ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Study on the Method of Rainfall Ensemble Probability Forecast Based on Bayesian Theory and Its Preliminary Experiments
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Abstract:
The paper applies BPO (Bayesian Processor of Output) method based on Bayesian theory to the method of rainfall ensemble probability forecast. Using ensemble prediction data and historical observational data, we develop a rainfall probability forecast model, and then revise a set of precipitation predicted value into a set of Bayesian precipitation probability forecast in the form of continuous probability distribution or continuous probability density. Besides, we obtain a group value of Informativeness Score (IS), which can express the prediction ability of each ensemble member. Furthermore, we fuse the probability forecast results of each member into an integration Bayesian precipitation probability forecast on the basis of IS and test the results with Continuous Ranked Probablity Score (CRPS). Experiment results show that the reliability of integration Bayesian precipitation probability forecast is higher than ensemble direct probability forecast.
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