Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

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
Download PDF
⟨ Previous Next ⟩

Abstract

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.

Keywords

References

  1. Atkinson, A., J. Cameron, B. Candy, and S. English, 2005: Bias correction of satellite data at the Met Office. ECMWF/EUMETSAT NWP-SAF Workshop on Bias estimation and correction in data assimilation, 9th November.
  2. Auligne, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631-642. https://doi.org/10.1002/qj.56
  3. Baker, N. L., T. F. Hogan, W. F. Campbell, R. L. Pauley, and S. D. Swadley, 2005: The impact of AMSU-A radiance assimilation in the U.S. Navy's Operational Global Atmospheric Prediction System (NOGAPS). NRL Memorandum Report (NRL/MR/7530-05-8836), Naval Research Laboratory, Monterey, CA.
  4. Cardinali, C., 2009: Monitoring the observation impact on the short-range forecast. Quart. J. Roy. Meteor. Soc., 135, 239-250. https://doi.org/10.1002/qj.366
  5. Dee, D. P., 2004: Variational bias correction of radiance data in the ECMWF system. Proceedings of the ECMWF workshop on assimilation of high spectral resolution sounders in NWP, Reading, UK, June 28-July 1, 97-112.
  6. Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteoro. Soc., 131, 3323-3343. https://doi.org/10.1256/qj.05.137
  7. Eyre, J. R., 1992: A bias correction scheme for simulated TOVS brightness temperature. ECMWF Tech. Memo, 176, ECMWF, Reading, UK.
  8. Goldberg, M. D., D. S. Crosby, and L. Zhou, 2001: The limb adjustment of AMSU-A Observations: Methodology and validation. J. Appl. Meteorol., 40, 70-83. https://doi.org/10.1175/1520-0450(2001)040<0070:TLAOAA>2.0.CO;2
  9. Grody, N., F. Weng, and R. Ferraro, 1999: Application of AMSU for obtaining water vapor, cloud liquid water, precipitation, snow cover and sea ice concentration. Technical Proceeding of the 10th International ATOVS Study Conference, Boulder, CO, Jan 27-Feb 2, 230-240.
  10. Grody, N., and Coauthors, 2001: Determination of precipitable water and cloud liquid water over oceans from the NOAA 15 advanced microwave sounding unit. J. Geophy. Res., 16, 2943-2953.
  11. Harris, B. A., and G. Kelly, 2001: A satellite radiance-bias correction scheme for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 1453-1468. https://doi.org/10.1002/qj.49712757418
  12. Hilton, F., N. C. Atkinson, S. J. English, and J. R. Eyre, 2009; Assimilation of IASI at the Met Office and assessment of its impact through observing system experiments. Quart. J. Roy. Meteor. Soc., 135, 495-505. https://doi.org/10.1002/qj.379
  13. Isaksen, L., 2012: The operational data assimilation system. ECMWF Stellite Data Assimilation Training Course.
  14. Jeong, D. H., C. Ziemkiewicz, W. Ribarsky, R. Chang, and C. V. Center, 2009: Understanding Principal Component Analysis Using a Visual Analytics Tool. Charlotte Visualization Center, UNC Charlotte.
  15. Jun, C.-H., M.-G. Jeong, and H.-S. Lee, 2004: Applied Statistics for Engineers, Chapter 9 Multiple linear regression (Korean edition), 165-205.
  16. Kim, J.-H., J.-H. Kang, and S. Lee, 2014: A comparison of observed and simulated brightness temperatures from two radiative transfer models of RTTOV and CRTM. J. Korean Earth Sci. Soc., 35, 19-28. https://doi.org/10.5467/JKESS.2014.35.1.19
  17. Kwon, S., 2004a: Survey analysis (SAS utilizing SPSS), Chapter 6 Multicollinearity (Korean edition), 134-154.
  18. Kwon, S., 2004b: Survey analysis (SAS utilizing SPSS), Chapter 10 Regression analysis (Korean edition), 211-239.
  19. Lee, S., J.-H. Kim, J.-H. Kang, and H.-W. Chun, 2013: Development of pre-processing and bias correction modules for AMSU-A satellite in the KIAPS observation processing system. Atmosphere, 23, 453-470. https://doi.org/10.14191/Atmos.2013.23.4.453
  20. Liao, D., and R. Valliant, 2012: Variance inflation factors in the analysis of complex survey data. Sur. Methodol., 38, 53-62.
  21. Marill, K. A., 2004: Advanced statistics: Linear regression, Part II: Multiple linear regression. Acad. Emerg. Med., 11, 94-102. https://doi.org/10.1197/j.aem.2003.09.006
  22. Rabier, F., H. Jarvinen, E. Klinker, J.-F. Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational data assimilation. I: experimental results and simplified physics. Quart. J. Roy. Meteor. Soc., 126, 1143-1170. https://doi.org/10.1002/qj.49712656415
  23. Rawlins, F., and Coauthors, 2007: The Met Office global four-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 133, 347-362. https://doi.org/10.1002/qj.32
  24. Smith, L. I., 2002: A tutorial on principal components analysis. http://nyx-www.informatik.uni-bremen.de/664/1/smith_tr_02.pdf.
  25. Wackerly, D., and W. Scheaffer, 2008: Mathematical Statistics with Applications (7 ed.). Belmont, CA, USA, Thomson Higher Education. ISBN 0-495-38508-5.

Cited by

  1. Development of an Observation Processing Package for Data Assimilation in KIAPS vol.54, pp.S1, 2018, https://doi.org/10.1007/s13143-018-0030-2