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    An accurate simulation of the sensible heat flux (H) over vegetation from thermal remote sensing requires an a priori estimate of roughness length and the excess resistance parameter kB-1. Despite being the subject of considerable interest in hydrometeorology, there still does not exist a uniform method for estimating roughness length from remote sensing techniques. This study demonstrates a turbulent diffusion method to simulate canopy‐air sensible heat. The performance of the roughness length scheme as described in Chen et al. (2013,‐D‐12‐056.1) was examined by comparing simulated H to measured values at 28 flux tower stations, which include seven different land covers (needle forest, broadleaf forest, shrub, savanna, grassland, cropland, and sparsely vegetated land). The model predictions of H for grass, crop, and sparsely vegetated land compare favorably with observed values, when actual canopy height is given. H is significantly underestimated at forest sites due to a high value of kB-1. Among the different physical representations for the canopy, canopy‐soil mixture, and soil component, it is found that such a high kB-1 value is caused by the high kB-1 value for the canopy part. The reasons for this high kB-1 were investigated from canopy‐air physical process of turbulent diffusion. This study introduces the vertical foliage density information into a column canopy‐air turbulent diffusion model to include the different momentum and heat transfer efficiencies in the vertical canopy layers to enhance the thermal turbulent transfer intensity above the tall canopy. The new model has been verified to provide accurate simulation over different canopy structures.

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    Chen, Xuelong; Massman, William J.; Su, Zhongbo. 2019. A column canopy‐air turbulent diffusion method for different canopy structures. Journal of Geophysical Research: Atmospheres. 124: 488-506.


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    turbulent sensible heat flux, canopy-air interaction, roughness length, leaf drag coefficient, leaf heat transfer coefficients, within-canopy wind

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