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首页> 外文期刊>Journal of hydrometeorology >Probabilistic quantitative precipitation forecast for flood prediction: An application
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Probabilistic quantitative precipitation forecast for flood prediction: An application

机译：概率定量降水预报在洪水预报中的应用

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摘要

This paper outlines a methodology to produce probabilistic quantitative precipitation forecasts by means of a dedicated uncertainty processor for weather model output. The uncertainty processor is developed as a component of a Bayesian forecasting system for river flow prediction. In this context the quantitative precipitation forecast is envisaged as a mixed binary-continuous predict and. The processor is applied to the quantitative precipitation forecasts and to precipitation observations covering a 5-yr period, whereby the forecasted and observed relative air humidity are used as ancillary meteorological indicators. The application of the processor to the selected dataset highlights a significantly larger skill of the quantitative precipitation forecast in predicting event occurrence rather than event depth and provides an objective quantification of the forecast uncertainty. The methodology applied here remains restricted to small basins, in which spatial variability of precipitation can be considered negligible. The need for processing the uncertainty induced by spatial variability of rainfall is briefly addressed.
机译：本文概述了一种通过专用的不确定性处理器生成天气模型输出的概率定量降水预报的方法。不确定性处理器被开发为用于河流流量预测的贝叶斯预测系统的组成部分。在这种情况下，定量降水预报被设想为混合二进制连续预报和。该处理器适用于定量降水预报和涵盖5年周期的降水观测，从而将预测和观测到的相对空气湿度用作辅助气象指标。处理器对所选数据集的应用突出了定量降水预测在预测事件发生而不是事件深度方面的显着更高的技能，并提供了对预测不确定性的客观量化。这里所采用的方法仍然仅限于小盆地，在这些小盆地中，降水的空间变化可以忽略不计。简要介绍了处理因降雨的空间变化而引起的不确定性的需求。

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