Application of MORUSES single‐layer urban canopy model in a tropical city: Results from Singapore

Abstract

A tropical version of the high‐resolution (300 m) UK Met Office forecast model (UM) using the MORUSES urban canopy parametrization (UCP) is adapted for Singapore. High‐resolution urban surface parameters are determined using a methodology based on Voronoi polygons applied to a 3D building database. The model is evaluated for clear sky and calm conditions at the neighbourhood scale by comparing its predictions with two sources of observations: energy balance data from an eddy covariance flux tower located in a low‐rise residential area, and a network of sensors measuring screen‐level temperature across the city. The model is able to reproduce the diurnal cycle of the surface energy balance fluxes. Net radiation is overestimated which likely follows from an underestimation in cloud cover and effective surface albedo. This overestimation partly explains the overestimation of the sensible‐ and latent‐heat fluxes. The higher model sensible‐heat flux is further hypothesised to be related to the overestimation of modelled canyon sensible‐heat flux. Its peak exhibits a 1 h delay, similar to simulated roof and canyon surface temperature. The model captures the diurnal cycle of temperature at a height of 20.5 m above ground and at screen level. A night‐time cold bias of 0.1–1.1 K and an overestimation of the daytime peak value are observed at both heights. These deviations are smaller than those predicted by other UCPs reported in the literature. A simple method to estimate the capability of an urban model to qualitatively distinguish screen‐level temperature differences across different urban morphologies is developed which shows that MORUSES is clearly able to represent the impacts of different neighbourhoods on the thermal environment.

Urban intensification of convective rainfall over the Singapore – Johor Bahru region

“…5 in Simón‐Moral et al . (2020)). JB is a more homogeneous city, with mostly 4–5 storey houses in the city centre, 2–3 storey houses in the suburbs and an increasing number of high‐rise buildings scattered throughout.…”

SINGV: A convective‐scale weather forecast model for Singapore

“…The SINGV downscaler is the basis for the SINGV data assimilation system (SINGV‐DA: Heng et al ., 2020) and the SINGV ensemble prediction system (SINGV‐EPS: Porson et al ., 2019). Furthermore, SINGV is also used for sub‐kilometre urban‐scale modelling (Simón‐Moral et al ., 2020), regional climate modelling (Timbal et al ., 2019) and coupled atmosphere–ocean modelling (Thompson et al ., 2019), at CCRS.…”

Characteristics of LDAPS-Predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

“…Their building-scale resolved air temperature model improved the performance of LDAPS air temperature prediction by reflecting the heating effect in urban areas [19]. Other previous studies have contributed to our understanding of LDAPS prediction characteristics [20][21][22][23][24]. However, the extensive numerical experiments including sensitivity tests to spatial and temporal resolutions and physical parameterization schemes have not distinctly explained the reasons for the spatial variability in LDAPS biases for surface wind speeds and temperatures over the Korean Peninsula yet.…”