The energetics of error-growth and the predictability analysis in precipitation prediction

Abstract

Sensitivity simulations are conducted in AREM (Advanced Regional Eta-Coordinate numerical heavy-rain prediction Model) for a torrential precipitation in June 2008 along South China to investigate the effect of initial uncertainty on precipitation predictability. It is found that the strong initial-condition sensitivity for precipitation prediction can be attributed to the upscale evolution of error growth. However, different modality of error growth can be observed in lower and upper layers. Compared with lower-level, significant error growth in the upper-layer appears over both convective area and high jet stream. It thus indicates that the error growth depends on both moist convection due to convective instability and the wind shear associated with dynamic instability. As heavy rainfall process can be described as a series of energy conversion, it reveals that the advection-term and latent heating serve as significant energy sources. Moreover, the dominant source terms of error-energy growth are nonlinearity advection (ADVT) and difference in latent heating (DLHT), with the latter being largely responsible for the rapid error growth in the initial stage. In this sense, the occurrence of precipitation and error-growth share the energy source, which implies the inherent predictability of heavy rainfall. In addition, a decomposition of ADVT further indicates that the flow-dependent error growth is closely related to the atmospheric instability. Thus the system growing from unstable flow regime has its intrinsic predictability.