Increasing Atmospheric Surface Spread in an Ensemble Model Using Land Cover Fraction Perturbations

Operational ensemble numerical weather prediction models are typically underspread near the land surface, with the Australian Bureau of Meteorology’s (BoM) global system being a typical example. In this study, land surface fraction values, representing the estimated proportions of various land cover types, are perturbed with the aim of increasing the ensemble spread at the surface. The perturbations are achieved by multiplying the existing land surface fraction estimates by spatially correlated random error structures that represent the uncertainties in these estimates. The methodology was trialed over a 75-day period during the Australian summer of 2017–2018 when both perturbed and unperturbed forecasting cycling experiments were run. The results showed that land surface fraction perturbations increased surface temperature, sensible heat flux, and latent heat flux ensemble spread significantly, especially in the tropics and over the Australian region. The screen-level temperature ensemble spread also increased, albeit by a relatively smaller magnitude compared to the surface temperature ensemble spread. Root-mean square error values—as measured relative to reanalysis data—were also found to be smaller in the perturbed runs, leading to significantly improved spread-to-skill ratio values.