On‐ and off‐line evaluation of the single‐layer urban canopy model in London summertime conditions

Tsiringakis, Aristofanis; Steeneveld, G.J.; Holtslag, A.A.M.; Kotthaus, Simone; Grimmond, Sue

doi:10.1002/qj.3505

Cited by 11 publications

(24 citation statements)

References 52 publications

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“…Boundary conditions for the 1-D WRF model are applied in the form of subsidence, geostrophic wind, and advection tendency terms for potential temperature, moisture, and u and v wind components. Geostrophic wind is derived from 6-hourly ECMWF operational reanalysis data (ECMWF, 2012) in combination with a 3-D WRF simulations (hourly data) (Steeneveld et al, 2017;Tsiringakis et al, 2019). Geostrophic wind values are given as 6-hourly means, with a tendency term applied to the u and v geostrophic wind components at each time step (of 1-D WRF) to ensure a smooth change in geostrophic wind through the 6-hourly blocks and avoid oscillations from imbalance between actual and geostrophic wind speeds.…”

Section: Case Study Descriptionmentioning

confidence: 99%

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Surface and Atmospheric Driven Variability of the Single‐Layer Urban Canopy Model Under Clear‐Sky Conditions Over London

et al. 2020

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Urban canopy models (UCMs) are parametrization schemes that are used to improve weather forecasts in urban areas. The performance of UCMs depends on understanding potential uncertainty sources that can generally originate from the (a) urban surface parameters, (b) atmospheric forcing, and (c) physical description. Here, we investigate the relative importance of surface and atmospheric driven model sensitivities of the single-layer urban canopy model when fully interactive with a 1-D configuration of the Weather Research and Forecasting model (WRF). The impact of different physical descriptions in UCMs and other key parameterization schemes of WRF is considered. As a case study, we use a 54-hr period with clear-sky conditions over London. Our analysis is focused on the surface radiation and energy flux partitioning and the intensity of turbulent mixing. The impact of changes in atmospheric forcing and surface parameter values on model performance appears to be comparable in magnitude. The advection of potential temperature, aerosol optical depth, exchange coefficient and roughness length for heat, surface albedo, and the anthropogenic heat flux are the most influential. Some atmospheric forcing variations have similar impact on the key physical processes as changes in surface parameters. Hence, error compensation may occur if one optimizes model performance using a single variable or combinations that have potential for carryover effects (e.g., temperature). Process diagrams help differences to be understood in the physical description of different UCMs, boundary layer, and radiation schemes and between the model and the observations.

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Section: Case Study Descriptionmentioning

confidence: 99%

“…Here we briefly evaluate the 50 m air temperature and net all-wave radiation (Q * ). For a more extensive evaluation see Tsiringakis et al (2019).…”

Section: Model Evaluation For the Reference Model Setupmentioning

confidence: 99%

Surface and Atmospheric Driven Variability of the Single‐Layer Urban Canopy Model Under Clear‐Sky Conditions Over London

et al. 2020

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“…At present, research on urban heat environment risk focuses on mapping heat risk and vulnerability in various environments [5][6][7], especially in the context of global climate change and heat waves [8][9][10]. Researchers have endeavored to employ theoretical and technical methods, such as statistical [11][12][13][14][15][16][17][18][19], energy-balance [3,4,20], numerical [21][22][23], analytical [24], and physical models [25]. For this process, researchers usually grade and evaluate air temperature data from meteorological stations and the LST data observed via remote sensing from the perspective of climate vulnerability or human exposure [26], and developed some vulnerability and risk indexes, such as manual indicator removal [27], as well as more complicated techniques, such as Monte Carlo simulation and variance-based global sensitivity analysis [28].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

De-chao

Sun

et al. 2019

Sustainability

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For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

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“…The scheme translates modelled values of heat, wind etc. down from the first model level into an urban canyon value when coupled to the actual atmospheric model (Tsiringakis et al, 2019). Since the UCM is only one layer, it circumvents the erratic vertical behaviour of turbulent quantities in the urban canyon.…”

Section: The Urban Problemmentioning

confidence: 99%

“…Most urban model studies are specifically interested in the Urban Canopy Layer and its vertical interactions with the air aloft and the surface (e.g. Tsiringakis et al, 2019), rather than the UBL as a whole. The bulk model is however very suitable to research how the different urban surface influences the entire boundary layer on a larger scale: the effect of the entire city, as it were.…”

Section: Conceptual Modellingmentioning

confidence: 99%

Understanding the urban atmosphere through conceptual modelling and opportunistic sensing

Droste

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Een promotie in tijden van corona. Het voelt raar, dit stuk te typen vandaag. Het is 13 Mei: de oorspronkelijke datum van mijn promotie. Maar ik sta niet stijf van de spanning in de Aula, gekleed in mij oneigen nette kledij. In plaats daarvan zit ik met sloffen aan, in pyjama achter m'n bureautje deze eerste paar zinnen te typen; mijn promotie uitgesteld tot een onbepaalde tijd. Waarom? Had ik wat harder door moeten werken? Nee, dit boek is al lang en breed goedgekeurd, ik heb mezelf het zweet in gewerkt om alles op tijd af te krijgen. De wereld is in de greep van COVID-19, het coronavirus, dat ons allemaal binnenhoudt en de wereld gegijzeld heeft. Maar ondertussen gaat de wereld door: de universiteit is open, al het onderwijs massaal online gegooid. Samen met wat collega's assisteer ik om een veldwerk vak volledig online te geven: een enorme inspanning voor student en docent, maar het gebeurt wel. We leren onszelf in rap tempo nieuwe skills aan: het maken en bewerken van instructiefilmpjes, omgaan met virtuele leeromgevingen, de beste houding om in te loungen tijdens eindeloze videovergaderingen... Die blijken we de maanden erna ook nodig te hebben: het virus gaat nergens heen, en we proberen ons dagelijks leven weer op te pakken, terwijl we ook de veiligheid in acht (trachten te) nemen. Het academisch jaar is inmiddels weer van start gegaan, en we mogen mondjesmaat weer de campus op: de tijden van elke dag naar de WUR fietsen lijken eeuwen geleden. We worden het thuiswerken wel zat, de sleur van je eigen huis, ik begon zelfs de dubieuze koffie op kantoor te missen. Vreemd genoeg blijkt eindeloos thuiszitten best stressvol te zijn. Maar tegelijkertijd leren we er ook wel wat uit: we zijn enorm flexibel, maar ook ontzettend kwetsbaar als ons dagelijke ritme zo wild verstoord wordt. Mocht, over hoe lang dan ook, deze situatie weer voorbij zijn, is het zaak om terug te kijken naar hoe we als samenleving ons hier doorheen hebben geslagen, en niet deze periode maar gewoon te vergeten als een nare herinnering. U, lezer, heeft dit boekje nu in de hand. Mijn nieuwe promotiedatum is inmiddels al lang bekend en nadert rap. Hopelijk treffen we elkaar in de Aula, en anders via een scherm. Hopelijk heeft u plezier aan het doornemen van dit werk, gemaakt in een andere wereld, toen we nog bij elkaar op kantoor mochten zitten en elkaar liedelijk de hand konden schudden.

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On‐ and off‐line evaluation of the single‐layer urban canopy model in London summertime conditions

Cited by 11 publications

References 52 publications

Surface and Atmospheric Driven Variability of the Single‐Layer Urban Canopy Model Under Clear‐Sky Conditions Over London

Surface and Atmospheric Driven Variability of the Single‐Layer Urban Canopy Model Under Clear‐Sky Conditions Over London

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

Understanding the urban atmosphere through conceptual modelling and opportunistic sensing

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