Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-91-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-91-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
09 Jan 2024
Development and technical paper |
| 09 Jan 2024
| 09 Jan 2024
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Department of Meteorology, University of Reading, Reading, UK
Institute for Risk and Disaster Reduction, University College London, London, UK
Hamidreza Omidvar
Institute for Risk and Disaster Reduction, University College London, London, UK
Zhenkun Li
Shanghai Climate Centre, Shanghai, China
Ning Zhang
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Wenjuan Huang
Shanghai Climate Centre, Shanghai, China
Simone Kotthaus
Institut Pierre-Simon Laplace, École Polytechnique, Palaiseau, France
Helen C. Ward
Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Zhiwen Luo
Welsh School of Architecture, Cardiff University, Cardiff, UK
Department of Meteorology, University of Reading, Reading, UK
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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.
Roland Stirnberg, Jan Cermak, Simone Kotthaus, Martial Haeffelin, Hendrik Andersen, Julia Fuchs, Miae Kim, Jean-Eudes Petit, and Olivier Favez
Atmos. Chem. Phys., 21, 3919–3948, https://doi.org/10.5194/acp-21-3919-2021, https://doi.org/10.5194/acp-21-3919-2021, 2021
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Air pollution endangers human health and poses a problem particularly in densely populated areas. Here, an explainable machine learning approach is used to analyse periods of high particle concentrations for a suburban site southwest of Paris to better understand its atmospheric drivers. Air pollution is particularly excaberated by low temperatures and low mixed layer heights, but processes vary substantially between and within seasons.
Yanxu Zhang, Xingpei Ye, Shibao Wang, Xiaojing He, Lingyao Dong, Ning Zhang, Haikun Wang, Zhongrui Wang, Yun Ma, Lei Wang, Xuguang Chi, Aijun Ding, Mingzhi Yao, Yunpeng Li, Qilin Li, Ling Zhang, and Yongle Xiao
Atmos. Chem. Phys., 21, 2917–2929, https://doi.org/10.5194/acp-21-2917-2021, https://doi.org/10.5194/acp-21-2917-2021, 2021
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Urban air quality varies drastically at street scale, but traditional methods are too coarse to resolve it. We develop a 10 m resolution air quality model and apply it for traffic-related carbon monoxide air quality in Nanjing megacity. The model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network and agrees well with a validation dataset. The model can be a vigorous part of the smart city system and inform urban planning and air quality management.
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.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, https://doi.org/10.5194/gmd-13-4891-2020, 2020
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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.
Cited articles
Alexander, P., Bechtel, B., Chow, W., Fealy, R., and Mills, G.: Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation, Urban Clim., 17, 196–215, https://doi.org/10.1016/j.uclim.2016.08.003, 2016. a
Alexander, P. J., Mills, G., and Fealy, R.: Using LCZ data to run an urban energy balance model, Urban Clim., 13, 14–37, https://doi.org/10.1016/j.uclim.2015.05.001, 2015. a
Allen, L., Lindberg, F., and Grimmond, C. S. B.: Global to city scale urban anthropogenic heat flux: Model and variability, Int. J. Climatol., 31, 1990–2005, https://doi.org/10.1002/joc.2210, 2010. a
Ao, X., Grimmond, C. S. B., Liu, D., Han, Z., Hu, P., Wang, Y., Zhen, X., and Tan, J.: Radiation Fluxes in a Business District of Shanghai, China, J. Appl. Meteorol. Clim., 55, 2451–2468, https://doi.org/10.1175/jamc-d-16-0082.1, 2016. a
Ao, X., Grimmond, C. S. B., Ward, H. C., Gabey, A. M., Tan, J., Yang, X.-Q., Liu, D., Zhi, X., Liu, H., and Zhang, N.: Evaluation of the Surface Urban Energy and Water Balance Scheme (SUEWS) at a Dense Urban Site in Shanghai: Sensitivity to Anthropogenic Heat and Irrigation, J. Hydrometeorol., 19, 1983–2005, https://doi.org/10.1175/jhm-d-18-0057.1, 2018. a, b, c
Baklanov, A., Grimmond, C., Carlson, D., Terblanche, D., Tang, X., Bouchet, V., Lee, B., Langendijk, G., Kolli, R., and Hovsepyan, A.: From urban meteorology, climate and environment research to integrated city services, Urban Clim., 23, 330–341, https://doi.org/10.1016/j.uclim.2017.05.004, 2018. a
Banks, R. F., Tiana-Alsina, J., Rocadenbosch, F., and Baldasano, J. M.: Performance Evaluation of the Boundary-Layer Height from Lidar and the Weather Research and Forecasting Model at an Urban Coastal Site in the North-East Iberian Peninsula, Bound.-Lay. Meteorol., 157, 265–292, https://doi.org/10.1007/s10546-015-0056-2, 2015. a, b
Banks, R. F., Tiana-Alsina, J., Baldasano, J. M., Rocadenbosch, F., Papayannis, A., Solomos, S., and Tzanis, C. G.: Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign, Atmos. Res., 176-177, 185–201, https://doi.org/10.1016/j.atmosres.2016.02.024, 2016. a
Barlow, J. F., Dunbar, T. M., Nemitz, E. G., Wood, C. R., Gallagher, M. W., Davies, F., O'Connor, E., and Harrison, R. M.: Boundary layer dynamics over London, UK, as observed using Doppler lidar during REPARTEE-II, Atmos. Chem. Phys., 11, 2111–2125, https://doi.org/10.5194/acp-11-2111-2011, 2011. a
Best, M. J. and Grimmond, C. S. B.: Key Conclusions of the First International Urban Land Surface Model Comparison Project, B. Am. Meteorol. Soc., 96, 805–819, https://doi.org/10.1175/bams-d-14-00122.1, 2015. a
Best, M. J., Grimmond, C. S. B., and Villani, M. G.: Evaluation of the Urban Tile in MOSES using Surface Energy Balance Observations, Bound.-Lay. Meteorol., 118, 503–525, https://doi.org/10.1007/s10546-005-9025-5, 2006. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bohnenstengel, S. I., Hamilton, I., Davies, M., and Belcher, S. E.: Impact of anthropogenic heat emissions on London's temperatures, Q. J. Roy. Meteor. Soc., 140, 687–698, https://doi.org/10.1002/qj.2144, 2013. a
Capel-Timms, I., Smith, S. T., Sun, T., and Grimmond, S.: Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation, Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, 2020. a, b
Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C. S. B., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., Miao, S., Sailor, D., Salamanca, F. P., Taha, H., Tewari, M., Wang, X., Wyszogrodzki, A. A., and Zhang, C.: The integrated WRF/urban modelling system: Development, evaluation, and applications to urban environmental problems, Int. J. Climatol., 31, 273–288, https://doi.org/10.1002/joc.2158, 2011. a
Chrysoulakis, N., Grimmond, S., Feigenwinter, C., Lindberg, F., Gastellu-Etchegorry, J.-P., Marconcini, M., Mitraka, Z., Stagakis, S., Crawford, B., Olofson, F., Landier, L., Morrison, W., and Parlow, E.: Urban energy exchanges monitoring from space, Sci. Rep., 8, 11498, https://doi.org/10.1038/s41598-018-29873-x, 2018. a
Demuzere, M., Harshan, S., Järvi, L., Roth, M., Grimmond, C. S. B., Masson, V., Oleson, K. W., Velasco, E., and Wouters, H.: Impact of urban canopy models and external parameters on the modelled urban energy balance in a tropical city, Q. J. Roy. Meteor. Soc., 143, 1581–1596, https://doi.org/10.1002/qj.3028, 2017. a
Dou, J., Grimmond, S., Cheng, Z., Miao, S., Feng, D., and Liao, M.: Summertime surface energy balance fluxes at two Beijing sites, Int. J. Climatol., 39, 2793–2810, https://doi.org/10.1002/joc.5989, 2019. a
Dyer, A. J.: A review of flux-profile relationships, Bound.-Lay. Meteorol., 7, 363–372, https://doi.org/10.1007/bf00240838, 1974. a
ECMWF: IFS Documentation CY47R3 – Part IV Physical processes, ECMWF, https://doi.org/10.21957/eyrpir4vj, 2021. a
Feng, J.-M., Wang, Y.-L., Ma, Z.-G., and Liu, Y.-H.: Simulating the Regional Impacts of Urbanization and Anthropogenic Heat Release on Climate across China, J. Climate, 25, 7187–7203, https://doi.org/10.1175/jcli-d-11-00333.1, 2012. a
Gabey, A. M., Grimmond, C. S. B., and Capel-Timms, I.: Anthropogenic heat flux: Advisable spatial resolutions when input data are scarce, Theor. Appl. Climatol., 135, 791–807, https://doi.org/10.1007/s00704-018-2367-y, 2018. a
Grimmond, C., Cleugh, H., and Oke, T.: An objective urban heat storage model and its comparison with other schemes, Atmos. Environ. B, 25, 311–326, https://doi.org/10.1016/0957-1272(91)90003-w, 1991. a, b, c
Grimmond, C. S. B. and Oke, T. R.: Aerodynamic Properties of Urban Areas Derived from Analysis of Surface Form, J. Appl. Meteorol., 38, 1262–1292, https://doi.org/10.1175/1520-0450(1999)038<1262:apouad>2.0.co;2, 1999. a, b, c
Grimmond, C. S. B. and Oke, T. R.: Turbulent Heat Fluxes in Urban Areas: Observations and a Local-Scale Urban Meteorological Parameterization Scheme (LUMPS), J. Appl. Meteorol., 41, 792–810, https://doi.org/10.1175/1520-0450(2002)041<0792:thfiua>2.0.co;2, 2002. a
Grimmond, C. S. B., Oke, T. R., and Steyn, D. G.: Urban Water Balance: 1. A Model for Daily Totals, Water Resour. Res., 22, 1397–1403, https://doi.org/10.1029/wr022i010p01397, 1986. a, b, c, d
Grimmond, C. S. B., Blackett, M., Best, M. J., Baik, J.-J., Belcher, S. E., Beringer, J., Bohnenstengel, S. I., Calmet, I., Chen, F., Coutts, A., Dandou, A., Fortuniak, K., Gouvea, M. L., Hamdi, R., Hendry, M., Kanda, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E. S., Lee, S.-H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Ooka, R., Pigeon, G., Porson, A., Ryu, Y.-H., Salamanca, F., Steeneveld, G., Tombrou, M., Voogt, J. A., Young, D. T., and Zhang, N.: Initial results from Phase 2 of the international urban energy balance model comparison, Int. J. Climatol., 31, 244–272, https://doi.org/10.1002/joc.2227, 2010a. a, b
Grimmond, C. S. B., Blackett, M., Best, M. J., Barlow, J., Baik, J.-J., Belcher, S. E., Bohnenstengel, S. I., Calmet, I., Chen, F., Dandou, A., Fortuniak, K., Gouvea, M. L., Hamdi, R., Hendry, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E. S., Lee, S.-H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Pigeon, G., Porson, A., Ryu, Y.-H., Salamanca, F., Shashua-Bar, L., Steeneveld, G.-J., Tombrou, M., Voogt, J., Young, D., and Zhang, N.: The International Urban Energy Balance Models Comparison Project: First Results from Phase 1, J. Appl. Meteorol. Clim., 49, 1268–1292, https://doi.org/10.1175/2010jamc2354.1, 2010b. a, b
Grimmond, S., Bouchet, V., Molina, L. T., Baklanov, A., Tan, J., Schlünzen, K. H., Mills, G., Golding, B., Masson, V., Ren, C., Voogt, J., Miao, S., Lean, H., Heusinkveld, B., Hovespyan, A., Teruggi, G., Parrish, P., and Joe, P.: Integrated urban hydrometeorological, climate and environmental services: Concept, methodology and key messages, Urban Clim., 33, 100623, https://doi.org/10.1016/j.uclim.2020.100623, 2020. a
Halios, C. H. and Barlow, J. F.: Observations of the Morning Development of the Urban Boundary Layer Over London, UK, Taken During the ACTUAL Project, Bound.-Lay. Meteorol., 166, 395–422, https://doi.org/10.1007/s10546-017-0300-z, 2017. a
Hamilton, I. G., Davies, M., Steadman, P., Stone, A., Ridley, I., and Evans, S.: The significance of the anthropogenic heat emissions of London's buildings: A comparison against captured shortwave solar radiation, Build. Environ., 44, 807–817, https://doi.org/10.1016/j.buildenv.2008.05.024, 2009. a
Hertwig, D., Grimmond, S., Hendry, M. A., Saunders, B., Wang, Z., Jeoffrion, M., Vidale, P. L., McGuire, P. C., Bohnenstengel, S. I., Ward, H. C., and Kotthaus, S.: Urban signals in high-resolution weather and climate simulations: Role of urban land-surface characterisation, Theor. Appl. Climatol., 142, 701–728, https://doi.org/10.1007/s00704-020-03294-1, 2020. a, b
Hogan, R. J.: Flexible Treatment of Radiative Transfer in Complex Urban Canopies for Use in Weather and Climate Models, Bound.-Lay. Meteorol., 173, 53–78, https://doi.org/10.1007/s10546-019-00457-0, 2019. a
Högström, U.: Non-dimensional wind and temperature profiles in the atmospheric surface layer: A re-evaluation, Bound.-Lay. Meteorol., 42, 55–78, https://doi.org/10.1007/bf00119875, 1988. a
Hollinger, D. Y. and Richardson, A. D.: Uncertainty in eddy covariance measurements and its application to physiological models, Tree Physiol., 25, 873–885, https://doi.org/10.1093/treephys/25.7.873, 2005. a
Hutchinson, T. A.: An adaptive time-step for increased model efficiency, in: Extended Abstracts, Eighth WRF Users' Workshop, 5 June 2009, Omaha, Nebraska, 4, 2007. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, 13103,, https://doi.org/10.1029/2008jd009944, 2008. a, b, c
Iamarino, M., Beevers, S., and Grimmond, C. S. B.: High-resolution (space, time) anthropogenic heat emissions: London 1970–2025, Int. J. Climatol., 32, 1754–1767, https://doi.org/10.1002/joc.2390, 2011. a, b
Janjić, Z. I.: The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes, Mon. Weather Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:tsmecm>2.0.co;2, 1994. a, b, c
Järvi, L., Grimmond, C. S. B., Taka, M., Nordbo, A., Setälä, H., and Strachan, I. B.: Development of the Surface Urban Energy and Water Balance Scheme (SUEWS) for cold climate cities, Geosci. Model Dev., 7, 1691–1711, https://doi.org/10.5194/gmd-7-1691-2014, 2014. a, b, c, d
Järvi, L., Grimmond, C. S. B., McFadden, J. P., Christen, A., Strachan, I. B., Taka, M., Warsta, L., and Heimann, M.: Warming effects on the urban hydrology in cold climate regions, Sci. Rep., 7, 1–8,, https://doi.org/10.1038/s41598-017-05733-y, 2017. a, b
Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmos. Meas. Tech., 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018. a
Järvi, L., Havu, M., Ward, H. C., Bellucco, V., McFadden, J. P., Toivonen, T., Heikinheimo, V., Kolari, P., Riikonen, A., and Grimmond, C. S. B.: Spatial Modeling of Local-Scale Biogenic and Anthropogenic Carbon Dioxide Emissions in Helsinki, J. Geophys. Res.-Atmos., 124, 8363–8384, https://doi.org/10.1029/2018jd029576, 2019. a, b, c
Jimenez, P. A., Hacker, J. P., Dudhia, J., Haupt, S. E., Ruiz-Arias, J. A., Gueymard, C. A., Thompson, G., Eidhammer, T., and Deng, A.: WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction, B. Am. Meteorol. Soc., 97, 1249–1264, https://doi.org/10.1175/bams-d-14-00279.1, 2016. a
Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., Oleson, K. W., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki, Q. J. Roy. Meteor. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2015. a
Kawai, T., Ridwan, M. K., and Kanda, M.: Evaluation of the Simple Urban Energy Balance Model Using Selected Data from 1-yr Flux Observations at Two Cities, J. Appl. Meteorol. Clim., 48, 693–715, https://doi.org/10.1175/2008jamc1891.1, 2009. a
Kim, Y., Sartelet, K., Raut, J.-C., and Chazette, P.: Evaluation of the Weather Research and Forecast/Urban Model Over Greater Paris, Bound.-Lay. Meteorol., 149, 105–132, https://doi.org/10.1007/s10546-013-9838-6, 2013. a, b
Kokkonen, T., Grimmond, C., Räty, O., Ward, H., Christen, A., Oke, T., Kotthaus, S., and Järvi, L.: Sensitivity of Surface Urban Energy and Water Balance Scheme (SUEWS) to downscaling of reanalysis forcing data, Urban Clim., 23, 36–52, https://doi.org/10.1016/j.uclim.2017.05.001, 2018a. a
Kokkonen, T. V., Grimmond, C. S. B., Christen, A., Oke, T. R., and Järvi, L.: Changes to the Water Balance Over a Century of Urban Development in Two Neighborhoods: Vancouver, Canada, Water Resour. Res., 54, 6625–6642, https://doi.org/10.1029/2017wr022445, 2018b. a
Kokkonen, T. V., Grimmond, S., Murto, S., Liu, H., Sundström, A.-M., and Järvi, L.: Simulation of the radiative effect of haze on the urban hydrological cycle using reanalysis data in Beijing, Atmos. Chem. Phys., 19, 7001–7017, https://doi.org/10.5194/acp-19-7001-2019, 2019. a, b
Kotthaus, S. and Grimmond, C.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Clim., 10, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a. a, b, c
Kotthaus, S. and Grimmond, C.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Clim., 10, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b. a, b, c, d
Kotthaus, S. and Grimmond, C. S. B.: Atmospheric boundary-layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds, Q. J. Roy. Meteor. Soc., 144, 1525–1538, https://doi.org/10.1002/qj.3299, 2018a. a, b, c, d
Kotthaus, S. and Grimmond, C. S. B.: Atmospheric boundary-layer characteristics from ceilometer measurements. Part 2: Application to London's urban boundary layer, Q. J. Roy. Meteor. Soc., 144, 1511–1524, https://doi.org/10.1002/qj.3298, 2018b. a
Kotthaus, S., O'Connor, E., Münkel, C., Charlton-Perez, C., Haeffelin, M., Gabey, A. M., and Grimmond, C. S. B.: Recommendations for processing atmospheric attenuated backscatter profiles from Vaisala CL31 ceilometers, Atmos. Meas. Tech., 9, 3769–3791, https://doi.org/10.5194/amt-9-3769-2016, 2016. a
Kotthaus, S., Halios, C. H., Barlow, J. F., and Grimmond, C.: Volume for pollution dispersion: London's atmospheric boundary layer during ClearfLo observed with two ground-based lidar types, Atmos. Environ., 190, 401–414, https://doi.org/10.1016/j.atmosenv.2018.06.042, 2018. a
Kotthaus, S., Bravo-Aranda, J. A., Collaud Coen, M., Guerrero-Rascado, J. L., Costa, M. J., Cimini, D., O'Connor, E. J., Hervo, M., Alados-Arboledas, L., Jiménez-Portaz, M., Mona, L., Ruffieux, D., Illingworth, A., and Haeffelin, M.: Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations, Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, 2023. a, b
Kusaka, H., Kondo, H., Kikegawa, Y., and Kimura, F.: A Simple Single-Layer Urban Canopy Model For Atmospheric Models: Comparison With Multi-Layer And Slab Models, Bound.-Lay. Meteorol., 101, 329–358, https://doi.org/10.1023/a:1019207923078, 2001. a, b
Lapo, K. E., Hinkelman, L. M., Sumargo, E., Hughes, M., and Lundquist, J. D.: A critical evaluation of modeled solar irradiance over California for hydrologic and land surface modeling, J. Geophys. Res.-Atmos., 122, 299–317, https://doi.org/10.1002/2016jd025527, 2017. a
Lindberg, F., Grimmond, C., Yogeswaran, N., Kotthaus, S., and Allen, L.: Impact of city changes and weather on anthropogenic heat flux in Europe 1995–2015, Urban Clim., 4, 1–15, https://doi.org/10.1016/j.uclim.2013.03.002, 2013. a, b
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Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban...
