Nonlinearity and predictability of convective rainfall associated with water vapor perturbations in a numerically simulated storm

Abstract

Abstract. Predictability of convective rainfall in a numerically simulated storm is assessed in conjunction with nonlinearity imposed by systematic perturbations in water vapor using a three-dimensional cloud model (ARPS). Nonlinearity is explicitly quantified using the tangent linear approximation, and predictability is measured by the noise-to-signal ratio. It is found that the behavior of nonlinearity at an early period of error insertion exerts strong influence on error dynamics and prcdictability in the future. The rate of increase in nonlinearity is larger for perturbations with larger magnitude and positive direction. Although dynamics of individual storms are sensitive to specified perturbations, the predictability limits generally go beyond 140 min after perturbations are introduced. The major contribution to the total error in the domain-integrated accumulated rainfall fields comes from the phase error rather than the amplitude error, especially under the region of secondary storms. The location and general behavior of the main storm (supercell) is predictable in a longer timescale than suggested by Lorenz's theoretical analysis. On the basis of the results from this study, some implications for practical storm-scale predictability are discussed. IntroductionThe growth and decay of perturbations in initial conditions essentially determine the predictability of atmospheric flow. Predictability refers to the degree of divergence among predictions started from nearby initial conditions through the time domain and thus depends on the sensitivity of system behavior to initial conditions. A predictability limit (or horizon) is a time after which prediction becomes useless no matter how accurately the initial condition may be obtained [Lighthill, 1986].Since Thompson [1957] 31,575

On the Predictability of Mesoscale Convective Systems: Three-Dimensional Simulations

“…Moisture perturbations are found to have a large impact on accumulated rainfall (Park 1999) and updraft strength (Gilmore and Wicker 1998;Park and Droegemeier 2000). Previous studies of the sensitivity of convection to initial perturbations often involve simulations of supercells and not MCSs, nevertheless, one might expect the results to be qualitatively applicable to MCSs, as well.…”

Atmospheric Predictability at Synoptic Versus Cloud-Resolving Scales

“…Such results have been obtained for barotropic flows (Tanguay et al 1995), for global NWP models at varying mesh sizes (e.g., Hartmann et al 1995;Buizza et al 1997;Simmons and Hollingsworth 2002;Gilmour et al 2001), for limited-area integrations (e.g., Ancell and Mass 2006;Errico and Raeder 1999;Zhang et al 2003), for idealized simulations of moist convection with cloud-resolving models (e.g., Park 1999;Park and Droegemeier 2000;Sun 2005), and from turbulence theory (e.g., Lorenz 1969;Leith 1971). The expected degradation of the tangent-linear approximation implies that the usefulness of adjoint-based methods may decrease with increasing resolution.…”

On the Predictability of Mesoscale Convective Systems: Two-Dimensional Simulations

“…The rapid error growth associated with convection is further documented by Hohenegger and Schär (2007). Crook (1996), Gilmore andWicker (1998), Park (1999), and Park and Droegemeier (2000) each examine the sensitivity of convection to changes in the within-and nearstorm environments, showing that perturbations in both the moisture and temperature fields can have a substantial impact on storm development and evolution.…”