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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 5
Nonlin. Processes Geophys., 20, 605-620, 2013
https://doi.org/10.5194/npg-20-605-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 20, 605-620, 2013
https://doi.org/10.5194/npg-20-605-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 11 Sep 2013

Research article | 11 Sep 2013

Multifractal properties of embedded convective structures in orographic precipitation: toward subgrid-scale predictability

M. Nogueira1,2, A. P. Barros2, and P. M. A. Miranda1 M. Nogueira et al.
  • 1Instituto Dom Luiz, University of Lisbon, Lisbon, Portugal
  • 2Pratt School of Engineering, Duke University, Durham, North Carolina, USA

Abstract. Rain and cloud fields produced by fully nonlinear idealized cloud resolving numerical simulations of orographic convective precipitation display statistical multiscaling behavior, implying that multifractal diagnostics should provide a physically robust basis for the downscaling and sub-grid scale parameterizations of moist processes. Our results show that the horizontal scaling exponent function (and respective multiscaling parameters) of the simulated rainfall and cloud fields varies with atmospheric and terrain properties, particularly small-scale terrain spectra, atmospheric stability, and advective timescale. This implies that multifractal diagnostics of moist processes for these simulations are fundamentally transient, exhibiting complex nonlinear behavior depending on atmospheric conditions and terrain forcing at each location. A particularly robust behavior found here is the transition of the multifractal parameters between stable and unstable cases, which has a clear physical correspondence to the transition from stratiform to organized (banded and cellular) convective regime. This result is reinforced by a similar behavior in the horizontal spectral exponent. Finally, our results indicate that although nonlinearly coupled fields (such as rain and clouds) have different scaling exponent functions, there are robust relationships with physical underpinnings between the scaling parameters that can be explored for hybrid dynamical-statistical downscaling.

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