Abstract
In order to obtain the characteristic variations of wind relating to the Japan-Sea coastal heavy snowfall, PCA (principal component analysis) is applied to upper-wind data and the correlations between the time-varying amplitude coefficients of PCA eigenvectors and snowfall are examined.
The first and second components of wind mainly represent large-scale variations, and the several succeeding components include the meso-scale wind field besides the synoptic-scale vorticity field and vertical wind shear.
Snowfall has the strongest correlation with the third component mainly representing vorticity over the southern part of the Sea of Japan. Also the fourth, fifth and sixth components have strong correlation with the snowfall in the Hokuriku District.
Clear clustering of the types of heavy snowfall in Niigata Prefecture classified by Akiyama (1981a, b) is seen in the score diagram of the first and second components but not in any other component, which means that the types of heavy snowfall in Niigata Prefecture are determined largely by the large-scale wind field.
