Abstract
This paper investigates the jumpiness index based modeling for precipitation prediction. The jumpiness index (JI) represents different forecast jumps and the inconsistency correlation for forecasts issued at different times but valid for the same time. Unlike researches that improve the accuracy of predictions mainly, this study attempts to analyze the characteristics of prediction consistency for the numerical model GDAPS using the jumpiness index. In this work, we try to minimize prediction errors through a combination of JI elements and symbolic functions using Cartesian Genetic Programming (CGP), which is a kind of evolutionary computation to solve nonlinear regression problems. The relationship between the time-by-time jumpiness indices and the prediction error is analyzed. Also, the applicability of a combination of jumpiness indices to improve the accuracy of future forecasts is examined. Experiments are conducted to model the relationship between JI and UM error values calculated by GDAPS data for 2013 and 2014 for ground precipitation data using CGP-based symbolic nonlinear regression.
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