Pre-processing and Bias Correction for AMSU-A Radiance Data Based on Statistical Methods

통계적 방법에 근거한 AMSU-A 복사자료의 전처리 및 편향보정

  • Lee, Sihye (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Kim, Sangil (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Chun, Hyoung-Wook (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Kim, Ju-Hye (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Kang, Jeon-Ho (Korea Institute of Atmospheric Prediction Systems (KIAPS))
  • 이시혜 (한국형수치예보모델개발사업단) ;
  • 김상일 (한국형수치예보모델개발사업단) ;
  • 전형욱 (한국형수치예보모델개발사업단) ;
  • 김주혜 (한국형수치예보모델개발사업단) ;
  • 강전호 (한국형수치예보모델개발사업단)
  • Received : 2014.08.29
  • Accepted : 2014.11.20
  • Published : 2014.12.31


As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.


Supported by : 기상청


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