Advanced hyper-local air temperature prediction in urban environments

The Science

Predicting detailed weather down to the air temperature on city streets is a challenging task due to the complex environment of urban areas. The traditional methods for modeling land surface conditions in urban areas involve a lot of computing power and aren’t efficient. This study introduces a way to estimate hourly air temperature on city streets. It combines traditional weather modeling with machine learning and advanced environmental monitoring networks. Tested in Chicago, researchers successfully estimated the air temperature at a very detailed level.

The Impact

The impact of this study is significant in two ways. First, it unifies past research on urban environments with new techniques like machine learning and urban climate informatics – the collection and use of climate data and urban information. This enhances the development of new urban climate models and guides city observations. Second, being able to estimate temperatures very locally is useful for urban planning, reducing the urban heat island effect, and protecting public health. City planners and architects can use this data to design buildings that improve airflow and cool the space. Overall, accurate temperature information leads to more effective city planning and makes cities safer and more comfortable to live in.

Summary

Estimating air temperature at street-level is a difficult task because of the complex environment in cities and the limitations of the current urban numerical models. In recent years, with the rapid development of data collection and analysis techniques, it is possible to fully utilize the hyper-local data harvested from urban areas by advanced machine learning algorithms. In this study, a modeling method is proposed to estimate point-scale street-level air temperature from conventional urban weather model and a suite of hyper-resolution urban data sets. These data sets were collected using state-of-art techniques, such as sub-meter level Light Detection and Ranging technology and wireless weather observation network. The proposed approach vastly improves the resolution of temperature predictions through an application to the City of Chicago. The modeling results have multiple real-world applications, such as providing navigation suggestions to reduce thermal discomfort of the pedestrians as an example. Moreover, it is possible to expand the use of our model to other areas given the current data availability. The results of this study can also help the development of the next-generation urban climate and weather models and guide observation efforts in cities. These together can build the strength for the holistic understanding of urban microclimate dynamics.