基于大数据驱动的人为热精细化计算模型构建及其与WRF耦合的城市化效应研究

人为热是城市地表能量平衡的重要组分，不仅能够驱动城市和区域气候变化，还是影响城市极端天气的重要因素。因此在进行城市天气气候数值模拟时，人为热的准确量化对提高数值天气模式的预报精度具有至关重要的作用。本项目瞄准国家建设气候适应型城市的战略需求，拟基于地理大数据及数据挖掘技术，通过改进并构建人为热精细化计算模型，并将其与中尺度天气预报模式（WRF）相耦合，开发一种具有高时空分辨率的城市天气气候模拟系统。同时基于大孔径闪烁仪（LAS）及解析足迹模型（Footprint），构建多样化典型城市功能区热通量验证实验方案。最终，基于该耦合系统研究得出城市化对极端天气气候的影响机理。本项目预期研究成果对气候变化监测及气候趋势预估具有重要科学及应用价值，能够为我国未来城市防灾减灾和城市精细化管理提供技术支持。

Anthropogenic heat is an important part of urban surface energy balance, which can not only drive urban and regional climate change, but also affect urban extreme weather. Therefore, in the numerical simulation of urban weather and climate, the accurate quantification of anthropogenic heat plays an important role in improving the prediction accuracy of numerical weather model. Aiming at the strategic demand of building climate-resilient cities in our country, and based on geographic big data and data mining technology, a high spatial-temporal resolution urban weather and climate simulation system is developed by improving and constructing an elaborate simulation model of anthropogenic heat and coupling it with the Weather Research and Forecasting Model (WRF). In addition, based on the Large Aperture Scintillometer (LAS) and Footprint model, the heat flux verification experiment scheme of typical urban functional areas is constructed. Finally, based on the coupling system, the impact mechanism of urbanization on extreme weather and climate is obtained. The expected research results of this project have important scientific and application value for climate change monitoring and climate trend prediction, and can provide technical support for future urban disaster prevention and mitigation and urban delicacy management in China.