Speakers
Description
Dense observations of near-surface air temperature are essential for studying local climate variability, yet conventional meteorological station networks remain spatially sparse. We present TelcoTemp, a system that derives air temperature observations from operational telemetry of telecommunication microwave link networks.
Microwave link units continuously record internal device temperatures as part of routine network diagnostics. These measurements reflect a combination of ambient air temperature, solar radiation, and internally generated heat. TelcoTemp brings a novel approach, which uses machine-learning models trained on reference meteorological observations to transform this operational telemetry into estimates of ambient air temperature.
The resulting dataset provides a dense layer of temperature observations from infrastructure already deployed across the landscape. Such measurements enable unique high-resolution analysis of spatial temperature variability and urban heat island effects.