The application of a high-density street-level air temperature observation network (HiSAN): The relationship between air temperature, urban development, and geographic features

Chen, Yu Cheng; Liao, Yu Jie; Yao, Chun Kuei; Honjo, Tsuyoshi; Wang, Chi-Kuei; Lin, Tzu Ping

doi:10.1016/j.scitotenv.2019.06.066

Cited by 35 publications

(20 citation statements)

References 42 publications

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“…Essentially speaking, urban climate, road axes, and building height are directly associated with Urban Heat Island attributes [57], and building height influences urban density, which in turn influences urban climate [62]. Relevant research has shown that urban thermal conditions are influenced by urban development factors [63]. Thus, location, building density, and urban form have varying degrees of influence on the thermal environment, which in turn impacts thermal comfort.…”

Section: Discussionmentioning

confidence: 99%

Influence of Weather Factors on Thermal Comfort in Subtropical Urban Environments

2020

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Urbanization has influenced the distribution of heat in urban environments. The mutual influence between weather factors and urban forms created by dense buildings intensify human perception of the deteriorating thermal environment in subtropics. Past studies have used real-world measurements and theoretical simulations to understand the relationship between climate factors and the urban heat island effect. However, few studies have examined how weather factors and urban forms are connected to the thermal environment. To understand the influence of various weather factors on urban thermal environments in various urban forms, this study applied structural equation modeling to assumptions of linear relationships and used quantitative statistical analysis of weather data as well as structural conversion of this data to establish the structural relationships between variables. Our objective was to examine the relationships among urban forms, weather factors, and thermal comfort. Our results indicate that weather factors do indeed exert influence on thermal comfort in urban environments. In addition, the thermal comfort of urban thermal environments varies with location and building density. In hot and humid environments in the subtropics, humidity and wind speed have an even more profound impact on the thermal environment. Apparent temperature can be used to examine differences in thermal comfort and urban forms. This study also proved that an urban wind field can effectively mitigate the urban heat island effect. Ventilation driven by wind and thermal buoyancy can dissipate heat islands and take the heat away from urban areas.

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Section: Discussionmentioning

confidence: 99%

Influence of Weather Factors on Thermal Comfort in Subtropical Urban Environments

2020

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“…The published articles mentioned in the second section confirmed the importance of vegetation zones in cities. They also pointed at negative effects of the UHI on air quality caused by the UHI releasing air pollutants, which leads to average temperature growth [15,16,17,20].…”

Section: Methodsmentioning

confidence: 99%

“…According to the authors, ozone and nitrogen dioxide increase when daily temperature reaches over 20 °C, and particulate matter has a negative effect on precipitation. The issue of heat generation in specific small size city zones known as urban heat island (UHI—often specified as the air temperature dissimilarity between the city and the paralleling air temperature of its surroundings) is discussed in [15].…”

Section: State Of the Artmentioning

confidence: 99%

A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network

Frnda

Ďurica

Nedoma

et al. 2019

Sensors

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This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people’s everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time.

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“…SWAQ adds to the growing number of urban meteorological networks (UMNs) deployed in the last decade worldwide, with the specific purpose of monitoring city-scale heat and air quality dynamics. Currently deployed UMNs include the Metropolitan Environmental Temperature and Rainfall Observation System (METROS) in Central Tokyo, Japan 6 , the Oklahoma City Micronet (OKCNET) in USA 7 , the Helsinki Testbed in Finland 8 , the Turku Urban Climate Research Project (TURCLIM) in Finland 9 , the Olomouc’s Metropolitan Station System in Czech Republic (MESSO) 10 , the Birmingham Urban Climate Laboratory (BULC) in the UK 11 , the HiSAN network in Tainan City, Taiwan 12 , and the MOCCA network in Ghent, Belgium 13 . SWAQ aligns with the above UMNs and devotes special attention to site documentation by following a standardized UMN metadata protocol 14 so as to improve site representativeness, maximize comparability across UMNs, and contribute to the buildup of a consistent database.…”

Section: Background and Summarymentioning

confidence: 99%

A citizen centred urban network for weather and air quality in Australian schools

et al. 2022

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High-quality, standardized urban canopy layer observations are a worldwide necessity for urban climate and air quality research and monitoring. The Schools Weather and Air Quality (SWAQ) network was developed and distributed across the Greater Sydney region with a view to establish a citizen-centred network for investigation of the intra-urban heterogeneity and inter-parameter dependency of all major urban climate and air quality metrics. The network comprises a matrix of eleven automatic weather stations, nested with a web of six automatic air quality stations, stretched across 2779 km2, with average spacing of 10.2 km. Six meteorological parameters and six air pollutants are recorded. The network has a focus on Sydney’s western suburbs of rapid urbanization, but also extends to many eastern coastal sites where there are gaps in existing regulatory networks. Observations and metadata are available from September 2019 and undergo routine quality control, quality assurance and publication. Metadata, original datasets and quality-controlled datasets are open-source and available for extended academic and non-academic use.

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The application of a high-density street-level air temperature observation network (HiSAN): The relationship between air temperature, urban development, and geographic features

Cited by 35 publications

References 42 publications

Influence of Weather Factors on Thermal Comfort in Subtropical Urban Environments

Influence of Weather Factors on Thermal Comfort in Subtropical Urban Environments

A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network

A citizen centred urban network for weather and air quality in Australian schools

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