Analysis study on perturbation energy and predictability of heavy precipitation in South China

The AREMv2.3 mesoscale numerical model is used to explore storm processes in South China during the pre-rainy season in 2006 by imposing perturbations on the initial fields of physical variables (temperature, humidity, and wind fields). Sensitivity experiments are performed to examine the impacts of...

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Bibliographic Details
Published in:Advances in atmospheric sciences 2010-03, Vol.27 (2), p.382-392
Main Authors: Zhu, Benlu, Lin, Wantao, Zhang, Yun
Format: Article
Language:English
Subjects:
Atmospheric Sciences
Earth and Environmental Science
Earth Sciences
Fluid dynamics
Geophysics/Geodesy
Mathematical models
Mesoscale convective complexes
Meteorology
Numerical analysis
Precipitation
Rainy season
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Summary:The AREMv2.3 mesoscale numerical model is used to explore storm processes in South China during the pre-rainy season in 2006 by imposing perturbations on the initial fields of physical variables (temperature, humidity, and wind fields). Sensitivity experiments are performed to examine the impacts of initial uncertainties on precipitation, on the error growth, and on the predictability of mesoscale precipitation in South China. The primary conclusion is that inherent initial condition uncertainties can significantly limit the predictability of storm. The 24-h accumulated precipitation is most sensitive to temperature perturbations. Larger-amplitude initial uncertainties generally lead to larger perturbation energies than those with smaller amplitude, but these kinds of differences decrease with time monotonically so the mechanism for the growth of perturbation energy is nonlinear. The power spectra of precipitation differences indicate that predictability increases with accumulated time. This also indicates the difficulties faced for short-term, small-scale precipitation forecasting.
ISSN:0256-1530
1861-9533
DOI:10.1007/s00376-009-8164-x