Estimating anthropogenic heat flux by assimilating meteorological observations with a Kalman filter approach

Anthropogenic heat (AH) emissions in urban environments alter the surface energy budget and significantly influence urban climates. However, these emissions vary greatly in both time and space, leading to considerable uncertainty in their estimation. As remote sensing in the urban environment advances, where the remotely sensed urban surface temperatures are becoming increasingly available, such as those retrieved from satellite observations and thermal cameras. Yet, assimilating these observations into surface energy modeling for AH estimation has not been fully explored. In this study, a model for AH estimation based on the Kalman filter and surface energy balance is developed (KF-SEB model). Urban meteorological data, including air temperature and building surface temperature, are assimilated into the Kalman filter, yielding sensible heat flux and building heat storage. AH is subsequently calculated using the SEB equation. The KF-SEB model is evaluated using a forward model with predefined AH emissions. The forward model employs a simple SEB approach at the building exterior surface and adopts a 1-D heat conduction equation for the wall. The results show that the KF-SEB model accurately captures the magnitude and temporal variation of AH, with reduced uncertainties compared to previous studies. This study offers a novel approach of AH estimation based on urban meteorological data and provides important insights into the feedback between urban microclimates and anthropogenic energy use.