Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters

摘要: [1] The estimation of surface heat fluxes based on the assimilation land temperature (LST) has been achieved within a variational data (VDA) framework. Variational approaches require development an adjoint model, which is difficult to derive and code in presence thresholds discontinuities. Also, it computationally expensive obtain background error covariance for approaches. Moreover, schemes cannot directly provide statistical information accuracy their estimates. To overcome these shortcomings, we develop alternative (DA) procedure ensemble Kalman smoother (EnKS) with state augmentation method. The unknowns scheme are neutral turbulent transfer coefficient (that scales sum fluxes) evaporative fraction, EF represents partitioning among fluxes). new methodology illustrated application First International Satellite Land Surface Climatology Project Field Experiment (FIFE) that includes areal average hydrometeorological forcings flux observations. results indicate EnKS model not only provides reasonably accurate estimates but also enables us determine uncertainty estimations under various hydrological conditions. compared those optimal (a dynamic model). It found less than optimal. However, degree suboptimality small, its outcomes roughly comparable smoother. Overall, from this test efficient flexible able extract useful available energy LST measurements eventually reliable fluxes.