Impact of Covariance Localization on Ensemble Estimation of Surface Downwelling Longwave and Shortwave Radiation Fluxes

Abstract

Abstract Accurate estimates of terrestrial hydrologic states and fluxes are, in large part, dependent on accurate estimates of the spatiotemporal variability and uncertainty of land surface forcings, including downwelling longwave (LW) and shortwave (SW) fluxes. However, such characterization of land surface forcings does not always receive proper attention. This study attempts to better estimate LW and SW fluxes, including their uncertainties, by merging different sources of information while considering horizontal error correlations via implementation of a 2D conditioning procedure within a Bayesian framework. A total of 25 experiments were performed utilizing four different, readily available downwelling radiation products. The localized region of space used to constrain horizontal error correlations was defined using an influence length, , specified a priori. Quantitative comparisons are made against an independent, ground-based observational network. In general, results suggest moderate improvement in cloudy-sky LW fluxes and modest improvement in clear-sky SW fluxes during certain times of the year when using the 2D framework relative to a more traditional 1D framework, but only up to a certain influence length scale. Beyond this length scale the flux estimates were typically degraded because of the introduction of spurious correlations. The influence length scale that yielded the greatest improvement in LW radiative flux estimation during cloudy-sky conditions, in general, increased with increasing cloud cover. These findings have implications for improving downwelling radiative flux estimation and further enhancing existing Land Data Assimilation System (LDAS) frameworks.