  Imperfections and uncertainties in forecast models are often represented in
ensemble prediction systems by stochastic perturbations of model equations. In
this article, we present a new technique to generate model perturbations. The
technique is termed Additive Model-uncertainty perturbations scaled by Physical
Tendencies (AMPT). The generated perturbations are independent between
different model variables and scaled by the local-area-averaged modulus of
physical tendency. The previously developed Stochastic Pattern Generator is
used to generate space and time-correlated pseudo-random fields. AMPT attempts
to address some weak points of the popular model perturbation scheme known as
Stochastically Perturbed Parametrization Tendencies (SPPT). Specifically, AMPT
can produce non-zero perturbations even at grid points where the physical
tendency is zero and avoids perfect correlations in the perturbation fields in
the vertical and between different variables. Due to a non-local link from
physical tendency to the local perturbation magnitude, AMPT can generate
significantly greater perturbations than SPPT without causing instabilities.
Relationships between biases and spreads caused by AMPT and SPPT were studied
in an ensemble of forecasts. The non-hydrostatic, convection-permitting
forecast model COSMO was used. In ensemble prediction experiments, AMPT
perturbations 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 but
weaker effects on near-surface wind speed and mixed effects on precipitation.
