Monte Carlo Simulations of Explosive Cyclogenesis

Steven L. Mullen Institute of Atmospheric Physics, The University of Arizona, Tucson, Arizona

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David P. Baumhefner National Center for Atmospheric Research Boulder, Colorado

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Abstract

The impact of initial condition uncertainty on short-range (0–48 h) simulations of explosive surface cyclogenesis is examined within the context of a perfect model environment. Eleven Monte Carlo simulations are performed on 10 cases of rapid oceanic cyclogenesis that occurred in a long-term, perpetual January integration of a global spectral model. The perturbations used to represent the initial condition error have a magnitude and spatial decomposition that closely matches estimates of global analysis error.

Large variability characterizes the error growth rates, both among the individual Monte Carlo simulations and among the case-average values. Some individual simulations display error growth doubling times as fast as approximately 12 h during the 24-h period of most rapid intensification, while others exhibit virtually no error growth. The variability is also reflected in the wide 90% confidence bounds for many surface weather elements such as the cyclone position and central pressure. However, no statistically significant differences are found between the initial states leading to large simulation errors and those leading to negligible errors. These results attest to the importance of initial condition uncertainty as the major cause of forecast variability and indicate a strong sensitivity to subtle differences in initial perturbation location and structure.

The effect that simple ensemble averaging has on reducing uncertainty is discussed. Averaging a 16-member ensemble decreases the random component of the initial data error by 80%–90% and the 90% confidence bounds by 70%–80% for cyclone position, central pressure, and 12-h pressure change. It is hypothesized that ensemble forecasting could benefit the utility of short-range forecasts for many weather elements of operational interest and conclude that research efforts should be directed at examining its effectiveness in an operational setting.

Abstract

The impact of initial condition uncertainty on short-range (0–48 h) simulations of explosive surface cyclogenesis is examined within the context of a perfect model environment. Eleven Monte Carlo simulations are performed on 10 cases of rapid oceanic cyclogenesis that occurred in a long-term, perpetual January integration of a global spectral model. The perturbations used to represent the initial condition error have a magnitude and spatial decomposition that closely matches estimates of global analysis error.

Large variability characterizes the error growth rates, both among the individual Monte Carlo simulations and among the case-average values. Some individual simulations display error growth doubling times as fast as approximately 12 h during the 24-h period of most rapid intensification, while others exhibit virtually no error growth. The variability is also reflected in the wide 90% confidence bounds for many surface weather elements such as the cyclone position and central pressure. However, no statistically significant differences are found between the initial states leading to large simulation errors and those leading to negligible errors. These results attest to the importance of initial condition uncertainty as the major cause of forecast variability and indicate a strong sensitivity to subtle differences in initial perturbation location and structure.

The effect that simple ensemble averaging has on reducing uncertainty is discussed. Averaging a 16-member ensemble decreases the random component of the initial data error by 80%–90% and the 90% confidence bounds by 70%–80% for cyclone position, central pressure, and 12-h pressure change. It is hypothesized that ensemble forecasting could benefit the utility of short-range forecasts for many weather elements of operational interest and conclude that research efforts should be directed at examining its effectiveness in an operational setting.

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