The Analytical Objective Hysteresis Model (AnOHM v1.0): methodology to determine bulk storage heat flux coefficients

Sun, Ting; Wang, Zhi‐Hua; Oechel, Walter C.; Grimmond, Sue

doi:10.5194/gmd-10-2875-2017

Cited by 24 publications

(19 citation statements)

References 74 publications

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“…Areas with low building density have, as expected, smaller ΔQ S values. Uncertainty is assessed using two alternative approaches implemented within the SUEWS 7 modelling platform: OHM 25 , and AnOHM 26 . All approaches produced similar results, but different spatial patterns are identified, mostly depending on the forcing data.…”

Section: Resultsmentioning

confidence: 99%

Urban energy exchanges monitoring from space

Chrysoulakis

Grimmond

Feigenwinter

et al. 2018

Sci Rep

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One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. Satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget fluxes, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (−64.1, +69.3 W m−2 for ±2 K perturbation). They also underestimate anthropogenic heat fluxes. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling.

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Section: Resultsmentioning

confidence: 99%

Urban energy exchanges monitoring from space

Chrysoulakis

Grimmond

Feigenwinter

et al. 2018

Sci Rep

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“…All three variables are important, given that ↓ directly drives the SEB system, T indicates the ambient thermal conditions, and WS implies the advection and convection efficiency. To attribute changes in heat storage from BC to HW to these variables, the Analytical Objective Hysteresis Model (AnOHM, for details refer to Sun et al (2017)) is used. AnOHM estimates the storage heat flux (ΔQ ) from net all-wave radiation (Q * ) following the Objective Hysteresis Model (Grimmond et al 1991).…”

Section: Attribution To Ambient Forcing Variablesmentioning

confidence: 99%

“…surface albedo, thermal conductivity, etc.). The applicability of AnOHM over different land cover types has been extensively evaluated (Sun et al 2017).…”

Section: Attribution To Ambient Forcing Variablesmentioning

confidence: 99%

Attribution and mitigation of heat wave-induced urban heat storage change

Sun

Kotthaus

et al. 2017

Environ. Res. Lett.

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When the urban heat island (UHI) effect coincides with a heat wave (HW), thermal stress in cities is exacerbated. Understanding the surface energy balance (SEB) responses to HWs is critical for improving predictions of the synergies between UHIs and HWs. This study evaluates observed SEB characteristics in four cities (Beijing, Łódź, London and Swindon), along with their ambient meteorological conditions, for both HW and background summer climate scenarios. Using the Analytical Objective Hysteresis Model (AnOHM), particular emphasis is on the heat storage. The results demonstrate that in London and Swindon the amount of daytime heat storage and its fraction relative to the net all-wave radiation increase under HWs. Results further demonstrate that such increases are strongly tied to lower wind speeds. The effects of different UHI mitigation measures on heat storage are assessed using AnOHM. Results reveal that use of reflective materials and maintaining higher soil moisture availability can offset the adverse effects of increased heat storage.

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“…However, in complex urban landscapes, this approach is impractical at the local scale. There are a range of methods to assess the storage heat fluxes in urban areas, including OHM, Objective Hysteresis Model Grimmond and Oke 1999); AnOHM, Analytical Objective Hysteresis Model (Sun et al 2017); RES, Residual, determination of the storage heat flux from the residual of the surface energy balance (Offerle et al 2005b); CAR, Complete Aspect Ratio (Rigo and Parlow 2007); TEB, Town Energy Balance model (Masson 2000) or other urban land surface models; and ESTM, Element Surface Temperature Method (Offerle et al 2005a). Some methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

Urban storage heat flux variability explored using satellite, meteorological and geodata

Lindberg

Olofson

Sun

et al. 2020

Theor Appl Climatol

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The storage heat flux (ΔQ S) is the net flow of heat stored within a volume that may include the air, trees, buildings and ground. Given the difficulty of measurement of this important and large flux in urban areas, we explore the use of Earth Observation (EO) data. EO surface temperatures are used with ground-based meteorological forcing, urban morphology, land cover and land use information to estimate spatial variations of ΔQ S in urban areas using the Element Surface Temperature Method (ESTM). First, we evaluate ESTM for four "simpler" surfaces. These have good agreement with observed values. ESTM coupled to SUEWS (an urban land surface model) is applied to three European cities (Basel, Heraklion, London), allowing EO data to enhance the exploration of the spatial variability in ΔQ S. The impervious surfaces (paved and buildings) contribute most to ΔQ S. Building wall area seems to explain variation of ΔQ S most consistently. As the paved fraction increases up to 0.4, there is a clear increase in ΔQ S. With a larger paved fraction, the fraction of buildings and wall area is lower which reduces the high values of ΔQ S .

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The Analytical Objective Hysteresis Model (AnOHM v1.0): methodology to determine bulk storage heat flux coefficients

Cited by 24 publications

References 74 publications

Urban energy exchanges monitoring from space

Urban energy exchanges monitoring from space

Attribution and mitigation of heat wave-induced urban heat storage change

Urban storage heat flux variability explored using satellite, meteorological and geodata

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