  Amid growing environmental concerns, interactive displays of data constitute
an important tool for exploring and understanding the impact of climate change
on the planet's ecosystemic integrity. This paper presents Tokyo kion-on, a
query-based sonification model of Tokyo's air temperature from 1876 to 2021.
The system uses a recurrent neural network architecture known as LSTM with
attention trained on a small dataset of Japanese melodies and conditioned upon
said atmospheric data. After describing the model's implementation, a brief
comparative illustration of the musical results is presented, along with a
discussion on how the exposed hyper-parameters can promote active and
non-linear exploration of the data.

Sound;Human-Computer Interaction;Machine Learning;Audio and Speech Processing