open access

https://hdl.handle.net/2115/58054

A new method for error degree estimation in numerical weather prediction via MKDA-based ordinal regression

英語

2014-07-22

Springer

Eurasip journal on advances in signal processing

2014

115

This paper presents a new method for estimating error degrees in numerical weather prediction via multiple kernel discriminant analysis (MKDA)-based ordinal regression. The proposed method tries to estimate how large prediction errors will occur in each area from known observed data. Therefore, ordinal regression based on KDA is used for estimating the prediction error degrees. Furthermore, the following points are introduced into the proposed approach. Since several meteorological elements are related to each other based on atmospheric movements, the proposed method merges such heterogeneous features in the target and neighboring areas based on a multiple kernel algorithm. This approach is based on the characteristics of actual meteorological data. Then, MKDA-based ordinal regression for estimating the prediction error degree of a target meteorological element in each area becomes feasible. Since the amount of training data obtained from known observed data becomes very large in the training stage of MKDA, the proposed method performs simple sampling of those training data to reduce the number of samples. We effectively use the remaining training data for determining the parameters of MKDA to realize successful estimation of the prediction error degree.

学術雑誌論文

Version of Record

Creative Commons Attribution-NonCommercial-ShareAlike 2.1 Japan

1687-6180