Predictability Mysteries in Cloud-Resolving Models

Abstract

The rapid amplification of small-amplitude perturbations by the chaotic nature of the atmospheric dynamics intrinsically limits the skill of deterministic weather forecasts. In this study, limited-area cloud-resolving numerical weather prediction (NWP) experiments are conducted to investigate the role of mesoscale processes in determining predictability. The focus is set on domain-internal error growth by integrating an ensemble of simulations using slightly modified initial conditions but identical lateral boundary conditions. It is found that the predictability of the three investigated cases taken from the Mesoscale Alpine Programme (MAP) differs tremendously. In terms of normalized precipitation spread, values between 0.05 (highly predictable) and 1 (virtually unpredictable) are obtained. Analysis of the derived ensemble spread demonstrates that the diabatic forcing associated with moist dynamics is the prime source of rapid error growth. However, in agreement with an earlier study it is found that the differentiation between convective and stratiform rain is unable to account for the distinctive precipitation spreads of the three cases. In particular, instability indices are demonstrated to be poor predictors of the predictability level. An alternate hypothesis is proposed and tested. It is inspired by the dynamical instability theory and states that significant loss of predictability only occurs over moist convectively unstable regions that are able to sustain propagation of energy against the mean flow. Using a linear analysis of gravity wave propagation, this hypothesis is shown to provide successful estimates of the predictability level for the three cases under consideration.