Urban Weather Research Articles

Impact of urban parameterization and integration of WUDAPT on the severe convection

Amplified rates of urban convective systems pose a severe peril to the life and property of the inhabitants over urban regions, requiring a reliable urban weather forecasting system. However, the city scale's accurate rainfall forecast has constantly been a challenge, as they are significantly affected by land use/ land cover changes (LULCC). Therefore, an attempt has been made to improve the forecast of the severe convective event by employing the comprehensive urban LULC map using Local Climate Zone (LCZ) classification from the World Urban Database and Access Portal Tools (WUDAPT) over the tropical city of Bhubaneswar in the eastern coast of India. These LCZs denote specific land cover classes based on urban morphology characteristics. It can be used in the Advanced Research version of the Weather Research and Forecasting (ARW) model, which also encapsulates the Building Effect Parameterization (BEP) scheme. The BEP scheme considers the buildings' 3D structure and allows complex land–atmosphere interaction for an urban area. The temple city Bhubaneswar, the capital of eastern state Odisha, possesses significant rapid urbanization during the recent decade. The LCZs are generated at 500 m grids using supervised classification and are ingested into the ARW model. Two different LULC dataset, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and WUDAPT derived LCZs and initial, and boundary conditions from NCEP GFS 6-h interval are used for two pre-monsoon severe convective events of the year 2016. The results from WUDAPT based LCZ have shown an improvement in spatial variability and reduction in overall BIAS over MODIS LULC experiments. The WUDAPT based LCZ map enhances high-resolution forecast from ARW by incorporating the details of building height, terrain roughness, and urban fraction.

A novel risk-based design framework for urban heat island: A case study of Kempten, Germany

Studies on the urban heat island (UHI) effect have increased over the past decades, mainly focusing on topics such as thermal comfort, building energy, and sensitivity studies. However, there is currently a research gap in the development of risk-based design criteria for UHI. This paper proposes a novel risk-based UHI design criteria using heat-related mortality risk. The design criteria was developed based on the annual hottest seven-day mean temperatures. Frequency-consequence curves (or F–N curves) were used to quantify tolerable levels of societal risk. Exposure-response relationships for temperature and mortality for southern Germany were also used to determine the heat-related mortality rate. The heat-related mortality rate was used with the F–N curves to obtain a maximum allowable UHI intensity (UHII) for several risk scenarios. This allowable UHII was compared to the actual 50-year UHII for a residential district in Kempten, Germany. The Urban Weather Generator was used to model the UHII. If the actual UHII did not exceed the allowable UHII, the analyzed area was considered adequate. The 50-year UHII of the study area was 0.17 °C. The calculated allowable UHII ranged from 0.3 °C–13.2 °C. Therefore, the study area was adequate for all risk scenarios that were considered. This research lays the foundation for decision-makers and urban planners to incorporate more precise design targets in a number of applications from planning new developments to mitigation of UHI in existing urban areas.

Counter-Mapping with Sounds in the Practices of Postdigital Pedagogy

The article analyses the methods of postdigital pedagogy based on critical media design (CMD), and collective practices which involve using sound recording and emission as a tool for counter-mapping the problems associated with the history and politics of digitally mediated urbanism. The article provides a detailed account of, and draws on materials from, the Emotional Urban Weather workshop conducted in 2014, in Warsaw, for an international group of professional young researchers, designers, artists and activists. The workshop took place in specially chosen districts of the Polish capital, in which the participants applied various sound design and critical media design strategies in order to address the historical, social, economic and political problems related to this place. Using technological tools capable of recording, emitting and measuring the parameters of sound, they prepared forms of sound intervention in the city. At the same time, this process became an experimental way of ‘opening up’ the problematics of a place by counter-mapping complex socio-cultural issues. Reflection on the workshop and its outcomes have contributed to the development of a postdigital pedagogy. The practices of critical media design (CDM), when combined with field recordings, affective listening and sound intervention, allow various technologies to be incorporated into theoretical critique and new forms of experimental engagement.

Impact of land-use land-cover datasets and urban parameterization on weather simulation over the Jakarta Metropolitan Area

Human-caused changes in land-use and land-cover (LULC) are most visible in metropolitan areas, when the majority of the land has been converted to urban land or built up. This study presents a modeling approach for simulating the spatiotemporal distribution of urban microclimate with the Weather Research and Forecasting (WRF) model using four urban parameterization schemes, namely a bulk, urban canopy model (UCM), building effect parameterization (BEP), and building energy model (BEM). The WRF model is set-up at 1 km spatial resolutions over the Jakarta Metropolitan Area to study the model’s sensitivity to the usage of alternative LULC datasets, the default MODIS and its modification 2017. The results show that the UCM and BEM schemes appear to be reliable in mapping urban weather conditions for all meteorological parameters examined. Given that the LULC categories in urban areas remained unchanged, changing the LULC in the model did not result in a large difference in error. The LULC dataset, on the other hand, can be used as information related to suburban areas that continue to grow in concurrently with urbanization. LULC updates can provide insight into how much temperature rise is occurring in urban areas and how it affects climate change.

Daily Weather Forecasting Based on Deep Learning Model: A Case Study of Shenzhen City, China

Daily weather conditions are closely related to every field of production and life, and the forecasting of weather conditions plays an important role in social development. Based on the data characteristics of urban weather conditions, a deep learning network was designed to forecast urban weather conditions, and its feasibility was proved by experiments. In view of the non-stationary and seasonal fluctuation of the time series of daily weather conditions in Shenzhen from 2015 to 2019, empirical mode decomposition (EMD) was used to carry out the stationary processing for the daily minimum humidity, minimum pressure, maximum temperature, maximum pressure, maximum wind speed and minimum temperature. The decomposed components, residual sequence and original sequence were reconstructed according to the degree of relevance. On this basis, a long short-term memory (LSTM) neural network for the Shenzhen daily weather forecast was used, using the advantages of the LSTM model in time-series data processing, using the grid search algorithm to find the optimal combination of the above parameters and combining with the gradient descent optimization algorithm to find optimal weights and bias, so as to improve the prediction accuracy of Shenzhen weather characteristics. The experimental results show that our design of the EMD-LSTM model has higher forecasting precision and efficiency than traditional models, which provides new ideas for the weather forecast.

Improvements, extensions, and validation of the Urban Weather Generator (UWG) for performance-oriented neighborhood planning

Policymakers have to assess and optimize neighborhood-scale planning aspects to profit from climate mitigation and adaptation measures due to various thermal environmental concerns. There are many models to evaluate the local thermal environment, but their outputs or complexity limit their application in performance-oriented neighborhood planning. In this study, the Urban Weather Generator (UWG) was updated to meet the needs of neighborhood planners. The model improves the radiation, vegetation, and convective heat transfer calculation processes to better match the actual physical representation of the neighborhood. Besides, the functions of the model are extended to calculate the mean radiant temperature (MRT) and water temperature for the evaluation of outdoor human thermal comfort and blue infrastructure effects. A new graphical interface also makes it easier for planning decision-makers to start quickly. Then, the prediction results of the updated model are evaluated with the neighborhood observations of the hot and humid regions of Guangzhou and Nanning. The air temperature, relative humidity (RH), and MRT results revealed that this novel model performs quite consistently and demonstrated the reliability and computational efficiency of the UWG model. In the early stages of thermal environment evaluation, the UWG model can be used for performance-oriented neighborhood planning.

Parameter Sensitivity Analysis and Optimization of the Single-Layer Urban Canopy Model in the Megacity of Shanghai

In order to meet the demand of more refined urban weather forecast, it is of great practical significance to improve and optimize the single-layer urban canopy model (SLUCM) suitable for the megacity of Shanghai. In this paper, based on the offline SLUCM model driven by a whole-year surface flux observation data in the Shanghai central business district, a series of parameter sensitivity tests are carried out by using the one at a time (OAT) method, the relative importance and a set of optimized parameters of the SLUCM suitable for high-density urban area are established, and the improvement of simulation is evaluated. The results show that SLUCM well reproduces the seasonal mean diurnal patterns of the net all-wave radiation flux ( Q ∗ ) and sensible heat flux (QH) but underestimates their magnitudes. Both Q ∗ and QH are linearly sensitive to the albedo, and most sensitive to the roof albedo, the second to the wall albedo, but relatively insensitive to the road albedo. The sensitivity of Q ∗ and QH to emissivity is not as strong as that of albedo, and the variation trend is also linear. Similar to albedo, Q ∗ and QH are most sensitive to roof emissivity. The effect of thermal parameters (heat capacity and conductivity) on fluxes is logarithmic. The sensitivity of surface fluxes to geometric parameters has no specific variation pattern. After parameter optimization, RMSE of Q ∗ decreases by about 3.4–18.7 Wm−2 in four seasons. RMSE of the longwave radiation (L↑) decreases by about 1.2–7.87 Wm−2. RMSE of QH decreases by about 2–5 Wm−2. This study provides guidance for future development of the urban canopy model parameterizations and urban climate risk response.

Creation and application of future typical weather files in the evaluation of indoor overheating in free-floating buildings

Expected Global warming and heatwaves coupled with the urban heat island effect (UHI) can overheat indoor environments of free-floating buildings in temperate climate regions. Overheating assessment requires practitioners to use appropriate climate data and suitable measurement indices. The aim of this article is first, to propose a practical approach to generate yearly and typical ready-to-use future typical weather datasets (FTWY) using high-resolution Regional Climate Model (RCM) data from Coordinated Regional Climate Downscaling Experiment (CORDEX), and second, investigate the potential of FTWYs in the assessment of indoor overheating, considering UHI effect.To achieve these objectives, three dynamically downscaled (DDS) FTWYs generated from RCMs (IPSL-SMHI, CNRM-ALADIN, MPI-REMO) were compared with one statistically downscaled (ESD) FTWY from Meteonorm, and observed heatwave weather data of 2003. Comparative analysis was performed in two stages: comparison of monthly statistical distribution of climate variables, and analysis of heatwave presence. Urban weather generator (UWG) was used to project UHI effect on two weather files for two buildings, and three overheating measurement indices were used to exploit results. Comparative analysis of weather files show that temperature in a FTWY in the medium future (2040–2070) is likely not as intense as the heatwave of 2003 for Nantes. Results also confirm that it is better to use two weather files, and at least two overheating indices to obtain reliable outputs. This study also revealed that indoor overheating is not limited to densely built areas where impact of UHI is highest; buildings located in sparsely built neighbourhoods are also at risk.

Development of a micro-scale meteorology-air quality model using a grid nesting method and potential application

Numerical weather prediction (NWP) facilitates weather forecasting and is important for determining future climate changes and providing advance warning of natural hazards. Numerical models can also be used to measure air quality and pollutants in the air. Dr Soo-Jin Park, Supercomputer Center, Pukyong National University, Korea, is using a grid nesting system in a CFD (computational fluid dynamics) model to improve the accuracy of urban weather and air quality prediction. Another goal of this work is to improve the efficiency of computing resources. Park's is applying the grid nesting system to a microscale model, which is a unique approach. Using the micro-scale meteorology-air quality model using a grid nesting system in CFD, the researchers can monitor wind speeds and the concentration of gases in the atmosphere such as ozone (O3) and nitrogen dioxide (NO2) and how these are affected by changes to topography. Using the model, Park and the team can perform numerical simulation of a larger area, as well as selectively high-resolution simulations for the target area. The researchers used the model to explore the effects of wall- and tree-type fences on the airflow and fine particulate matter (PM2.5) concentration around a school by simulating flows and pollutant dispersion and were able to provide detailed information regarding the air quality.

An urban thermal tool chain to simulate summer thermal comfort in passive urban buildings

This paper presents a simulation tool chain for the prediction of thermal comfort in passive urban buildings during summer and under heat wave conditions. The tool chain encompasses EnergyPlus building energy model and the Urban Weather Generator and UrbaWind tools to consider the impacts of the urban environment on building loads. This chain of tools is computationally efficient and does not require notable expertise for the simulations. To assess its accuracy, this simulation results are compared to in situ measurements. This paper describes the measurement setup, analyzes the measurement results and reveals a satisfactory model accuracy through a comparison to the measurement data. The average nighttime urban heat island between July and September 2020 reached 2.31 °C in the city of Lyon. Not considering the urban heat island effect (employing rural weather files) in urban thermal simulations could induce a 1 °C bias in indoor air temperature predictions. This could also result in overpredicting the cooling potential of natural ventilation during summer. Key parameters of the simulation accuracy are identified. These are the action schedule of occupants in regard to opening devices (shutters, windows and doors) and the urban boundary layer height at night.

Spatial variability of forward modelled attenuated backscatter in clear-sky conditions over a megacity: Implications for observation network design.

Sensors that measure the attenuated backscatter coefficient (e.g., automatic lidars and ceilometers [ALCs]) provide information on aerosols that can impact urban climate and human health. To design an observational network of ALC sensors for supporting data assimilation and to improve prediction of urban weather and air quality, a methodology is needed. In this study, spatio‐temporal patterns of aerosol‐attenuated backscatter coefficient are modelled using Met Office numerical weather prediction (NWP) models at two resolutions, 1.5 km (UKV) and 300 m (London Model [LM]), for 28 clear‐sky days and nights. Initially, attenuated backscatter coefficient data are analysed using S‐mode principal component analysis (PCA) with varimax rotation. Four to seven empirical orthogonal functions (EOFs) are produced for each model level, with common EOFs found across different heights (day and night) for both NWP models. EOFs relate strongly to orography, wind, and emissions source location, highlighting these as critical controls of attenuated backscatter coefficient spatial variability across the megacity. Urban–rural differences are largest when wind speeds are low and vertical boundary‐layer dynamics can more effectively distribute near‐surface aerosol emissions vertically. In several night‐time EOFs, gravity‐wave features are found for both NWP models. Increasing the horizontal resolution of native ancillaries (model input parameters) and improving the urban surface scheme in the LM may enhance the urban signal in the EOFs. PCA output, with agglomerative Ward cluster analysis (CA), minimises intra‐group variance. The UKV and LM CA shape and size results are similar and strongly related to orography. PCA‐CA is a simple, but adaptable methodology, allowing close alignment with observation network design goals. Here, CA is used with wind roses to suggest the optimised ALC deployment is one in the city to observe the urban plume and others surrounding the city, with priority given to cluster size and frequency of upwind advection.

Incorporating an urban canopy model with artificial water dissipation into weather research and forecasting: A case study for Beijing

AbstractUrban artificial water dissipation is a concomitant process of human water use in built‐up areas that can absorb heat through evapotranspiration, reduce air temperature, transfer surface water to the atmosphere, and participate in the urban water cycle. In the context of increased urbanization, the impact of artificial water dissipation on urban climates cannot be ignored. In this study, calculation models for artificial water dissipation from different underlying surface types (buildings, hardened ground, soil, and vegetation) were introduced into an urban canopy model and coupled with the weather research and forecasting (WRF) model for mesoscale weather simulations of the Beijing area. Observational datasets of temperature and humidity from automatic weather stations in Beijing were used for validation. Results showed that the coupled model could reproduce the temperature and humidity of urban weather stations in Beijing more accurately compared to simulations that did not employ an urban canopy model or that adopted a traditional urban canopy model. In the Beijing urban area, the latent heat flux of NON, urban canopy model, and artificial water dissipation into the UCM are approximately 0, 35, and 65 W/m2, respectively, and the anthropogenic latent heat is about 30 W/m2. Incorporating urban artificial water dissipation into the urban canopy model reduced the urban air temperature by 0.2°C and increased the specific humidity by 0.6 g/kg, alleviating urban heat island effects and increasing humidity. These findings indicate that artificial urban water dissipation plays an important role in urban weather and climate that should be considered by urban canopy models. In the future, this model can be coupled into WRF for more accurate mesoscale weather simulations.

Improved Urban Finescale Forecasting During a Heat Wave by Using High-Resolution Urban Canopy Parameters

For urban weather finescale forecasting, obtaining accurate and up-to-date urban canopy parameters (UCPs) is necessary and still a challenge. In this study, a high-resolution dataset of UCPs was developed by using vector-format building information and then applied in the WRF/urban system with the single-layer urban canopy model (SLUCM)/building effect parameterization (BEP) model to improve the urban finescale forecasting of a typical heat wave event during summer 2016 in Hangzhou. A series of sensitivity experiments were conducted, and the results showed that the high-resolution UCP data improved the model skill in simulating the spatial distributions and diurnal variations of 2-m temperature, 2-m relative humidity, and 10-m wind speed in the urban areas of Hangzhou, especially for the BEP model. Better results were produced when refining the computation domain due to more realistic urban morphological characteristics were adopted. The sensitive experiments suggest that the high-resolution UCPs played a significant role in representing the UHI effect though changing the surface thermodynamic parameters (e.g., roughness length), hereafter increasing the sensible heat and surface heat flux, and finally resulting a notable urban heat island (UHI) effect.

Does air pollution inhibit manufacturing productivity in Yangtze River Delta, China? Moderating effects of temperature

China has been experiencing serious and recurrent incidences of air pollution in recent years. The frequency and timespans of such incidences are uncertain because of variable urban weather conditions, especially temperature, that inhibit the productivity of manufacturing companies. Matching data about listed manufacturing companies in China's Yangtze River Delta urban cluster from 2003 to 2018 with data on urban air pollution and weather, we explored the effects of air pollution on corporate productivity and the moderating role of temperature. We found that air pollution significantly inhibited the productivity of these companies, which decreased by about 0.1% for 1% increase in the concentration of PM2.5. Regarding industry heterogeneity, high-energy-consumption and low-technology manufacturing industries were more sensitive to the negative effects of air pollution. Furthermore, we concluded that low temperatures played an important role in causing significant increases in the negative effects of air pollution. In the fall and winter (October to January), the lower the temperatures resulted in stronger inhibitory effects of air pollution on corporate productivity. When the average daily temperature is 0°C–3°C, the moderating effects of temperature are even more obvious. To minimize the inhibitory effects of air pollution on productivity, governments and companies should implement positive adaptions to simultaneously confront air pollution and temperature change.

Tuning urban microclimate: A morpho-patch approach for multi-scale building group energy simulation

• A morpho-patch generation approach based on urban street networks. • Urban microclimate estimation with urban building morphological features. • Localized microcliamte computation through tuning cities’ TMY weather. • More reliable urban building energy simulation with high-resoluation microclimates. Building energy simulation is a powerful tool for developing sustainable and low-emission urban built environments. The conventional simulation model relies on the climate conditions of the entire city that are measured by few weather stations. However, due to urban morphologies, natural conditions, and man-made structures, microclimate conditions vary across geographic locations. This results in unrealistic and unreliable simulation outputs and failure to properly support urban sustainability decision-making. To fill this gap, this study proposed a microclimate tuning approach based on the morphological analysis of local buildings and green land. The proposed tuning approach implements a morpho-patch as the analyzing unit to incorporate various morphological conditions for localized microclimate estimation. Extending the urban weather generator model, the inter-building effects are introduced in the urban canopy component computation and a unique microclimate weather conditions for each patch can be computed. To illustrate the proposed model, this study t simulated the energy dynamic of the entire city of Tucson [AZ, USA] using the proposed model. The results suggested that the root mean square temperature differences between using the single weather conditions and the tunned microclimates can be up to 2.49 °C. For one sample patch, this difference in underestimation of cooling energy demand by 6.69 MW•h in June and overestimation of the heating energy demand by 7.85 MW•h in November. Thus, the proposed method provides more accurate and realistic microclimate estimation for large-scale energy dynamic simulation for cities.

Integrated Urban Environmental System of Systems for Weather Ready Cities in India

Abstract Global urban population is projected to double by 2050. This rapid urbanization is the driver of economic growth but has environmental challenges. To that end, there is an urgent need to understand, simulate, and disseminate information about extreme events, routine city operations, and long-term planning decisions. This paper describes an effort underway in India involving an interdisciplinary community of meteorology, hydrology, air quality, and computer science from national and international institutes. The urban collaboratory is a system of systems for simulating weather, hydrology, air quality, health, energy, transport, and economy and its interactions. Study and prediction of urban events involve multiscale observations and cross-sector models, heterogeneous data management, and enormous computing power. The consortia program (NSM_Urban) is part of “weather ready cities,” under the aegis of India’s National Supercomputing Mission. The ecosystem “Urban Environment Science to Society” (UES2S) builds on the integrated cyberinfrastructure with a science gateway for community research and end-user service with modeling and interoperable data. The collaboratory has urban computing, stakeholder participation, and a coordinated means to scaffold projects and ideas into operational tools. It discusses the design and the utilization of high-performance computing (HPC) as a science cloud platform for bridging urban environment and data science, participatory stakeholder applications, and decision-making. The system currently integrates models for high-impact urban weather, flooding, air quality, and simulating street- and building-scale wind flow and dispersion. The program with the work underway is ripe for interfacing with regional and international partners, and this paper provides an avenue toward that end.

Population management in an urban center using the dynamic integrated solution for an adequate atmospheric environmental quality

Accelerated growth in urban populations has become a powerful force for human development, particularly in developed countries. Metropolitan cities are centers for technical and economic advancement, but air pollution, overflowing of water, and other climate effects still pose significant problems related to nature, climate, and the environment. Cities are vulnerable to increasingly dense, diverse, and interdependent urban systems. A single extreme occurrence can contribute to a systemic break-up of a city's infrastructure, often like dominoes. In this paper, a dynamic integration-assisted population management solution (DI-PMS) has been proposed. DI-PMS recognizes that the latest facilities' optimal usage of knowledge and technologies is needed to increase urbanization. They are one of the critical priorities of the weather community. Such integrated urban weather, environmental, and climate services will help cities address dangers including storms, floods, heatwaves, and air pollution, especially in climate change. The goal is to create urban facilities that satisfy communities' unique needs by high-resolution forecasting and pollution reduction climate services, which allow the construction of durable, robust, and thriving cities that support the Sustainable Development Goals. Several recent international surveys to explore these topics have been undertaken. DI-PMS gives a brief description of urban hydrometeorological, climate and air pollution activities, outlines the new urban integrated weather and environmental services concept, and emphasizes the need for science to be implemented.

Assessment and Prediction of Climate Risks in Three Major Urban Agglomerations of Eastern China

In the context of global climate change and urban expansion, extreme urban weather events occur frequently and cause significant social problems and economic losses. To study the climate risks associated with rapid urbanization in the global context of climate change, the vulnerability degree of urban agglomeration is constructed by the Grey Model (GM (1, 1)). Based on the sixth phase of the Coupled Model Intercomparison Project (CMIP6) data sets SSP1-2.6, SSP2-4.5, and SSP5-8.5, drought, heat wave, and flood hazards under different emission scenarios are calculated. The vulnerability degree of the urban agglomeration and the climate change hazard were input into the climate change risk assessment model to evaluate future climate change risk. The analysis results show regional differences, with the Beijing–Tianjin–Hebei urban agglomeration having good urban resilience, the Yangtze River Delta urban agglomeration having slightly higher overall risk, and the Pearl River Delta urban agglomeration having the highest relative risk overall. On the whole, the higher the emission intensity is, the greater the risk of climate change to each urban agglomeration under different emission scenarios.

A practical approach to the evaluation of local urban overheating– A coastal city case-study

In response to urbanization and global warming, which amplify heatwave effects and might lead to urban heat stress, this paper proposes a practical approach to characterize the local microclimate at the neighborhood scale. In this approach, the local urban climate is described using suitable indicators, to support the ecodistrict design process or refurbishment. Experimental and numerical results illustrate the approach in a case study of a French coastal city, La Rochelle. In the first step, we set up urban and rural weather stations to characterize the local urban climate over a summer period and to identify local temperature differences. The measurements highlighted a daytime urban cooling effect due to the local sea breeze. While the Urban Weather Generator (UWG) simulation tool used for this study does not capture coastal effects, the results were consistent with the urban heat island (UHI) measurements. We proposed two indicators to quantify the local climate modifications: local UHI and overheating intensity. The parameters of the adaptation strategies were assessed through a sensitivity analysis for these two indicators. For this case-study, we identified vegetation cover, building height and road albedo as key parameters that can be used to mitigate local overheating.

Impact of urban morphology on urban microclimate and building energy loads

Due to insufficient records and limited number of weather stations, prediction models must be used to forecast local climate conditions. Accurate prediction is required in the case of emerging cities because rapid growth in urban development causes changes in the attributes of the urban environment, particularly the local microclimate. With the help of the Urban Weather Generator (UWG) and a locally established weather station, this research explores the validity of UWG processed open weather data (i.e., World Weather Online and Open Weather Map datasets). The Marina district in the city of Lusail near Doha, Qatar, saw a 26% increase in temperature prediction accuracy. A more detailed analysis of a representative residential building load prediction reveals that cooling estimate gaps are reduced by 2.7% to 7.3% when compared to the underestimated loads from the rural weather dataset. The impact of urban morphology on urban climate is further studied. The results show that increasing building construction, which results in increased building footprint density in the studied area, increases cooling consumption of the representative residential building by more than 11,000 kWh under certain conditions. Whereas, increase in greenery only results in savings of around 250 kWh. Additionally, a uniform random sensitivity analysis of 10 UWG characteristics showed that cooling consumption can vary between 10,000 kWh and 47,500 kWh compared to the predicted cooling consumption when the baseline weather dataset is used.