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Imperfections and uncertainties in forecast models are often represented inensemble prediction systems by stochastic perturbations of model equations. Inthis article, we present a new technique to generate model perturbations. Thetechnique is termed Additive Model-uncertainty perturbations scaled by PhysicalTendencies (AMPT). The generated perturbations are independent betweendifferent model variables and scaled by the local-area-averaged modulus ofphysical tendency. The previously developed Stochastic Pattern Generator isused to generate space and time-correlated pseudo-random fields. AMPT attemptsto address some weak points of the popular model perturbation scheme known asStochastically Perturbed Parametrization Tendencies (SPPT). Specifically, AMPTcan produce non-zero perturbations even at grid points where the physicaltendency is zero and avoids perfect correlations in the perturbation fields inthe vertical and between different variables. Due to a non-local link fromphysical tendency to the local perturbation magnitude, AMPT can generatesignificantly greater perturbations than SPPT without causing instabilities.Relationships between biases and spreads caused by AMPT and SPPT were studiedin an ensemble of forecasts. The non-hydrostatic, convection-permittingforecast model COSMO was used. In ensemble prediction experiments, AMPTperturbations led to statistically significant improvements (compared to SPPT)in probabilistic performance scores such as spread-skill relationship, CRPS,Brier Score, and ROC area for near-surface temperature. AMPT had similar butweaker effects on near-surface wind speed and mixed effects on precipitation.

Additive Model Perturbations Scaled by Physical Tendencies for Use in Ensemble Prediction