Urban meteorological forcing data for building energy simulations

Tang, Yihao; Sun, Ting; Luo, Zhiwen; Omidvar, Hamidreza; Theeuwes, Natalie; Xie, Xiaoxiong; Xiong, Jie; Yao, Runming; Grimmond, Sue

doi:10.1016/j.buildenv.2021.108088

Cited by 27 publications

(23 citation statements)

References 106 publications

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“…Also by using CFD, the local wind can be modified by the neighbourhood, which will further influence the surface temperatures and building energy consumption. While in our study the influence of neighbourhood on wind is not considered in this study, but is included in our new work (Tang et al, 2021).…”

Section: 4impact Of Longwave Radiation Methods On Building Energy Dem...mentioning

confidence: 99%

“…In these simulation, although other variables (e.g. air temperature, wind) that are also impacted by the surroundings (Tang et al, 2021), they do not vary from their original TMY values at each time step. If Ta is assigned to adj buildings (boiadj←a), the default EnergyPlus view factor calculation method is used.…”

Section: 2inter-building Longwave Radiation Exchangementioning

confidence: 99%

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Impact of inter-building longwave radiative exchanges on building energy performance and indoor overheating

Xie

Luo

Grimmond

et al. 2022

Building and Environment

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Section: 4impact Of Longwave Radiation Methods On Building Energy Dem...mentioning

confidence: 99%

Section: 2inter-building Longwave Radiation Exchangementioning

confidence: 99%

Impact of inter-building longwave radiative exchanges on building energy performance and indoor overheating

Xie

Luo

Grimmond

et al. 2022

Building and Environment

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“…These biases can be partly explained by the ERA5 framework not including an urban surface model. For example, the well-documented warmer air temperature in cities (urban heat island) are not modelled in ERA5 because natural land surfaces are assumed in simulations (Table 4), although ERA5 can include an urban signal if the data assimilated are from within urban areas (Tang et al, 2021). Qair shows a general positive bias as evapotranspiration will be overestimated in ERA5 without an urban land surface representation.…”

Section: Lnmentioning

confidence: 99%

Harmonized gap-filled datasets from 20 urban flux tower sites

Lipson

Grimmond

Best

et al. 2022

Earth Syst. Sci. Data

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Abstract. A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022).

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“…5.1 SUEWS configuration and evaluation SUEWS v2020a (Tang et al, 2021) is evaluated using its Python wrapper SuPy v2021.3.18 (Sun and Grimmond, 2019) with parameters (Table 1) and gap-filled 30 or 60 min meteorological forcing data (Table 2) based on FLUXNET2015 dataset. Simulations are conducted, with forcing data interpolated to a 5 min time step (Ward et al, Table 7.…”

Section: Suews Performance In Vegetated Areasmentioning

confidence: 99%

“…This requires both the "rural" context -usually characterised by pervious land cover -and the urban area of focus to be simulated appropriately ideally using the same modelling framework. As SUEWS v2020a (Tang et al, 2021) can diagnose nearsurface meteorology in the roughness sub-layer and canopy layer (e.g. air temperature and humidity at 2 m a.g.l.…”

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confidence: 99%

Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset

et al. 2022

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Abstract. To compare the impact of surface–atmosphere exchanges from rural and urban areas, fully vegetated areas (e.g. deciduous trees, evergreen trees and grass) commonly found adjacent to cities need to be modelled. Here we provide a general workflow to derive parameters for SUEWS (Surface Urban Energy and Water Balance Scheme), including those associated with vegetation phenology (via leaf area index, LAI), heat storage and surface conductance. As expected, attribution analysis of bias in SUEWS-modelled QE finds that surface conductance (gs) plays the dominant role; hence there is a need for more estimates of surface conductance parameters. The workflow is applied at 38 FLUXNET sites. The derived parameters vary between sites with the same plant functional type (PFT), demonstrating the challenge of using a single set of parameters for a PFT. SUEWS skill at simulating monthly and hourly latent heat flux (QE) is examined using the site-specific derived parameters, with the default NOAH surface conductance parameters (Chen et al., 1996). Overall evaluation for 2 years has similar metrics for both configurations: median hit rate between 0.6 and 0.7, median mean absolute error less than 25 W m−2, and median mean bias error ∼ 5 W m−2. Performance differences are more evident at monthly and hourly scales, with larger mean bias error (monthly: ∼ 40 W m−2; hourly ∼ 30 W m−2) results using the NOAH-surface conductance parameters, suggesting that they should be used with caution. Assessment of sites with contrasting QE performance demonstrates how critical capturing the LAI dynamics is to the SUEWS prediction skills of gs and QE. Generally gs is poorest in cooler periods (more pronounced at night, when underestimated by ∼ 3 mm s−1). Given the global LAI data availability and the workflow provided in this study, any site to be simulated should benefit.

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Urban meteorological forcing data for building energy simulations

Cited by 27 publications

References 106 publications

Impact of inter-building longwave radiative exchanges on building energy performance and indoor overheating

Impact of inter-building longwave radiative exchanges on building energy performance and indoor overheating

Harmonized gap-filled datasets from 20 urban flux tower sites

Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset

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