Spatio-temporal variability of sensible heat fluxes: Large-Aperture Scintillometry observations during urbisphere-Berlin

Accurate modelling of turbulent sensible heat fluxes (Q_H) within cities is essential for understanding energy exchange between the urban surface and the atmosphere, with implications for several fields, including climate modelling, weather prediction, and urban environmental management. In this study, six Large-Aperture Scintillometers (LAS) operating over different neighbourhoods of Berlin within the urbisphere-Berlin campaign (Fenner et al., 2024) are used to derive Q_H. Following Saunders et al. (2024), the LAS source areas are determined by combining multiple footprints along each path using the optical path-weighting function, with iteratively derived surface roughness parameters (z₀, z_d, z_f) obtained using the Kanda et al. (2013) method combined with a detailed building-vegetation digital surface model. Geospatial data resolution is adjusted with varying atmospheric stability conditions to ensure adequate domain representation. The LAS-derived Q_H uses observed refractive index structure parameter (C_n²), with meteorological variables, roughness parameters and Monin-Obukhov Similarity Theory.

The six LAS paths span both inner and outer city areas of Berlin, enabling analysis of the spatial and temporal variability of Q_H for different building density and  vegetation, as well as land use (e.g. inner city, residential). Results are presented across season and synoptic conditions, highlighting the influence of land cover and boundary layer dynamics on Q_H. Source-area land cover composition shows a clear urban gradient, with vegetation decreasing from  ≈ 50-55% in outer-city paths to ≈ 35-50% in inner-city paths, while building fractions increase from ≈ 15-17% to ≈ 20-32%, alongside consistently high paved surface contributions (≈ 28-35%). LAS observed Q_H compared to eddy-covariance measurements show strong consistency. Q_H from high resolution (O(100 m)) numerical weather prediction modelling, undertaken with the Met Office Unified model during selected days, are compared to the LAS Q_H using the footprint characteristic to assess simulation capability.