Estimation of urban sensible heat flux using a dense wireless network of observations

The determination of the sensible heat flux over urban terrain is challenging due to irregular surface geometry and surface types. To address this, in 2006-07, a major field campaign (LUCE) took place at the École Polytechnique Fédérale de Lausanne campus, a moderately occupied urban site. A distributed network of 92 wireless weather stations was combined with routine atmospheric profiling, offering high temporal and spatial resolution meteorological measurements. The objective of this study is to estimate the sensible heat flux over the built environment under convective conditions. Calculations were based on Monin-Obukhov similarity for temperature in the surface layer. The results illustrate a good agreement between the sensible heat flux inferred from the thermal roughness length approach and independent calibrated measurements from a scintillometer located inside the urban canopy. It also shows that using only one well-selected station can provide a good estimate of the sensible heat flux over the campus for convective conditions. Overall, this study illustrates how an extensive network of meteorological measurements can be a useful tool to estimate the sensible heat flux in complex urban environment

Approximating turbulent and non-turbulent events with the Tensor Train decomposition method

Low-rank multilevel approximation methods are often suited to attack high-dimensional problems successfully and they allow very compact representation of large data sets. Specifically, hierarchical tensor product decomposition methods, e.g., the Tree-Tucker format and the Tensor Train format emerge as a promising approach for application to data that are concerned with cascade-of-scales problems as, e.g., in turbulent fluid dynamics. Beyond multilinear mathematics, those tensor formats are also successfully applied in e.g., physics or chemistry, where they are used in many body problems and quantum states. Here, we focus on two particular objectives, that is, we aim at capturing self-similar structures that might be hidden in the data and we present the reconstruction capabilities of the Tensor Train decomposition method tested with 3D channel turbulence flow data

Estimation of urban sensible heat flux using a dense wireless network of observations

The determination of the sensible heat flux over urban terrain is challenging due to irregular surface geometry and surface types. To address this, in 2006–07, a major field campaign (LUCE) took place at the École Polytechnique Fédérale de Lausanne campus, a moderately occupied urban site. A distributed network of 92 wireless weather stations was combined with routine atmospheric profiling, offering high temporal and spatial resolution meteorological measurements. The objective of this study is to estimate the sensible heat flux over the built environment under convective conditions. Calculations were based on Monin–Obukhov similarity for temperature in the surface layer. The results illustrate a good agreement between the sensible heat flux inferred from the thermal roughness length approach and independent calibrated measurements from a scintillometer located inside the urban canopy. It also shows that using only one well-selected station can provide a good estimate of the sensible heat flux over the campus for convective conditions. Overall, this study illustrates how an extensive network of meteorological measurements can be a useful tool to estimate the sensible heat flux in complex urban environments