DETERMINATION METHOD OF EVAPORATIVE EFFICIENCY AND ANTHROPOGENIC HEAT COMPONENT ON SIMULATION OF URBAN AIR TEMPERATURE BASED ON ONE DIMENSIONAL HEAT BUDGET MODEL

Mitigating high air temperatures and heat waves is vital for decreasing air pollution and protecting public health. To improve understanding of microscale urban air temperature variation, this paper performed measurements of air temperature and relative humidity in a field of Wuhan City in the afternoon of hot summer days, and used path analysis and genetic support vector regression (SVR) to quantify the independent influences of land cover and humidity on air temperature variation. The path analysis shows that most effect of the land cover is mediated through relative humidity difference, more than four times as much as the direct effect, and that the direct effect of relative humidity difference is nearly six times that of land cover, even larger than the total effect of the land cover. The SVR simulation illustrates that land cover and relative humidity independently contribute 16.3% and 83.7%, on average, to the rise of the air temperature over the land without vegetation in the study site. An alternative strategy of increasing the humidity artificially is proposed to reduce high air temperatures in urban areas. The study would provide scientific support for the regulation of the microclimate and the mitigation of the high air temperature in urban areas.

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Spatial and Temporal Effects of Built Environment on Urban Air Temperature in Seoul City, Korea: An Application of Spatial Regression Models

Journal of Asian Architecture and Building Engineering ◽

10.3130/jaabe.16.123 ◽

2017 ◽

Vol 16 (1) ◽

pp. 123-130

Author(s):

Sugie Lee ◽

Jaehyun Ha ◽

Hyemin Cho

Keyword(s):

Built Environment ◽

Air Temperature ◽

Regression Models ◽

Spatial Regression ◽

Urban Air ◽

Temporal Effects ◽

Spatial Regression Models

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IMPACT OF THE ACTUAL SEA SURFACE TEMPERATURE OF TOKYO BAY ON URBAN AIR TEMPERATURE

PROCEEDINGS OF HYDRAULIC ENGINEERING ◽

10.2208/prohe.52.283 ◽

2008 ◽

Vol 52 ◽

pp. 283-288

Author(s):

Ryoko ODA ◽

Manabu KANDA ◽

Ryo MORIWAKI ◽

Tadashi YAMADA

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Air Temperature ◽

Sea Surface ◽

Tokyo Bay ◽

Urban Air

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A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

10.5194/amt-2021-301 ◽

2021 ◽

Author(s):

Shihan Chen ◽

Yuanjian Yang ◽

Fei Deng ◽

Yanhao Zhang ◽

Duanyang Liu ◽

...

Keyword(s):

High Resolution ◽

Random Forest ◽

Urban Heat Island ◽

Air Temperature ◽

Anthropogenic Heat ◽

Heat Island ◽

Model Framework ◽

Rapid Urbanization ◽

Urban Heat ◽

The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

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Urban weather generation: the intercomparison of three emerging models

10.5194/egusphere-egu21-16001 ◽

2021 ◽

Author(s):

Csilla Gal

Keyword(s):

Land Use ◽

Wind Speed ◽

Air Temperature ◽

Numerical Models ◽

Weather Generator ◽

Weather Data ◽

Urban Air ◽

Area Of Interest ◽

Built Form ◽

Urban Weather

Cities modify the background climate through the surface-atmosphere interaction. This modification is function of urban design features, such as the configuration of buildings and the amount of vegetation. Compared to the undisturbed climate of the region, the climate of cities is characterized by higher temperature and lower wind speed. This modification is especially pronounce in dense urban areas. The climate modification of cities is not static, but varies in space and time. The spatial variations are governed by land use and built form differences, as well as by the presence or absence of green and blue infrastructures. Due to the spatial complexity of cities and the general lack of urban weather station networks in most places, the amount of available urban weather data is limited. As a consequence, planners, engineers and public health professionals can only approximate the climate impact of built environments in their respective fields.Over the past years, several numerical simulation models have emerged that are able to model the influence of built areas on the atmosphere at the local scale and thus, deliver urban weather data for an area of interest. The aim of this study is to assess the performance of three numerical models with an ability to predict site-specific urban air temperature. The evaluated models are the Urban Weather Generator (UWG), the Vertical City Weather Generator (VCWG) and the Surface Urban Energy and Water Balance Scheme (SUEWS). Although the models differ in their scopes, modeling approaches and applications, they all derive the urban weather data from rural observations considering the land use and built form characteristics of the site.The models are evaluated against air temperature measurements from the dense, 13th District of Budapest (Hungary). The field measurement utilized simple air temperature and relative humidity loggers placed in non-aspirated solar radiation screens at four shaded sites. The two week measurement period encompassed a five-day-long anticyclonic period with clear sky and low wind speed. Preliminary results indicate a good general agreement between modeled and observed values with root mean square error below or at 2&#186;C and index of agreement between 0.92-0.96. During the anticyclonic period most models slightly overestimate the daily maximum and underestimated the daily minimum urban air temperature.

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Urban air temperature model using GOES-16 LST and a diurnal regressive neural network algorithm

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111495 ◽

2020 ◽

Vol 237 ◽

pp. 111495 ◽

Cited By ~ 4

Author(s):

Joshua Hrisko ◽

Prathap Ramamurthy ◽

Yunyue Yu ◽

Peng Yu ◽

David Melecio-Vázquez

Keyword(s):

Neural Network ◽

Air Temperature ◽

Urban Air ◽

Temperature Model ◽

Network Algorithm ◽

Neural Network Algorithm

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Impact of a Detailed Urban Parameterization on Modeling the Urban Heat Island in Beijing Using TEB-RAMS

Advances in Meteorology ◽

10.1155/2014/602528 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11

Author(s):

Lei Jiang ◽

Lixin Lu ◽

Lingmei Jiang ◽

Yuanyuan Qi ◽

Aqiang Yang

Keyword(s):

Land Use ◽

Urban Heat Island ◽

Air Temperature ◽

Metropolitan Area ◽

Atmospheric Modeling ◽

Anthropogenic Heat ◽

Heat Island ◽

Urban Heat ◽

Four Levels ◽

Geometric Morphology

The Town Energy Budget (TEB) model coupled with the Regional Atmospheric Modeling System (RAMS) is applied to simulate the Urban Heat Island (UHI) phenomenon in the metropolitan area of Beijing. This new model with complex and detailed surface conditions, called TEB-RAMS, is from Colorado State University (CSU) and the ASTER division of Mission Research Corporation. The spatial-temporal distributions of daily mean 2 m air temperature are simulated by TEB-RAMS during the period from 0000 UTC 01 to 0000 UTC 02 July 2003 over the area of 116°E~116.8°E, 39.6°N~40.2°N in Beijing. The TEB-RAMS was run with four levels of two-way nested grids, and the finest grid is at 1 km grid increment. An Anthropogenic Heat (AH) source is introduced into TEB-RAMS. A comparison between the Land Ecosystem-Atmosphere Feedback model (LEAF) and the detailed TEB parameterization scheme is presented. The daily variations and spatial distribution of the 2 m air temperature agree well with the observations of the Beijing area. The daily mean 2 m air temperature simulated by TEB-RAMS with the AH source is 0.6 K higher than that without specifying TEB and AH over the metropolitan area of Beijing. The presence of urban underlying surfaces plays an important role in the UHI formation. The geometric morphology of an urban area characterized by road, roof, and wall also seems to have notable effects on the UHI intensity. Furthermore, the land-use dataset from USGS is replaced in the model by a new land-use map for the year 2010 which is produced by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS). The simulated regional mean 2 m air temperature is 0.68 K higher from 01 to 02 July 2003 with the new land cover map.

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