
Short summary
Short summary
SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025, https://doi.org/10.5194/gmd-18-3921-2025, 2025
Short summary
Short summary
It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025, https://doi.org/10.5194/gmd-18-3965-2025, 2025
Short summary
Short summary
Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
Short summary
Short summary
RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Short summary
Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Short summary
This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
Short summary
Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Short summary
This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Short summary
We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary
Short summary
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
Short summary
Short summary
In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
Short summary
Short summary
We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Short summary
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
Short summary
Short summary
This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
Short summary
Short summary
This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
Short summary
Short summary
We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
Short summary
Short summary
Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
Short summary
Short summary
We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Short summary
This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary
Short summary
Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
Short summary
Short summary
The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
Short summary
Short summary
The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Joseph Mouallem, Kun Gao, Brandon G. Reichl, Lauren Chilutti, Lucas Harris, Rusty Benson, Niki Zadeh, Jing Chen, Jan-Huey Chen, and Cheng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1690, https://doi.org/10.5194/egusphere-2025-1690, 2025
Short summary
Short summary
We introduce a new high-resolution model that couple the atmosphere and ocean to better simulate extreme weather events. It combines GFDL’s advanced atmospheric and ocean models with a powerful coupling system that allows robust and efficient two-way interactions. Simulations show the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model is a key step toward improving extreme weather forecast.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
Short summary
Short summary
This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
Short summary
Short summary
The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Short summary
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
Short summary
Short summary
The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary
Short summary
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
Short summary
Short summary
The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
Short summary
Short summary
Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
Short summary
Short summary
Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
Short summary
Short summary
This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary
Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
Short summary
Short summary
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
Short summary
Short summary
Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
Short summary
Short summary
Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
Short summary
Short summary
This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
Short summary
Short summary
This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
Short summary
Short summary
An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
Short summary
Short summary
As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
Cited articles
Ambirajan, A. and Venkateshan, S. P.: Accurate determination of diffuse view
factors between planar surfaces, Int. J. Heat Mass Transf., 36, 2203–2208,
https://doi.org/10.1016/S0017-9310(05)80151-6, 1993. a
American Society of Civil Engineers Task Committee on Outdoor Human Comfort ASCE: Outdoor human comfort and its assessment: state of the art, American Society of Civil Engineers (ASCE), Reston, VA, 68 pp., ISBN 0784406847, 2004. a
American Society of Civil Engineers Task Committee on Urban Aerodynamics ASCE: Urban aerodynamics: wind engineering for urban planners and designers, American Society of Civil Engineers (ASCE), Reston, VA, 63 pp., ISBN 9780784411797, 2011. a
American Society of Heating Refrigerating and Air-Conditioning Engineers Inc. (ASHRAE): 2011 ASHRAE Handbook: Heating, Ventilating, and Air-Conditioning Applications, chapter 35, Solar energy use, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., 1108 pp., ISBN 978-1-936504-07-7, 2011. a
Aoyagi, T. and Takahashi, S.: Development of an Urban Multilayer Radiation
Scheme and Its Application to the Urban Surface Warming Potential,
Bound.-Lay. Meteorol., 142, 305–328, https://doi.org/10.1007/s10546-011-9679-0,
2012. a
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical
processes of the UCLA general circulation model, in: Methods Comput. Phys.,
edited by: Chang, J., vol. 17, Academic Press, New York, NY,
USA, pp. 173–265, https://doi.org/10.1016/B978-0-12-460817-7.50009-4, 1977. a
Arnfield, A. J.: Two decades of urban climate research: A review of
turbulence, exchanges of energy and water, and the urban heat island, Int.
J. Climatol., 23, 1–26, https://doi.org/10.1002/joc.859, 2003. a
Arnold, S., ApSimon, H., Barlow, J. F., Belcher, S. E., Bell, M., Boddy, J.,
Britter, R., Cheng, H., Clark, R., and Colvile, R.: Introduction to the
DAPPLE Air Pollution Project, Sci. Total Environ., 332,
139–153, https://doi.org/10.1016/j.scitotenv.2004.04.020, 2004. a
Barlow, J. F.: Progress in observing and modelling the urban boundary layer,
Urban Clim., 10, 216–240, https://doi.org/10.1016/j.uclim.2014.03.011, 2014. a, b
Bartholomew, P., Deskos, G., Frantz, R. A., Schuch, F. N., Lamballais, E., and
Laizet, S.: Xcompact3D: An open-source framework for solving turbulence
problems on a Cartesian mesh, SoftwareX, 12, 100550,
https://doi.org/10.1016/j.softx.2020.100550, 2020. a
Blocken, B.: Computational Fluid Dynamics for urban physics: Importance,
scales, possibilities, limitations and ten tips and tricks towards accurate
and reliable simulations, Build. Environ., 91, 219–245,
https://doi.org/10.1016/j.buildenv.2015.02.015, 2015. a, b
Blocken, B., Roels, S., and Carmeliet, J.: A numerical study of wind nuisance
for a high-rise building group, in: Proceedings of the 2nd International
conference on Research in Building Physics, Leuven,
Belgium, 14–18 September 2003, edited by: Carmeliet, J., Hens, H., and Vermeir, G., pp. 981–990, https://doi.org/10.1201/9781003078852,
2003. a
Blocken, B., Stathopoulos, T., and van Beeck, J.: Pedestrian-level wind
conditions around buildings: Review of wind-tunnel and CFD techniques and
their accuracy for wind comfort assessment, Build. Environ., 100, 50–81, https://doi.org/10.1016/j.buildenv.2016.02.004, 2016. a
Bohnenstengel, S., Evans, S., Clark, P. A., and Belcher, S.: Simulations of the
London urban heat island, Q. J. Roy. Meteor.
Soc., 137, 1625–1640, 2011. a
Bolton, D.: The computation of equivalent potential temperature, Mon. Weather
Rev., 108, 1046–1053,
https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2, 1980. a
Borge, R., Santiago, J. L., de la Paz, D., Martín, F., Domingo, J., Valdés, C., Sánchez, B., Rivas, E., Rozas, M. T., Lázaro, S., Pérez, J., and Fernández, Á.: Application of a short term air quality action plan in Madrid (Spain) under a high-pollution episode-Part II: Assessment from multi-scale modelling, Sci. Total Environ., 635, 1574–1584, https://doi.org/10.1016/j.scitotenv.2018.04.323, 2018. a
Bozovic, R., Maksimovic, C., Mijic, A., Smith, K., Suter, I., and Van Reeuwijk,
M.: Blue Green Solutions. A Systems Approach to Sustainable and
Cost-effective Urban Development, Tech. Rep., Imperial College London,
https://doi.org/10.13140/RG.2.2.30628.07046, 2017. a
Cai, X.-M.: Effects of differential wall heating in street canyons on
dispersion and ventilation characteristics of a passive scalar, Atmos.
Environ., 51, 268–277, https://doi.org/10.1016/j.atmosenv.2012.01.010,
2012b. a
Carpentieri, M. and Robins, A. G.: Tracer Flux Balance at an Urban Canyon
Intersection, Bound.-Lay. Meteorol., 135, 229–242,
https://doi.org/10.1007/s10546-010-9471-6, 2010. a, b, c
Carpentieri, M., Robins, A. G., and Baldi, S.: Three-Dimensional Mapping of Air
Flow at an Urban Canyon Intersection, Bound.-Lay. Meteorol., 133,
277–296, https://doi.org/10.1007/s10546-009-9425-z, 2009. a, b
Carpentieri, M., Hayden, P., and Robins, A. G.: Wind tunnel measurements of
pollutant turbulent fluxes in urban intersections, Atmos. Environ.,
46, 669–674, https://doi.org/10.1016/j.atmosenv.2011.09.083, 2012. a, b
Castleton, H. F., Stovin, V., Beck, S. B. M., and Davison, J. B.: Green roofs;
building energy savings and the potential for retrofit, Energy Build., 42,
1582–1591, https://doi.org/10.1016/j.enbuild.2010.05.004, 2010. a
Caton, F., Britter, R. E., and Dalziel, S.: Dispersion mechanisms in a street
canyon, Atmos. Environ., 37, 693–702,
https://doi.org/10.1016/S1352-2310(02)00830-0, 2003. a, b
Cheng, H. and Robins, A. G.: Wind tunnel simulation of field tracer release in London, in: Fourth International Conference on Fluid Mechanics, Dalian, China, 28–31 July 2004, 2004. a
COST Action 732: Model evaluation guidance and protocol document, edited by: Britter, R. E. and Schatzmann, M., Tech. Rep.,
University of Hamburg, 28 pp., ISBN 3-00-018312-4, https://mi-pub.cen.uni-hamburg.de/fileadmin/files/forschung/techmet/cost/cost_732/pdf/GUIDANCE_AND_PROTOCOL_DOCUMENT_1-5-2007_www.pdf (last access: 8 June 2022), 2007. a
Ehrhard, J., Khatib, I. A., Winkler, C., Kunz, R., Moussiopoulos, N., and
Ernst, G.: The microscale model MIMO: development and assessment, J. Wind
Eng. Ind. Aerodyn., 85, 163–176, https://doi.org/10.1016/S0167-6105(99)00137-3, 2000. a
Emanuel, K. A.: Atmospheric convection, Oxford University Press on Demand, ISBN 9780195066302,
1994. a
Erdélyi, R., Wang, Y., Guo, W., Hanna, E., and Colantuono, G.:
Three-dimensional SOlar RAdiation Model (SORAM) and its application to 3-D
urban planning, Sol. Energy, 101, 63–73,
https://doi.org/10.1016/j.solener.2013.12.023, 2014. a
Erell, E., Pearlmutter, D., and Williamson, T.: Urban microclimate – Designing
the spaces between buildings, 1st edn., Routledge, ISBN 9781844074679, 2011. a
Garratt, J. R.: The atmospheric boundary layer, 1st edn., Cambridge Univeristy Press, 316 pp., ISBN 0521380529,
1992. a
Girard, P., Nadeau, D. F., Pardyjak, E. R., Overby, M., Willemsen, P., Stoll,
R., Bailey, B. N., and Parlange, M. B.: Evaluation of the QUIC-URB wind
solver and QESRadiant radiation-transfer model using a dense array of urban
meteorological observations, Urban Clim., 24, 657–674,
https://doi.org/10.1016/j.uclim.2017.08.006, 2017. a
Grimmond, C. S. B., Blackett, M., Best, M. J., Barlow, J., Baik, J. J.,
Belcher, S. E., and Bohnenstengel, S. I.: The International Urban Energy
Balance Models Comparison Project: First Results from Phase 1, J. Appl.
Meteor. Clim., 49, 1268–1292, https://doi.org/10.1175/2010JAMC2354.1, 2010. a, b
Grimmond, S.: Urbanization and global environmental change: local effects of
urban warming, Geogr. J., 173, 83–88,
https://doi.org/10.1111/j.1475-4959.2007.232_3.x, 2007. a
Grylls, T.: Simulating air pollution in the urban microclimate, PhD thesis,
Imperial College London, https://doi.org/10.25560/91884, 2020. a
Grylls, T. and van Reeuwijk, M.: Tree model with drag, transpiration, shading
and deposition: Identification of cooling regimes and large-eddy simulation,
Agr. Forest Meteorol., 298–299, 108288,
https://doi.org/10.1016/j.agrformet.2020.108288, 2021. a
Grylls, T., Le Cornec, C. M., Salizzoni, P., Soulhac, L., Stettler, M. E.,
and van Reeuwijk, M.: Evaluation of an operational air quality model using
large-eddy simulation, Atmos. Environ. X, 3, 100041,
https://doi.org/10.1016/j.aeaoa.2019.100041, 2019. a, b, c, d
Grylls, T., Suter, I., Sützl, B., Owens, S., Meyer, D., and van Reeuwijk, M.:
uDALES: large-eddy-simulation software for urban flow, dispersion, and
microclimate modelling, J. Open Source Softw., 6, 3055,
https://doi.org/10.21105/joss.03055, 2021a. a
Grylls, T., Suter, I., Sützl, B., Owens, S., Meyer, D., and van Reeuwijk, M.: uDALES: large-eddy-simulation software for urban flow, dispersion, and microclimate modelling (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.5111497, 2021b. a
Hanjalić, K. and Kenjereš, S.: Some developments in turbulence
modeling for wind and environmental engineering, J. Wind Eng.
Ind. Aerod., 96, 1537–1570, 2008. a
Heus, T., van Heerwaarden, C. C., Jonker, H. J. J., Pier Siebesma, A., Axelsen, S., van den Dries, K., Geoffroy, O., Moene, A. F., Pino, D., de Roode, S. R., and Vilà-Guerau de Arellano, J.: Formulation of the Dutch Atmospheric Large-Eddy Simulation (DALES) and overview of its applications, Geosci. Model Dev., 3, 415–444, https://doi.org/10.5194/gmd-3-415-2010, 2010. a, b, c, d, e, f
Hölling, M. and Herwig, H.: Asymptotic analysis of the near-wall region
of turbulent natural convection flows, J. Fluid Mech., 541, 383–397,
https://doi.org/10.1017/S0022112005006300, 2005. a
Hundsdorfer, W., Koren, B., vanLoon, M., and Verwer, J. G.: A positive finite-difference
advection scheme, J. Comput. Phys., 117, 35–46, https://doi.org/10.1006/jcph.1995.1042, 1995. a
Huttner, S.: Further development and application of the 3D microclimate
simulation ENVI-met, PhD thesis, Johannes Gutenberg-Universität
Mainz, https://doi.org/10.25358/openscience-2022, 2012. a
Isymov, N. and Davenport, A. G.: The ground level wind environment in built up
areas, in: Proceedings of the 4th International Conference on Wind Effects on
Buildings and Structures, edited by: Eaton, K. J., Heathrow, 1975, pp.
403–422, Cambridge University Press, ISBN 9780521208017, 1975. a
Järvi, L., Grimmond, C. S. B., and Christen, A.: The surface urban
energy and water balance scheme (SUEWS): Evaluation in Los Angeles and
Vancouver, J. Hydrol., 411, 219–237, https://doi.org/10.1016/j.jhydrol.2011.10.001,
2011. a
Jarvis, P. G.: The Interpretation of the Variations in Leaf Water Potential
and Stomatal Conductance Found in Canopies in the Field, Philos. Trans. R.
Soc. London B Biol. Sci., 273, 593–610, https://doi.org/10.1098/rstb.1976.0035, 1976. a
Kong, H., Choi, H., and Lee, J. S.: Direct numerical simulation of turbulent
thermal boundary layers, Phys. Fluids, 12, 2555–2568,
https://doi.org/10.1063/1.1287912, 2000. a
Kotthaus, S. and Grimmond, C. S. B.: Atmospheric boundary-layer characteristics
from ceilometer measurements. Part 2: Application to London's urban boundary
layer, Q. J. Roy. Meteorol. Soc., 144,
1511–1524, 2018. a
Krayenhoff, E. S. and Voogt, J.: A microscale three-dimensional urban energy
balance model for studying surface temperatures, Bound.-Lay. Meteorol.,
123, 433–461, https://doi.org/10.1007/s10546-006-9153-6, 2007. a, b
Krayenhoff, E. S., Christen, A., Martilli, A., and Oke, T. R.: A Multi-layer
Radiation Model for Urban Neighbourhoods with Trees, Bound.-Lay.
Meteorol., 151, 139–178, https://doi.org/10.1007/s10546-013-9883-1, 2014. a, b
Lawson, T. V. and Penwarden, A. D.: The effect of wind on people in the
vicinity of buildings, in: Proceedings of the 4th International Conference on
Wind Effects on Buildings and Structures, Heathrow, 1975, edited by: Eaton,
K. J., Cambridge University Press, ISBN 9780521208017, 1975. a
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The
contribution of outdoor air pollution sources to premature mortality on a
global scale, Nature, 525, 367–371,
https://doi.org/10.1038/nature15371, 2015. a, b
Li, N. and Laizet, S.: 2DECOMP&FFT – A Highly Scalable 2D Decomposition
Library and FFT Interface,
https://www.turbulencesimulation.com/uploads/5/8/7/2/58724623/2010_laizet_nag.pdf (last access: 8 June 2022), 2010. a
Lindberg, F., Holmer, B., and Thorsson, S.: SOLWEIG 1.0 – Modelling spatial
variations of 3D radiant fluxes and mean radiant temperature in complex urban
settings, Int. J. Biometeorol., 52, 697–713,
https://doi.org/10.1007/s00484-008-0162-7, 2008. a
Llaguno-Munitxa, M. and Bou-Zeid, E.: Shaping buildings to promote street
ventilation: A large-eddy simulation study, Urban Clim., 26, 76–94, 2018. a
Luc Int Panis, Steven Broekx, R. L.: Modelling instantaneous traffic emission
and the influence of traffic speed limits, Sci. Total Environ.,
371, 270–285, 2006. a
Macdonald, R. W., Griffiths, R. F., and Hall, D. J.: An improved method for the
estimation of surface roughness of obstacle arrays, Atmos. Environ.,
32, 1857–1864, https://doi.org/10.1016/S1352-2310(97)00403-2, 1998. a
Maronga, B., Banzhaf, S., Burmeister, C., Esch, T., Forkel, R., Fröhlich, D., Fuka, V., Gehrke, K. F., Geletič, J., Giersch, S., Gronemeier, T., Groß, G., Heldens, W., Hellsten, A., Hoffmann, F., Inagaki, A., Kadasch, E., Kanani-Sühring, F., Ketelsen, K., Khan, B. A., Knigge, C., Knoop, H., Krč, P., Kurppa, M., Maamari, H., Matzarakis, A., Mauder, M., Pallasch, M., Pavlik, D., Pfafferott, J., Resler, J., Rissmann, S., Russo, E., Salim, M., Schrempf, M., Schwenkel, J., Seckmeyer, G., Schubert, S., Sühring, M., von Tils, R., Vollmer, L., Ward, S., Witha, B., Wurps, H., Zeidler, J., and Raasch, S.: Overview of the PALM model system 6.0, Geosci. Model Dev., 13, 1335–1372, https://doi.org/10.5194/gmd-13-1335-2020, 2020. a
Masson, V.: A physically-based scheme for the urban energy budget in
atmospheric models, Bound.-Lay. Meteorol., 94, 357–397,
https://doi.org/10.1023/A:1002463829265, 2000. a
Matzarakis, A., Rutz, F., and Mayer, H.: Modelling radiation fluxes in simple
and complex environments: basics of the RayMan model, Int. J. Biometeorol.,
54, 131–139, https://doi.org/10.1007/s00484-006-0061-8, 2010. a
Mirzaei, P. A.: Recent challenges in modeling of urban heat island, Sustain.
Cities Soc., 19, 200–206, https://doi.org/10.1016/j.scs.2015.04.001, 2015. a
Mirzaei, P. A. and Haghighat, F.: Approaches to study urban heat island –
abilities and limitations, Build. Environ., 45, 2192–2201, 2010. a
Mittal, R. and Iaccarino, G.: Immersed Boundary Methods, Annu. Rev. Fluid
Mech., 37, 239–261, https://doi.org/10.1146/annurev.fluid.37.061903.175743, 2005. a
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface
layer of the atmosphere, Contrib. Geophys. Inst. Acad. Sci. USSR, 24,
163–187, 1954. a
Moonen, P., Defraeye, T., Dorer, V., Blocken, B., and Carmeliet, J.: Urban
Physics: Effect of the micro-climate on comfort, health and energy demand,
Front. Archit. Res., 1, 197–228, https://doi.org/10.1016/j.foar.2012.05.002, 2012. a
Morris, C. W.: The anisotropy of diffuse solar radiation, PhD thesis,
Texas Tech University, http://hdl.handle.net/2346/9001 (last access: 8 June 2022), 1969. a
Murphy, D. M. and Koop, T.: Review of the vapour pressures of ice and
supercooled water for atmospheric applications, Q. J. Roy. Meteor. Soc.,
131, 1539–1565, https://doi.org/10.1256/qj.04.94, 2005. a
Musy, M., Malys, L., Morille, B., and Inard, C.: The use of SOLENE-microclimat
model to assess adaptation strategies at the district scale, Urban Clim.,
14, 213–223, https://doi.org/10.1016/j.uclim.2015.07.004, 2015. a
NOAA: Solar Position Calculator,
https://www.esrl.noaa.gov/gmd/grad/solcalc/azel.html (last access: 8 June 2022), 2018. a
Noilhan, J. and Planton, S.: A Simple Parameterization of Land Surface
Processes for Meteorological Models, Mon. Weather Rev., 117, 536–549,
https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2, 1989. a
Obukhov, A. M.: Turbulence in an atmosphere with inhomogeneous temperature,
Tr, Inst. Teor. Geofis. Akad. Nauk. SSSR, 1, 95–115, 1946. a
Oke, T. R.: The energetic basis of the urban heat island, Q. J. Roy. Meteor.
Soc., 108, 1–24, https://doi.org/10.1002/qj.49710845502, 1982. a
Oke, T. R., Mills, G., Christen, A., and Voogt, J. A.: Urban Climates, 1st edn.,
Cambridge University Press, ISBN 9780521849500, https://doi.org/10.1017/9781139016476, 2017. a, b, c, d
Peyret, R. and Taylor, T. D.: Computational methods for fluid flow, 1st edn., Springer
Science & Business Media, 358 pp., ISBN 9783642859526, https://doi.org/10.1007/978-3-642-85952-6, 1983. a
Pourquie, M., Breugem, W. P., and Boersma, B. J.: Some Issues Related to the
Use of Immersed Boundary Methods to Represent Square Obstacles, Int. J.
Multiscale Comput. Eng., 7, 509–522, https://doi.org/10.1615/IntJMultCompEng.v7.i6.30,
2009. a
PTV AG: PTV VISSIM 9 User Manual, PTV AG, 1084 pp., https://www.myptv.com/en/mobility-software/ptv-vissim (last access: 8 June 2022), 2017. a
Pyrgou, A. and Santamouris, M.: Increasing Probability of Heat-Related
Mortality in a Mediterranean City Due to Urban Warming, Int. J. Environ. Res.
Public Health, 15, 1571, https://doi.org/10.3390/ijerph15081571, 2018. a
Rao, V. R. and Sastri, V. M. K.: Efficient evaluation of diffuse view factors
for radiation, Int. J. Heat Mass Transf., 39, 1281–1286,
https://doi.org/10.1016/0017-9310(95)00203-0, 1996. a, b
Resler, J., Krč, P., Belda, M., Juruš, P., Benešová, N., Lopata, J., Vlček, O., Damašková, D., Eben, K., Derbek, P., Maronga, B., and Kanani-Sühring, F.: PALM-USM v1.0: A new urban surface model integrated into the PALM large-eddy simulation model, Geosci. Model Dev., 10, 3635–3659, https://doi.org/10.5194/gmd-10-3635-2017, 2017. a
Resler, J., Eben, K., Geletič, J., Krč, P., Rosecký, M., Sühring, M., Belda, M., Fuka, V., Halenka, T., Huszár, P., Karlický, J., Benešová, N., Ďoubalová, J., Honzáková, K., Keder, J., Nápravníková, Š., and Vlček, O.: Validation of the PALM model system 6.0 in a real urban environment: a case study in Dejvice, Prague, the Czech Republic, Geosci. Model Dev., 14, 4797–4842, https://doi.org/10.5194/gmd-14-4797-2021, 2021. a
Rizwan, A. M., Dennis, L. Y. C., and Liu, C.: A review on the generation,
determination and mitigation of Urban Heat Island, J. Environ. Sci., 20,
120–128, https://doi.org/10.1016/S1001-0742(08)60019-4, 2008. a
Rosenzweig, C., Solecki, W., Romero-Lankao, P., Mehrotra, S., Dhakal, S.,
Bowman, T., and Ibrahim, S. A.: ARC3.2 Summary for City Leaders – Climate Change and Cities: Second
Assessment Report of the Urban Climate Change Research Network, Tech. Rep., Urban
Climate Change Research Network (UCCRN), https://pubs.giss.nasa.gov/abs/ro02510w.html (last access: 8 June 2022), 2015. a
Salim, M. H., Schlünzen, K. H., Grawe, D., Boettcher, M., Gierisch, A. M. U., and Fock, B. H.: The microscale obstacle-resolving meteorological model MITRAS v2.0: model theory, Geosci. Model Dev., 11, 3427–3445, https://doi.org/10.5194/gmd-11-3427-2018, 2018. a
Santamouris, M.: Cooling the cities – A review of reflective and green roof
mitigation technologies to fight heat island and improve comfort in urban
environments, Sol. Energy, 103, 682–703,
https://doi.org/10.1016/j.solener.2012.07.003, 2014. a
Schlünzen, K. H., Hinneburg, D., Knoth, O., Lambrecht, M., Leitl, B.,
Lopez, S., Lüpkes, C., Panskus, H., Renner, E., and Schatzmann, M.:
Flow and transport in the obstacle layer: First results of the micro-scale
model MITRAS, J. Atmos. Chem., 44, 113–130, 2003. a
Seneviratne, S., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A.,
Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh,
M., Vicente-Serrano, S., Wehner, M., and Zhou, B.: 2021: Weather and Climate
Extreme Events in a Changing Climate, in: Climate Change 2021: The Physical
Science Basis, Contribution of Working Group I to the Sixth Assessment Report
of the Intergovernmental Panel on Climate Change, Tech. Rep.,
Intergovernmental Panel on Climate Change, https://www.ipcc.ch/report/ar6/wg1/downloads/ (last access: 8 June 2022), 2021. a
Siegel, R. and Howell, J. R.: Thermal radiation heat transfer, 4th edn.,
Taylor and Francis-Hemisphere, Washington, ISBN 1560328398, 2001. a
Sievers, U.: Das kleinskalige Strömungsmodell MUKLIMO_3. Teil 2:
Thermodynamische Erweiterungen, Deutscher Wetterdienst, 151 pp., ISBN 9783881484909, https://www.dwd.de/DE/leistungen/pbfb_verlag_berichte/pdf_einzelbaende/248_pdf.html (last access: 8 June 2022), 2016. a
Sikkema, J. K., Ong, S.-K., and Alleman, J. E.: Photocatalytic concrete
pavements: Laboratory investigation of NO oxidation rate under varied
environmental conditions, Constr. Build. Mater., 100, 305–314,
2015. a
Smagorinsky, J.: General circulation experiments with the primitive
equations, Mon. Weather Rev., 91, 99–164,
https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963. a
Stewart, J. B.: Modelling surface conductance of pine forest, Agric. For.
Meteorol., 43, 19–35, https://doi.org/10.1016/0168-1923(88)90003-2, 1988. a
Suter, I.: Simulating the impact of blue-green infrastructure on the
microclimate of urban areas, PhD thesis, Imperial College London, https://doi.org/10.25560/78715, 2019. a, b, c, d
Suter, I.: uDALES 1.0: a large-eddy-simulation model for urban environments, Zenodo [data set], https://doi.org/10.5281/zenodo.5480826, 2021. a
Suter, I., Maksimović, Č., and van Reeuwijk, M.: A
neighbourhood-scale estimate for the cooling potential of green roofs, Urban
Clim., 20, 33–45, https://doi.org/10.1016/j.uclim.2017.02.007, 2017. a
Suter, I., Grylls, T., Sützl, B., Owens, S., Meyer, D., and van Reeuwijk, M.: uDALES, Github [code], https://github.com/uDALES/u-dales, last access: 8 June 2022. a
Sützl, B. S., Rooney, G. G., and van Reeuwijk, M.: Drag Distribution in
Idealized Heterogeneous Urban Environments, Bound.-Lay. Meteorol., 178,
225–248, https://doi.org/10.1007/s10546-020-00567-0, 2021b. a, b
Swinbank, W. C.: Long-wave radiation from clear skies, Q. J. Roy. Meteor.
Soc., 89, 339–348, https://doi.org/10.1002/qj.49708938105, 1963. a
Tomas, J. M.: Obstacle-resolving large-eddy simulation of dispersion in urban
environments Effects of stability and roughness geometry, PhD thesis, TU
Delft, https://doi.org/10.4233/uuid:5d93a697-be49-4f63-b871-b763bc327139, 2016. a
Tomas, J. M., Pourquie, M. J. B. M., and Jonker, H. J. J.: Stable
Stratification Effects on Flow and Pollutant Dispersion in Boundary Layers
Entering a Generic Urban Environment, Bound.-Lay. Meteorol., 159,
221–239, https://doi.org/10.1007/s10546-015-0124-7, 2016. a
United Nations Framework Convention on Climate Change (UNFCCC): Report of the Conference of the Parties on its twenty-fifth session, held in Madrid from 2 to 15 December 2019; Addendum Part two: Action taken by the Conference of the Parties at its twenty-fifth session, Tech. Rep.,
United Nations Framework Convention on Climate Change, https://unfccc.int/documents/210471 (last access: 8 June 2022), 2020. a
United Nations Population Fund (UNFPA): State of World Population 2012, Tech. Rep., United Nations Population
Fund, 128 pp., ISBN 978-1-61800-009-5, https://www.unfpa.org/publications/state-world-population-2012 (last access: 8 June 2022), 2012. a
Uno, I., Cai, X. M., Steyn, D. G., and Emori, S.: A simple extension of the
Louis method for rough surface layer modelling, Bound.-Lay. Meteorol.,
76, 395–409, https://doi.org/10.1007/BF00709241, 1995. a, b
Van den Hurk, B., Viterbo, P., Beljaars, A. C. M., and Betts, A. K.: Offline
validation of the ERA40 surface scheme, 295, Tech. Rep., ECMWF, https://www.ecmwf.int/node/12900 (last access: 8 June 2022), 2000. a
Van Reeuwijk, M.: Efficient simulation of non-hydrostatic free-surface flow,
Master's thesis, TU Delft, Delft, http://resolver.tudelft.nl/uuid:be73f057-804a-41bf-9170-c577e2f14d83 (last access: 8 June 2022), 2002. a
Vreman, A. W.: An eddy-viscosity subgrid-scale model for turbulent shear flow:
Algebraic theory and applications, Phys. Fluids, 16, 3670–3681,
https://doi.org/10.1063/1.1785131, 2004. a
Walker, T., Xue, S.-C., and Barton, G. W.: Numerical determination of
radiative view factors using ray tracing, J. Heat Transfer, 132, 72702,
https://doi.org/10.1115/1.4000974, 2010. a
Wallace, J. M. and Hobbs, P. V.: Chapter 5 – Atmospheric Chemistry, in:
Atmospheric Science: An Introductory Survey, second edn., edited by: Wallace, J. M. and Hobbs,
P. V., Academic Press, San Diego, pp. 153–207, ISBN 9780127329512, 2006. a
Wicker, L. J. and Skamarock, W. C.: Time-Splitting Methods for Elastic Models
Using Forward Time Schemes, Mon. Weather Rev., 130, 2088–2097,
https://doi.org/10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2, 2002. a
World Heath Organization: WHO global air quality guidelines: particulate
matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon
monoxide, Tech. Rep., WHO, ISBN 9789240034228, https://apps.who.int/iris/handle/10665/345329 (last access: 8 June 2022), 2021. a
Wu, L.: URBAN4: An Urban Canopy Layer Surface Energy Balance Climate Model,
PhD thesis, University of California, Los Angeles, Dissertation Abstracts International, Section: B, 56–11, 6009, 1995. a
Xie, Z.-T. and Castro, I. P.: Efficient Generation of Inflow Conditions for Large Eddy Simulation of Street-Scale Flows, Flow Turbul. Combust., 81,
449–470, https://doi.org/10.1007/s10494-008-9151-5, 2008. a
Xie, Z.-T. and Castro, I. P.: Large-eddy simulation for flow and dispersion in
urban streets, Atmos. Environ., 43, 2174–2185,
https://doi.org/10.1016/j.atmosenv.2009.01.016, 2009. a, b
Yaghoobian, N. and Kleissl, J.: Effect of reflective pavements on building
energy use, Urban Clim., 2, 25–42, https://doi.org/10.1016/j.uclim.2012.09.002, 2012. a
Zhong, J., Cai, X.-M., and Bloss, W. J.: Large eddy simulation of reactive
pollutants in a deep urban street canyon: Coupling dynamics with O3-NOx-VOC
chemistry, Environ. Pollut., 224, 171–184, 2017. a
Short summary
Cities are increasingly moving to the fore of climate and air quality research due to their central role in the population’s health and well-being, while suitable models remain scarce. This article describes the development of a new urban LES model, which allows examining the effects of various processes, infrastructure and vegetation on the local climate and air quality. Possible applications are demonstrated and a comparison to an experiment is shown.
Cities are increasingly moving to the fore of climate and air quality research due to their...
