Field data acquisition campaign study for physically based temperature modeling in complex urban environments

Modeling the spatial and temporal distributions of urban temperature is critical for understanding complex urban environments and developing adaptive countermeasures for intensifying urban heat. Statistical model and physically based model using data from existing weather observation networks, which often suffer from spatial representativeness, lead to unexplained discrepancies to those for real-world systems. The urban temperature forecasting accuracy can be enhanced through a custom-built mobile meteorological measurement system combined with an optimized field campaign. By integrating mobile data into physically based models, biases inherent in traditional observation networks can be minimized. In order to overcome the high costs and inefficiencies of mobile data collection, this study proposes an optimization method for field data acquisition campaign using multiple datasets under a complex topological and landuse environment. The proposed protocol improves high-resolution temperature prediction accuracy while minimizing operational costs.构建城市气温时空分布模型，对于理解复杂城市环境、制定应对城市热效应加剧的适应性对策至关重要。依托现有气象观测网络数据构建的统计模型与物理过程模型（physically based model），往往存在空间代表性缺陷，导致其模拟结果与真实世界系统的实际状态存在无法解释的偏差。通过定制化移动气象测量系统结合优化后的外场观测方案，可有效提升城市气温预报精度。将移动观测数据融入物理过程模型，可最大限度削弱传统观测网络固有的偏差。为解决移动数据采集成本高昂、效率低下的问题，本研究提出一种在复杂地形拓扑与土地利用环境下，基于多数据集的外场数据采集方案优化方法。所提出的研究方案在降低运营成本的同时，可显著提升高分辨率气温预测精度。