Premium
A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction
Author(s) -
Ershadi Ali,
McCabe Matthew F.,
Evans Jason P.,
Mariethoz Gregoire,
Kavetski Dmitri
Publication year - 2013
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/wrcr.20231
Subject(s) - forcing (mathematics) , bayesian inference , bayesian probability , environmental science , uncertainty analysis , sensible heat , inference , estimation , scaling , meteorology , heat flux , econometrics , computer science , climatology , statistics , mathematics , heat transfer , geography , engineering , artificial intelligence , geology , systems engineering , physics , geometry , thermodynamics
Key Points Bayesian inference with prior info is well suited for input uncertainty analysis The land surface temperature has large uncertainties in flux estimation Scaling of surface temperature is required to capture spatial variability