Comments on “Observational Analysis of the Predictability of Mesoscale Convective Systems”

Abstract

Jirak and Cotton (2007, hereafter JC07) proposed a new index to assist in forecasting the development of mesoscale convective systems (MCSs). This ‘‘MCS index’’ is the summation of three components that are a function of the 1) ‘‘best’’ lifted index (LI), 2) 0–3-km shear vector magnitude (SVM), and 3) 700-hPa temperature advection (TAdv). JC07’s study also reemphasized important aspects of MCS development, namely, the importance of the low-level jet (e.g., Junker et al. 1999) and low-level warm advection (e.g., Maddox and Doswell 1982) in the development and sustenance of MCSs. Their MCS index attempts to account for these important physical processes. The use of indices has become ubiquitous in operational weather forecasting, especially in the realm of deep moist convection. However, indices easily can be misused and overused, as discussed by Doswell and Schultz (2006). Some indices have been developed arbitrarily, and others lack a robust physical foundation. This may be complicated further when multiple variables are combined into a single index. As a consequence, before operational forecasters can utilize indices to their potential advantage, they need a solid understanding of both how the indices were developed and the relative importance of their underlying components. The intent of this comment is to consider the MCS index and its three components, and subsequently to use the findings as motivation for others—in training and operational roles—to investigate such indices before they transfer them to operations. This study was conceived as the MCS index was being tested for operational use at the Weather Forecast Office (WFO) in Rapid City, South Dakota (RAP). Although the results do not refute the MCS index per se, there is concern this index (and possibly others) may be misapplied using standard operational gridded datasets. It is ultimately shown that the MCS index, in its present form (JC07), is not suitable for operational forecasting.