Investigation of subgrid-scale scalar flux and its production rate using measurement data (HATS)

The subgrid-scale (SGS) scalar flux in the atmospheric surface layer is studied using field measurements data. We analyze the means of the SGS scalar flux, the SGS scalar flux production, the SGS scalar variance production, the SGS stress, and the SGS stress production conditional on both the resolvable-scale velocity and scalar, which must be reproduced by SGS models for large-eddy simulation to reproduce the one-point resolvable-scale joint velocity-scalar statistics. The results show that the conditional scalar flux and its production rate depend on the resolvable-scale velocity and temperature fluctuations, suggesting that these conditional variables have strong influences on the resolvable-scale statistics. The dependences are due to the effects of buoyancy and flow history. Furthermore, the dependences for the conditional flux and the conditional flux production rate have similar trends. The positive temperature fluctuations associated with updrafts are found to have qualitatively a different influence on the dependences than the negative temperature fluctuations associated with downdrafts. The results show that the conditional vertical scalar flux affects the conditional horizontal scalar flux production rate. However, the conditional horizontal scalar flux has no direct effect on the conditional vertical scalar production rate. Therefore, correct modeling of the conditional vertical scalar flux components is extremely crucial. Predictions of the conditional scalar flux and the scalar flux production rate predicted using several SGS models are compared with measurements. The Smagorinsky model can predict well the conditional vertical scalar flux and the conditional horizontal scalar flux production rate. The nonlinear model can predict well the conditional horizontal scalar flux. In a convective atmospheric surface layer, where there exist a dominant vertical gradient, predictions using the nonlinear model are found to be closely related to predictions using the Smagorinsky model. The similarities and the dynamics connections between the conditional scalar flux and its production rate provide the potential of using the conditional scalar flux production rate to model the scalar flux in convective ABLs.

Supported by NSF