TITLE:
Missing Data Imputations for Upper Air Temperature at 24 Standard Pressure Levels over Pakistan Collected from Aqua Satellite
AUTHORS:
Muhammad Usman Saleem, Sajid Rashid Ahmed
KEYWORDS:
Missing Data Imputations, Spatial Interpolation, AQUA Satellite, Upper Level Air Temperature, AIRX3STML
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.4 No.3,
September
6,
2016
ABSTRACT: This research was an effort to select best imputation method for missing upper air temperature
data over 24 standard pressure levels. We have implemented four imputation techniques like inverse
distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations.
Performance indicators for these techniques were the root mean square error (RMSE), absolute
mean error (AME), correlation coefficient and coefficient of determination ( R2 ) adopted in this
research. We randomly make 30% of total samples (total samples was 324) predictable from 70%
remaining data. Although four interpolation methods seem good (producing 0.99) found between actual and predicted
air temperature data through this method. The high value of the coefficient of determination (0.99)
through bilinear interpolation method, tells us best fit to the surface. We have also found similar
results for imputation with natural interpolation method in this research, but after investigating
scatter plots over each month, imputations with this method seem to little obtuse in certain
months than bilinear method.