Estimating surface turbulent heat fluxes from land surface temperature and soil moisture observations using the particle batch smoother

Abstract

Surface heat fluxes interact with the overlying atmosphere and play a crucial role in meteorology, hydrology, and climate change studies, but in situ observations are costly and difficult. It has been demonstrated that surface heat fluxes can be estimated from assimilation of land surface temperature (LST). One approach is to estimate a neutral bulk heat transfer coefficient (CHN) to scale the sum of turbulent heat fluxes, and an evaporative fraction (EF) that represents the partitioning between fluxes. Here the newly developed particle batch smoother (PBS) is implemented. The PBS makes no assumptions about the prior distributions and is therefore well‐suited for non‐Gaussian processes. It is also particularly advantageous for parameter estimation by tracking the entire prior distribution of parameters using Monte Carlo sampling. To improve the flux estimation on wet or densely vegetated surfaces, a simple soil moisture scheme is introduced to further constrain EF, and soil moisture observations are assimilated simultaneously. This methodology is implemented with the FIFE 1987 and 1988 data sets. Validation against observed fluxes indicates that assimilating LST using the PBS significantly improves the flux estimates at both daily and half‐hourly timescales. When soil moisture is assimilated, the estimated EFs become more accurate, particularly when the surface heat flux partitioning is energy‐limited. The feasibility of extending the methodology to use remote sensing observations is tested by limiting the number of LST observations. Results show that flux estimates are greatly improved after assimilating soil moisture, particularly when LST observations are sparse.

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“…Bateni & Liang, 2012;Lu et al, 2016), intercomparing different data assimilation techniques(Xu et al, 2019) and assessing the benefit of model parameter calibration(Koster et al, 2018).…”