Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations

By Yunjun Yao, Shunlin Liang, Xianglan Li, Yang Hong, Joshua B. Fisher, Nannan Zhang, Jiquan Chen, Jie Cheng, Shaohua Zhao, Xiaotong Zhang and 7 more. 2014

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Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression, and robust regression models in three arid environments (2017, background): “2012), heat flux (Ershadi et al. 2013; Yao et al. 2014), solar radiation (Iizumi et al.”
Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau (2017, methodology): “…weights considered the individual uncertainties through the use of probabilistic likelihood, which yielded correct values given the gauge observations and, thus, was more accurate than traditional ensemble methods, that is, SMA and OOR (Duan et al., 2007; Raftery et al., 2005; Yao et al., 2014).”
Parameter Analysis and Estimates for the MODIS Evapotranspiration Algorithm and Multiscale Verification (2019, background): “2225 structure and the quality of the forcing data, the model parameters impose considerable uncertainties when using theMOD16 algorithm, particularly for global E applications (Kim et al., 2012; Liu et al., 2013; Ramoelo et al., 2014; Talsma et al., 2018; Yang et al., 2015; Yao et al., 2014).”
Multi‐model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging (2016, methodology): “In addition, the normal conditional density function was commonly used in practice when merging the multiple ET products (Yao et al., 2014; Chen et al., 2015).”