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Real-time urban climate monitoring provides useful information that can beutilized to help monitor and adapt to extreme events, including urbanheatwaves. Typical approaches to the monitoring of climate data include weatherstation monitoring and remote sensing. However, climate monitoring stations arevery often distributed spatially in a sparse manner, and consequently, this hasa significant impact on the ability to reveal exposure risks due to extremeclimates at an intra-urban scale. Additionally, traditional remote sensing datasources are typically not received and analyzed in real-time which is oftenrequired for adaptive urban management of climate extremes, such as suddenheatwaves. Fortunately, recent social media, such as Twitter, furnishesreal-time and high-resolution spatial information that might be useful forclimate condition estimation. The objective of this study is utilizinggeo-tagged tweets (participatory sensing data) for urban temperature analysis.We first detect tweets relating hotness (hot-tweets). Then, we studyrelationships between monitored temperatures and hot-tweets via a statisticalmodel framework based on copula modelling methods. We demonstrate that thereare strong relationships between "hot-tweets" and temperatures recorded at anintra-urban scale. Subsequently, we then investigate the application of"hot-tweets" informing spatio-temporal Gaussian process interpolation oftemperatures as an application example of "hot-tweets". We utilize acombination of spatially sparse weather monitoring sensor data and spatiallyand temporally dense lower quality twitter data. Here, a spatial best linearunbiased estimation technique is applied. The result suggests that tweetsprovide some useful auxiliary information for urban climate assessment. Lastly,effectiveness of tweets toward a real-time urban risk management is discussedbased on the results.

A spatiotemporal analysis of participatory sensing data "tweets" and extreme climate events toward real-time urban risk management