  The rising temperature is one of the key indicators of a warming climate, and
it can cause extensive stress to biological systems as well as built
structures. Due to the heat island effect, it is most severe in urban
environments compared to other landscapes due to the decrease in vegetation
associated with a dense human-built environment. It is essential to adequately
monitor the local temperature dynamics to mitigate risks associated with
increasing temperatures, which can include short term strategy to protect
people and animals, to long term strategy to how to build a new structure and
cope with extreme events. Observed temperature is also a very important input
for atmospheric models, and accurate data can lead to better future forecasts.
  Ambient temperature collected at ground level can have a higher variability
when compared to regional weather forecasts, which fail to capture the local
dynamics. There remains a clear need for an accurate air temperature prediction
at the sub-urban scale at high temporal and spatial resolution. This research
proposed a framework based on Long Short-Term Memory (LSTM) deep learning
network to generate day-ahead hourly temperature forecast with high spatial
resolution. A case study is shown which uses historical in-situ observations
and Internet of Things (IoT) observations for New York City, USA. By leveraging
the historical air temperature data from in-situ observations, the LSTM model
can be exposed to more historical patterns that might not be present in the IoT
observations. Meanwhile, by using IoT observations, the spatial resolution of
air temperature predictions is significantly improved.
