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Precipitation uncertainty processor for probabilistic river stage forecasting
Author(s) -
Kelly Karen S.,
Krzysztofowicz Roman
Publication year - 2000
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2000wr900061
Subject(s) - probabilistic logic , precipitation , weibull distribution , quantitative precipitation forecast , stage (stratigraphy) , probabilistic forecasting , equivalence (formal languages) , probability distribution , probability density function , function (biology) , environmental science , statistical model , computer science , meteorology , statistics , mathematics , geology , geography , paleontology , discrete mathematics , evolutionary biology , biology
The precipitation uncertainty processor (PUP) is a component of the Bayesian forecasting system which produces a short‐term probabilistic river stage forecast (PRSF) based on a probabilistic quantitative precipitation forecast (PQPF). The task of the PUP is to process a probability distribution of the total precipitation amount through a deterministic hydrologic model (of any complexity) into a probability distribution of the model river stage. An analytic‐numerical PUP is developed based on the theory of response functions and empirical data simulated from the operational forecast system of the National Weather Service for a 1430 km 2 headwater basin. The PUP outputs a five‐parameter two‐piece Weibull distribution of the model river stage. The corresponding response function is a two‐piece power function. Structural properties of the PUP are investigated empirically, including the deterministic equivalence principle: Under certain conditions a deterministic forecast of the temporal disaggregation of the total precipitation amount is equivalent to a probabilistic forecast. This considerably simplifies the PQPF, without affecting the optimality of the PRSF.