Articles | Volume 13, issue 10
https://doi.org/10.5194/gmd-13-4891-2020
© Author(s) 2020. 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-13-4891-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Oct 2020
Model description paper |
| 15 Oct 2020
| 15 Oct 2020
Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation
Isabella Capel-Timms
Department of Meteorology, University of Reading, Reading RG6 6ET, UK
School of the Built Environment, University of Reading, Reading RG6 6DF, UK
Stefán Thor Smith
School of the Built Environment, University of Reading, Reading RG6 6DF, UK
Department of Meteorology, University of Reading, Reading RG6 6ET, UK
Department of Meteorology, University of Reading, Reading RG6 6ET, UK
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Ruidong Li, Jiapei Liu, Ting Sun, Shao Jian, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3780, https://doi.org/10.5194/egusphere-2024-3780, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This work presents a new approach to simulate sewer drainage effects for urban flooding with key missing information like flow directions and nodal depths estimated from incomplete information. 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.
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
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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
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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.
Nimra Iqbal, Marvin Ravan, Zina Mitraka, Joern Birkmann, Sue Grimmond, Denise Hertwig, Nektarios Chrysoulakis, Giorgos Somarakis, and Angela Wendnagel-Beck
EGUsphere, https://doi.org/10.5194/egusphere-2024-1907, https://doi.org/10.5194/egusphere-2024-1907, 2024
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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 urban center, however, human vulnerability and adaptive capacities depend stronger on inner-variations and differences between urban structures. Planning policies and adaptation strategies should account for these differences.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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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.
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
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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
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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.
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
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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
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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.
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
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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
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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.
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
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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
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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.
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, David Carruthers, Sue Grimmond, Yiqun Han, Pingqing Fu, and Simone Kotthaus
Atmos. Chem. Phys., 21, 13687–13711, https://doi.org/10.5194/acp-21-13687-2021, https://doi.org/10.5194/acp-21-13687-2021, 2021
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Heat-related illnesses are of increasing concern in China given its rapid urbanisation and our ever-warming climate. We examine the relative impacts that land surface properties and anthropogenic heat have on the urban heat island (UHI) in Beijing using ADMS-Urban. Air temperature measurements and satellite-derived land surface temperatures provide valuable means of evaluating modelled spatiotemporal variations. This work provides critical information for urban planners and UHI mitigation.
Claire E. Reeves, Graham P. Mills, Lisa K. Whalley, W. Joe F. Acton, William J. Bloss, Leigh R. Crilley, Sue Grimmond, Dwayne E. Heard, C. Nicholas Hewitt, James R. Hopkins, Simone Kotthaus, Louisa J. Kramer, Roderic L. Jones, James D. Lee, Yanhui Liu, Bin Ouyang, Eloise Slater, Freya Squires, Xinming Wang, Robert Woodward-Massey, and Chunxiang Ye
Atmos. Chem. Phys., 21, 6315–6330, https://doi.org/10.5194/acp-21-6315-2021, https://doi.org/10.5194/acp-21-6315-2021, 2021
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The impact of isoprene on atmospheric chemistry is dependent on how its oxidation products interact with other pollutants, specifically nitrogen oxides. Such interactions can lead to isoprene nitrates. We made measurements of the concentrations of individual isoprene nitrate isomers in Beijing and used a model to test current understanding of their chemistry. We highlight areas of uncertainty in understanding, in particular the chemistry following oxidation of isoprene by the nitrate radical.
Wenhua Wang, Longyi Shao, Claudio Mazzoleni, Yaowei Li, Simone Kotthaus, Sue Grimmond, Janarjan Bhandari, Jiaoping Xing, Xiaolei Feng, Mengyuan Zhang, and Zongbo Shi
Atmos. Chem. Phys., 21, 5301–5314, https://doi.org/10.5194/acp-21-5301-2021, https://doi.org/10.5194/acp-21-5301-2021, 2021
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We compared the characteristics of individual particles at ground level and above the mixed-layer height. We found that the particles above the mixed-layer height during haze periods are more aged compared to ground level. More coal-combustion-related primary organic particles were found above the mixed-layer height. We suggest that the particles above the mixed-layer height are affected by the surrounding areas, and once mixed down to the ground, they might contribute to ground air pollution.
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, Archit Mehra, Stephen D. Worrall, Asan Bacak, Thomas J. Bannan, Hugh Coe, Carl J. Percival, Bin Ouyang, Roderic L. Jones, Leigh R. Crilley, Louisa J. Kramer, William J. Bloss, Tuan Vu, Simone Kotthaus, Sue Grimmond, Yele Sun, Weiqi Xu, Siyao Yue, Lujie Ren, W. Joe F. Acton, C. Nicholas Hewitt, Xinming Wang, Pingqing Fu, and Dwayne E. Heard
Atmos. Chem. Phys., 21, 2125–2147, https://doi.org/10.5194/acp-21-2125-2021, https://doi.org/10.5194/acp-21-2125-2021, 2021
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To understand how emission controls will impact ozone, an understanding of the sources and sinks of OH and the chemical cycling between peroxy radicals is needed. This paper presents measurements of OH, HO2 and total RO2 taken in central Beijing. The radical observations are compared to a detailed chemistry model, which shows that under low NO conditions, there is a missing OH source. Under high NOx conditions, the model under-predicts RO2 and impacts our ability to model ozone.
Rutambhara Joshi, Dantong Liu, Eiko Nemitz, Ben Langford, Neil Mullinger, Freya Squires, James Lee, Yunfei Wu, Xiaole Pan, Pingqing Fu, Simone Kotthaus, Sue Grimmond, Qiang Zhang, Ruili Wu, Oliver Wild, Michael Flynn, Hugh Coe, and James Allan
Atmos. Chem. Phys., 21, 147–162, https://doi.org/10.5194/acp-21-147-2021, https://doi.org/10.5194/acp-21-147-2021, 2021
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Black carbon (BC) is a component of particulate matter which has significant effects on climate and human health. Sources of BC include biomass burning, transport, industry and domestic cooking and heating. In this study, we measured BC emissions in Beijing, finding a dominance of traffic emissions over all other sources. The quantitative method presented here has benefits for revising widely used emissions inventories and for understanding BC sources with impacts on air quality and climate.
Freya A. Squires, Eiko Nemitz, Ben Langford, Oliver Wild, Will S. Drysdale, W. Joe F. Acton, Pingqing Fu, C. Sue B. Grimmond, Jacqueline F. Hamilton, C. Nicholas Hewitt, Michael Hollaway, Simone Kotthaus, James Lee, Stefan Metzger, Natchaya Pingintha-Durden, Marvin Shaw, Adam R. Vaughan, Xinming Wang, Ruili Wu, Qiang Zhang, and Yanli Zhang
Atmos. Chem. Phys., 20, 8737–8761, https://doi.org/10.5194/acp-20-8737-2020, https://doi.org/10.5194/acp-20-8737-2020, 2020
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Significant air quality problems exist in megacities like Beijing, China. To manage air pollution, legislators need a clear understanding of pollutant emissions. However, emissions inventories have large uncertainties, and reliable field measurements of pollutant emissions are required to constrain them. This work presents the first measurements of traffic-dominated emissions in Beijing which suggest that inventories overestimate these emissions in the region during both winter and summer.
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, Michael Hollaway, David Carruthers, Jie Li, Qiang Zhang, Ruili Wu, Simone Kotthaus, Sue Grimmond, Freya A. Squires, James Lee, and Zongbo Shi
Atmos. Chem. Phys., 20, 2755–2780, https://doi.org/10.5194/acp-20-2755-2020, https://doi.org/10.5194/acp-20-2755-2020, 2020
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Ambient air pollution is a major cause of premature death in China. We examine the street-scale variation of pollutant levels in Beijing using air pollution dispersion and chemistry model ADMS-Urban. Campaign measurements are compared with simulated pollutant levels, providing a valuable means of evaluating the impact of key processes on urban air quality. Air quality modelling at such fine scales is essential for human exposure studies and for informing choices on future emission controls.
Ting Sun and Sue Grimmond
Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, https://doi.org/10.5194/gmd-12-2781-2019, 2019
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A Python-enhanced urban land surface model, SuPy (SUEWS in Python), is presented with its development (the SUEWS interface modification, F2PY configuration and Python frontend implementation), cross-platform deployment (PyPI, Python Package Index) and demonstration (online tutorials in Jupyter notebooks for users of different levels). SuPy represents a significant enhancement that supports existing and new model applications, reproducibility and enhanced functionality.
Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
Atmos. Chem. Phys., 19, 7519–7546, https://doi.org/10.5194/acp-19-7519-2019, https://doi.org/10.5194/acp-19-7519-2019, 2019
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APHH-Beijing is a collaborative international research programme to study the sources, processes and health effects of air pollution in Beijing. This introduction to the special issue provides an overview of (i) the APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it and (iii) the air quality and meteorological conditions during joint intensive field campaigns as a core activity within APHH-Beijing.
Fushan Wang, Guangheng Ni, William J. Riley, Jinyun Tang, Dejun Zhu, and Ting Sun
Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019, https://doi.org/10.5194/gmd-12-2119-2019, 2019
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The current lake model in the Weather Research and Forecasting system was reported to be insufficient in simulating deep lakes and reservoirs. We thus revised the lake model by improving its spatial discretization scheme, surface property parameterization, diffusivity parameterization, and convection scheme. The revised model was evaluated at a deep reservoir in southwestern China and the results were in good agreement with measurements.
Tom V. Kokkonen, Sue Grimmond, Sonja Murto, Huizhi Liu, Anu-Maija Sundström, and Leena Järvi
Atmos. Chem. Phys., 19, 7001–7017, https://doi.org/10.5194/acp-19-7001-2019, https://doi.org/10.5194/acp-19-7001-2019, 2019
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This is the first study to evaluate and correct the WATCH WFDEI reanalysis product in a highly polluted urban environment. It gives an important understanding of the uncertainties in reanalysis products in local-scale urban modelling in polluted environments and identifies and corrects the most important variables in hydrological modelling. This is also the first study to examine the effects of haze on the local-scale urban hydrological cycle.
Dantong Liu, Rutambhara Joshi, Junfeng Wang, Chenjie Yu, James D. Allan, Hugh Coe, Michael J. Flynn, Conghui Xie, James Lee, Freya Squires, Simone Kotthaus, Sue Grimmond, Xinlei Ge, Yele Sun, and Pingqing Fu
Atmos. Chem. Phys., 19, 6749–6769, https://doi.org/10.5194/acp-19-6749-2019, https://doi.org/10.5194/acp-19-6749-2019, 2019
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This study provides source attribution and characterization of BC in the Beijing urban environment in both winter and summer. For the first time, the physically and chemically based source apportionments are compared to evaluate the primary source contribution and secondary processing of BC-containing particles. A method is proposed to isolate the BC from the transportation sector and coal combustion sources.
Roy M. Harrison, David C. S. Beddows, Mohammed S. Alam, Ajit Singh, James Brean, Ruixin Xu, Simone Kotthaus, and Sue Grimmond
Atmos. Chem. Phys., 19, 39–55, https://doi.org/10.5194/acp-19-39-2019, https://doi.org/10.5194/acp-19-39-2019, 2019
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Particle number size distributions were measured simultaneously at five sites in London during a campaign. Observations are interpreted in terms of both evaporative shrinkage of traffic-generated particles and condensational growth, probably of traffic-generated particles under cool nocturnal conditions, as well as the influence of particles emitted from Heathrow Airport at a distance of about 22 km. The work highlights the highly dynamic behaviour of nanoparticles within the urban atmosphere.
Ting Sun, Zhi-Hua Wang, Walter C. Oechel, and Sue Grimmond
Geosci. Model Dev., 10, 2875–2890, https://doi.org/10.5194/gmd-10-2875-2017, https://doi.org/10.5194/gmd-10-2875-2017, 2017
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The diurnal hysteresis behaviour found between the net storage heat flux and net all-wave radiation has been captured in the Objective Hysteresis Model (OHM). To facilitate use, and enhance physical interpretations of the OHM coefficients, we develop the Analytical Objective Hysteresis Model (AnOHM) using an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the surface energy balance relationship.
Wen-Yu Yang, Guang-Heng Ni, You-Cun Qi, Yang Hong, and Ting Sun
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-388, https://doi.org/10.5194/amt-2016-388, 2016
Revised manuscript has not been submitted
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Using a dataset consisting of one-year measurements by an X-band radar and distrometer, we found that error corrections greatly improve X-band-radar-based rainfall estimation. Specifically, the greatest improvement is realized by the beam integration. Derivation of localized Z-R relationships for specific rainfall systems is also of great importance. Moreover, wind drift correction improves quantitative estimates and temporal consistency.
Carole Helfter, Anja H. Tremper, Christoforos H. Halios, Simone Kotthaus, Alex Bjorkegren, C. Sue B. Grimmond, Janet F. Barlow, and Eiko Nemitz
Atmos. Chem. Phys., 16, 10543–10557, https://doi.org/10.5194/acp-16-10543-2016, https://doi.org/10.5194/acp-16-10543-2016, 2016
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There are relatively few long-term, direct measurements of pollutant emissions in urban settings. We present over 3 years of measurements of fluxes of CO, CO2 and CH4, study their respective temporal and spatial dynamics and offer an independent verification of the London Atmospheric Emissions Inventory. CO and CO2 were strongly controlled by traffic and well characterised by the inventory whilst measured CH4 was two-fold larger and linked to natural gas usage and perhaps biogenic sources.
Simone Kotthaus, Ewan O'Connor, Christoph Münkel, Cristina Charlton-Perez, Martial Haeffelin, Andrew M. Gabey, and C. Sue B. Grimmond
Atmos. Meas. Tech., 9, 3769–3791, https://doi.org/10.5194/amt-9-3769-2016, https://doi.org/10.5194/amt-9-3769-2016, 2016
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Ceilometers lidars are useful to study clouds, aerosol layers and atmospheric boundary layer structures. As sensor optics and acquisition algorithms can strongly influence the observations, sensor specifics need to be incorporated into the physical interpretation. Here, recommendations are made for the operation and processing of profile observations from the widely deployed Vaisala CL31 ceilometer. Proposed corrections are shown to increase data quality and even data availability at times.
J. Lindén, C.S.B. Grimmond, and J. Esper
Adv. Sci. Res., 12, 157–162, https://doi.org/10.5194/asr-12-157-2015, https://doi.org/10.5194/asr-12-157-2015, 2015
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Long term meteorological records from stations associated with villages are generally classified as rural and assumed to have no urban influence. Using temperature sensor networks installed around two such stations, spatial variations of the same order magnitude as the long-term temperature trend from these stations were found. The potential bias in the long term series therefore warrants careful consideration in temperature trend evaluation also in village stations.
H. C. Ward, J. G. Evans, C. S. B. Grimmond, and J. Bradford
Atmos. Meas. Tech., 8, 1385–1405, https://doi.org/10.5194/amt-8-1385-2015, https://doi.org/10.5194/amt-8-1385-2015, 2015
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Two-wavelength scintillometry, a ground-based remote sensing technique for deriving large-area heat fluxes, has been used over an urban area for the first time. The long data set enables investigation of the performance of the technique and characteristics of turbulent transport processes at sub-daily to inter-annual timescales. In this first paper, the structure parameters of temperature and humidity, and the correlation between temperature and humidity, are presented and analysed.
H. C. Ward, J. G. Evans, and C. S. B. Grimmond
Atmos. Meas. Tech., 8, 1407–1424, https://doi.org/10.5194/amt-8-1407-2015, https://doi.org/10.5194/amt-8-1407-2015, 2015
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Two-wavelength scintillometry, a ground-based remote sensing technique for deriving large-area heat fluxes, has been used over an urban area for the first time. The long data set enables investigation of the performance of the technique and characteristics of turbulent transport processes at sub-daily to inter-annual timescales. In this second paper, sensible and latent heat fluxes representative of an area of 5--10 km2 are presented and analysed.
L. Järvi, C. S. B. Grimmond, M. Taka, A. Nordbo, H. Setälä, and I. B. Strachan
Geosci. Model Dev., 7, 1691–1711, https://doi.org/10.5194/gmd-7-1691-2014, https://doi.org/10.5194/gmd-7-1691-2014, 2014
A. Font, C. S. B. Grimmond, J.-A. Morguí, S. Kotthaus, M. Priestman, and B. Barratt
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-13465-2013, https://doi.org/10.5194/acpd-13-13465-2013, 2013
Revised manuscript not accepted
H. C. Ward, J. G. Evans, and C. S. B. Grimmond
Atmos. Chem. Phys., 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, https://doi.org/10.5194/acp-13-4645-2013, 2013
Related subject area
Atmospheric sciences
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
Assessment of object-based indices to identify convective organization
The Global Forest Fire Emissions Prediction System version 1.0
Development of A Fast Radiative Transfer Model for Ground-based Microwave Radiometers (ARMS-gb v1.0): Validation and Comparison to RTTOV-gb
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework
Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model
GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator
Preliminary evaluation of the effect of electro-coalescence with conducting sphere approximation on the formation of warm cumulus clouds using SCALE-SDM version 0.2.5–2.3.0
Similarity-Based Analysis of Atmospheric Organic Compounds for Machine Learning Applications
Cell tracking -based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
Improving the EnSRF in the Community Inversion Framework: a case study with ICON-ART 2024.01
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
EGUsphere, https://doi.org/10.5194/egusphere-2024-2884, https://doi.org/10.5194/egusphere-2024-2884, 2024
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Assimilating Ground-based microwave radiometers' observations into numerical weather prediction models holds significant promise for enhancing forecast accuracy. Radiative transfer models (RTM) are crucial for direct data assimilation. We propose a new RTM capable of simulating brightness temperatures observed by GMRs and their Jacobians. Several improvements are introduced to achieve higher accuracy.The RTM align with RTTOV-gb well and can achieve smaller STD in water vapor absorption channels.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
Short summary
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-145, https://doi.org/10.5194/gmd-2024-145, 2024
Revised manuscript accepted for GMD
Short summary
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements in 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
Short summary
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Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
Short summary
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AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
Short summary
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Hilda Sandström and Patrick Rinke
EGUsphere, https://doi.org/10.48550/arXiv.2406.18171, https://doi.org/10.48550/arXiv.2406.18171, 2024
Short summary
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Machine learning has the potential to aid the identification 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 model in atmospheric sciences.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-99, https://doi.org/10.5194/gmd-2024-99, 2024
Revised manuscript accepted for GMD
Short summary
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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 rainfall. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and then the model skill is evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with 4 open-source models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
Short summary
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Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
Short summary
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In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2197, https://doi.org/10.5194/egusphere-2024-2197, 2024
Short summary
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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 more efficient implementation of the serial and batch versions of the Ensemble Square Root Filter (EnSRF) algorithm in CIF.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
Short summary
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A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
Short summary
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The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
Short summary
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Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
Short summary
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This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Cited articles
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, 2011.
ASHRAE: ANSI/ASHRAE Standard 140-2017, Standard Method of Test for the
Evaluation of Building Energy Analysis Computer Programs, 2017.
Baetens, R. and Saelens, D.: Modelling uncertainty in district energy
simulations by stochastic residential occupant behaviour, J. Build. Perform.
Simu., 9, 431–447, https://doi.org/10.1080/19401493.2015.1070203, 2016.
BCO: Guide to Specification 2009, British Council for Offices, 2009.
BEIS: Department for Business, Energy & Industrial Strategy: Sub-national
electricity consumption data, available at:
https://www.gov.uk/government/collections/sub-national-electricity-consumption-data#lsoa/msoa-data,
last access: 14 July 2017a.
BEIS: Department for Business, Energy & Industrial Strategy: Sub-national
gas consumption data, available at:
https://www.gov.uk/government/collections/sub-national-gas-consumption-data,
last access: 14 July 2017b.
BEIS: Department for Business, Energy & Industrial Strategy: Sub-national
total final energy consumption data, available at:
https://www.gov.uk/government/collections/total-final-energy-consumption-at-sub-national-level,
last access: 14 July 2017c.
BEIS: Department for Business, Energy & Industrial Strategy: Sub-National
Consumption Statisitics: Methodology and guidance booklet, 2018.
Bergman, T. L., Lavine, A. S., Incropera, F. P., and DeWitt, D. P.:
Fundamentals of Heat and Mass Transfer, 8th Edn., Wiley Global Education,
2017.
Best, M. J. and Grimmond, C. S. B.: Investigation of the impact of
anthropogenic heat flux within an urban land surface model and PILPS-urban,
Theor. Appl. Climatol., 126, 51–60, https://doi.org/10.1007/s00704-015-1554-3,
2016.
Björkegren, A. and Grimmond, C. S. B.: Net carbon dioxide emissions from
central London, Urban Clim., 23, 131–158, https://doi.org/10.1016/j.uclim.2016.10.002,
2018.
Blitzstein, J. K. and Hwang, J.: Introduction to Probability, 2nd Edn.,
Chapman and Hall, CRC Press, 2019.
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, 2014.
BSI: BS 6700: Specification for Design, installation, testing and
maintenance of services supplying water for domestic use within buildings
and their curtilages, British Standards Institution, 1997.
Bueno, B., Pigeon, G., Norford, L. K., Zibouche, K., and Marchadier, C.: Development and evaluation of a building energy model integrated in the TEB scheme, Geosci. Model Dev., 5, 433–448, https://doi.org/10.5194/gmd-5-433-2012, 2012.
Busby, J.: UK shallow ground temperatures for ground coupled heat
exchangers, Q. J. Eng. Geol. Hydroge., 48, 248–260,
https://doi.org/10.1144/qjegh2015-077, 2015.
Butcher, K. (Ed.): CIBSE Guide K: Electricity in buildings, Chartered
Institution of Building Services Engineers, 2004.
Butcher, K. (Ed.): CIBSE Guide F: Energy Efficiency in Buildings, Chartered
Institution of Building Services Engineers, 2012.
Butcher, K. (Ed.): CIBSE Guide G: Public health and plumbing engineering,
Chartered Institute of Building Service Engineers, 2014.
Butcher, K. and Craig, B. (Eds.): CIBSE Guide A: Environmental design,
Chartered Institute of Building Service Engineers, London, 2016.
Casey, H. J.: The law of retail gravitation applied to traffic engineering,
Traffic Q., 9, 313–321, 1955.
Capel-Timms, I., Smith, S. T., Sun, T., and Grimmond, S.: Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): Development and evaluation (Version 1.2), Zenodo, https://doi.org/10.5281/zenodo.3936025, 2020.
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, 1–8,
https://doi.org/10.1038/s41598-018-29873-x, 2018.
Cleveland, W. S.: LOWESS: A Program for Smoothing Scatterplots by Robust
Locally Weighted Regression, Am. Stat., 35, 54, https://doi.org/10.2307/2683591, 1988.
Cole, R. J. and Sturrock, N. S.: The convective heat exchange at the
external surface of buildings, Build. Environ., 12, 207–214,
https://doi.org/10.1016/0360-1323(77)90021-X, 1977.
Crawford, B., Grimmond, C. S. B., Ward, H. C., Morrison, W., and Kotthaus,
S.: Spatial and temporal patterns of surface–atmosphere energy exchange in
a dense urban environment using scintillometry, Q. J. Roy. Meteor. Soc.,
143, 817–833, https://doi.org/10.1002/qj.2967, 2017.
Crawley, D. B., Lawrie, L. K., Pedersen, C. O., and Winkelmann, F. C.:
EnergyPlus: Energy Simulation Program, ASHRAE J., 42, 49–56, 2000.
Crooks, A. T. and Heppenstall, A. J.: Introduction to Agent-Based Modelling,
in: Agent-Based Models of Geographical Systems, 85–105, 2012.
DECC: Non-Domestic National Energy Efficiency Data-Framework: Energy
Statistics 2006-12, Department of Energy and Climate Change, available at:
https://www.gov.uk/government/statistics/non-domestic-national-energy-efficiency-data-framework-energy-statistics-2006-12
(last access: 11 February 2020), 2015.
DECC and BRE: Energy Follow Up Survey (EFUS) 2011, Department of Energy and
Climate Change and Building Research Establishment, available at:
https://www.gov.uk/government/statistics/energy-follow-up-survey-efus-2011
(last access: 11 February 2020), 2016.
de Munck, C., Pigeon, G., Masson, V., Meunier, F., Bousquet, P., Tréméac, B., Merchat, M., Poeuf, P., and Marchadier, C.: How much can air conditioning increase air temperatures for a city like Paris, France?, Int. J. Climatol., 33, 210–227, https://doi.org/10.1002/joc.3415, 2013.
DfE: Schools, Pupils and their Characteristics, January 2019 – Accompanying
Table, Department for Education, available at:
https://www.gov.uk/government/statistics/schools-pupils-and-their-characteristics-january-2019
(last access: 11 February 2020), 2019.
DfT: Department for Transport, TRA0203 – Motor vehicle traffic (vehicle
kilometres) by road class and region and country in Great Britain, annual
2014, available at:
https://www.gov.uk/government/statistical-data-sets/road-traffic-statistics-tra
(last access: 10 February 2016), 2014a.
DfT: Department for Transport, TRA0204 – Road traffic (vehicle kilometres)
by vehicle type and road class in Great Britain, annual 2014,
available at:
https://www.gov.uk/government/statistical-data-sets/road-traffic-statistics-tra
(last access: 10 February 2016), 2014b.
DfT: Department for Transport, National Travel Survey, 2002–2016, [data
collection], 12th Edn., UK Data Service, SN: 5340, https://doi.org/10.5255/UKDA-SN-5340-8,
2017.
DfT and DVLA: Department for Transport (DfT) and Driver and Vehicle
Licensing Agency (DVLA), Data on all licensed and registered vehicles
(VEH01), available at:
https://www.gov.uk/government/statistical-data-sets/all-vehicles-veh01
(last access: 10 February 2020), 2019.
Dong, Y., Varquez, A. C. G., and Kanda, M.: Global anthropogenic heat flux
database with high spatial resolution, Atmos. Environ., 150, 276–294,
https://doi.org/10.1016/j.atmosenv.2016.11.040, 2017.
Druckman, A. and Jackson, T.: Household energy consumption in the UK: A
highly geographically and socio-economically disaggregated model, Energ.
Policy, 36, 3167–3182, https://doi.org/10.1016/j.enpol.2008.03.021, 2008.
EnergyPlus: Knowledgebase: Downloads, Testing and validation, ANSI/ASHRAE
Standard 140 models, Knowledgebase Downloads, available at:
http://energyplus.helpserve.com/Knowledgebase/List/Index/49, last access: 30
June 2020.
Evans, S., Liddiard, R., and Steadman, P.: Modelling a whole building stock:
domestic, non-domestic and mixed use, Build. Res. Inf., 47, 156–172,
https://doi.org/10.1080/09613218.2017.1410424, 2019.
Ferreira, M. J., de Oliveira, A. P., and Soares, J.: Anthropogenic heat in
the city of São Paulo, Brazil, Theor. Appl. Climatol., 104,
43–56, https://doi.org/10.1007/s00704-010-0322-7, 2011.
Field, J.: TM46: Energy Benchmarks, Chartered Institute of Building Service
Engineers, 2008.
Firth, S., Lomas, K., Wright, A., and Wall, R.: Identifying trends in the use
of domestic appliances from household electricity consumption measurements,
Energ. Buildings, 40, 926–936, https://doi.org/10.1016/j.enbuild.2007.07.005, 2008.
Fisher, K. and Gershuny, J.: Coming full circle – introducing the
multinational Time Use Study Simple File, Electron. Int. J. Time Use Res.,
10, 91–96, https://doi.org/10.1016/j.physbeh.2017.03.040, 2013.
Flamco: Indirect Water Heaters (mains water systems), available
at: https://flamcogroup.com/media/files/documentation/EXP17_PSTEST_LR_v09022017_Chapter_6.pdf (last access: 7 March 2019), 2017.
Foucquier, A., Robert, S., Suard, F., Stéphan, L., and Jay, A.: State of
the art in building modelling and energy performances prediction: A review,
Renew. Sust. Energ Rev., 23, 272–288, https://doi.org/10.1016/j.rser.2013.03.004,
2013.
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, 2019.
Gershuny, J. and Sullivan, O.: United Kingdom Time Use Survey, 2014–2015,
[data collection], UK Data Service, SN: 8128,
https://doi.org/10.5255/UKDA-SN-8128-1, 2017.
GLA: Greater London Authority, Statistical GIS Boundary Files for London,
available at:
https://data.london.gov.uk/dataset/statistical-gis-boundary-files-london
(last access: 11 February 2020), 2011.
GLA: Greater London Authority, London Schools Atlas, available
at: https://data.london.gov.uk/dataset/london-schools-atlas (last access: 7 September 2020), 2014.
Google: Google Directions API, available at:
https://developers.google.com/maps/documentation/directions/start (last access:
31 January 2020), 2019.
Greenshields, B. D., Bibbins, J. R., Channing, W. S., and Miller, H. H.: A
Study of Traffic Capacity, in Proceedings of the highway research board,
Highway Research Board, Washington, D. C., 14, 448–477, 1935.
Grimmond, C. S. B.: The Suburban Energy Balance?: Methodological
Considerations and Results for a Mid-Latitude West, Int. J., 12, 481–497,
https://doi.org/10.1002/joc.3370120506, 1992.
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.
Grimmond, C. S. B., Cleugh, H. A., and Oke, T. R.: An objective urban heat
storage model and its comparison with other schemes, Atmos. Environ., 25, 311–326, https://doi.org/10.1016/0957-1272(91)90003-W, 1991.
Grimmond, C. S. B., Potter, S. K., Zutter, H. N., and Souch, C.: Rapid
methods to estimate sky view factors applied to urban areas, Int. J. Climatol.,
21, 903–913, https://doi.org/10.1002/joc.659, 2001.
Hawkins, G.: Rules of Thumb, Guidelines for Building Services, 5th Edn.,
BSRIA, Building Services Research and Information Association, 2011.
HCA: Employment Densities Guide: 2nd Edition, Homes and Communities Agency,
available at:
https://www.gov.uk/government/publications/employment-densities-guide (last access: 7 September 2020), 2010.
Heaviside, C., Vardoulakis, S., and Cai, X.-M.: Attribution of mortality to
the urban heat island during heatwaves in the West Midlands, UK, Environ.
Health, 15 Suppl 1, 50–59, https://doi.org/10.1186/s12940-016-0100-9, 2016.
Heiple, S. and Sailor, D. J.: Using building energy simulation and
geospatial modeling techniques to determine high resolution building sector
energy consumption profiles, Energ. Buildings, 40, 1426–1436,
https://doi.org/10.1016/j.enbuild.2008.01.005, 2008.
Hermanns, H.: Interactive Markov Chains: The Quest for Quantified Quality,
Springer, 2003.
Highways Agency: Traffic Capcity of Urban Roads. Design Manual for Roads and
Bridges: TA 79/99, Highways Agency, available at:
http://www.standardsforhighways.co.uk/ha/standards/dmrb/vol5/section1/ta7999.pdf
(last access: 10 February 2020), 2017.
Hinkel, K. M., Nelson, F. E., Klene, A. E., and Bell, J. H.: The urban heat
island in winter at Barrow, Alaska, Int. J. Climatol., 23, 1889–1905,
https://doi.org/10.1002/joc.971, 2003.
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, 2012.
IOP: Institute of Plumbing. Plumbing Services Engineering Design Guide ISBN
9781871956405, 2002.
Kikegawa, Y., Genchi, Y., Yoshikado, H., and Kondo, H.: Development of a
numerical simulation system toward comprehensive assessments of urban
warming countermeasures including their impacts upon the urban buildings'
energy-demands, Appl. Energ., 76, 449–466,
https://doi.org/10.1016/S0306-2619(03)00009-6, 2003.
Kikegawa, Y., Tanaka, A., Ohashi, Y., Ihara, T., and Shigeta, Y.: Observed
and simulated sensitivities of summertime urban surface air temperatures to
anthropogenic heat in downtown areas of two Japanese Major Cities, Tokyo and
Osaka, Theor. Appl. Climatol., 117, 175–193,
https://doi.org/10.1007/s00704-013-0996-8, 2014.
Kim, Y. S. and Srebric, J.: Impact of occupancy rates on the building
electricity consumption in commercial buildings, Energ. Buildings, 138,
591–600, https://doi.org/10.1016/j.enbuild.2016.12.056, 2017.
Klein, S. A., Duffie, J. A., and Mitchell, J. C.: TRNSYS 18: A Transient
System Simulation Program, Solar Energy Laboratory, University of Wisconsin,
available at: https://sel.me.wisc.edu/trnsys/ (last access: 31
January 2020), 2017.
Knudsen, S.: Heat transfer in a “tank in tank” combi store, BYG Rapport, No. R-025, 2002.
Kotthaus, S. and Grimmond, C. S. B.: Identification of Micro-scale
Anthropogenic CO2, heat and moisture sources – Processing eddy covariance
fluxes for a dense urban environment, Atmos. Environ., 57, 301–316,
https://doi.org/10.1016/j.atmosenv.2012.04.024, 2012.
Kotthaus, S. and Grimmond, C. S. B.: 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, 2014.
Lee, S. H., Song, C. K., Baik, J. J., and Park, S. U.: Estimation of
anthropogenic heat emission in the Gyeong-In region of Korea, Theor. Appl.
Climatol., 96, 291–303, https://doi.org/10.1007/s00704-008-0040-6, 2009.
LGA: Local Government Association website, available at:
https://www.local.gov.uk/about/what-local-government, last access: 26 November
2019.
Lindberg, F., Grimmond, C. S. B., 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.
London Datastore: London Atmospheric Emissions Inventory (LAEI) 2013 –
Supporting information: key GIS geographies and road traffic flows and
vehicle-kilometres, available at:
https://data.london.gov.uk/dataset/london-atmospheric-emissions-inventory-2013
(last access: 11 February 2020), 2014.
Lu, Y., Wang, Q., Zhang, Y., Sun, P., and Qian, Y.: An estimate of
anthropogenic heat emissions in China, Int. J. Climatol., 36, 1134–1142,
https://doi.org/10.1002/joc.4407, 2016.
Macal, C. and North, M.: Tutorial on agent-based modelling and simulation, J.
Simul., 4, 151–162, https://doi.org/10.1057/jos.2010.3, 2010.
Mavrogianni, A., Wilkinson, P., Davies, M., Biddulph, P., and Oikonomou, E.:
Building characteristics as determinants of propensity to high indoor summer
temperatures in London dwellings, Build. Environ., 55, 117–130,
https://doi.org/10.1016/j.buildenv.2011.12.003, 2012.
McKenna, E., Krawczynski, M., and Thomson, M.: Four-state domestic building
occupancy model for energy demand simulations, Energ. Buildings, 96, 30–39,
https://doi.org/10.1016/j.enbuild.2015.03.013, 2015.
MWS: McDonald Water Storage Ltd: Hot Water Tanks Specifications and Sizing,
available at:
https://www.mcdonaldwaterstorage.com/rectangular-tank-sizing-specifications,
last access: 7 March 2019.
National Bureau of Statistics of China: China Statistical Information
Network, available at:
http://www.stats.gov.cn/english/statisticaldata/censusdata/ (last access: 11
February 2020), 2017.
NG: National Grid: Transmission operational data, available at:
https://www.nationalgridgas.com/data-and-operations/transmission-operational-data,
last access: 28 November 2015.
Nie, W. S., Sun, T., and Ni, G. H.: Spatiotemporal characteristics of
anthropogenic heat in an urban environment: A case study of Tsinghua Campus,
Build. Environ., 82, 675–686, https://doi.org/10.1016/j.buildenv.2014.10.011, 2014.
Offerle, B., Grimmond, C. S. B., and Fortuniak, K.: Heat storage and
anthropogenic heat flux in relation to the energy balance of a central
European city centre, Int. J. Climatol., 25, 1405–1419,
https://doi.org/10.1002/joc.1198, 2005.
Oke, T. R.: The urban energy balance, Prog. Phys. Geogr., 12, 471–508,
https://doi.org/10.1177/030913338801200401, 1988.
ONS: Office for National Statistics, QS406EW – Household size, available at: https://www.nomisweb.co.uk/census/2011/qs406ew (last access: 10
February 2020), 2011.
ONS: Office for National Statistics, WP101EW Population (Workplace
population), available at:
https://www.nomisweb.co.uk/query/construct/summary.asp?reset=yes&mode=construct&dataset=1300&version=0&anal=1&initsel=%0D
(last access: 31 January 2020), 2014a.
ONS: Office for National Statistics, WU03UK Location of usual residence and
place of work by method of travel to work, available at:
https://www.nomisweb.co.uk/query/construct/summary.asp?reset=yes&mode=construct&dataset=1207&version=0&anal=1&initsel=
(last access: 31 January 2020), 2014b.
ONS: Office for National Statistics, Mid-2015 Population Estimates for
Census Output Areas in London by Single Year of Age and Sex, available at:
https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/censusoutputareaestimatesinthelondonregionofengland,
last access: 25 May 2015.
ONS: Office for National Statistics, Census Geography, available
at:
https://www.ons.gov.uk/methodology/geography/ukgeographies/censusgeography,
last access: 11 February 2017a.
ONS: Office for National Statistics, Labour force survey – Families and
Households, available at:
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/families/datasets/familiesandhouseholdsfamiliesandhouseholds,
last access: 30 October 2017b.
ONS: Estimated average calorific values of fuels 2017 – Digest of UK Energy
Statistics (DUKES): calorific values, available at:
https://www.gov.uk/government/statistics/dukes-calorific-values (last access: 31
January 2020), 2018.
ONS: Office for National Statistics, UK Business Counts – local units by
industry and employment size band, available at:
https://www.nomisweb.co.uk/datasets/idbrlu (last access: 17 January 2020), 2019.
OpenStreetMap: OpenStreetMap data of Greater London, available at: https://www.openstreetmap.org, last access: 31 January 2017.
OS: OS MasterMap®, available at:
http://digimap.edina.ac.uk (last access: 11 October 2015), 2014.
OS: London digital speed limit map and private communication, Ordnance
Survey, 2015.
OS: OS Open Roads, available at:
https://www.ordnancesurvey.co.uk/opendatadownload/products.html (last access: 30
August 2016), 2016.
O'Sullivan, D., Millington, J., Perry, G., and Wainwright, J.: Agent-Based
Models – Because They're Worth It?, in: Agent-Based Models of Geographical
Systems, 109–123, 2012.
Page, J., Robinson, D., Morel, N., and Scartezzini, J. L.: A generalised
stochastic model for the simulation of occupant presence, Energ. Buildings,
40, 83–98, https://doi.org/10.1016/j.enbuild.2007.01.018, 2008.
Palmer, E. (Ed.): CIBSE Guide B1: Heating, Chartered Institution of Building
Services Engineers, 2016.
Pigeon, G., Legain, D., Durand, P., and Masson, V.: Anthropogenic heat
release in an old European agglomeration (Toulouse, France), Int. J.
Climatol., 27, 1969–1981, 2007.
Reilly, W. J.: The Law of Retail Gravitation (1931), 2nd Edn., Pilsbury
Publishers, New York, 1953.
Richardson, I., Thomson, M., and Infield, D.: A high-resolution domestic
building occupancy model for energy demand simulations, Energ. Buildings,
40, 1560–1566, https://doi.org/10.1016/j.enbuild.2008.02.006, 2008.
Richardson, I., Thomson, M., Infield, D., and Clifford, C.: Domestic
electricity use: A high-resolution energy demand model, Energ. Buildings,
42, 1878–1887, https://doi.org/10.1016/j.enbuild.2010.05.023, 2010.
Sailor, D. J.: A review of methods for estimating anthropogenic heat and
moisture emissions in the urban environment, Int. J. Climatol., 31,
189–199, https://doi.org/10.1002/joc.2106, 2011.
Sailor, D. J. and Lu, L.: A top-down methodology for developing diurnal and
seasonal anthropogenic heating profiles for urban areas, Atmos. Environ.,
38, 2737–2748, https://doi.org/10.1016/j.atmosenv.2004.01.034, 2004.
Salamanca, F. P., Georgescu, M., Mahalov, A., Moustaoui, M., and Wang, M.: Anthropogenic heating of the urban environment due to air conditioning, J. Geophys. Res.-Atmos., 119, 5949–5965, https://doi.org/10.1002/2013jd021225, 2014.
Salter, R. J.: The relationship between space, flow and density of a highway
traffic stream, in: Highway Traffic Analysis and Design,
Palgrave Macmillan, 119–120, 1989.
Santamouris, M., Papanikolaou, N., Livada, I., Koronakis, I., Georgakis, C.,
Argiriou, A., and Assimakopoulos, D. N.: On the impact of urban climate on
the energy consumption of buildings, Sol. Energy, 70, 201–216,
https://doi.org/10.1016/S0038-092X(00)00095-5, 2001.
Schoetter, R., Masson, V., Bourgeois, A., Pellegrino, M., and Lévy, J.-P.: Parametrisation of the variety of human behaviour related to building energy consumption in the Town Energy Balance (SURFEX-TEB v. 8.2), Geosci. Model Dev., 10, 2801–2831, https://doi.org/10.5194/gmd-10-2801-2017, 2017.
SciPy: Numpy random sampling, available at:
https://docs.scipy.org/doc/numpy/reference/random/index.html, last access: 30
November 2019.
Sellers, W. D.: Physical Climatology, 4th Edn., Univeristy of Chicago Press,
Ltd, 1972.
Sericola, B.: Markov chains: theory, algorithms and applications, John Wiley
& Sons, Inc., 2013.
Smith, C., Lindley, S. and Levermore, G.: Estimating spatial and temporal
patterns of urban anthropogenic heat fluxes for UK cities: The case of
Manchester, Theor. Appl. Climatol., 98, 19–35,
https://doi.org/10.1007/s00704-008-0086-5, 2009.
Spitler, J. D.: Thermal Load and Energy performance prediction, in: Building
performance simulation for design and operation, edited by: Hensen, J. L. and
Lamberts, R., Spon Press, 2011.
Statistics Bureau of Japan: Japanese census data resolution, available at: https://www.stat.go.jp/english/data/index.html (last access: 7 September 2020), 2017.
Statistics Canada: Statistical Area Classification (SAC), available at:
https://www150.statcan.gc.ca/n1/pub/92-195-x/2016001/other-autre/sac-css/sac-css-eng.htm
(last access: 11 February 2020), 2017.
Steadman, P., Bruhns, H. R., and Rickaby, P. A.: An introduction to the
national Non-Domestic Building Stock database, Environ. Plann. B,
27, 3–10, https://doi.org/10.1068/bst2, 2000.
Stewart, I. D., Oke, T. R., and Krayenhoff, E. S.: Evaluation of the “local
climate zone” scheme using temperature observations and model simulations,
Int. J. Climatol., 34, 1062–1080, https://doi.org/10.1002/joc.3746, 2014.
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.
TfL: Transport for London. Bus service usage, passengers and kilometres
operated by route (2014–2015), available at:
https://tfl.gov.uk/corporate/publications-and-reports/buses#on-this-page-1
(last access: 12 February 2020), 2018.
TfL: Transport for London. Number of Buses by Type of Bus in London,
available at:
https://data.london.gov.uk/dataset/number-buses-type-bus-london (last access: 11
February 2020), 2019.
Thorsson, S., Rocklöv, J., Konarska, J., Lindberg, F., Holmer, B.,
Dousset, B., and Rayner, D.: Mean radiant temperature – A predictor of heat
related mortality, Urban Clim., 10, 332–345,
https://doi.org/10.1016/j.uclim.2014.01.004, 2014.
Underwood, C. P. and Yik, F.: Modelling methods for energy in buildings,
Blackwell Publishing, Malden, 2004.
U.S. Department of Energy: EnergyPlusVersion 9.3.0: Engineering Reference,
2020.
US Census Bureau: US Census Geography, available at:
https://www.census.gov/data.html (last access: 11 February 2020), 2019.
VOA: Dwellings by Property Build Period and Type, available at:
https://data.london.gov.uk/dataset/property-build-period-lsoa (last access: 31
January 2020), 2015.
Ward, H. C. and Grimmond, C. S. B.: Assessing the impact of changes in
surface cover, human behaviour and climate on energy partitioning across
Greater London, Landscape Urban Plan., 165, 142–161, 2017.
Ward, H. C., Kotthaus, S., Järvi, L., and Grimmond, C. S. B.: Surface
Urban Energy and Water Balance Scheme (SUEWS): Development, Evaluation and
Application, Urban Clim., 18, 1–32, https://doi.org/10.1016/j.uclim.2016.05.001, 2016.
Widén, J. and Wäckelgård, E.: A high-resolution stochastic model
of domestic activity patterns and electricity demand, Appl. Energ., 87,
1880–1892, https://doi.org/10.1016/j.apenergy.2009.11.006, 2010.
Widén, J., Nilsson, A. M., and Wäckelgård, E.: A combined
Markov-chain and bottom-up approach to modelling of domestic lighting
demand, Energ. Buildings, 41, 1001–1012,
https://doi.org/10.1016/j.enbuild.2009.05.002, 2009a.
Widén, J., Lundh, M., Vassileva, I., Dahlquist, E., Ellegård, K., and
Wäckelgård, E.: Constructing load profiles for household electricity
and hot water from time-use data-Modelling approach and validation, Energ.
Buildings, 41, 753–768, https://doi.org/10.1016/j.enbuild.2009.02.013, 2009b.
Wu, N.: A new Approach for Modeling of Fundamental Diagrams and its
Applications, Transp. Res. B, 36, 867–884, 2000.
Yohanis, Y. G., Mondol, J. D., Wright, A., and Norton, B.: Real-life energy
use in the UK: How occupancy and dwelling characteristics affect domestic
electricity use, Energ. Buildings, 40, 1053–1059,
https://doi.org/10.1016/j.enbuild.2007.09.001, 2008.
Zheng, Y. and Weng, Q.: High spatial- and temporal-resolution anthropogenic
heat discharge estimation in Los Angeles County, California, J. Environ.
Manage., 206, 1274–1286, https://doi.org/10.1016/j.jenvman.2017.07.047, 2017.
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Short summary
Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and larger-scale urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, and socio-economic factors and in response to environmental conditions.
Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and...
