Evaluating and Modeling the Reliability of Continuous No-Rain Forecast from TIGGE Based on the First-Passage Problem and Fuzzy Mathematics

Abstract

Abstract As an important reference of reservoir regulation, more and more attention has been paid to the numeric precipitation forecast. Due to the uncertainty of meteorological prediction, reservoir regulation based on precipitation forecasts may lead to flood control risks. Therefore, the reliability of precipitation forecasts is crucial to the formulation of reservoir regulation strategy based on it. In this paper, a reliability assessment model for a continuous precipitation forecast is proposed based on the first-passage problem and fuzzy mathematics. The uncertainty of precipitation forecast is described by the generalized Bayesian model, and the fuzzy reliability of a continuous precipitation forecast can be obtained by the first-passage fuzzy probability model (FFPM). Due to the importance of a no-rain period in flood resource utilization, the no-rain forecasts from four different forecast centers in the Meishan basin are used as an example. The results show that the fuzzy mathematics is helpful in describing the uncertainty of the boundary for the no-rain set, and the fuzzy reliability of the no-rain forecast is affected by the selection of the range for the no-rain forecast, while the influence of the membership function is limited. Furthermore, due to the downward trend of fuzzy reliability as the lead time increases, there is a contradiction between excess water storage of the reservoir and the fuzzy reliability of the no-rain forecast. A longer continuous no-rain period means more excess water storage, but it also faces lower reliability. In actual reservoir regulation, the results of FFPM can be combined with more information to formulate better strategies for reservoir regulation.