Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services

Lindberg, Fredrik; Grimmond, Sue; Gabey, A. M.; Huang, Bei; Kent, Christoph W.; Sun, Ting; Theeuwes, Natalie; Jrvi, Leena; Ward, Helen C.; Capel-Timms, Isabella; Chang, Yuanyong; Jonsson, Per R.; Krave, Niklas; Li, Dongwei; Meyer, David E.; Olofson, K. Frans G.; Tan, Jianwei; Wstberg, Dag; Xue, Lingbo; Zhang, Zhe

doi:10.1016/j.envsoft.2017.09.020

Cited by 210 publications

(126 citation statements)

References 78 publications

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“…Where K ↓ and K ↑ are incoming and outgoing shortwave radiation; L ↓ and L ↑ are incoming and outgoing longwave radiation; Q H and Q E are turbulent sensible and latent heat fluxes; Q F is anthropogenic heat flux calculated by LQF version (Gabey et al, ; Lindberg et al, ) of the LUCY model (Allen et al, ; Lindberg et al, ); storage heat flux Q res = Q * + Q F − ( Q H + Q E ); albedo = K ↓ /K ↑ ; τ transmissivity = K ↓ /G 0. …”

Section: Resultsmentioning

confidence: 99%

Summertime surface energy balance fluxes at two Beijing sites

Dou

Grimmond

Zou

et al. 2019

Intl Journal of Climatology

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Summertime (June–August 2015) radiative and turbulent heat fluxes were measured concurrently at two sites (urban and suburban) in Beijing. The urban site has slightly lower incoming and outgoing shortwave radiation, lower atmospheric transmissivity and a lower surface albedo than the suburban site. Both sites receive similar incoming longwave radiation. Although the suburban site had larger daytime outgoing longwave radiation (L↑), differences in the daily mean L↑ values are small, as the urban site has higher nocturnal L↑. Overall, both the midday and daily mean net all‐wave radiation (Q*) for the two sites are nearly equal. However, there are significant differences between the sites in the surface energy partitioning. The urban site has smaller turbulent sensible heat (Q H) (21–25% of Q* [midday–daily]) and latent heat (Q E) fluxes (21–45% of Q*). Whereas, the suburban proportions of Q* are Q H 32–32% and Q E 39–66%. The daily (midday) mean Bowen ratio (Q H/Q E) was 0.56 and 0.49 (0.98 and 0.83) for the urban and suburban sites, respectively. These values are low compared with other urban and suburban areas with similar or larger fractions of vegetated cover. Likely, these are caused by the widespread external water use for road cleaning/wetting, greenbelts, and air conditioners. Our suburban site has quite different land cover to most previous suburban studies as crop irrigation supplements rainfall. These results are important in enhancing our understanding of surface–atmosphere energy exchanges in Chinese cities and can aid the development and evaluation of urban climate models and inform urban planning strategies in the context of rapid global urbanization and climate change.

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Section: Resultsmentioning

confidence: 99%

Summertime surface energy balance fluxes at two Beijing sites

Dou

Grimmond

Zou

et al. 2019

Intl Journal of Climatology

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“…Both, roughness length (for heat and momentum) and displacement height are needed in r a calculation. Input for the calculation of roughness parameters, i.e., the morphometry, is derived from a digital surface model, including the heights of buildings and trees, in high spatial resolution (between 1 and 5 m) using the open-source Geographic Information System software QGIS and the Urban Multi-scale Environmental Predictor (UMEP) [11]. UMEP output provides building heights (mean, standard deviation, maximum) and the morphological parameters plane area index and frontal area index aggregated to the chosen grid size.…”

Section: A Flux Calculationmentioning

confidence: 99%

Spatial Distribution of Sensible and Latent Heat Flux in the City of Basel (Switzerland)

Feigenwinter

Vogt

Parlow

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Urban surfaces are a complex mixture of different land covers and surface materials; the relative magnitudes of the surface energy balance components therefore vary widely across a city. Eddy covariance (EC) measurements provide the best estimates of turbulent heat fluxes but are restricted to the source area. Land surface modeling with earth observation (EO) data is beneficial for extrapolation of a larger area since citywide information is possible. Turbulent sensible and latent heat fluxes are calculated by a combination of micrometeorological approaches (the aerodynamic resistance method, ARM), EO data, and GIS techniques. Input data such as land cover fractions and surface temperatures are derived from Landsat 8 OLI and TIRS, urban morphology was calculated from high-resolution digital building models and GIS data layers, and meteorological data were provided by flux tower measurements. Twenty-two Landsat scenes covering all seasons and different meteorological conditions were analyzed. Sensible heat fluxes were highest for industrial areas, railway stations, and areas with high building density, mainly corresponding to the pixels with highest surface-to-air temperature differences. The spatial distribution of latent heat flux is strongly related to the saturation deficit of vapor and the (minimum) stomatal resistance of vegetation types. Seasonal variations are highly dependent on meteorological conditions, i.e., air temperature, water vapor saturation deficit, and wind speed. Comparison of measured fluxes with modeled fluxes in the weighted source area of the flux towers is moderately accurate due to known drawbacks in the modeling approach and uncertainties inherent to EC measurements, particularly in urban areas.

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“…The LUCY and GreaterQF models have been reimplemented (under the Urban Multiscale Environmental Predictor (UMEP) suite- Lindberg et al 2018) to interface with the Quantum GIS Geographical Information System (2016) to use standard GIS datasets as inputs. The models are termed BLQF^(LUCY) and BGQF^(GreaterQF) hereafter to emphasise the new implementations presented here, which make it easier to update input data to represent different historical or hypothetical conditions.…”

Section: Introductionmentioning

confidence: 99%

Anthropogenic heat flux: advisable spatial resolutions when input data are scarce

Gabey

Grimmond

Capel-Timms

2018

Theor Appl Climatol

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Anthropogenic heat flux (Q F ) may be significant in cities, especially under low solar irradiance and at night. It is of interest to many practitioners including meteorologists, city planners and climatologists. Q F estimates at fine temporal and spatial resolution can be derived from models that use varying amounts of empirical data. This study compares simple and detailed models in a European megacity (London) at 500 m spatial resolution. The simple model (LQF) uses spatially resolved population data and national energy statistics. The detailed model (GQF) additionally uses local energy, road network and workday population data. The Fractions Skill Score (FSS) and bias are used to rate the skill with which the simple model reproduces the spatial patterns and magnitudes of Q F , and its sub-components, from the detailed model. LQF skill was consistently good across 90% of the city, away from the centre and major roads. The remaining 10% contained elevated emissions and Bhot spots^representing 30-40% of the total city-wide energy. This structure was lost because it requires workday population, spatially resolved building energy consumption and/or road network data. Daily total building and traffic energy consumption estimates from national data were within ± 40% of local values. Progressively coarser spatial resolutions to 5 km improved skill for total Q F , but important features (hot spots, transport network) were lost at all resolutions when residential population controlled spatial variations. The results demonstrate that simple Q F models should be applied with conservative spatial resolution in cities that, like London, exhibit timevarying energy use patterns.

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Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services

Cited by 210 publications

References 78 publications

Summertime surface energy balance fluxes at two Beijing sites

Summertime surface energy balance fluxes at two Beijing sites

Spatial Distribution of Sensible and Latent Heat Flux in the City of Basel (Switzerland)

Anthropogenic heat flux: advisable spatial resolutions when input data are scarce

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