The Impact of Moist Physics on the Sensitive Area Identification for Heavy Rainfall Associated Weather Systems

The impact of moist physics on the sensitive areas identified by conditional nonlinear optimal perturbation (CNOP) is examined based on four typical heavy rainfall cases in northern China through performing numerical experiments with and without moist physics. Results show that the CNOP with moist physics identifies sensitive areas corresponding to both the lower- (850–700 hPa) and upper-level (300–100 hPa) weather systems, while the CNOP without moist physics fails to capture the sensitive areas at lower levels. The reasons for the CNOP peaking at different levels can be explained in both algorithm and physics aspects. Firstly, the gradient of the cost function with respect to initial perturbations peaks at the upper level without moist physics which results in the upper-level peak of the CNOP, while it peaks at both the upper and lower levels with moist physics which results in both the upper- and lower-level peaks of the CNOP. Secondly, the upper-level sensitive area is associated with high baroclinicity, and these dynamic features can be captured by both CNOPs with and without moist physics. The lower-level sensitive area is associated with moist processes, and this thermodynamic feature can be captured only by the CNOP with moist physics. This result demonstrates the important contribution of the initial error of lower-level systems that are related to water vapor transportation to the forecast error of heavy rainfall associated weather systems, which could be an important reference for heavy rainfall observation targeting.