Machine Learning-Based Downscaling of Urban Air Temperature Using Lidar Data DOI

Fatemeh Chajaei,

Hossein Bagheri

Опубликована: Янв. 1, 2023

Climate models typically provide air temperature estimates at lower resolutions, lacking the necessary details for urban climate studies. These require significant computational resources and time to estimate temperatures higher resolution, which are not easily accessible city scale. In contrast, data-driven approaches offer accuracy speed in downscaling. this study, a framework downscaling derived from such as UrbClim was developed. The proposed utilized morphological features extracted LiDAR data. To extract features, first three-dimensional building model created using data deep learning models. Then, these were integrated with meteorological parameters wind, humidity, etc., downscale machine algorithms. results demonstrated that developed effectively Deep algorithms played crucial role generating extracting aforementioned features. Also, evaluation of various indicated LightGBM had best performance an RMSE 0.352°K MAE 0.215°K. Furthermore, examination final maps showed successfully estimated enabling identification local patterns street level. source codes corresponding research paper available on GitHub via https://github.com/FatemehCh97/Air-Temperature-Downscaling

Язык: Английский

Optimizing pedestrian thermal comfort in urban street canyons for summer and winter: Tree planting or low-albedo pavements? DOI
Tailong Zhang,

Xiaotong Fu,

Feng Qi

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер 120, С. 106143 - 106143

Опубликована: Янв. 12, 2025

Язык: Английский

Процитировано

3

Investigation of the outdoor workers’ thermal comfort and improving technology DOI
Jiahao Yang,

Yini Fan,

Zhuotong Wu

и другие.

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115332 - 115332

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

3

Dynamics of cool surface performance on urban microclimate: A full-scale experimental study in Singapore DOI

E. V. S. Kiran Kumar Donthu,

Yong Ping Long,

Man Pun Wan

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер 102, С. 105218 - 105218

Опубликована: Янв. 20, 2024

Язык: Английский

Процитировано

9

Machine learning framework for high-resolution air temperature downscaling using LiDAR-derived urban morphological features DOI

Fatemeh Chajaei,

Hossein Bagheri

Urban Climate, Год журнала: 2024, Номер 57, С. 102102 - 102102

Опубликована: Авг. 23, 2024

Язык: Английский

Процитировано

6

An in-depth review of phase change materials in concrete for enhancing building energy-efficient temperature control systems DOI Creative Commons

Zizheng Yu,

Ruizhe Shao, Jun Li

и другие.

Journal of Energy Storage, Год журнала: 2024, Номер 104, С. 114533 - 114533

Опубликована: Ноя. 9, 2024

Язык: Английский

Процитировано

5

Assessment of the Effectiveness of Cool Pavements on Outdoor Thermal Environment in Urban Areas DOI

Hasna Fawzi Elmagri,

Tarek M. Kamel, Hasan Özer

и другие.

Building and Environment, Год журнала: 2024, Номер 266, С. 112095 - 112095

Опубликована: Сен. 16, 2024

Язык: Английский

Процитировано

4

Research on the impact of land use and land cover changes on local meteorological conditions and surface ozone in the north China plain from 2001 to 2020 DOI Creative Commons
Chunsheng Fang,

Xinlong Li,

Juan Li

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 15, 2025

Land use and land cover changes (LULCC) alter local surface attributes, thereby modifying energy balance material exchanges, ultimately impacting meteorological parameters air quality. The North China Plain (NCP) has undergone rapid urbanization in recent decades, leading to dramatic cover. This study utilizes the 2020 data obtained from MODIS satellite replace default 2001 Weather Research Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model. It simulates analyzes direct impact of LULCC on indirect ozone (O3) concentration through physical chemical processes during July summer. Six rapidly urbanizing cities were selected represent Plain. results show that significantly increased sensible heat flux 2-m temperature areas throughout diurnal cycle, with more pronounced effects daytime, ranging 6.49 23.46 W/m2 0.20–0.59 °C, respectively. 10-m wind speed decreased at night day, − 0.43 0.27 m/s 0.16 0.15 day. planetary boundary layer height generally increased, a larger rise 23.63 84.74 m. Simultaneously, O3 concentrations both daytime nighttime. increase ranged 2.89 9.82 μg/m3, while nighttime 1.76 7.77 μg/m3. enhanced as well vertical transport, an O3. At same time, it reduced horizontal transport dry deposition processes. These are related variations. was not limited but extended top (approximately 1500 m). Below 500 m, concentrations, concentrations. Additionally, induced by showed above surface, whereas process had smaller surface. reveals significant urban expansion regional optimizes model's simulation quality provides new insights into understanding conditions

Язык: Английский

Процитировано

0

The impact of urban morphology on sunlight availability at urban and neighborhood scales: a systematic review DOI
Ehsan Rostami, Nazanin Nasrollahi

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106194 - 106194

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Understanding the Factors Driving Species Composition Similarity of Urban Spontaneous Plants DOI
Min Guo, Hua Zheng, Xinxin Wang

и другие.

Urban forestry & urban greening, Год журнала: 2025, Номер unknown, С. 128766 - 128766

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Drone-Based Vertical Atmospheric Temperature Profiling in Urban Environments DOI Creative Commons

Jokūbas Laukys,

Bernardas Maršalka, Ignas Daugėla

и другие.

Drones, Год журнала: 2023, Номер 7(11), С. 645 - 645

Опубликована: Окт. 24, 2023

The accurate and detailed measurement of the vertical temperature, humidity, pressure, wind profiles atmosphere is pivotal for high-resolution numerical weather prediction, determination atmospheric stability, as well investigation small-scale phenomena such urban heat islands. Traditional approaches, balloons, have been indispensable but are constrained by cost, environmental impact, data sparsity. In this article, we investigate uncrewed aerial systems (UASs) an innovative platform in situ probing. By comparing from a drone-mounted semiconductor temperature sensor (TMP117) with traditional radiosonde measurements, spotlight UAS-collected data’s accuracy system suitability surface layer measurement. Our research encountered challenges linked inherent delays achieving ambient readings. However, applying specific processing techniques, including smoothing methodologies like Savitzky–Golay filter, iterative smoothing, time shift, Newton’s law cooling, improved consistency. 28 flights were examined certain patterns between different sensors observed. Temperature differentials assessed over range 100 m. article highlights notable achievement 0.16 ± 0.014 °C 95% confidence when cooling comparison to RS41’s data. findings demonstrate potential UASs capturing profiles. This work posits that UASs, further refinements, could revolutionize collection.

Язык: Английский

Процитировано

3