Abstract
A base case and the best time-coherent, regional analog to that case, identified in Part I, were analyzed to determine the precise mechanisms responsible for the differences in precipitation amounts. This analysis was conducted using two modeling approaches. A model dataset that provides time continuity and dynamic coupling among the various fields was generated using a continuous four-dimensional data assimilation procedure for 60-h integrations within the Pennsylvania State University–National Center for Atmospheric Research fifth generation Mesoscale Model (PSU–NCAR MM5). A nonhydrostatic, axisymmetric cloud model with large-scale convergent forcing was also used to perform selected experiments to assess the triggering of convection in a controlled setting.
The findings from these analyses are the following. Differences in moisture source origins of parcels arriving within the precipitation region, observationally linked in Part I to variations in the structure and intensity of the subtropical jet, were sufficient to explain the precipitation variation between the base case and the analog during the first 24 h. Coupling of an upper- and lower-level jet streak allowed near-surface lifting to occur in the base case during the second 24 h. Although the synoptic environment was similar in the analog during this time (rich moisture, potential instability, deep tropospheric ascent beneath the right entrance region of an upper jet streak), the jet streak coupling did not occur in the analog and reduced near-surface lifting relative to the base case. These differences were sufficient to explain the precipitation variation during the second 24-h period.
From the standpoint of predictability studies, the marked sensitivity of precipitation to details for this type of case (Rocky Mountain lee cyclogenesis) suggests that some synoptic flows may exhibit step function rather than smooth divergence of phase-space trajectories. For such flows, it may be necessary to target specific triggering features in the estimation of initial condition uncertainty such that the true dispersion of the probability density function can be measured and a relationship between variance and skill in short-range ensemble forecasts can be attained.
Corresponding author address: Paul J. Roebber, Department of Mathematical Sciences, University of Wisconsin—Milwaukee, 3200 N. Cramer Ave., Milwaukee, WI 53211. Email: roebber@uwm.edu
