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  • Author or Editor: Lisheng Xu x
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Guotong Zhang, Lisheng Xu, and Hongbin Chen

Abstract

A new parameterization scheme for the shortwave radiative properties of water clouds is presented, using versatile cloud drop size distributions ( DSDs). As for single-scattering properties, a new parameterization for cloud optical thickness is proposed by using the separation of the dependence of r on the total number of theDSDs, the cloud thickness, and the liquid water content, combined with equivalent radius. The cloud bulk radiative properties are obtained from the delta-Eddington approximation for our cloud models. It is shown that the flux reflectivity, transmissivity, and absorptivity are uniquely fitted by a dimensionless parameter X or the cloud optical thickness 7. The parameterization is compared with other schemes. The features and potential use of the scheme are discussed.

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Tongren Xu, Sayed M. Bateni, Steven A. Margulis, Lisheng Song, Shaomin Liu, and Ziwei Xu

Abstract

The primary objective of this study is to assess the accuracy of the two-source variational data assimilation (TVDA) system for partitioning evapotranspiration (ET) into soil evaporation (ETS) and canopy transpiration (ETC). Its secondary aim is to compare performance of the TVDA system with the commonly used two-source surface energy balance (TSEB) method. A combination of eddy-covariance-based ET observations and stable-isotope-based measurements of the ratio of evaporation and transpiration to total evapotranspiration (ETS/ET and ETC/ET) over an irrigated cropland site (the so-called Daman site) in the middle reach of the Heihe River basin (northwestern China) was used to investigate these objectives. The results indicate that the TVDA method predicts ETS and ETC more accurately than TSEB. Root-mean-square errors (RMSEs) of midday (1300–1500 LT) averaged soil and canopy latent heat flux (LES and LEC) estimates from TVDA are 23.1 and 133.0 W m−2, respectively. Corresponding RMSE values from TSEB are 41.9 and 156.0 W m−2. Compared to TSEB, the TVDA method takes advantage of all of the information in land surface temperature observations in the estimation period by leveraging a dynamic model (the heat diffusion equation) and thus can generate more accurate LES and LEC estimates.

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