Summertime heavy precipitation associated with the quasi-stationary Mei-Yu front often causes severe flooding along the Yangtze river basin in China. This study explores the mesoscale predictability of one such event near Wuhan, the capital city of Hubei Province. The 20-21 July rainfall event contributed to making the 1998 flood season the worst in this region since 1954. Various sensitivity experiments were performed to examine the impact of both realistic and idealized initial condition uncertainties of different scales and amplitudes on the prediction of the mesoscale precipitation systems along the Mei-Yu front. While it is found that mesoscale model simulations initialized with global analyses at a 36-h lead time can depict the evolution of the synoptic environment reasonably well, there are large variations between different experiments in the prediction of the mesoscale details and heavy precipitation of this event. It was also found that larger-scale, larger-amplitude initial uncertainties generally led to larger forecast divergence than did uncertainties of smaller scales and small amplitudes. However, the forecast errors induced by perturbations of the same amplitude but at different scales are very similar if the initial error is sufficiently small. Error growth is strongly nonlinear and small-amplitude initial errors, which are far smaller than those of current observational networks, may grow rapidly and quickly saturate at smaller scales. They subsequently grow upscale, leading to significant forecast uncertainties at increasingly larger scales. In agreement with previous studies, moist convection is found to be the key to the rapid error growth leading to limited mesoscale predictability. These findings further suggest that, while there is significant scope for improving forecast skill by improving forecast models and initial conditions, mesoscale predictability of such a heavy precipitation event is inherently limited.
|Original language||English (US)|
|Number of pages||17|
|Journal||Quarterly Journal of the Royal Meteorological Society|
|State||Published - Jan 1 2007|
All Science Journal Classification (ASJC) codes
- Atmospheric Science