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EURASIP Journal on Advances in Signal Processing

Table 3 Estimation results of the kernel weights for ‘temperature on the ground’

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

Meteorological element

Dataset 1

Dataset 2

Dataset 3

γ0 (temperature)

0.7

0.7

0.9

γ1 (relative humidity)

0.2

0.2

0.0

γ2 (wind velocity)

0.1

0.1

0.1

  1. Results of γ l (l = 0, 1, 2) obtained by the proposed method when estimating the prediction error degree of ‘temperature on the ground’. γ0, γ1, and γ2 respectively represent the weights of the kernels for temperature, relative humidity, and wind velocity.
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