KIAPS 관측자료 처리시스템에서의 AMSU-A 위성자료 초기 전처리와편향보정 모듈 개발
Development of Pre-Processing and Bias Correction Modules for AMSU-A Satellite Data in the KIAPS Observation Processing System
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초록
As a part of the KIAPS Observation Processing System (KOPS), we have developedthe modules of satellite radiance data pre-processing and quality control, which includeobservation operators to interpolate model state variables into radiances in observation space. AMSU-A (Advanced Microwave Sounding Unit-A) level-1d radiance data have been extractedusing the BUFR (Binary Universal Form for the Representation of meteorological data)decoder and a first guess has been calculated with RTTOV (Radiative Transfer for TIROSOperational Vertical Sounder) version 10.2. For initial quality checks, the pixels contaminatedby large amounts of cloud liquid water, heavy precipitation, and sea ice have been removed. Channels for assimilation, rejection, or monitoring have been respectively selected for differentsurface types since the errors from the skin temperature are caused by inaccurate surfaceemissivity. Correcting the bias caused by errors in the instruments and radiative transfer modelis crucial in radiance data pre-processing. We have developed bias correction modules in twosteps based on 30-day innovation statistics (observed radiance minus background; O-B). Thescan bias correction has been calculated individually for each channel, satellite, and scan position. Then a multiple linear regression of the scan-bias-corrected innovations with several predictorshas been employed to correct the airmass bias.
Abstract
As a part of the KIAPS Observation Processing System (KOPS), we have developedthe modules of satellite radiance data pre-processing and quality control, which includeobservation operators to interpolate model state variables into radiances in observation space. AMSU-A (Advanced Microwave Sounding Unit-A) level-1d radiance data have been extractedusing the BUFR (Binary Universal Form for the Representation of meteorological data)decoder and a first guess has been calculated with RTTOV (Radiative Transfer for TIROSOperational Vertical Sounder) version 10.2. For initial quality checks, the pixels contaminatedby large amounts of cloud liquid water, heavy precipitation, and sea ice have been removed. Channels for assimilation, rejection, or monitoring have been respectively selected for differentsurface types since the errors from the skin temperature are caused by inaccurate surfaceemissivity. Correcting the bias caused by errors in the instruments and radiative transfer modelis crucial in radiance data pre-processing. We have developed bias correction modules in twosteps based on 30-day innovation statistics (observed radiance minus background; O-B). Thescan bias correction has been calculated individually for each channel, satellite, and scan position. Then a multiple linear regression of the scan-bias-corrected innovations with several predictorshas been employed to correct the airmass bias.
참고문헌 (19)
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