Parameter Range Study of Numerically-Simulated Isolated Multicellular Convection Z. DuFran, B. Baranowski, C. Doswell III, and D. Weber This work is supported primarily by the National Science Foundation Grant ATM Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation. Introduction Convective storms contain areas with marked ascent which is commonly called the updraft. Although the magnitude of updrafts was once assumed to be constant, more recent observations have proven otherwise. The goal of this study is to simulate the non-continuous nature of updrafts in isolated multicell convection similar to observations, and to relate the timing of the updraft regeneration to our selected background environmental parameters. Computing Extensive numerical cloud models are necessary for experiments in meteorology. This study uses ARPI, the research version of the Advanced Regional Prediction System (ARPS), developed by Dan Weber of the Center for Analysis and Prediction of Storms (CAPS). The model solution statistically converges for decreasing resolution, but the calculation and memory requirements increase quickly (see Table 1). In order to complete this project in a human lifetime, model runs must be carried out on the available supercomputing resources, using message passing interface (MPI) domain decomposition. 4 processors are being use in each horizontal direction (16 processors) for each job 5400 second simulations 48 x 48 x 20 km model domain with 100 m resolution. This study will use OSCER’s Topdawg, as well as NCSA and PSC machines. Simplifications to the Problem The Buckingham PI-theorem is used to conglomerate these variables and reduce the number of model runs required to span the desired parameter ranges. This cuts the number of runs from potentially tens of thousands to 270. Additionally, Ben Baranowski has developed a script that creates the necessary model input file and batch script needed to run the model for a range of non-dimensional parameter values, specified by the user. This script also submits those jobs to the computing resource being used. Analysis Although we are using continuous forcing mechanisms, the buoyant parcels (called “bubbles”) pinch off from the forcing region and rise through the cloud at discrete intervals. The last unanswered question is how we define the bubbles so that we can determine the regeneration rate. Figure 1 (below) includes contours of cloud water content that act to visually map out the cloud outline of the simulated storm. This particular image is a vertical slice through the center of the storm at 2700 seconds (half way through the simulation). Circle B is a convective “bubble” that has just lifted away from the forcing region and a new “bubble” is forming within circle A. The ARPI model includes a plotting routine that allows us to look at many useful variables throughout the evolution of each simulation. However, the number of model runs that are being executed in this study will impair our ability to qualitatively inspect each run. We will devise a method to analyze the large amount of data generated by these model runs. Table 1. Number of computations and time required for a single model run with various resolutions (TopDawg) Summary Sensitive numerical experiments for isolated multicellular convection are limited. There have been several studies concentrating on updraft regeneration in two- dimensional storms like squall lines. However, the factors influencing the regeneration rate for isolated convection is still speculation. It is our hope that there will be several relationships between the updraft regeneration rate and the chosen parameters (within the respective ranges). The proposed range of parameter values will require 270 model simulations. These initial simulations might show a high sensitivity to one or more of the parameters, motivating additional study and simulations over a smaller range to properly characterize those relationships. Resolution Total Number of Computations Time for 1 job using 1 processor (hrs) Time for 1 job using 1000 processors (hrs) Time for 1000 Jobs using 1000 processors (years) 150m0.2 PetaFLOPS m1.1 PetaFLOPS m3.7 PetaFLOPS m19.0 PetaFLOPS m304.2 PetaFLOPS Experiment Design The parameters of interest to this study are those describing the geometry of the forcing region (horizontal and vertical radii), the magnitudes of each type of forcing (convergence and thermal buoyancy), the relative buoyancy and the environmental wind shear. We have chosen to represent the magnitude of thermal buoyancy with the convective available potential energy (CAPE) of the most unstable parcel. The CAPE of the environment is used to represent the background instability or buoyancy. The nature of the CAPE calculation causes some ambiguity in the value computed. For instance, a typical value of CAPE for a convective environment might be 2500 m 2 s -2. But this value can be attained by a large amount of buoyancy over a shallow layer in the atmosphere, or by a deep layer with marginal buoyancy. Therefore, this study uses a CAPE value that has been normalized with respect to the depth over which that CAPE was computed. The CAPE computation is part of a pre-processing step that prepares the input file, adjusts the sounding profile and other job control parameter for use by the simulation and supercomputing resource. Parameters to be testedRanges Source Horizontal Size(r x, r y )500 m – 15 km Source Vertical Extent(r z )700 m – 1.5 km max normalized parcel CAPE(NCAPE p ) J kg -1 m -1 (ms -2 ) max normalized environment CAPE(NCAPE e )0.008 – 0.33 J kg -1 m -1 (ms -2 ) Wind Shear (  u/  z) s -1 Convergence (-  ) – s -1 Table 2. Dimensional parameters and respective ranges Figure 1. East-West cross section of cloud water content from a numerical simulation.