Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters
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Entekhabi_Surface heat.pdf
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Author(s)
Bateni, S. M.
Entekhabi, Dara
Date Issued
August 2012
Journal
Water Resources Research
Publisher
American Geophysical Union (Wiley platform)
Citation
Bateni, S. M., and D. Entekhabi. “Surface Heat Flux Estimation with the Ensemble Kalman Smoother: Joint Estimation of State and Parameters.” Water Resources Research 48.8 (2012). ©2012. American Geophysical Union. All Rights Reserved.
Version
Final published version
Abstract
[1] The estimation of surface heat fluxes based on the assimilation of land surface temperature (LST) has been achieved within a variational data assimilation (VDA) framework. Variational approaches require the development of an adjoint model, which is difficult to derive and code in the presence of thresholds and discontinuities. Also, it is computationally expensive to obtain the background error covariance for the variational approaches. Moreover, the variational schemes cannot directly provide statistical information on the accuracy of their estimates. To overcome these shortcomings, we develop an alternative data assimilation (DA) procedure based on ensemble Kalman smoother (EnKS) with the state augmentation method. The unknowns of the assimilation scheme are neutral turbulent heat transfer coefficient (that scales the sum of turbulent heat fluxes) and evaporative fraction, EF (that represents partitioning among the turbulent fluxes). The new methodology is illustrated with an application to the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) that includes areal average hydrometeorological forcings and flux observations. The results indicate that the EnKS model not only provides reasonably accurate estimates of EF and turbulent heat fluxes but also enables us to determine the uncertainty of estimations under various land surface hydrological conditions. The results of the EnKS model are also compared with those of an optimal smoother (a dynamic variational model). It is found that the EnKS model estimates are less than optimal. However, the degree of suboptimality is small, and its outcomes are roughly comparable to those of an optimal smoother. Overall, the results from this test indicate that EnKS is an efficient and flexible data assimilation procedure that is able to extract useful information on the partitioning of available surface energy from LST measurements and eventually provides reliable estimates of turbulent heat fluxes.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology)
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DOI of Published Version
https://doi.org/10.1029/2011WR011542
