Abstract
Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP) neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric variables. Downscaled precipitation was then used as inputs to the super tank model (runoff model) for flood prediction. The case study was conducted for the Thu Bon River Basin, located in Central Vietnam. Study results showed that the precipitation predicted by MLP outperformed that directly obtained from model outputs or downscaled using multiple linear regression. Consequently, flood forecast based on the downscaled precipitation was very encouraging. It has demonstrated as a robust technology, simple to implement, reliable, and universal application for flood prediction through the combination of downscaling model and super tank model.
Highlights
Numerical weather prediction NWP has demonstrated its breakthrough in flood forecast recently
In terms of forecast lead time, the flood prediction based upon numerical weather prediction outputs tends to outperform other conventional forecasts which are based on realtime observation, especially in small- to medium-size basins where runoff concentration is relatively short
Since land surface is averaged within very coarse grid cells; small-scale effects of topography may not be resolved in the global NWP model
Summary
Numerical weather prediction NWP has demonstrated its breakthrough in flood forecast recently. In terms of forecast lead time, the flood prediction based upon numerical weather prediction outputs tends to outperform other conventional forecasts which are based on realtime observation, especially in small- to medium-size basins where runoff concentration is relatively short. The forecast lead times given by NWP are ranging from short-term forecast a couple of hours to few days to medium-range forecast up to ten days or more. This allows implementing effective action plans to minimize flood risk. Downscaling is a familiar technique used in climate research and weather forecast that aims to utilize information derived from the global NWP model outputs, in the assessment of hydrological implication driven by global climate models or attempting for runoff prediction
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