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.

SINGV‐DA is a convective‐scale numerical weather prediction system with regional data assimilation for Singapore and the surrounding region. This article documents SINGV‐DA's current operational configuration and the sensitivity studies that influenced its development. We show that background error covariances derived by bootstrapping (via the lagged National Meteorological Centre method) contain spurious vertical structures at higher model levels that may degrade forecast performance. We found that SINGV‐DA precipitation forecasts are sensitive to horizontal resolution and lateral boundary conditions. Our observing system experiments reveal that satellite radiance assimilation, while clearly beneficial for precipitation forecasts in this region, adversely affected model background temperatures and winds at higher altitudes. Benchmarked against the forecast model in isolation, the regional DA system adds significant value to precipitation forecasts in the nowcasting range, but not at longer lead times. Our findings point to the need for further research and development to improve the system.

Simulations of five November months (2010–2014) using the urban version of the numerical weather prediction system of the Meteorological Service Singapore (uSINGV) are used to analyse the urban effect on convective precipitation over Singapore and Johor Bahru (Malaysia). The model is able to closely predict locations where rainfall peaks occur, but rainfall totals are overestimated compared to radar data. The temporal variability of rainfall in the region shows that urban areas increase the frequency and severity of rainfall events and that such impact increases with the rainfall intensity. Results show that low‐level moisture advection is enhanced in this coastal conurbation as a result of the strengthening of wind convergence. The latter is likely caused by increasing sea‐breeze strength due to lower surface pressure over the urban area, and higher urban surface roughness, respectively. As a consequence, more precipitable water is available in the region, enhancing convection and increasing the probability of heavy rainfall over the centre and north of Singapore island and Johor Bahru. Stronger convection further increases moisture advection from the vicinity. By studying the temporal variability and the spatial distribution of rainfall events, the present study provides new insights on the urban impact on heavy rainfall in tropical areas. The conclusions are only valid for the November inter‐monsoon period, when local forcing, rather than large‐scale influences, dominates rainfall generation.

Biases in simulating the diurnal cycle of convection near the western coast of the island of Sumatra have been investigated using the data from the pilot field campaign of the Years of the Maritime Continent (pre-YMC). The campaign was carried out at a sea [Research Vessel (R/V) Mirai] and a land (Bengkulu, Sumatra) site. Simulations are performed using a tropical configuration of the Met Office model at a grid resolution of 1.5 km in a limited-area mode. The focus of this study is to understand how biases in the input conditions from ECMWF high-resolution deterministic forecast affect the diurnal cycle. Modeled precipitation is found to be delayed and weak, with cold SST bias in the model as the key contributing factor affecting convection at both sites. Colder SST causes a delay in the trigger of convection at Bengkulu by delaying the onset of the local land breeze, which in turn delays the local convergence. The cold outflow from precipitation over the adjacent mountain is also found to be delayed in the model, contributing to the total delay. This delay in the evening convection at Bengkulu is shown to directly affect the timing of nighttime convection at Mirai. Weaker convection at Bengkulu is argued to be due to lower-tropospheric dry humidity bias in the model initial condition. Convection at Mirai is shown to be caused by the convergence of the cold outflow from Bengkulu with the prevailing landward wind over the sea. Both thermodynamic and dynamic conditions near the cold outflow front are found to be less favorable for intense convection in the simulation, the reason for which is argued to be a combination of the cold SST bias and a weaker cold outflow.