Stable gap-filling for longer eddy covariance data gaps: A globally validated machine-learning approach for carbon dioxide, water, and energy fluxes
Songyan Zhu, Robert Clement, Jon McCalmont, Christian A. Davies, Timothy C. Hill
Topics & Concepts
Eddy covarianceSensible heatEnvironmental scienceRange (aeronautics)Standard deviationAtmospheric sciencesFlux (metallurgy)EcosystemStatisticsMathematicsEngineeringEcologyPhysicsChemistryBiologyAerospace engineeringOrganic chemistryPlant Water Relations and Carbon DynamicsHydrology and Watershed Management StudiesClimate variability and models