통계적 방법에 근거한 AMSU-A 복사자료의 전처리 및 편향보정
Pre-processing and Bias Correction for AMSU-A Radiance Data Based on Statistical Methods
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참고문헌
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초록
As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Packagefor Observation Processing (KPOP), we have developed the modules for Advanced MicrowaveSounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP systemcalculates the airmass bias correction coefficients via the method of multiple linear regressionin which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and10~1 hPa are respectively used for dependent and independent variables. Among the four airmasspredictors, 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 fromthe multiple linear regression. The analysis shows that the order of performances is multiplelinear, principal component, and simple linear regressions. For bias correction for the AMSU-Achannel 4 which is the most sensitive to the lower troposphere, the multiple linear regressionwith 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.
Abstract
As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Packagefor Observation Processing (KPOP), we have developed the modules for Advanced MicrowaveSounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP systemcalculates the airmass bias correction coefficients via the method of multiple linear regressionin which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and10~1 hPa are respectively used for dependent and independent variables. Among the four airmasspredictors, 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 fromthe multiple linear regression. The analysis shows that the order of performances is multiplelinear, principal component, and simple linear regressions. For bias correction for the AMSU-Achannel 4 which is the most sensitive to the lower troposphere, the multiple linear regressionwith 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.
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