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

Models that describe spatial and temporal distributions of urban temperatures are crucial for a better understanding of complex urban environments, but driving and validating these models requires an enormous amount of field data. The existing fixed observation networks are not spatially representative and can produce unexplained differences from the actual system. In order to improve urban temperature forecasting accuracy, this study introduced a self-manufactured mobile meteorological measurement system. As a result of using the collected data as input to the physics-based model, interpolation errors between the existing observation network and the model were minimized. As a further solution to the high costs and inefficiencies of mobile data collection campaigns, a new optimization methodology for the field campaign was proposed. With this protocol, measurement costs can be minimized and model accuracy can be enhanced when performing high-resolution environmental modeling on a limited budget.