On the Assessment of a Cooling Tower Scheme for High-Resolution Numerical Weather Modeling for Urban Areas

Yu, Miao; González, Jorge E.; Miao, Shiguang; Ramamurthy, Prathap

doi:10.1175/jamc-d-18-0126.1

Cited by 11 publications

(2 citation statements)

References 51 publications

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“…・ Consideration of the cooling tower: In BEP+BEM, all Q FB is emitted as sensible heat, irrespective of building use. However, cooling towers exist in offices, and some Q FB is discharged as latent heat during the cooling season, as demonstrated by the detailed cooling tower model in BEP+BEM (e.g., Yu et al 2019) and in our separately developed CM-BEM. Therefore, in SLUCM+BEM, simplicity is emphasised, and fractions are introduced in Eqs.…”

Section: Discussionmentioning

confidence: 95%

SLUCM+BEM: A simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban

Takane,

Kikegawa,

Nakajima

et al. 2024

Preprint

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We propose a simple dynamic anthropogenic heat (QF) parameterisation for the Weather Research and Forecasting (WRF)-single-layer urban canopy model (SLUCM). The SLUCM is a remarkable physically based urban canopy model that is widely used worldwide. However, a limitation of SLUCM is that it considers a statistically based diurnal pattern of QF. Consequently, QF is not affected by outdoor temperature changes and the diurnal pattern of QF is constant throughout the simulation period. To address these limitations, based on the concept of a building energy model (BEM), which has been officially introduced in WRF, we propose a parameterisation to dynamically and simply simulate QF from buildings (QFB) through physically based calculation of the indoor heat load and input parameters for BEM and SLUCM. This method allows model users to simulate dynamic QF and electricity consumption (EC) according to factors such as outdoor temperature changes, building insulation, and heating and air conditioning (HAC) performance simply by setting the AHOPTION option in URBPRAM.TBL to 2. SLUCM+BEM was shown to simulate temporal variations of QFB and EC for HAC (ECHAC) and broadly reproduce the ECHAC estimates of more sophisticated BEM and ECHAC observations in the world’s largest metropolis, Tokyo. Our results demonstrate that SLUCM-BEM can be applied to urban climates worldwide.

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Section: Discussionmentioning

confidence: 95%

SLUCM+BEM: A simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban

Takane,

Kikegawa,

Nakajima

et al. 2024

Preprint

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“…The PLUTO data have been aggregated from its tax lot-based resolution to 1-km aggregates for the fine resolution domain. Accounting for the mechanical and thermal effects of buildings has also resulted in more accurate estimates of urban temperatures and winds [56,57].…”

Section: Weather Research and Forecasting Model (Wrf)mentioning

confidence: 99%

Spatiotemporal Variability of Heat Storage in Major U.S. Cities—A Satellite-Based Analysis

et al. 2020

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Heat storage, ΔQs, is quantified for 10 major U.S. cities using a method called the thermal variability scheme (TVS), which incorporates urban thermal mass parameters and the variability of land surface temperatures. The remotely sensed land surface temperature (LST) is retrieved from the GOES-16 satellite and is used in conjunction with high spatial resolution land cover and imperviousness classes. New York City is first used as a testing ground to compare the satellite-derived heat storage model to two other methods: a surface energy balance (SEB) residual derived from numerical weather model fluxes, and a residual calculated from ground-based eddy covariance flux tower measurements. The satellite determination of ΔQs was found to fall between the residual method predicted by both the numerical weather model and the surface flux stations. The GOES-16 LST was then downscaled to 1-km using the WRF surface temperature output, which resulted in a higher spatial representation of storage heat in cities. The subsequent model was used to predict the total heat stored across 10 major urban areas across the contiguous United States for August 2019. The analysis presents a positive correlation between population density and heat storage, where higher density cities such as New York and Chicago have a higher capacity to store heat when compared to lower density cities such as Houston or Dallas. Application of the TVS ultimately has the potential to improve closure of the urban surface energy balance.

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Atmospheric-Boundary-Layer-Height Variation over Mountainous and Urban Sites in Beijing as Derived from Radar Wind-Profiler Measurements

Solanki

Guo

et al. 2021

Boundary-Layer Meteorol

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On the Assessment of a Cooling Tower Scheme for High-Resolution Numerical Weather Modeling for Urban Areas

Cited by 11 publications

References 51 publications

SLUCM+BEM: A simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban

SLUCM+BEM: A simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban

Spatiotemporal Variability of Heat Storage in Major U.S. Cities—A Satellite-Based Analysis

Atmospheric-Boundary-Layer-Height Variation over Mountainous and Urban Sites in Beijing as Derived from Radar Wind-Profiler Measurements

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