Articles | Volume 13, issue 10
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation
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
No articles found.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev. Discuss.,
Preprint under review for GMDShort summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
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,Short summary
The hydroxyl (OH) and closely coupled hydroperoxyl (HO2) radicals are vital for their role in the removal of atmospheric pollutants. In less polluted regions, atmospheric models over-predict HO2 concentrations. In this modelling study, the impact of heterogeneous uptake of HO2 onto aerosol surfaces on radical concentrations and the ozone production regime in Beijing in the summertime is investigated, and the implications for emissions policies across China are considered.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778,Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Megan Alice Stretton, William Morrison, Robin Hogan, and Sue Grimmond
Cities materials and form impact radiative fluxes. The SPARTACUS-Urban multi-layer approach to modelling longwave radiation, describing realistic 3D geometry statistically, is evaluated using the explicit DART (Discrete Anisotropic Radiative Transfer) model. Temperatures configurations used are derived from thermal camera observations. SPARTACUS-Urban accurately predicts longwave fluxes, with low computational time cf. DART, but has larger errors when sunlit/shaded surface temperatures are used.
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,Short summary
We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Will S. Drysdale, Adam R. Vaughan, Freya A. Squires, Sam J. Cliff, Stefan Metzger, David Durden, Natchaya Pingintha-Durden, Carole Helfter, Eiko Nemitz, C. Sue B. Grimmond, Janet Barlow, Sean Beevers, Gregor Stewart, David Dajnak, Ruth M. Purvis, and James D. Lee
Atmos. Chem. Phys., 22, 9413–9433,Short summary
Measurements of NOx emissions are important for a good understanding of air quality. While there are many direct measurements of NOx concentration, there are very few measurements of its emission. Measurements of emissions provide constraints on emissions inventories and air quality models. This article presents measurements of NOx emission from the BT Tower in central London in 2017 and compares them with inventories, finding that they underestimate by a factor of ∼1.48.
Yiqing Liu, Zhiwen Luo, and Sue Grimmond
Atmos. Chem. Phys., 22, 4721–4735,Short summary
Anthropogenic heat emission from buildings is important for atmospheric modelling in cities. The current building anthropogenic heat flux is simplified by building energy consumption. Our research proposes a novel approach to determine ‘real’ building anthropogenic heat emission from the changes in energy balance fluxes between occupied and unoccupied buildings. We hope to provide new insights into future parameterisations of building anthropogenic heat flux in urban climate models.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078,Short summary
This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
Junxia Dou, Sue Grimmond, Shiguang Miao, Bei Huang, Huimin Lei, and Mingshui Liao
Atmos. Chem. Phys. Discuss.,
Revised manuscript accepted for ACPShort summary
Multi-timescale variations in surface energy fluxes at a suburb of Beijing are analyzed using 16-months 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 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.
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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.,
Revised manuscript has not been submittedShort summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,
A. Font, C. S. B. Grimmond, J.-A. Morguí, S. Kotthaus, M. Priestman, and B. Barratt
Atmos. Chem. Phys. Discuss.,
Revised manuscript not accepted
H. C. Ward, J. G. Evans, and C. S. B. Grimmond
Atmos. Chem. Phys., 13, 4645–4666,
Related subject area
Atmospheric sciencesMetrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversionImproved representation of volcanic sulfur dioxide depletion in Lagrangian transport simulations: a case study with MPTRAC v2.4Use of threshold parameter variation for tropical cyclone trackingPassive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemesThe High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scaleA gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filterPlume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport modelA method to derive Fourier–wavelet spectra for the characterization of global-scale waves in the mesosphere and lower thermosphere and its MATLAB and Python software (fourierwavelet v1.1)Dynamic Meteorology-induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmisVisual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductanceA Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction modelsThe development and validation of the Inhomogeneous Wind Scheme for Urban Street (IWSUS-v1)GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 modelsBreakups are complicated: an efficient representation of collisional breakup in the superdroplet methodAn optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parametersSensitivity of tropospheric ozone to halogen chemistry in the chemistry–climate model LMDZ-INCA vNMHCSegmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plantsEvaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 modelsThe Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applicationsAn approach to refining the ground meteorological observation stations for improving PM2.5 forecasts in the Beijing–Tianjin–Hebei regionAssessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUSConvective-gust nowcasting based on radar reflectivity and a deep learning algorithmSelf-nested large-eddy simulations in PALM model system v21.10 for offshore wind prediction under different atmospheric stability conditionsHow does cloud-radiative heating over the North Atlantic change with grid spacing, convective parameterization, and microphysics scheme in ICON version 2.1.00?Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-AtmosphereUpdated isoprene and terpene emission factors for the Interactive BVOC (iBVOC) emission scheme in the United Kingdom Earth System Model (UKESM1.0)Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution modelEvaluating WRF-GC v2.0 predictions of boundary layer and vertical ozone profiles during the 2021 TRACER-AQ campaign in Houston, TexasIntercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modelingHalogen chemistry in volcanic plumes: a 1D framework based on MOCAGE 1D (version R1.18.1) preparing 3D global chemistry modellingPyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysisCLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcastingLong-term evaluation of surface air pollution in CAMSRA and MERRA-2 global reanalyses over Europe (2003–2020)A simplified non-linear chemistry-transport model for analyzing NO2 column observationsEvaluating Three Decades of Precipitation in the Upper Colorado River Basin from a High-Resolution Regional Climate ModelEmulating aerosol optics with randomly generated neural networksDevelopment of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air qualityApplication of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality in AfricaComparison of ozone formation attribution techniques in the northeastern United StatesDescription and performance of the CARMA sectional aerosol microphysical model in CESM2Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolutionData fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning modelEmulating lateral gravity wave propagation in a global chemistry-climate model (EMAC v2.55.2) through horizontal flux redistributionModelling concentration heterogeneities in streets using the street-network model MUNICHAccelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236,Short summary
Measuring the performance of inversions in linear Bayesian problems is crucial in real-life applications. In this work, we provide analytical forms of the local and global sensitivities of the estimated fluxes with respect to various inputs. We provide methods to uniquely map the observational signal to spatiotemporal domains. Utilizing this, we also show techniques to assess correlations between the Jacobians that naturally translate to nonstationary covariance matrix components.
Mingzhao Liu, Lars Hoffmann, Sabine Griessbach, Zhongyin Cai, Yi Heng, and Xue Wu
Geosci. Model Dev., 16, 5197–5217,Short summary
We introduce new and revised chemistry and physics modules in the Massive-Parallel Trajectory Calculations (MPTRAC) Lagrangian transport model aiming to improve the representation of volcanic SO2 transport and depletion. We test these modules in a case study of the Ambae eruption in July 2018 in which the SO2 plume underwent wet removal and convection. The lifetime of SO2 shows highly variable and complex dependencies on the atmospheric conditions at different release heights.
Bernhard M. Enz, Jan P. Engelmann, and Ulrike Lohmann
Geosci. Model Dev., 16, 5093–5112,Short summary
An algorithm to track tropical cyclones in model simulation data has been developed. The algorithm uses many combinations of varying parameter thresholds to detect weaker phases of tropical cyclones while still being resilient to false positives. It is shown that the algorithm performs well and adequately represents the tropical cyclone activity of the underlying simulation data. The impact of false positives on overall tropical cyclone activity is shown to be insignificant.
Sepehr Fathi, Mark Gordon, and Yongsheng Chen
Geosci. Model Dev., 16, 5069–5091,Short summary
We have combined various capabilities within a WRF model to generate simulations of atmospheric pollutant dispersion at 50 m resolution. The study objective was to resolve transport processes at the scale of measurements to assess and optimize aircraft-based emission rate retrievals. Model performance evaluation resulted in agreement within 5 % of observed meteorological and within 1–2 standard deviations of observed wind fields. Mass was conserved in the model within 5 % of input emissions.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068,Short summary
The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882,Short summary
Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852,Short summary
A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810,Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Geosci. Model Dev., 16, 4749–4766,Short summary
The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676,Short summary
To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann
Geosci. Model Dev., 16, 4617–4638,Short summary
Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579,Short summary
The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
Simone Dietmüller, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Christian Weder, Volker Grewe, Feijia Yin, and Federica Castino
Geosci. Model Dev., 16, 4405–4425,Short summary
Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact due to aviation emissions. This requires spatially and temporally resolved information on aviation's climate impact. We propose using algorithmic climate change functions (aCCFs) for CO2 and non-CO2 effects (ozone, methane, water vapor, contrail cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft-specific parameters and climate metrics. Technically this is done with a Python library.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450,Short summary
We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Zhenxin Liu, Yuanhao Chen, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev., 16, 4385–4403,Short summary
The heterogeneous layout of urban buildings leads to the complex wind field in and over the urban canopy. Large discrepancies between the observations and the current simulations result from misunderstanding the character of the wind field. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) was developed to simulate the heterogeneity of the wind speed in a typical street and then improve the simulated energy budget in the lower atmospheric layer over the urban canopy.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
Geosci. Model Dev., 16, 4367–4383,Short summary
Offline performance experiment results show that the GPU-HADVPPM on a V100 GPU can achieve up to 1113.6 × speedups to its original version on an E5-2682 v4 CPU. A series of optimization measures are taken, and the CAMx-CUDA model improves the computing efficiency by 128.4 × on a single V100 GPU card. A parallel architecture with an MPI plus CUDA hybrid paradigm is presented, and it can achieve up to 4.5 × speedup when launching eight CPU cores and eight GPU cards.
Geosci. Model Dev., 16, 4265–4281,Short summary
This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211,Short summary
In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller fragments. Particle-based modeling approaches often do not represent breakup because of the computational demands of creating new droplets. We present a particle-based breakup method that preserves the computational efficiency of these methods. In a series of simple demonstrations, we show that this representation alters cloud processes in reasonable and expected ways.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154,Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Cyril Caram, Sophie Szopa, Anne Cozic, Slimane Bekki, Carlos A. Cuevas, and Alfonso Saiz-Lopez
Geosci. Model Dev., 16, 4041–4062,Short summary
We studied the role of halogenated compounds (containing chlorine, bromine and iodine), emitted by natural processes (mainly above the oceans), in the chemistry of the lower layers of the atmosphere. We introduced this relatively new chemistry in a three-dimensional climate–chemistry model and looked at how this chemistry will disrupt the ozone. We showed that the concentration of ozone decreases by 22 % worldwide and that of the atmospheric detergent, OH, by 8 %.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016,Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951,Short summary
We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891,Short summary
The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Lichao Yang, Wansuo Duan, and Zifa Wang
Geosci. Model Dev., 16, 3827–3848,Short summary
An approach is proposed to refine a ground meteorological observation network to improve the PM2.5 forecasts in the Beijing–Tianjin–Hebei region. A cost-effective observation network is obtained and makes the relevant PM2.5 forecasts assimilate fewer observations but achieve the forecasting skill comparable to or higher than that obtained by assimilating all ground station observations, suggesting that many of the current ground stations can be greatly scattered to avoid much unnecessary work.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
Geosci. Model Dev., 16, 3699–3722,Short summary
Stakeholders need high-resolution regional climate data for applications such as assessing water availability and mountain snowpack. This study examines 3 h and 24 h historical precipitation over the contiguous United States in the 12 km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao
Geosci. Model Dev., 16, 3611–3628,Short summary
Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG nowcasting has remained unattainable. Here, we developed a deep learning model — namely CGsNet — for 0—2 h of quantitative CG nowcasting, first achieving minute—kilometer-level forecasts. Based on the CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.
Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen
Geosci. Model Dev., 16, 3553–3564,Short summary
Local refinement of the grid is a powerful method allowing us to reduce the computational time while preserving the accuracy in the area of interest. Depending on the implementation, the local refinement may introduce unwanted numerical effects into the results. We study the wind speed common to the wind turbine operational speeds and confirm strong alteration of the result when the heat fluxes are present, except for the specific refinement scheme used.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551,Short summary
Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavcic, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben J. Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
3D climate models are one of the best tools we have to study planetary atmospheres. Here, we apply LFRic-Atmosphere, a new model developed by the Met Office, to seven different scenarios for terrestrial planetary climates, including four for the exoplanet TRAPPIST-1e, a primary target for future observations. LFRic-Atmosphere reproduces these scenarios within the spread of the existing models across a range of key climatic variables, justifying its use in future exoplanet studies.
James Weber, James A. King, Katerina Sindelarova, and Maria Val Martin
Geosci. Model Dev., 16, 3083–3101,Short summary
The emissions of volatile organic compounds from vegetation (BVOCs) influence atmospheric composition and contribute to certain gases and aerosols (tiny airborne particles) which play a role in climate change. BVOC emissions are likely to change in the future due to changes in climate and land use. Therefore, accurate simulation of BVOC emission is important, and this study describes an update to the simulation of BVOC emissions in the United Kingdom Earth System Model (UKESM).
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081,Short summary
We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 Tracking Aerosol Convection Experiment Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Xiaohan Li, Yi Zhang, Xindong Peng, Baiquan Zhou, Jian Li, and Yiming Wang
Geosci. Model Dev., 16, 2975–2993,Short summary
The weather and climate physics suites used in GRIST-A22.7.28 are compared using single-column modeling. The source of their discrepancies in terms of modeling cloud and precipitation is explored. Convective parameterization is found to be a key factor responsible for the differences. The two suites also have intrinsic differences in the interaction between microphysics and other processes, resulting in different cloud features and time step sensitivities.
Virginie Marécal, Ronan Voisin-Plessis, Tjarda Jane Roberts, Alessandro Aiuppa, Herizo Narivelo, Paul David Hamer, Béatrice Josse, Jonathan Guth, Luke Surl, and Lisa Grellier
Geosci. Model Dev., 16, 2873–2898,Short summary
We implemented a halogen volcanic chemistry scheme in a one-dimensional modelling framework preparing for further use in a three-dimensional global chemistry-transport model. The results of the simulations for an eruption of Mt Etna in 2008, including various sensitivity tests, show a good consistency with previous modelling studies.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776,Short summary
PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi
Geosci. Model Dev., 16, 2737–2752,Short summary
Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Aleksander Lacima, Hervé Petetin, Albert Soret, Dene Bowdalo, Oriol Jorba, Zhaoyue Chen, Raúl F. Méndez Turrubiates, Hicham Achebak, Joan Ballester, and Carlos Pérez García-Pando
Geosci. Model Dev., 16, 2689–2718,Short summary
Understanding how air pollution varies across space and time is of key importance for the safeguarding of human health. This work arose in the context of the project EARLY-ADAPT, for which the Barcelona Supercomputing Center developed an air pollution database covering all of Europe. Through different statistical methods, we compared two global pollution models against measurements from ground stations and found significant discrepancies between the observed and the modeled surface pollution.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and evaluate modeled results against TROPOMI v2 over multiple power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind direction and prior emissions.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev. Discuss.,
Revised manuscript has not been submittedShort summary
It's important to know how well atmospheric models do in the mountains, but there aren't very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado river basin against the data that's available. The model works pretty well but, there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we couldn't before.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370,Short summary
Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Joey C. Y. Lam, Amos P. K. Tai, Jason A. Ducker, and Christopher D. Holmes
Geosci. Model Dev., 16, 2323–2342,Short summary
We developed a new component within an atmospheric chemistry model to better simulate plant ecophysiological processes relevant for ozone air quality. We showed that it reduces simulated biases in plant uptake of ozone in prior models. The new model enables us to explore how future climatic changes affect air quality via affecting plants, examine ozone–vegetation interactions and feedbacks, and evaluate the impacts of changing atmospheric chemistry and climate on vegetation productivity.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations, and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model-satellite discrepancies, we find that future field campaigns in an East African region (30° E – 45° E, 5° S – 5° N) could substantially improve the predictive skill of air quality models.
Qian Shu, Sergey L. Napelenok, William T. Hutzell, Kirk R. Baker, Barron H. Henderson, Benjamin N. Murphy, and Christian Hogrefe
Geosci. Model Dev., 16, 2303–2322,Short summary
Source attribution methods are generally used to determine culpability of precursor emission sources to ambient pollutant concentrations. However, source attribution of secondarily formed pollutants such as ozone and its precursors cannot be explicitly measured, making evaluation of source apportionment methods challenging. In this study, multiple apportionment approach comparisons show common features but still reveal wide variations in predicted sector contribution and species dependency.
Simone Tilmes, Michael J. Mills, Yunqian Zhu, Charles G. Bardeen, Francis Vitt, Pengfei Yu, David Fillmore, Xiaohong Liu, Brian Toon, and Terry Deshler
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
We implemented an alternative aerosol scheme in the high and low-top model versions of the Community Earth System Model Version 2 (CESM2) with a more detailed description of tropospheric and stratospheric aerosol size distributions than the existing aerosol model. The development enables the comparison of different aerosol schemes with different complexity in the same model framework and identifies improvements in comparison to a range of observations in both the troposphere and stratosphere.
Rüdiger Brecht, Lucie Bakels, Alex Bihlo, and Andreas Stohl
Geosci. Model Dev., 16, 2181–2192,Short summary
We use neural-network-based single-image super-resolution to improve the upscaling of meteorological wind fields to be used for particle dispersion models. This deep-learning-based methodology improves the standard linear interpolation typically used in particle dispersion models. The improvement of wind fields leads to substantial improvement in the computed trajectories of the particles.
Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodriguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba
Geosci. Model Dev., 16, 2193–2213,Short summary
This work aims to derive and evaluate a general statistical post-processing tool specifically designed for the street scale that can be applied to any urban air quality system. Our data fusion methodology corrects NO2 fields based on continuous hourly observations and experimental campaigns. This study enables us to obtain exceedance probability maps of air quality standards. In 2019, 13 % of the Barcelona area had a 70 % or higher probability of exceeding the annual legal NO2 limit of 40 µg/m3.
Liang Wang, Bingcheng Wan, Shaohui Zhou, Haofei Sun, and Zhiqiu Gao
Geosci. Model Dev., 16, 2167–2179,Short summary
The past 24 h TC trajectories and meteorological field data were used to forecast TC tracks in the northwestern Pacific from hours 6–72 based on GRU_CNN, which we proposed in this paper and which has better prediction results than traditional single deep-learning methods. The historical steering flow of cyclones has a significant effect on improving the accuracy of short-term forecasting, while, in long-term forecasting, the SST and geopotential height will have a particular impact.
Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchar, Patrick Jöckel, Astrid Kerkweg, and Bastian Kern
Dynamical model biases result from the columnar approach of gravity wave (GW) schemes, but parallel decomposition makes horizontal GW propagation computationally unfeasible. In the global model EMAC, we approximate it by GW redistribution at one altitude using tailor-made redistribution maps generated with a ray-tracer. More spread-out GW drag helps reconciling the model with observations and closing the 60S GW gap. Polar vortex dynamics are improved, enhancing climate model credibility.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Thomas Berkemeier, Matteo Krüger, Aryeh Feinberg, Marcel Müller, Ulrich Pöschl, and Ulrich K. Krieger
Geosci. Model Dev., 16, 2037–2054,Short summary
Kinetic multi-layer models (KMs) successfully describe heterogeneous and multiphase atmospheric chemistry. In applications requiring repeated execution, however, these models can be too expensive. We trained machine learning surrogate models on output of the model KM-SUB and achieved high correlations. The surrogate models run orders of magnitude faster, which suggests potential applicability in global optimization tasks and as sub-modules in large-scale atmospheric models.
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.
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
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...