Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data.

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP-BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Numerical analysis of the impact of anthropogenic emissions on the urban environment of Singapore.

Increased urbanization and anthropogenic activities in tropical cities lead to the temperature gradient between the urban and rural environments, causing the urban heat island (UHI) phenomenon. This study is a pioneering attempt that examines the changes in the temporal evolution of the surface energy budget induced by urbanization known as the Anthropogenic Influence (AI) in modifying the urban climate of a tropical city using Weather Research and Forecasting (WRF) numerical modeling system. The AI from buildings, traffic and power plants is determined in five different scenarios and the model is validated with high temporal resolution in-situ data. These increased AIs provide improved WRF capability with root mean square error (RMSE) less than 2 °C and mean bias error (MBE) less than 0.5 °C between different performance indicators. Building envelopes (without indoor activity/equipment) are found to be a major contributor in exacerbating the island wide urban heat ∆TaAI, max to 3.7 °C compared to baseline all green scenario. This is followed by the air-conditioner (AC) systems that contribute up to 1.4 °C. The maximum local contribution of traffic and power plants to urban heat is 0.9 °C and 0.4 °C, respectively.

A computational fluid dynamic modelling approach to assess the representativeness of urban monitoring stations.

Air quality measurements of urban monitoring stations have a limited spatial representativeness due to the complexity of urban meteorology and emissions distribution. In this work, a methodology based on a set of computational fluid dynamics simulations based on Reynolds-Averaged Navier-Stokes equations (RANS-CFD) for different meteorological conditions covering several months is developed in order to analyse the spatial representativeness of urban monitoring stations and to complement their measured concentrations. The methodology has been applied to two urban areas nearby air quality traffic-oriented stations in Pamplona and Madrid (Spain) to analyse nitrogen oxides concentrations. The computed maps of pollutant concentrations around each station show strong spatial variability being very difficult to comply with the European legislation concerning the spatial representativeness of traffic-oriented air quality stations.