[1] The partitioning of available energy into dissipative fluxes over land surfaces is dependent on the state variable of the surface energy balance (land surface temperature) and the state variable of the surface water balance (soil moisture). The direct measurement of the turbulent fluxes is achieved with in situ instruments at tower sites. These point-scale measurements are sparsely distributed. Broader scale mapping of the turbulent fluxes is mostly dependent on land surface temperature (LST) and optical/infrared vegetation that can be sensed remotely. There are several data assimilation approaches currently in use that intake sequences of daytime LST that attain different diurnal amplitudes depending on available energy and the relative efficiency of surface energy balance to infer the magnitude of surface flux components such as latent and sensible heat flux. In this study we perform stability analysis on the evolution of LST in order to provide insights into the physical bases for why LST variations can be used to diagnose surface energy balance (SEB) components. The derived relative efficiencies of SEB components in dissipating available energy at the land surface are tested using two field experiment measurements. The results show that the theoretically derived relative efficiencies of SEB components agree well with field observations. The study provides insight into how LST sequences implicitly contain the signature of partitioning of available energy among SEB components and can be used to infer their magnitudes.

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.Citation: Bateni, S. M., and D. Entekhabi (2012), Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters, Water Resour. Res., 48, W08521,

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[1] Recently, a number of studies have assimilated land surface temperature (LST) within a variational data assimilation (VDA) framework to estimate turbulent heat fluxes. These VDA models have mainly considered soil and vegetation as a combined source (CS) and have not accounted for the difference between soil and canopy temperatures and turbulent exchange rates, although soil and canopy can exhibit very different behaviors. Hence, in this study the contribution of soil and canopy to the LST and turbulent heat fluxes is taken into account separately by developing a dual-source ( ), which represents a significant improvement over the previous study.Citation: Bateni, S. M., and S. Liang (2012), Estimating surface energy fluxes using a dual-source data assimilation approach adjoined to the heat diffusion equation,

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Recently, a number of studies have focused on estimating surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) observations into variational data assimilation (VDA) schemes. Using the full heat diffusion equation as a constraint, the surface energy balance equation can be solved via assimilation of sequences of LST within a VDA framework. However, the VDA methods have been tested only in limited field sites that span only a few climate and land use types. Hence, in this study, combined-source (CS) and dual-source (DS) VDA schemes are tested extensively over six FluxNet sites with different vegetation covers (grassland, cropland, and forest) and climate conditions. The CS model groups the soil and canopy together as a single source and does not consider their different contributions to the total turbulent heat fluxes, while the DS model considers them to be different sources. LST data retrieved from the Geostationary Operational Environmental Satellites are assimilated into these two VDA schemes. Sensible and latent heat flux estimates from the CS and DS models are compared with the corresponding measurements from flux tower stations. The results indicate that the performance of both models at dry, lightly vegetated sites is better than that at wet, densely vegetated sites. Additionally, the DS model outperforms the CS model at all sites, implying that the DS scheme is more reliable and can characterize the underlying physics of the problem better.

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[1] A variational data assimilation model is developed to estimate surface energy fluxes from remotely sensed land surface temperature (LST). Components of the surface energy balance (sensible, latent, and ground heat fluxes) have different degrees of efficiency in dissipating available energy. LST is the state variable of the surface energy balance (SEB). Land surface models that capture the exchange and storage of energy in the soil and vegetation media use LST as a prognostic variable. Sequences of LST measurements implicitly contain information on partitioning of available energy among the components of SEB. In this study, we focus on the estimation of the sum of the turbulent fluxes as well as the partitioning among them. Two dimensionless parameters are used to characterize the sum and the partitioning. Using LST observations from a constellation of satellites, these parameters are mapped over a large region. The remotely sensed LST is assimilated to the heat diffusion equation within the SEB framework. In addition, a model error term is added to the SEB equation such that the variational data assimilation scheme includes model uncertainty as well as observation error. The framework is tested over the Southern Great Plains region. The mapped results of the surface evaporation estimation are used to study the surface control on evaporation. Independent mapped soil moisture estimates from an airborne microwave campaign are used. The dependence of the evaporation control-soil moisture relationship on vegetation cover and plant functional types over large regions is examined in this first and exploratory study.Citation: Bateni, S. M., D. Entekhabi, and F. Castelli (2013), Mapping evaporation and estimation of surface control of evaporation using remotely sensed land surface temperature from a constellation of satellites, Water Resour. Res., 49,