Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-91-2024
© Author(s) 2024. 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-17-91-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
09 Jan 2024
Development and technical paper |
| 09 Jan 2024
| 09 Jan 2024
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Department of Meteorology, University of Reading, Reading, UK
Institute for Risk and Disaster Reduction, University College London, London, UK
Hamidreza Omidvar
Institute for Risk and Disaster Reduction, University College London, London, UK
Zhenkun Li
Shanghai Climate Centre, Shanghai, China
Ning Zhang
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Wenjuan Huang
Shanghai Climate Centre, Shanghai, China
Simone Kotthaus
Institut Pierre-Simon Laplace, École Polytechnique, Palaiseau, France
Helen C. Ward
Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Zhiwen Luo
Welsh School of Architecture, Cardiff University, Cardiff, UK
Department of Meteorology, University of Reading, Reading, UK
Related authors
Shuo-Jun Mei, Zhanmin Wu, Jiang-Tao Hu, Taihan Chen, Guanwen Chen, and Ting Sun
EGUsphere, https://doi.org/10.5194/egusphere-2026-5226, https://doi.org/10.5194/egusphere-2026-5226, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Cities trap and redistribute sunlight in ways shaped by buildings and trees. We built a computer model that traces sunlight through whole cities in three dimensions, treating buildings and tree canopies explicitly. Tests confirmed its accuracy. Applied to Guangzhou, it showed that reflectivity alone does not reveal where energy is absorbed; trees and dense buildings move it between ground, walls, and roofs. Cooling strategies should be judged by where energy is absorbed, not reflectivity.
Bu Li, Ting Sun, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2026-4628, https://doi.org/10.5194/egusphere-2026-4628, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Hydrological models are increasingly combining scientific knowledge with AI to improve predictions, but it remains unclear whether these improvements also affect the reliability of model results. We found that AI improved prediction accuracy but also increased uncertainty in the processes it directly represented, while internal water storage retained and accumulated this uncertainty. These findings provide guidance for developing more reliable models for understanding water systems.
Fu Luo, Yuanjian Yang, Ting Sun, Zuofang Zheng, Xueyan Bi, Guoyu Ren, and Simone Lolli
EGUsphere, https://doi.org/10.5194/egusphere-2026-3392, https://doi.org/10.5194/egusphere-2026-3392, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Cities are hotter than surrounding countryside, and heatwaves intensify this contrast. How combined warming reshapes air above different urban areas remains unclear. Using a high-resolution model, we examined two 2020 heatwaves over Guangzhou, China, finding extra warming strongest above densely built neighbourhoods and weakest over open low-rise areas. Dense areas retain heat overnight, adding to next-day warming and showing how urban form shapes extreme heat from surface to lower atmosphere.
Shuo-Jun Mei, Guanwen Chen, Jian Hang, and Ting Sun
Geosci. Model Dev., 18, 10143–10167, https://doi.org/10.5194/gmd-18-10143-2025, https://doi.org/10.5194/gmd-18-10143-2025, 2025
Short summary
Short summary
Cities face growing heat challenges due to dense buildings, but predicting surface temperatures is complex because sunlight, airflow, and heat radiation interact. By simulating how sunlight bounces between structures and how heat transfers through materials, we accurately predicted temperatures on roofs, roads, and walls. The model successfully handled intricate city layouts thanks to GPU speed. By revealing which heat matters most, we aim to guide smarter city designs for a warming climate.
Ruidong Li, Jiapei Liu, Ting Sun, Jian Shao, Fuqiang Tian, and Guangheng Ni
Hydrol. Earth Syst. Sci., 29, 5677–5694, https://doi.org/10.5194/hess-29-5677-2025, https://doi.org/10.5194/hess-29-5677-2025, 2025
Short summary
Short summary
This work presents a new approach to simulate sewer drainage effects from incomplete information in the context of urban flooding, given missing information like flow directions and nodal depths. Tested in Yinchuan, China, our approach exhibits high accuracy in reproducing flood depths and reliably outperforms existing methods in various rainfall scenarios. Our method offers a reliable tool for cities with limited sewer data to improve flood simulation performance.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, https://doi.org/10.5194/gmd-15-3041-2022, 2022
Short summary
Short summary
This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
Shuo-Jun Mei, Zhanmin Wu, Jiang-Tao Hu, Taihan Chen, Guanwen Chen, and Ting Sun
EGUsphere, https://doi.org/10.5194/egusphere-2026-5226, https://doi.org/10.5194/egusphere-2026-5226, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Cities trap and redistribute sunlight in ways shaped by buildings and trees. We built a computer model that traces sunlight through whole cities in three dimensions, treating buildings and tree canopies explicitly. Tests confirmed its accuracy. Applied to Guangzhou, it showed that reflectivity alone does not reveal where energy is absorbed; trees and dense buildings move it between ground, walls, and roofs. Cooling strategies should be judged by where energy is absorbed, not reflectivity.
Bu Li, Ting Sun, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2026-4628, https://doi.org/10.5194/egusphere-2026-4628, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Hydrological models are increasingly combining scientific knowledge with AI to improve predictions, but it remains unclear whether these improvements also affect the reliability of model results. We found that AI improved prediction accuracy but also increased uncertainty in the processes it directly represented, while internal water storage retained and accumulated this uncertainty. These findings provide guidance for developing more reliable models for understanding water systems.
Fu Luo, Yuanjian Yang, Ting Sun, Zuofang Zheng, Xueyan Bi, Guoyu Ren, and Simone Lolli
EGUsphere, https://doi.org/10.5194/egusphere-2026-3392, https://doi.org/10.5194/egusphere-2026-3392, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Cities are hotter than surrounding countryside, and heatwaves intensify this contrast. How combined warming reshapes air above different urban areas remains unclear. Using a high-resolution model, we examined two 2020 heatwaves over Guangzhou, China, finding extra warming strongest above densely built neighbourhoods and weakest over open low-rise areas. Dense areas retain heat overnight, adding to next-day warming and showing how urban form shapes extreme heat from surface to lower atmosphere.
Alohotsy Rafalimanana, Thomas Lauvaux, Charbel Abdallah, Mali Chariot, Michel Ramonet, Josselin Doc, Olivier Laurent, Morgan Lopez, Anja Raznjevic, Maarten Krol, Leena Järvi, Andreas Christen, Dana Looschelders, Leslie David, Olivier Sanchez, Laura Bignotti, Benjamin Loubet, Sue Grimmond, and William Morrison
EGUsphere, https://doi.org/10.5194/egusphere-2026-2581, https://doi.org/10.5194/egusphere-2026-2581, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Using WRF-Chem simulations over Greater Paris at resolutions from 900 m to 100 m (LES), we show that horizontal resolution critically shapes urban CO2 plume structure. Mesoscale runs underestimate spatial contrasts by a factor of 2–3, with representativeness errors of 29–61 % (near-surface) and 21–59 % (XCO2). LES improves intra-urban variability and enables more accurate spatial attribution of emissions, a key step toward reliable top-down estimates for city-scale climate mitigation.
Dana Looschelders, Andreas Christen, Sue Grimmond, Simone Kotthaus, and William Morrison
EGUsphere, https://doi.org/10.5194/egusphere-2026-2444, https://doi.org/10.5194/egusphere-2026-2444, 2026
Short summary
Short summary
Using seven automatic lidar-ceilometers located on a 105 km transect in the Paris region, we investigate modifications of the mixed-layer height (MLH) dynamics. Over and downwind of Paris, MLHs are deeper and grow faster during the morning transition. In summer, the spatial extent of urban effects (> 60 km downwind) and absolute MLH spatial differences are larger. In winter, relative MLH differences are larger. The MLH is deeper in the city at the start of the morning transition.
Sihan Liu, Tianwen Wei, Ning Zhang, Fangzhi Wei, Xinyuan Liang, Qian Huang, Yuan Wang, and Haiyun Xia
EGUsphere, https://doi.org/10.5194/egusphere-2026-2865, https://doi.org/10.5194/egusphere-2026-2865, 2026
Short summary
Short summary
Using wind lidar, ERA5, and WRF data, this study explores nocturnal tropospheric gravity waves in a double-jet environment over the Taklimakan Desert. Results show 10–20 min oscillations within the weak-wind layer between a lower easterly and upper westerly jet, triggered by shear instability. Thermal and Doppler ducting jointly trap these waves. Notably, lower jet intensification compresses the waveguide, shifting trapped modes to higher frequencies.
Alohotsy Rafalimanana, Thomas Lauvaux, Charbel Abdallah, Mali Chariot, Michel Ramonet, Josselin Doc, Olivier Laurent, Morgan Lopez, Anja Raznjevic, Maarten Krol, Leena Järvi, Andreas Christen, Dana Looschelders, Leslie David, Olivier Sanchez, Laura Bignotti, Benjamin Loubet, Sue Grimmond, and William Morrison
EGUsphere, https://doi.org/10.5194/egusphere-2026-2109, https://doi.org/10.5194/egusphere-2026-2109, 2026
Short summary
Short summary
This study explores how city design and buildings affect CO2 levels in the air over Paris. Using a transport model, we tested different urban canopy models to represent the city environment and its influence on air motion. We found that realistic descriptions of urban surfaces are crucial, especially in winter, for accurately showing how CO2 builds up and disperses, leading to better satellite monitoring of city CO2 emissions.
Robert Schoetter, Cyril Caliot, Florent Retailleau, Tim Nagel, Christophe Coustet, Vincent Eymet, Vincent Forest, Frédéric Hourdin, Atsushi Inagaki, Simone Kotthaus, Valéry Masson, William Morrison, and Najda Villefranque
EGUsphere, https://doi.org/10.5194/egusphere-2026-2061, https://doi.org/10.5194/egusphere-2026-2061, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Temperature, humidity, radiation, and wind can have a negative impact on human comfort and infrastructure in urban areas. Building-resolving meteorological models can help to quantify such meteorological impacts and the potential benefits of adaptation measures to climate change. Here, a new unified algorithm to calculate the radiation, heat conduction, and sensible heat exchange in urban areas is coupled with an atmospheric model, and evaluated against experimental data.
Yuya Takane, Yukihiro Kikegawa, Zhiwen Luo, Hiroyuki Kusaka, and Sue Grimmond
EGUsphere, https://doi.org/10.5194/egusphere-2026-1307, https://doi.org/10.5194/egusphere-2026-1307, 2026
Short summary
Short summary
We developed and released the single-layer urban canopy model coupled with building energy model v2. This incorporates a new scheme enabling dynamic changes in indoor temperature. This allows the model to be applied not only to air-conditioned conditions but also to non-air-conditioned scenarios, making it applicable to all seasons and cities worldwide. This upgrade facilitates the assessment of climate change adaptation measures for both outdoor and indoor environments.
Tiancheng Ma, Denise Hertwig, Megan McGrory, Matthew Paskin, and Sue Grimmond
EGUsphere, https://doi.org/10.5194/egusphere-2025-6060, https://doi.org/10.5194/egusphere-2025-6060, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Transport impacts emissions (heat, pollutants), which needs to be represented in high-resolution urban models. We introduce the transport model MATSDA and evaluate its route finding for car journeys in London, UK. The model shows high skill at identifying major travel corridors and associated travel durations, capturing day type and time-of-day dependency (rush-hour v. off-peak). MATSDA offers dynamic human movement patterns for transport research in the context of urban climate modelling.
Shuo-Jun Mei, Guanwen Chen, Jian Hang, and Ting Sun
Geosci. Model Dev., 18, 10143–10167, https://doi.org/10.5194/gmd-18-10143-2025, https://doi.org/10.5194/gmd-18-10143-2025, 2025
Short summary
Short summary
Cities face growing heat challenges due to dense buildings, but predicting surface temperatures is complex because sunlight, airflow, and heat radiation interact. By simulating how sunlight bounces between structures and how heat transfers through materials, we accurately predicted temperatures on roofs, roads, and walls. The model successfully handled intricate city layouts thanks to GPU speed. By revealing which heat matters most, we aim to guide smarter city designs for a warming climate.
Russell H. Glazer, Sue Grimmond, Lewis Blunn, Daniel Fenner, Humphrey Lean, Andreas Christen, Will Morrison, Dana Looschelders, and Jonathan K. P. Shonk
Weather Clim. Dynam., 6, 1723–1742, https://doi.org/10.5194/wcd-6-1723-2025, https://doi.org/10.5194/wcd-6-1723-2025, 2025
Short summary
Short summary
In this study we use very high resolution numerical weather prediction model simulations of the Berlin, Germany region along with assessment of field campaign observations to understand better the impact of urban areas on the near-surface boundary layer. We find that there a clear affect of urban areas up to 15 km downwind of the city centre in both the field campaign observations and the high resolution model.
William Morrison, Dana Looschelders, Jonnathan Céspedes, Bernie Claxton, Marc-Antoine Drouin, Jean-Charles Dupont, Aurélien Faucheux, Martial Haeffelin, Christopher C. Holst, Simone Kotthaus, Valéry Masson, James McGregor, Jeremy Price, Matthias Zeeman, Sue Grimmond, and Andreas Christen
Earth Syst. Sci. Data, 17, 6507–6529, https://doi.org/10.5194/essd-17-6507-2025, https://doi.org/10.5194/essd-17-6507-2025, 2025
Short summary
Short summary
We conducted research using sophisticated wind sensors to better understand wind patterns in Paris. By installing these sensors across the city, we gathered detailed data on wind speeds and directions from 2022 to 2024. This information helps improve weather and climate models, making them more accurate for city environments. Our findings offer valuable insights for scientists studying urban air and weather, improving predictions and understanding of city-scale atmospheric processes.
Laura Bouillon, Valérie Gros, Morgan Lopez, Nicolas Bonnaire, Carole Philippon, Camille Yver Kwok, Leslie David, Olivier Perrussel, Olivier Sanchez, Simone Kotthaus, Jean-Eudes Petit, Philippe Ciais, and Michel Ramonet
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-602, https://doi.org/10.5194/essd-2025-602, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
The Saclay observatory, 20 km southwest of Paris, monitored greenhouse and reactive gases, as well as aerosols, over 10 years to assess the impact of urban emissions. By comparing downwind and background conditions, the study revealed significant reductions in pollutants linked to traffic: -35.6 % for CO, -52.3 % for NOₓ, -56.7 % for eBC, and -15 % for CO2. These trends align with emission decrease estimates from AIRPARIF inventories over the same period.
Ruidong Li, Jiapei Liu, Ting Sun, Jian Shao, Fuqiang Tian, and Guangheng Ni
Hydrol. Earth Syst. Sci., 29, 5677–5694, https://doi.org/10.5194/hess-29-5677-2025, https://doi.org/10.5194/hess-29-5677-2025, 2025
Short summary
Short summary
This work presents a new approach to simulate sewer drainage effects from incomplete information in the context of urban flooding, given missing information like flow directions and nodal depths. Tested in Yinchuan, China, our approach exhibits high accuracy in reproducing flood depths and reliably outperforms existing methods in various rainfall scenarios. Our method offers a reliable tool for cities with limited sewer data to improve flood simulation performance.
Jinhong Xian, Zongxu Qiu, Huayan Rao, Zhigang Cheng, Xiaoling Lin, Chao Lu, Honglong Yang, and Ning Zhang
Atmos. Chem. Phys., 25, 8427–8441, https://doi.org/10.5194/acp-25-8427-2025, https://doi.org/10.5194/acp-25-8427-2025, 2025
Short summary
Short summary
We studied how turbulence kinetic energy (TKE) changes in the lower atmosphere over Shenzhen, focusing on its role in weather and climate. Using advanced wind lidar technology, we tracked how TKE varies with height and across seasons. We found that heat near the ground drives turbulence, while wind effects become stronger higher up. Our results help improve weather and climate models by providing better data on how turbulence behaves in the atmosphere, aiding understanding of climate change.
Nimra Iqbal, Marvin Ravan, Zina Mitraka, Joern Birkmann, Sue Grimmond, Denise Hertwig, Nektarios Chrysoulakis, Giorgos Somarakis, Angela Wendnagel-Beck, and Emmanouil Panagiotakis
Nat. Hazards Earth Syst. Sci., 25, 2481–2502, https://doi.org/10.5194/nhess-25-2481-2025, https://doi.org/10.5194/nhess-25-2481-2025, 2025
Short summary
Short summary
This work deepens the understanding of how perceived heat stress, human vulnerability (e.g. age, income) and adaptive capacities (e.g. green, shaded spaces) are coupled with urban structures. The results show that perceived heat stress decreases with distance from the urban center, however, human vulnerability and adaptive capacities depend more strongly on inner variations and differences between urban structures. Planning policies and adaptation strategies should account for these differences.
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data, 17, 2535–2551, https://doi.org/10.5194/essd-17-2535-2025, https://doi.org/10.5194/essd-17-2535-2025, 2025
Short summary
Short summary
The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP) which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Ludovico Di Antonio, Matthias Beekmann, Guillaume Siour, Vincent Michoud, Christopher Cantrell, Astrid Bauville, Antonin Bergé, Mathieu Cazaunau, Servanne Chevaillier, Manuela Cirtog, Joel F. de Brito, Paola Formenti, Cecile Gaimoz, Olivier Garret, Aline Gratien, Valérie Gros, Martial Haeffelin, Lelia N. Hawkins, Simone Kotthaus, Gael Noyalet, Diana L. Pereira, Jean-Eudes Petit, Eva Drew Pronovost, Véronique Riffault, Chenjie Yu, Gilles Foret, Jean-François Doussin, and Claudia Di Biagio
Atmos. Chem. Phys., 25, 4803–4831, https://doi.org/10.5194/acp-25-4803-2025, https://doi.org/10.5194/acp-25-4803-2025, 2025
Short summary
Short summary
The summer of 2022 has been considered a proxy for future climate scenarios due to its hot and dry conditions. In this paper, we use the measurements from the Atmospheric Chemistry of the Suburban Forest (ACROSS) campaign, conducted in the Paris area in June–July 2022, along with observations from existing networks, to evaluate a 3D chemistry transport model (WRF–CHIMERE) simulation. Results are shown to be satisfactory, allowing us to explain the gas and aerosol variability at the campaign sites.
Jinhong Xian, Zongxu Qiu, Hongyan Luo, Yuanyuan Hu, Xiaoling Lin, Chao Lu, Yan Yang, Honglong Yang, and Ning Zhang
Atmos. Chem. Phys., 25, 441–457, https://doi.org/10.5194/acp-25-441-2025, https://doi.org/10.5194/acp-25-441-2025, 2025
Short summary
Short summary
The existing methods for observing turbulent kinetic energy (TKE) budget terms can only rely on ground-based towers. We have developed a new detection method that can directly observe and analyze the generation and dissipation mechanisms of turbulent energy at different heights in the vertical direction of the boundary layer. This research result will extend our study of TKE budget terms from near the ground to high altitude, providing a higher and more detailed perspective.
Martial Haeffelin, Jean-François Ribaud, Jonnathan Céspedes, Jean-Charles Dupont, Aude Lemonsu, Valéry Masson, Tim Nagel, and Simone Kotthaus
Atmos. Chem. Phys., 24, 14101–14122, https://doi.org/10.5194/acp-24-14101-2024, https://doi.org/10.5194/acp-24-14101-2024, 2024
Short summary
Short summary
This study highlights how the state of the urban atmospheric boundary layer impacts urban park cooling effect intensity at night. Under summertime heat wave conditions, the urban atmosphere becomes stable at night, which inhibits turbulent motions. Under those specific conditions, urban parks and woods cool much more efficiently than the surrounding built-up neighbourhoods in the evening and through the night, providing cooler air temperatures by 4 to 6° C depending on park size.
Matthias Zeeman, Andreas Christen, Sue Grimmond, Daniel Fenner, William Morrison, Gregor Feigel, Markus Sulzer, and Nektarios Chrysoulakis
Geosci. Instrum. Method. Data Syst., 13, 393–424, https://doi.org/10.5194/gi-13-393-2024, https://doi.org/10.5194/gi-13-393-2024, 2024
Short summary
Short summary
This study presents an overview of a data system for documenting, processing, managing, and publishing data streams from research networks of atmospheric and environmental sensors of varying complexity in urban environments. Our solutions aim to deliver resilient, near-time data using freely available software.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Jonnathan Céspedes, Simone Kotthaus, Jana Preissler, Clément Toupoint, Ludovic Thobois, Marc-Antoine Drouin, Jean-Charles Dupont, Aurélien Faucheux, and Martial Haeffelin
Atmos. Chem. Phys., 24, 11477–11496, https://doi.org/10.5194/acp-24-11477-2024, https://doi.org/10.5194/acp-24-11477-2024, 2024
Short summary
Short summary
The low-level jet (LLJ) is common in Paris during summer. The LLJ core height and speed significantly influence vertical mixing in the urban boundary layer, which affects air temperature variations between the urban canopy layer and surrounding rural areas, determining the urban heat island (UHI) intensity. This study highlights the importance of wind profile observations for understanding urban boundary layer dynamics and near-surface atmospheric conditions relevant to health.
Jinhong Xian, Chao Lu, Xiaoling Lin, Honglong Yang, Ning Zhang, and Li Zhang
Atmos. Meas. Tech., 17, 1837–1850, https://doi.org/10.5194/amt-17-1837-2024, https://doi.org/10.5194/amt-17-1837-2024, 2024
Short summary
Short summary
Improving the monitoring capability of atmospheric turbulence can help unravel the mystery of turbulence. Based on some assumptions, scientists have proposed various detection methods. However, these assumptions limit their applicability. We abandoned these assumptions and proposed a more accurate method, revealing some new results. Our method can provide more accurate three-dimensional features of turbulence, which will have a huge driving effect on the development of turbulence.
Megan A. Stretton, William Morrison, Robin J. Hogan, and Sue Grimmond
Geosci. Model Dev., 16, 5931–5947, https://doi.org/10.5194/gmd-16-5931-2023, https://doi.org/10.5194/gmd-16-5931-2023, 2023
Short summary
Short summary
Cities' materials and forms impact radiative fluxes. We evaluate the SPARTACUS-Urban multi-layer approach to modelling longwave radiation, describing realistic 3D geometry statistically using the explicit DART (Discrete Anisotropic Radiative Transfer) model. The temperature configurations used are derived from thermal camera observations. SPARTACUS-Urban accurately predicts longwave fluxes, with a low computational time (cf. DART), but has larger errors with sunlit/shaded surface temperatures.
Junxia Dou, Sue Grimmond, Shiguang Miao, Bei Huang, Huimin Lei, and Mingshui Liao
Atmos. Chem. Phys., 23, 13143–13166, https://doi.org/10.5194/acp-23-13143-2023, https://doi.org/10.5194/acp-23-13143-2023, 2023
Short summary
Short summary
Multi-timescale variations in surface energy fluxes in a suburb of Beijing are analyzed using 16-month observations. Compared to previous suburban areas, this study site has larger seasonal variability in energy partitioning, and summer and winter Bowen ratios are at the lower and higher end of those at other suburban sites, respectively. Our analysis indicates that precipitation, irrigation, crop/vegetation growth activity, and land use/cover all play critical roles in energy partitioning.
Jinghui Lian, Thomas Lauvaux, Hervé Utard, François-Marie Bréon, Grégoire Broquet, Michel Ramonet, Olivier Laurent, Ivonne Albarus, Mali Chariot, Simone Kotthaus, Martial Haeffelin, Olivier Sanchez, Olivier Perrussel, Hugo Anne Denier van der Gon, Stijn Nicolaas Camiel Dellaert, and Philippe Ciais
Atmos. Chem. Phys., 23, 8823–8835, https://doi.org/10.5194/acp-23-8823-2023, https://doi.org/10.5194/acp-23-8823-2023, 2023
Short summary
Short summary
This study quantifies urban CO2 emissions via an atmospheric inversion for the Paris metropolitan area over a 6-year period from 2016 to 2021. Results show a long-term decreasing trend of about 2 % ± 0.6 % per year in the annual CO2 emissions over Paris. We conclude that our current capacity can deliver near-real-time CO2 emission estimates at the city scale in under a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.
Joanna E. Dyson, Lisa K. Whalley, Eloise J. Slater, Robert Woodward-Massey, Chunxiang Ye, James D. Lee, Freya Squires, James R. Hopkins, Rachel E. Dunmore, Marvin Shaw, Jacqueline F. Hamilton, Alastair C. Lewis, Stephen D. Worrall, Asan Bacak, Archit Mehra, Thomas J. Bannan, Hugh Coe, Carl J. Percival, Bin Ouyang, C. Nicholas Hewitt, Roderic L. Jones, Leigh R. Crilley, Louisa J. Kramer, W. Joe F. Acton, William J. Bloss, Supattarachai Saksakulkrai, Jingsha Xu, Zongbo Shi, Roy M. Harrison, Simone Kotthaus, Sue Grimmond, Yele Sun, Weiqi Xu, Siyao Yue, Lianfang Wei, Pingqing Fu, Xinming Wang, Stephen R. Arnold, and Dwayne E. Heard
Atmos. Chem. Phys., 23, 5679–5697, https://doi.org/10.5194/acp-23-5679-2023, https://doi.org/10.5194/acp-23-5679-2023, 2023
Short summary
Short summary
The hydroxyl (OH) and closely coupled hydroperoxyl (HO2) radicals are vital for their role in the removal of atmospheric pollutants. In less polluted regions, atmospheric models over-predict HO2 concentrations. In this modelling study, the impact of heterogeneous uptake of HO2 onto aerosol surfaces on radical concentrations and the ozone production regime in Beijing in the summertime is investigated, and the implications for emissions policies across China are considered.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Simone Kotthaus, Juan Antonio Bravo-Aranda, Martine Collaud Coen, Juan Luis Guerrero-Rascado, Maria João Costa, Domenico Cimini, Ewan J. O'Connor, Maxime Hervo, Lucas Alados-Arboledas, María Jiménez-Portaz, Lucia Mona, Dominique Ruffieux, Anthony Illingworth, and Martial Haeffelin
Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, https://doi.org/10.5194/amt-16-433-2023, 2023
Short summary
Short summary
Profile observations of the atmospheric boundary layer now allow for layer heights and characteristics to be derived at high temporal and vertical resolution. With novel high-density ground-based remote-sensing measurement networks emerging, horizontal information content is also increasing. This review summarises the capabilities and limitations of various sensors and retrieval algorithms which need to be considered during the harmonisation of data products for high-impact applications.
Andreas Plach, Rolf Rüfenacht, Simone Kotthaus, and Markus Leuenberger
EGUsphere, https://doi.org/10.5194/egusphere-2022-1019, https://doi.org/10.5194/egusphere-2022-1019, 2022
Preprint archived
Short summary
Short summary
Greenhouse gases emissions are contributing to global warming and it is essential to better understand where they originate from and how they are transported. In this study we analyze greenhouse gas observations at a Swiss tall tower where measurements are taken more than 200 m above ground and investigate their origin by looking at the condition of the atmosphere at the time of the observations. We find that most pollution at this site is caused from emissions transported from further away.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
Short summary
Short summary
We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Will S. Drysdale, Adam R. Vaughan, Freya A. Squires, Sam J. Cliff, Stefan Metzger, David Durden, Natchaya Pingintha-Durden, Carole Helfter, Eiko Nemitz, C. Sue B. Grimmond, Janet Barlow, Sean Beevers, Gregor Stewart, David Dajnak, Ruth M. Purvis, and James D. Lee
Atmos. Chem. Phys., 22, 9413–9433, https://doi.org/10.5194/acp-22-9413-2022, https://doi.org/10.5194/acp-22-9413-2022, 2022
Short summary
Short summary
Measurements of NOx emissions are important for a good understanding of air quality. While there are many direct measurements of NOx concentration, there are very few measurements of its emission. Measurements of emissions provide constraints on emissions inventories and air quality models. This article presents measurements of NOx emission from the BT Tower in central London in 2017 and compares them with inventories, finding that they underestimate by a factor of ∼1.48.
Helen Claire Ward, Mathias Walter Rotach, Alexander Gohm, Martin Graus, Thomas Karl, Maren Haid, Lukas Umek, and Thomas Muschinski
Atmos. Chem. Phys., 22, 6559–6593, https://doi.org/10.5194/acp-22-6559-2022, https://doi.org/10.5194/acp-22-6559-2022, 2022
Short summary
Short summary
This study examines how cities and their surroundings influence turbulent exchange processes responsible for weather and climate. Analysis of a 4-year observational dataset for the Alpine city of Innsbruck reveals several similarities with other (flat) city centre sites. However, the mountain setting leads to characteristic daily and seasonal flow patterns (valley winds) and downslope windstorms that have a marked effect on temperature, wind speed, turbulence and pollutant concentration.
Yiqing Liu, Zhiwen Luo, and Sue Grimmond
Atmos. Chem. Phys., 22, 4721–4735, https://doi.org/10.5194/acp-22-4721-2022, https://doi.org/10.5194/acp-22-4721-2022, 2022
Short summary
Short summary
Anthropogenic heat emission from buildings is important for atmospheric modelling in cities. The current building anthropogenic heat flux is simplified by building energy consumption. Our research proposes a novel approach to determine ‘real’ building anthropogenic heat emission from the changes in energy balance fluxes between occupied and unoccupied buildings. We hope to provide new insights into future parameterisations of building anthropogenic heat flux in urban climate models.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, https://doi.org/10.5194/gmd-15-3041-2022, 2022
Short summary
Short summary
This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
Jean-François Ribaud, Martial Haeffelin, Jean-Charles Dupont, Marc-Antoine Drouin, Felipe Toledo, and Simone Kotthaus
Atmos. Meas. Tech., 14, 7893–7907, https://doi.org/10.5194/amt-14-7893-2021, https://doi.org/10.5194/amt-14-7893-2021, 2021
Short summary
Short summary
PARAFOG is a near-real-time decision tool that aims to retrieve pre-fog alert levels minutes to hours prior to fog onset. The second version of PARAFOG allows us to discriminate between radiation and stratus lowering fog situations. It is based upon the combination of visibility observations and automatic lidar and ceilometer measurements. The overall performance of the second version of PARAFOG over more than 300 fog cases at five different locations presents a good perfomance.
Cited articles
Alexander, P., Bechtel, B., Chow, W., Fealy, R., and Mills, G.: Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation, Urban Clim., 17, 196–215, https://doi.org/10.1016/j.uclim.2016.08.003, 2016. a
Alexander, P. J., Mills, G., and Fealy, R.: Using LCZ data to run an urban energy balance model, Urban Clim., 13, 14–37, https://doi.org/10.1016/j.uclim.2015.05.001, 2015. a
Allen, L., Lindberg, F., and Grimmond, C. S. B.: Global to city scale urban anthropogenic heat flux: Model and variability, Int. J. Climatol., 31, 1990–2005, https://doi.org/10.1002/joc.2210, 2010. a
Ao, X., Grimmond, C. S. B., Liu, D., Han, Z., Hu, P., Wang, Y., Zhen, X., and Tan, J.: Radiation Fluxes in a Business District of Shanghai, China, J. Appl. Meteorol. Clim., 55, 2451–2468, https://doi.org/10.1175/jamc-d-16-0082.1, 2016. a
Ao, X., Grimmond, C. S. B., Ward, H. C., Gabey, A. M., Tan, J., Yang, X.-Q., Liu, D., Zhi, X., Liu, H., and Zhang, N.: Evaluation of the Surface Urban Energy and Water Balance Scheme (SUEWS) at a Dense Urban Site in Shanghai: Sensitivity to Anthropogenic Heat and Irrigation, J. Hydrometeorol., 19, 1983–2005, https://doi.org/10.1175/jhm-d-18-0057.1, 2018. a, b, c
Baklanov, A., Grimmond, C., Carlson, D., Terblanche, D., Tang, X., Bouchet, V., Lee, B., Langendijk, G., Kolli, R., and Hovsepyan, A.: From urban meteorology, climate and environment research to integrated city services, Urban Clim., 23, 330–341, https://doi.org/10.1016/j.uclim.2017.05.004, 2018. a
Banks, R. F., Tiana-Alsina, J., Rocadenbosch, F., and Baldasano, J. M.: Performance Evaluation of the Boundary-Layer Height from Lidar and the Weather Research and Forecasting Model at an Urban Coastal Site in the North-East Iberian Peninsula, Bound.-Lay. Meteorol., 157, 265–292, https://doi.org/10.1007/s10546-015-0056-2, 2015. a, b
Banks, R. F., Tiana-Alsina, J., Baldasano, J. M., Rocadenbosch, F., Papayannis, A., Solomos, S., and Tzanis, C. G.: Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign, Atmos. Res., 176-177, 185–201, https://doi.org/10.1016/j.atmosres.2016.02.024, 2016. a
Barlow, J. F., Dunbar, T. M., Nemitz, E. G., Wood, C. R., Gallagher, M. W., Davies, F., O'Connor, E., and Harrison, R. M.: Boundary layer dynamics over London, UK, as observed using Doppler lidar during REPARTEE-II, Atmos. Chem. Phys., 11, 2111–2125, https://doi.org/10.5194/acp-11-2111-2011, 2011. a
Best, M. J. and Grimmond, C. S. B.: Key Conclusions of the First International Urban Land Surface Model Comparison Project, B. Am. Meteorol. Soc., 96, 805–819, https://doi.org/10.1175/bams-d-14-00122.1, 2015. a
Best, M. J., Grimmond, C. S. B., and Villani, M. G.: Evaluation of the Urban Tile in MOSES using Surface Energy Balance Observations, Bound.-Lay. Meteorol., 118, 503–525, https://doi.org/10.1007/s10546-005-9025-5, 2006. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bohnenstengel, S. I., Hamilton, I., Davies, M., and Belcher, S. E.: Impact of anthropogenic heat emissions on London's temperatures, Q. J. Roy. Meteor. Soc., 140, 687–698, https://doi.org/10.1002/qj.2144, 2013. a
Capel-Timms, I., Smith, S. T., Sun, T., and Grimmond, S.: Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation, Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, 2020. a, b
Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C. S. B., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., Miao, S., Sailor, D., Salamanca, F. P., Taha, H., Tewari, M., Wang, X., Wyszogrodzki, A. A., and Zhang, C.: The integrated WRF/urban modelling system: Development, evaluation, and applications to urban environmental problems, Int. J. Climatol., 31, 273–288, https://doi.org/10.1002/joc.2158, 2011. a
Chrysoulakis, N., Grimmond, S., Feigenwinter, C., Lindberg, F., Gastellu-Etchegorry, J.-P., Marconcini, M., Mitraka, Z., Stagakis, S., Crawford, B., Olofson, F., Landier, L., Morrison, W., and Parlow, E.: Urban energy exchanges monitoring from space, Sci. Rep., 8, 11498, https://doi.org/10.1038/s41598-018-29873-x, 2018. a
Demuzere, M., Harshan, S., Järvi, L., Roth, M., Grimmond, C. S. B., Masson, V., Oleson, K. W., Velasco, E., and Wouters, H.: Impact of urban canopy models and external parameters on the modelled urban energy balance in a tropical city, Q. J. Roy. Meteor. Soc., 143, 1581–1596, https://doi.org/10.1002/qj.3028, 2017. a
Dou, J., Grimmond, S., Cheng, Z., Miao, S., Feng, D., and Liao, M.: Summertime surface energy balance fluxes at two Beijing sites, Int. J. Climatol., 39, 2793–2810, https://doi.org/10.1002/joc.5989, 2019. a
Dyer, A. J.: A review of flux-profile relationships, Bound.-Lay. Meteorol., 7, 363–372, https://doi.org/10.1007/bf00240838, 1974. a
ECMWF: IFS Documentation CY47R3 – Part IV Physical processes, ECMWF, https://doi.org/10.21957/eyrpir4vj, 2021. a
Feng, J.-M., Wang, Y.-L., Ma, Z.-G., and Liu, Y.-H.: Simulating the Regional Impacts of Urbanization and Anthropogenic Heat Release on Climate across China, J. Climate, 25, 7187–7203, https://doi.org/10.1175/jcli-d-11-00333.1, 2012. a
Gabey, A. M., Grimmond, C. S. B., and Capel-Timms, I.: Anthropogenic heat flux: Advisable spatial resolutions when input data are scarce, Theor. Appl. Climatol., 135, 791–807, https://doi.org/10.1007/s00704-018-2367-y, 2018. a
Grimmond, C., Cleugh, H., and Oke, T.: An objective urban heat storage model and its comparison with other schemes, Atmos. Environ. B, 25, 311–326, https://doi.org/10.1016/0957-1272(91)90003-w, 1991. a, b, c
Grimmond, C. S. B. and Oke, T. R.: Aerodynamic Properties of Urban Areas Derived from Analysis of Surface Form, J. Appl. Meteorol., 38, 1262–1292, https://doi.org/10.1175/1520-0450(1999)038<1262:apouad>2.0.co;2, 1999. a, b, c
Grimmond, C. S. B. and Oke, T. R.: Turbulent Heat Fluxes in Urban Areas: Observations and a Local-Scale Urban Meteorological Parameterization Scheme (LUMPS), J. Appl. Meteorol., 41, 792–810, https://doi.org/10.1175/1520-0450(2002)041<0792:thfiua>2.0.co;2, 2002. a
Grimmond, C. S. B., Oke, T. R., and Steyn, D. G.: Urban Water Balance: 1. A Model for Daily Totals, Water Resour. Res., 22, 1397–1403, https://doi.org/10.1029/wr022i010p01397, 1986. a, b, c, d
Grimmond, C. S. B., Blackett, M., Best, M. J., Baik, J.-J., Belcher, S. E., Beringer, J., Bohnenstengel, S. I., Calmet, I., Chen, F., Coutts, A., Dandou, A., Fortuniak, K., Gouvea, M. L., Hamdi, R., Hendry, M., Kanda, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E. S., Lee, S.-H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Ooka, R., Pigeon, G., Porson, A., Ryu, Y.-H., Salamanca, F., Steeneveld, G., Tombrou, M., Voogt, J. A., Young, D. T., and Zhang, N.: Initial results from Phase 2 of the international urban energy balance model comparison, Int. J. Climatol., 31, 244–272, https://doi.org/10.1002/joc.2227, 2010a. a, b
Grimmond, C. S. B., Blackett, M., Best, M. J., Barlow, J., Baik, J.-J., Belcher, S. E., Bohnenstengel, S. I., Calmet, I., Chen, F., Dandou, A., Fortuniak, K., Gouvea, M. L., Hamdi, R., Hendry, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E. S., Lee, S.-H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Pigeon, G., Porson, A., Ryu, Y.-H., Salamanca, F., Shashua-Bar, L., Steeneveld, G.-J., Tombrou, M., Voogt, J., Young, D., and Zhang, N.: The International Urban Energy Balance Models Comparison Project: First Results from Phase 1, J. Appl. Meteorol. Clim., 49, 1268–1292, https://doi.org/10.1175/2010jamc2354.1, 2010b. a, b
Grimmond, S., Bouchet, V., Molina, L. T., Baklanov, A., Tan, J., Schlünzen, K. H., Mills, G., Golding, B., Masson, V., Ren, C., Voogt, J., Miao, S., Lean, H., Heusinkveld, B., Hovespyan, A., Teruggi, G., Parrish, P., and Joe, P.: Integrated urban hydrometeorological, climate and environmental services: Concept, methodology and key messages, Urban Clim., 33, 100623, https://doi.org/10.1016/j.uclim.2020.100623, 2020. a
Halios, C. H. and Barlow, J. F.: Observations of the Morning Development of the Urban Boundary Layer Over London, UK, Taken During the ACTUAL Project, Bound.-Lay. Meteorol., 166, 395–422, https://doi.org/10.1007/s10546-017-0300-z, 2017. a
Hamilton, I. G., Davies, M., Steadman, P., Stone, A., Ridley, I., and Evans, S.: The significance of the anthropogenic heat emissions of London's buildings: A comparison against captured shortwave solar radiation, Build. Environ., 44, 807–817, https://doi.org/10.1016/j.buildenv.2008.05.024, 2009. a
Hertwig, D., Grimmond, S., Hendry, M. A., Saunders, B., Wang, Z., Jeoffrion, M., Vidale, P. L., McGuire, P. C., Bohnenstengel, S. I., Ward, H. C., and Kotthaus, S.: Urban signals in high-resolution weather and climate simulations: Role of urban land-surface characterisation, Theor. Appl. Climatol., 142, 701–728, https://doi.org/10.1007/s00704-020-03294-1, 2020. a, b
Hogan, R. J.: Flexible Treatment of Radiative Transfer in Complex Urban Canopies for Use in Weather and Climate Models, Bound.-Lay. Meteorol., 173, 53–78, https://doi.org/10.1007/s10546-019-00457-0, 2019. a
Högström, U.: Non-dimensional wind and temperature profiles in the atmospheric surface layer: A re-evaluation, Bound.-Lay. Meteorol., 42, 55–78, https://doi.org/10.1007/bf00119875, 1988. a
Hollinger, D. Y. and Richardson, A. D.: Uncertainty in eddy covariance measurements and its application to physiological models, Tree Physiol., 25, 873–885, https://doi.org/10.1093/treephys/25.7.873, 2005. a
Hutchinson, T. A.: An adaptive time-step for increased model efficiency, in: Extended Abstracts, Eighth WRF Users' Workshop, 5 June 2009, Omaha, Nebraska, 4, 2007. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, 13103,, https://doi.org/10.1029/2008jd009944, 2008. a, b, c
Iamarino, M., Beevers, S., and Grimmond, C. S. B.: High-resolution (space, time) anthropogenic heat emissions: London 1970–2025, Int. J. Climatol., 32, 1754–1767, https://doi.org/10.1002/joc.2390, 2011. a, b
Janjić, Z. I.: The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes, Mon. Weather Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:tsmecm>2.0.co;2, 1994. a, b, c
Järvi, L., Grimmond, C. S. B., Taka, M., Nordbo, A., Setälä, H., and Strachan, I. B.: Development of the Surface Urban Energy and Water Balance Scheme (SUEWS) for cold climate cities, Geosci. Model Dev., 7, 1691–1711, https://doi.org/10.5194/gmd-7-1691-2014, 2014. a, b, c, d
Järvi, L., Grimmond, C. S. B., McFadden, J. P., Christen, A., Strachan, I. B., Taka, M., Warsta, L., and Heimann, M.: Warming effects on the urban hydrology in cold climate regions, Sci. Rep., 7, 1–8,, https://doi.org/10.1038/s41598-017-05733-y, 2017. a, b
Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmos. Meas. Tech., 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018. a
Järvi, L., Havu, M., Ward, H. C., Bellucco, V., McFadden, J. P., Toivonen, T., Heikinheimo, V., Kolari, P., Riikonen, A., and Grimmond, C. S. B.: Spatial Modeling of Local-Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki, J. Geophys. Res.-Atmos., 124, 8363–8384, https://doi.org/10.1029/2018jd029576, 2019. a, b, c
Jimenez, P. A., Hacker, J. P., Dudhia, J., Haupt, S. E., Ruiz-Arias, J. A., Gueymard, C. A., Thompson, G., Eidhammer, T., and Deng, A.: WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction, B. Am. Meteorol. Soc., 97, 1249–1264, https://doi.org/10.1175/bams-d-14-00279.1, 2016. a
Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., Oleson, K. W., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki, Q. J. Roy. Meteor. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2015. a
Kawai, T., Ridwan, M. K., and Kanda, M.: Evaluation of the Simple Urban Energy Balance Model Using Selected Data from 1-yr Flux Observations at Two Cities, J. Appl. Meteorol. Clim., 48, 693–715, https://doi.org/10.1175/2008jamc1891.1, 2009. a
Kim, Y., Sartelet, K., Raut, J.-C., and Chazette, P.: Evaluation of the Weather Research and Forecast/Urban Model Over Greater Paris, Bound.-Lay. Meteorol., 149, 105–132, https://doi.org/10.1007/s10546-013-9838-6, 2013. a, b
Kokkonen, T., Grimmond, C., Räty, O., Ward, H., Christen, A., Oke, T., Kotthaus, S., and Järvi, L.: Sensitivity of Surface Urban Energy and Water Balance Scheme (SUEWS) to downscaling of reanalysis forcing data, Urban Clim., 23, 36–52, https://doi.org/10.1016/j.uclim.2017.05.001, 2018a. a
Kokkonen, T. V., Grimmond, C. S. B., Christen, A., Oke, T. R., and Järvi, L.: Changes to the Water Balance Over a Century of Urban Development in Two Neighborhoods: Vancouver, Canada, Water Resour. Res., 54, 6625–6642, https://doi.org/10.1029/2017wr022445, 2018b. a
Kokkonen, T. V., Grimmond, S., Murto, S., Liu, H., Sundström, A.-M., and Järvi, L.: Simulation of the radiative effect of haze on the urban hydrological cycle using reanalysis data in Beijing, Atmos. Chem. Phys., 19, 7001–7017, https://doi.org/10.5194/acp-19-7001-2019, 2019. a, b
Kotthaus, S. and Grimmond, C.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Clim., 10, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a. a, b, c
Kotthaus, S. and Grimmond, C.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Clim., 10, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b. a, b, c, d
Kotthaus, S. and Grimmond, C. S. B.: Atmospheric boundary-layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds, Q. J. Roy. Meteor. Soc., 144, 1525–1538, https://doi.org/10.1002/qj.3299, 2018a. a, b, c, d
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. Meteor. Soc., 144, 1511–1524, https://doi.org/10.1002/qj.3298, 2018b. a
Kotthaus, S., O'Connor, E., Münkel, C., Charlton-Perez, C., Haeffelin, M., Gabey, A. M., and Grimmond, C. S. B.: Recommendations for processing atmospheric attenuated backscatter profiles from Vaisala CL31 ceilometers, Atmos. Meas. Tech., 9, 3769–3791, https://doi.org/10.5194/amt-9-3769-2016, 2016. a
Kotthaus, S., Halios, C. H., Barlow, J. F., and Grimmond, C.: Volume for pollution dispersion: London's atmospheric boundary layer during ClearfLo observed with two ground-based lidar types, Atmos. Environ., 190, 401–414, https://doi.org/10.1016/j.atmosenv.2018.06.042, 2018. a
Kotthaus, S., Bravo-Aranda, J. A., Collaud Coen, M., Guerrero-Rascado, J. L., Costa, M. J., Cimini, D., O'Connor, E. J., Hervo, M., Alados-Arboledas, L., Jiménez-Portaz, M., Mona, L., Ruffieux, D., Illingworth, A., and Haeffelin, M.: Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations, Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, 2023. a, b
Kusaka, H., Kondo, H., Kikegawa, Y., and Kimura, F.: A Simple Single-Layer Urban Canopy Model For Atmospheric Models: Comparison With Multi-Layer And Slab Models, Bound.-Lay. Meteorol., 101, 329–358, https://doi.org/10.1023/a:1019207923078, 2001. a, b
Lapo, K. E., Hinkelman, L. M., Sumargo, E., Hughes, M., and Lundquist, J. D.: A critical evaluation of modeled solar irradiance over California for hydrologic and land surface modeling, J. Geophys. Res.-Atmos., 122, 299–317, https://doi.org/10.1002/2016jd025527, 2017. a
Lindberg, F., Grimmond, C., Yogeswaran, N., Kotthaus, S., and Allen, L.: Impact of city changes and weather on anthropogenic heat flux in Europe 1995–2015, Urban Clim., 4, 1–15, https://doi.org/10.1016/j.uclim.2013.03.002, 2013. a, b
Lindberg, F., Grimmond, C., Gabey, A., Huang, B., Kent, C. W., Sun, T., Theeuwes, N. E., Järvi, L., Ward, H. C., Capel-Timms, I., Chang, Y., Jonsson, P., Krave, N., Liu, D., Meyer, D., Olofson, K. F. G., Tan, J., Wästberg, D., Xue, L., and Zhang, Z.: Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services, Environ. Model. Softw., 99, 70–87, https://doi.org/10.1016/j.envsoft.2017.09.020, 2018. a, b
Loridan, T., Grimmond, C. S. B., Offerle, B. D., Young, D. T., Smith, T. E. L., Järvi, L., and Lindberg, F.: Local-Scale Urban Meteorological Parameterization Scheme (LUMPS): Longwave Radiation Parameterization and Seasonality-Related Developments, J. Appl. Meteorol. Clim., 50, 185–202, https://doi.org/10.1175/2010jamc2474.1, 2011. a
Loridan, T., Lindberg, F., Jorba, O., Kotthaus, S., Grossman-Clarke, S., and Grimmond, C. S. B.: High Resolution Simulation of the Variability of Surface Energy Balance Fluxes Across Central London with Urban Zones for Energy Partitioning, Bound.-Lay. Meteorol., 147, 493–523, https://doi.org/10.1007/s10546-013-9797-y, 2013. a, b, c
Mahrt, L., Thomas, C., Richardson, S., Seaman, N., Stauffer, D., and Zeeman, M.: Non-stationary Generation of Weak Turbulence for Very Stable and Weak-Wind Conditions, Bound.-Lay. Meteorol., 147, 179–199, https://doi.org/10.1007/s10546-012-9782-x, 2012. a
Marconcini, M., Heldens, W., Del Frate, F., Latini, D., Mitraka, Z., and Lindberg, F.: EO-based products in support of urban heat fluxes estimation, in: 2017 Joint Urban Remote Sensing Event (JURSE), 1–4, IEEE, https://doi.org/10.1109/jurse.2017.7924592, 2017. a
Martilli, A., Clappier, A., and Rotach, M. W.: An Urban Surface Exchange Parameterisation for Mesoscale Models, Bound.-Lay. Meteorol., 104, 261–304, https://doi.org/10.1023/a:1016099921195, 2002. 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
Masson, V., Lemonsu, A., Hidalgo, J., and Voogt, J.: Urban Climates and Climate Change, Annu. Rev. Env. Resour., 45, 411–444, https://doi.org/10.1146/annurev-environ-012320-083623, 2020. a
Meyer, D., Schoetter, R., Riechert, M., Verrelle, A., Tewari, M., Dudhia, J., Masson, V., van Reeuwijk, M., and Grimmond, S.: WRF-TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model, J. Adv. Model. Earth Syst., 12, e2019MS001961, https://doi.org/10.1029/2019ms001961, 2020. a
Mitchell, V. G., Cleugh, H. A., Grimmond, C. S. B., and Xu, J.: Linking urban water balance and energy balance models to analyse urban design options, Hydrol. Process., 22, 2891–2900, https://doi.org/10.1002/hyp.6868, 2008. a
Mitraka, Z., Del Frate, F., and Carbone, F.: Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover Mapping, #IEEE_J_STARS#, 9, 3340–3350, https://doi.org/10.1109/jstars.2016.2522181, 2016. a
Monteith, J. L.: Evaporation and environment, in: Symposia of the society for experimental biology, 19, 205–234, Cambridge University Press (CUP) Cambridge, 1965. a
Narumi, D., Kondo, A., and Shimoda, Y.: Effects of anthropogenic heat release upon the urban climate in a Japanese megacity, Environ. Res., 109, 421–431, https://doi.org/10.1016/j.envres.2009.02.013, 2009. a
Offerle, B., Grimmond, C. S. B., and Oke, T. R.: Parameterization of Net All-Wave Radiation for Urban Areas, J. Appl. Meteorol., 42, 1157–1173, https://doi.org/10.1175/1520-0450(2003)042<1157:ponarf>2.0.co;2, 2003. a
Oke, T. R.: Boundary Layer Climates, Routledge, ISBN 9781134951345, https://doi.org/10.4324/9780203407219, 2002. a, b
Omidvar, H., Sun, T., Grimmond, S., Bilesbach, D., Black, A., Chen, J., Duan, Z., Gao, Z., Iwata, H., and McFadden, J. P.: Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset, Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, 2022. a, b, c, d, e
Onomura, S., Grimmond, C., Lindberg, F., Holmer, B., and Thorsson, S.: Meteorological forcing data for urban outdoor thermal comfort models from a coupled convective boundary layer and surface energy balance scheme, Urban Clim., 11, 1–23, https://doi.org/10.1016/j.uclim.2014.11.001, 2015. a
Porson, A., Clark, P. A., Harman, I. N., Best, M. J., and Belcher, S. E.: Implementation of a new urban energy budget scheme into MetUM. Part II: Validation against observations and model intercomparison, Q. J. Roy. Meteor. Soc., 136, 1530–1542, https://doi.org/10.1002/qj.572, 2010. a
Rafael, S., Martins, H., Marta-Almeida, M., Sá, E., Coelho, S., Rocha, A., Borrego, C., and Lopes, M.: Quantification and mapping of urban fluxes under climate change: Application of WRF-SUEWS model to Greater Porto area (Portugal), Environ. Res., 155, 321–334, https://doi.org/10.1016/j.envres.2017.02.033, 2017. a
Ryder, C. and Toumi, R.: An urban solar flux island: Measurements from London, Atmos. Environ., 45, 3414–3423, https://doi.org/10.1016/j.atmosenv.2011.03.045, 2011. a
Sailor, D. J. and Vasireddy, C.: Correcting aggregate energy consumption data to account for variability in local weather, Environ. Model. Softw., 21, 733–738, https://doi.org/10.1016/j.envsoft.2005.08.001, 2006. a
Shuttleworth, W. J.: A simplified one-dimensional theoretical description of the vegetation-atmosphere interaction, Bound.-Lay. Meteorol., 14, 3–27, https://doi.org/10.1007/bf00123986, 1978. a
Shuttleworth, W. J.: Evaporation Models in the Global Water Budget, in: Variations in the Global Water Budget, 147–171, Springer Netherlands, ISBN 9789400969568, 9789400969544, https://doi.org/10.1007/978-94-009-6954-4_11, 1983. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Duda, M. G., Barker, D. M., Huang, X.-Y., Wang, W., Powers, J. G., Liu, Z., and Berner, J.: A description of the advanced research WRF model version 4, National Center for Atmospheric Research: Boulder, CO, USA, 145, 550, 2019. a
Sun, J., Wang, Z., Zhou, W., Xie, C., Wu, C., Chen, C., Han, T., Wang, Q., Li, Z., Li, J., Fu, P., Wang, Z., and Sun, Y.: Measurement report: Long-term changes in black carbon and aerosol optical properties from 2012 to 2020 in Beijing, China, Atmos. Chem. Phys., 22, 561–575, https://doi.org/10.5194/acp-22-561-2022, 2022. a
Sun, T. and Grimmond, S.: A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration, Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, 2019. a, b, c
Sun, T., Grimmond, C. S. B., and Ni, G.-H.: How do green roofs mitigate urban thermal stress under heat waves?, J. Geophys. Res.-Atmos., 121, 5320–5335, https://doi.org/10.1002/2016jd024873, 2016. a
Sun, T., Jarvi, L., Grimmond, S., Lindberg, F., Li, Z., Tang, Y., and Ward, H.: C.: Urban-meteorology-reading/suews: 2018c Release, Zenodo [code], https://doi.org/10.5281/ZENODO.3267306, 2019. a, b
Sun, T., Omidvar, H., and Grimmond, S.: WRF(v4.0)-SUEWS(2018c): Input data for the evaluation at two UK sites, Zenodo [data set], https://doi.org/10.5281/zenodo.7957903, 2023a. a, b
Sun, T., Omidvar, H., Li, Z., and Grimmond, S.: WRF-SUEWS source code for GMD submission, Zenodo [code], https://doi.org/10.5281/zenodo.8137708, 2023b. a
Tang, Y., Sun, T., Luo, Z., Omidvar, H., Theeuwes, N., Xie, X., Xiong, J., Yao, R., and Grimmond, S.: Urban meteorological forcing data for building energy simulations, Build. Environ., 204, 108088, https://doi.org/10.1016/j.buildenv.2021.108088, 2021. a, b
Theeuwes, N. E., Ronda, R. J., Harman, I. N., Christen, A., and Grimmond, C. S. B.: Parametrizing Horizontally-Averaged Wind and Temperature Profiles in the Urban Roughness Sublayer, Bound.-Lay. Meteorol., 173, 321–348, https://doi.org/10.1007/s10546-019-00472-1, 2019. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008mwr2387.1, 2008. a
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models, Mon. Weather Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:acmfsf>2.0.co;2, 1989. a
Tsiringakis, A., Steeneveld, G.-J., Holtslag, A. A. M., Kotthaus, S., and Grimmond, S.: On- and off-line evaluation of the single-layer urban canopy model in London summertime conditions, Q. J. Roy. Meteor. Soc., 145, 1474–1489, https://doi.org/10.1002/qj.3505, 2019. a
UK ONS: Population, latest available census and estimates (2010 - 2011), in: Statistical Papers – United Nations (Ser. A), Population and Vital Statistics Report, 5–14, UN, ISBN 9789210559881, https://doi.org/10.18356/a7dbb328-en, 2013. a
Van Ulden, A. P. and Holtslag, A. A. M.: Estimation of Atmospheric Boundary Layer Parameters for Diffusion Applications, J. Clim. Appl. Meteorol., 24, 1196–1207, https://doi.org/10.1175/1520-0450(1985)024<1196:eoablp>2.0.co;2, 1985. a
Vilà-Guerau de Arellano, J., Hartogensis, O., Benedict, I., de Boer, H., Bosman, P. J. M., Botía, S., Cecchini, M. A., Faassen, K. A. P., González-Armas, R., van Diepen, K., Heusinkveld, B. G., Janssens, M., Lobos-Roco, F., Luijkx, I. T., Machado, L. A. T., Mangan, M. R., Moene, A. F., Mol, W. B., van der Molen, M., Moonen, R., Ouwersloot, H. G., Park, S.-W., Pedruzo-Bagazgoitia, X., Röckmann, T., Adnew, G. A., Ronda, R., Sikma, M., Schulte, R., van Stratum, B. J. H., Veerman, M. A., van Zanten, M. C., and van Heerwaarden, C. C.: Advancing understanding of land–atmosphere interactions by breaking discipline and scale barriers, Ann. NY Acad. Sci., 1522, 74–97, https://doi.org/10.1111/nyas.14956, 2023. a
Ward, H. and Grimmond, C.: Assessing the impact of changes in surface cover, human behaviour and climate on energy partitioning across Greater London, Landscape Urban Plan., 165, 142–161, https://doi.org/10.1016/j.landurbplan.2017.04.001, 2017. a, b, c
Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmos. Chem. Phys., 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, 2013. a, b, c, d
Ward, H. C., Tan, Y. S., Gabey, A. M., Kotthaus, S., and Grimmond, C. S. B.: Impact of temporal resolution of precipitation forcing data on modelled urban-atmosphere exchanges and surface conditions, Int. J. Climatol., 38, 649–662, https://doi.org/10.1002/joc.5200, 2017. a
Warren, E., Charlton-Perez, C., Kotthaus, S., Lean, H., Ballard, S., Hopkin, E., and Grimmond, S.: Evaluation of forward-modelled attenuated backscatter using an urban ceilometer network in London under clear-sky conditions, Atmos. Environ., 191, 532–547, https://doi.org/10.1016/j.atmosenv.2018.04.045, 2018. a
Willmott, C., Robeson, S., and Matsuura, K.: Climate and Other Models May Be More Accurate Than Reported, Eos (Washington DC), DC, https://doi.org/10.1029/2017eo074939, 2017. a
Xu, J., Li, C., Shi, H., He, Q., and Pan, L.: Analysis on the impact of aerosol optical depth on surface solar radiation in the Shanghai megacity, China, Atmos. Chem. Phys., 11, 3281–3289, https://doi.org/10.5194/acp-11-3281-2011, 2011. a
Yang, Y., Guo, M., Ren, G., Liu, S., Zong, L., Zhang, Y., Zheng, Z., Miao, Y., and Zhang, Y.: Modulation of Wintertime Canopy Urban Heat Island (CUHI) Intensity in Beijing by Synoptic Weather Pattern in Planetary Boundary Layer, J. Geophys. Res.-Atmos., 127, e2021JD035988, https://doi.org/10.1029/2021jd035988, 2022. a
Zhang, C., Wang, Y., and Hamilton, K.: Improved Representation of Boundary Layer Clouds over the Southeast Pacific in ARW-WRF Using a Modified Tiedtke Cumulus Parameterization Scheme*, Mon. Weather Rev., 139, 3489–3513, https://doi.org/10.1175/mwr-d-10-05091.1, 2011. a
Zhang, N. and Chen, Y.: A Case Study of the Upwind Urbanization Influence on the Urban Heat Island Effects along the Suzhou–Wuxi Corridor, J. Appl. Meteorol. Clim., 53, 333–345, https://doi.org/10.1175/jamc-d-12-0219.1, 2014. a
Zhang, N., Wang, X., Chen, Y., Dai, W., and Wang, X.: Numerical simulations on influence of urban land cover expansion and anthropogenic heat release on urban meteorological environment in Pearl River Delta, Theor. Appl. Climatol., 126, 469–479, https://doi.org/10.1007/s00704-015-1601-0, 2015. a
Zheng, Z., Ren, G., Gao, H., and Yang, Y.: Urban ventilation planning and its associated benefits based on numerical experiments: A case study in beijing, China, Build. Environ., 222, 109383, https://doi.org/10.1016/j.buildenv.2022.109383, 2022. a
Zong, L., Yang, Y., Xia, H., Gao, M., Sun, Z., Zheng, Z., Li, X., Ning, G., Li, Y., and Lolli, S.: Joint occurrence of heatwaves and ozone pollution and increased health risks in Beijing, China: role of synoptic weather pattern and urbanization, Atmos. Chem. Phys., 22, 6523–6538, https://doi.org/10.5194/acp-22-6523-2022, 2022. a
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban...
