Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data

Abstract

Considering the different types of error and the nonlinearity of the meteorological measurement, this paper proposes a nonlinear partial least squares method for consistency analysis of meteorological data. For a meteorological element from one automated weather station, the proposed method builds the prediction model based on the corresponding meteorological elements of other surrounding automated weather stations to determine the abnormality of the measured values. For the proposed method, the latent variables of the independent variables and the dependent variables are extracted by the partial least squares (PLS), and then they are, respectively, used as the inputs and outputs of neural network to build the nonlinear internal model of PLS. The proposed method can deal with the limitation of traditional nonlinear PLS whose inner model is the fixed quadratic function or the spline function. Two typical neural networks are used in the proposed method, and they are the back propagation neural network and the adaptive neuro-fuzzy inference system (ANFIS). Moreover, the experiments are performed on the real data from the atmospheric observation equipment operation monitoring system of Shaanxi Province of China. The experimental results verify that the nonlinear PLS with the internal model of ANFIS has higher effectiveness and could realize the consistency analysis of meteorological data correctly.