Land-Surface Parameter and State Perturbations in the Global Ensemble Forecast System

The National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) is under-spread near the surface, a common characteristic of ensemble prediction systems. Here, several methods for increasing the spread are tested, including perturbing soil initial conditions, soil tendencies, and surface parameters with physically-based perturbations. Perturbations are applied to the soil initial conditions based on empirical orthogonal functions (EOFs) of differences between normalized soil moisture states from different Land Surface Models (LSMs). Perturbations of roughness lengths for heat and momentum, soil hydraulic conductivity, stomatal resistance, vegetation fraction and albedo are applied, with the amplitude and perturbation scales based on previous research. Soil moisture and temperature tendencies are also perturbed.
Overall, results show that surface perturbations have a modest impact on the spread of near-surface temperature and other variables in the GEFS. Adjusting the forecasts using an estimate of the systematic bias shows that bias correction has a larger impact on the forecast reliability than surface perturbations.
The results indicate that surface perturbations, through their impact on near-surface spread, have a modest positive impact on the skill of short-range ensemble forecasts, but that systematic bias in the model needs to be addressed as well.