Estimation of Turbulent Heat Fluxes by Assimilation of Land Surface Temperature Observations From GOES Satellites Into an Ensemble Kalman Smoother Framework. Issue 5 (2nd March 2018)

• Record Type: Journal Article
• Authors: Xu, Tongren; Bateni, S. M.; Neale, C. M. U.; Auligne, T.; Liu, Shaomin
• Is Part Of: Journal of geophysical research. Volume 123:Issue 5(2018)
• Page Start: 2409
• Page End: 2423
• Publication Date: 2018-03-02
• Subjects: data assimilation -- evapotranspiration -- land surface temperature -- neutral bulk heat transfer coefficient -- evaporative fraction -- uncertainty; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD027732
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: In different studies, land surface temperature (LST) observations have been assimilated into the variational data assimilation (VDA) approaches to estimate turbulent heat fluxes. The VDA methods yield accurate turbulent heat fluxes, but they need an adjoint model, which is difficult to derive and code. They also cannot directly calculate the uncertainty of their estimates. To overcome the abovementioned drawbacks, this study assimilates LST data from Geostationary Operational Environmental Satellite into the ensemble Kalman smoother (EnKS) data assimilation system to estimate turbulent heat fluxes. EnKS does not need to derive the adjoint term and directly generates statistical information on the accuracy of its predictions. It uses the heat diffusion equation to simulate LST. EnKS with the state augmentation approach finds the optimal values for the unknown parameters (i.e., evaporative fraction and neutral bulk heat transfer coefficient, C HN ) by minimizing the misfit between LST observations from Geostationary Operational Environmental Satellite and LST estimations from the heat diffusion equation. The augmented EnKS scheme is tested over six Ameriflux sites with a wide range of hydrological and vegetative conditions. The results show that EnKS can predict not only the model parameters and turbulent heat fluxes but also their uncertainties over a variety of land surface conditions. Compared to the variational method, EnKS yields suboptimal turbulent heat fluxes. However, suboptimality of EnKS is small, and its results are comparable to those of the VDA method. Overall, EnKS is a feasible and reliable method for estimation of turbulent heat fluxes. Key Points: An ensemble Kalman smoother (EnKS) data assimilation scheme for turbulent heat flux estimation is developed and test over six FLUXNET sites The EnKS model can produce both turbulent heat flux estimates and their uncertainties over a variety of land surface conditions The performance of EnKS method is comparable to the variational data assimilation (VDA) method with assimilation of GOES LST data