Urban Surface Energy Balance Models: Model Characteristics and Methodology for a Comparison Study

Grimmond, Sue; Best, Martin; Barlow, Janet F.; Arnfield, A. John; Baik, Jong‐Jin; Baklanov, Alexander; Belcher, Stephen E.; Bruse, Michael; Calmet, I.; Chen, Fei; Clark, Peter A.; Dandou, A.; Erell, Evyatar; Fortuniak, Krzysztof; Hamdi, Rafiq; Kanda, Manabu; Kawai, Tōru; Kumakura, Hiroaki; Krayenhoff, Scott; Lee, S. H.; Limor, S.-B.; Martilli, Alberto; Masson, Valéry; Miao, Shiguang; Mills, Gerald; Moriwaki, Ryo; Oleson, Keith W.; Porson, Aurore; Sievers, Uwe; Tombrou, M.; Voogt, James A.; Williamson, Terence

doi:10.1007/978-3-642-00298-4_11

Cited by 32 publications

(35 citation statements)

References 75 publications

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“…Urban energy balance models can be classified in a number of ways (see also Grimmond et al 2009a), for example, they vary in terms of the fluxes they calculate ('F' in Table 2). While all the models examined here calculate K↑, L↑, Q* and Q H , some do not model either Q E or the Q F , and some model neither.…”

Section: The Characteristics Of Urban Energy Balance Modelsmentioning

confidence: 99%

The International Urban Energy Balance Models Comparison Project: First Results from Phase 1

Grimmond

Blackett

Best

et al. 2010

Journal of Applied Meteorology and Climatology

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333

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Section: The Characteristics Of Urban Energy Balance Modelsmentioning

confidence: 99%

The International Urban Energy Balance Models Comparison Project: First Results from Phase 1

Grimmond

Blackett

Best

et al. 2010

Journal of Applied Meteorology and Climatology

Self Cite

455

333

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“…It is well known that the urban surface material and building morphology affect meteorology in various ways including the increase in temperature, leading to the urban heat island effect (Bornstein, 1968;Oke, 1973;Landsberg, 1981;Arnfield, 2003;Kalnay and Cai, 2003;Kim and Baik, 2005;Grimmond, 2006); decrease or increase in the temporal variation of absolute humidity due to impervious surfaces and anthropogenic water use (Unger, 1999;Kuttler et al, 2007); increase in haze, cloud, and precipitation (Bornstein and Lin, 2000;Dixon and Mote, 2003;Shepherd, 2005;Carrio et al, 2010); decrease in visibility due to anthropogenic aerosols (Cheng and Tsai, 2000;Singh et al, 2008;Nichol et al, 2010); increase in the turbulent intensity and change of wind speed due to high-rise buildings (Roth, 2000;Arnfield, 2003;Grimmond et al, 2004;Barlow et al, 2011;Song et al, 2013); decrease in solar radiation due to manmade air pollutants (Peterson et al, 1978;Robaa, 2009); increase in the sensible heat flux and heat storage due to anthropogenic heat release from the urban surface; and decrease in the latent heat flux (Nunez and Oke, 1977;Christen and Vogt, 2004;Harman and Belcher, 2006;Grimmond et al, 2009;Nordbo et al, 2012;Park et al, 2014a). When the synoptic wind becomes strong, the area receiving most of the precipitation with strong upward motion moves more downwind (Bornstein and Lin, 2000;Lin et al, 2011;Han et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Physics schemes, including microphysics (interactions among water vapor, cloud water, cloud ice, rain drop, snow, and graupel), cumulus (updraft, downdraft, entrainment, and detrainment in clouds), radiation (absorption, emission, scattering, reflection, and transpiration in the atmosphere for radiative energy), surface (surface EB and energy/moisture transfer between the surface and ground), and atmospheric boundary-layer schemes (energy and moisture transfer between the surface and atmospheric boundary layer), interact with each other (Dudhia, 1989). Irregular surface morphologies and materials in urban areas affect the surface optical, physical, and thermal properties such as thermal conductivity, heat capacity, roughness, displacement length, albedo, and emissivity (Masson, 2006;Lee and Park, 2008;Grimmond et al, 2009). These properties change the energy partition dramatically over urban surfaces compared with rural surfaces.…”

Section: Introductionmentioning

confidence: 99%

High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea

Park

Chae

et al. 2017

Atmos. Meas. Tech.

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Abstract. To improve our knowledge of urban meteorology, including those processes applicable to high-resolution meteorological models in the Seoul Metropolitan Area (SMA), the Weather Information Service Engine (WISE) Urban Meteorological Observation System (UMS-Seoul) has been designed and installed. The UMS-Seoul incorporates 14 surface energy balance (EB) systems, 7 surface-based threedimensional (3-D) meteorological observation systems and applied meteorological (AP) observation systems, and the existing surface-based meteorological observation network. The EB system consists of a radiation balance system, sonic anemometers, infrared CO 2 /H 2 O gas analyzers, and many sensors measuring the wind speed and direction, temperature and humidity, precipitation, and air pressure. The EBproduced radiation, meteorological, and turbulence data will be used to quantify the surface EB according to land use and to improve the boundary-layer and surface processes in meteorological models. The 3-D system, composed of a wind lidar, microwave radiometer, aerosol lidar, or ceilometer, produces the cloud height, vertical profiles of backscatter by aerosols, wind speed and direction, temperature, humidity, and liquid water content. It will be used for highresolution reanalysis data based on observations and for the improvement of the boundary-layer, radiation, and microphysics processes in meteorological models. The AP system includes road weather information, mosquito activity, water quality, and agrometeorological observation instruments. The standardized metadata for networks and stations are documented and renewed periodically to provide a detailed observation environment. The UMS-Seoul data are designed to support real-time acquisition and display and automatically quality check within 10 min from observation. After the quality check, data can be distributed to relevant potential users such as researchers and policy makers. Finally, two case studies demonstrate that the observed data have a great potential to help to understand the boundary-layer structures more deeply, improve the performance of high-resolution meteorological models, and provide useful information customized based on the user demands in the SMA.

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“…Knowledge based modeling consists in splitting the entire city into several volumes and to apply on each of them an energy balance (Grimmond et al, 2009). One of the major shortcomings of these models is the complexity of both the method (numerical simulation) and the physical concept (fluid dynamics, radiation, convection and conduction principles) used.…”

Section: Introductionmentioning

confidence: 99%

Urban heat island temporal and spatial variations: Empirical modeling from geographical and meteorological data

Bernard

Musy

Calmet

et al. 2017

Building and Environment

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Urban air temperature varies along time and space. This contribution proposes a methodology to model these variations from empirical models. Four air temperature networks are implemented in three west France cities. The period [1.4 -1.8] of a normalized night is investigated. Three empirical models are established, in order to predict temporal and spatial air temperature variation. They are gathered under the name of model group. Urban heat island (UHI) intensity is explained according to one temporal model using meteorological variables. Temperature spatial variations are explained from two models using geographical informations averaged in a 500 m radius buffer circle. The first one represents the mean UHI value for a given location, the second the variability of the UHI around its mean value. 32 model groups are calibrated from data sampled by one network. The accuracy of their estimations is tested comparing estimated to observed values from the three other networks. The most accurate model is identified and its performances are analyzed. Night-time UHI intensity variations are explained all along the year by wind speed and nebulosity values calculated after sunset. During spring and summer season UHI variations are mainly driven by Normalized Difference Vegetation Index (NDVI) value whereas building density or surface density are predominant explicative variables during colder seasons. The model is finally applied to the city of Nantes during a summer night to illustrate the interest of this work to urban planning applications. Several warm areas located outside the city center are identified according to this method. Their high UHI value is mainly caused by their low NDVI value.

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Urban Surface Energy Balance Models: Model Characteristics and Methodology for a Comparison Study

Cited by 32 publications

References 75 publications

The International Urban Energy Balance Models Comparison Project: First Results from Phase 1

The International Urban Energy Balance Models Comparison Project: First Results from Phase 1

High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea

Urban heat island temporal and spatial variations: Empirical modeling from geographical and meteorological data

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