EMS Annual Meeting Abstracts
Vol. 18, EMS2021-3, 2021
EMS Annual Meeting 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

The combination use of microclimate measurement network, urban type classification and mesoscale climate prediction model to estimate the thermal condition distribution

Yu Cheng Chen, Fang Yi Cheng, Cheng Pei Yang, and Tzu Ping Lin
Yu Cheng Chen et al.

The climate in Taiwan is hot and humid, and urban show high-density development. The dense urban development has increased the heat storage of the ground and buildings. The compact arrangement of tall buildings causes the narrowness of the urban space to block the sky's view and hinders the relatively smooth airflow, which will cause problems such as poor ventilation in the city and cause high thermal risk in the city. In the past, when obtaining climate data, if only the climate stations set by the Central Meteorological Bureau were used, the distance between the stations was too far, and the coverage of the ground around the distribution area was almost the same, resulting in significant differences between the predicted results and the actual climate conditions. Therefore, this research established a microclimate measurement network to obtain climate data. For the urban environment information, the urban built environment data, such as digital surface model, and building information, were regulated data or required to purchase. Therefore, this study uses the Local Climate Zone (LCZ) which can consider land use and land cover simultaneously and can be freely produced by satellite images. The typology classification method can be used to view the city by the height and density of obstacles. LCZ can solve the inaccuracy of estimation caused by the mixed land-use in Taiwan and assign various types of related data from the scheme such as heat capacity, albedo, and roughness through classification results. This study herein applies LCZ combined with a mesoscale climate prediction model Weather Research and Forecasting (WRF) to predict the climate conditions within the study's scope and compare them with actual measured values. It can be used for urban climate assessment. The research results preliminarily show that by applying the LCZ classification and its corresponding factors to WRF with Multi-layer urban canopy model which can consider vertical surfaces such as building volume facades horizontal surfaces such as streets and roofs. The predicted temperature and actual temperature will be slightly underestimated, and it can be approximately between 1.5°C to 2.5°C in the urban area at night and 0.5°C to 1°C during the day. This phenomenon may be due to the relative ratio of buildings and road width in Taiwan, making the actual night. The heat dissipation effect is poor, and it is easy to cause heat accumulation in the urban area.

How to cite: Chen, Y. C., Cheng, F. Y., Yang, C. P., and Lin, T. P.: The combination use of microclimate measurement network, urban type classification and mesoscale climate prediction model to estimate the thermal condition distribution, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-3, https://doi.org/10.5194/ems2021-3, 2021.

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