DOI QR코드

DOI QR Code

Estimation of Total Precipitable Water from MODIS Infrared Measurements over East Asia

MODIS 적외 자료를 이용한 동아시아 지역의 총가강수량 산출

  • Park, Ho-Sun (ROKAF 73rd Weather Group) ;
  • Sohn, Byung-Ju (School of Earth and Environmental Sciences, Seoul National University) ;
  • Chung, Eui-Seok (School of Earth and Environmental Sciences, Seoul National University)
  • 박호순 (공군 제73기상전대) ;
  • 손병주 (서울대학교 지구환경과학부) ;
  • 정의석 (서울대학교 지구환경과학부)
  • Published : 2008.08.30

Abstract

In this study the retrieval algorithms have been developed to retrieve total precipitable water (TPW) from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) infrared measurements using a physical iterative retrieval method and a split-window technique over East Asia. Retrieved results from these algorithms were validated against Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) over ocean and radiosonde observation over land and were analyzed for investigating the key factors affecting the accuracy of results and physical processes of retrieval methods. Atmospheric profiles from Regional Data Assimilation and Prediction System (RDAPS), which produces analysis and prediction field of atmospheric variables over East Asia, were used as first-guess profiles for the physical retrieval algorithm. We used RTTOV-7 radiative transfer model to calculate the upwelling radiance at the top of the atmosphere. For the split-window technique, regression coefficients were obtained by relating the calculated brightness temperature to the paired radiosonde-estimated TPW. Physically retrieved TPWs were validated against SSM/I and radiosonde observations for 14 cases in August and December 2004 and results showed that the physical method improves the accuracy of TPW with smaller bias in comparison to TPWs of RDAPS data, MODIS products, and TPWs from split-window technique. Although physical iterative retrieval can reduce the bias of first-guess profiles and bring in more accurate TPWs, the retrieved results show the dependency upon initial guess fields. It is thought that the dependency is due to the fact that the water vapor absorption channels used in this study may not reflect moisture features in particular near surface.

Terra/Aqua MODIS의 적외관측 자료를 이용하여 동아시아 지역에서 물리적 방법과 split-window 방법으로 총가강수량을 산출하는 알고리즘을 개발하였다. 물리적 방법에서는 동아시아 지역에 대한 분석 예측 자료를 생산하는 RDAPS 자료를 알고리즘의 초기 추정치로 사용하였다. 이 과정에서 복사전달계산을 위해 빠르고 정확도가 높은 RTTOV-7 모델을 이용하였다. Split-window를 이용한 총가강수량 산출에서는 동아시아 지역의 라디오존데 관측자료를 훈련자료로 사용하여 밝기온도를 계산하였고, 이로부터 관측된 밝기온도로부터 총가강수량을 산출할 수 있는 회귀식을 도출하였다. 위의 두 알고리즘을 2004년 8월과 12월의 MODIS 적외 자료에 적용하여 산출한 결과를 해양에서는 DMSP SSM/I 결과와 육지에서는 라디오존데 관측 결과와 비교하여 검증하였고, 이를 바탕으로 총가강수량의 정확성에 영향을 미치는 요인과 산출과정에 중요한 물리과정을 분석하였다. 비교결과 RDAPS, MODIS, split-window 방법에 비해 물리적 방법을 이용한 총가강수량의 산출 정확성이 높은 것으로 나타났다. 그러나 물리적 방법은 초기 추정치에 따라 산출결과가 상이하게 나타나는 단점을 가지고 있는 것으로 파악되었다. 따라서 TIGR 자료와 같은 기후 평균값을 초기치로 적용함에 있어 주의가 요구된다. 이러한 원인으로 지표 부근의 수증기에 대한 정보 부족 등을 들 수 있다. 이러한 단점에도 불구하고 지표와 지형의 변화가 큰 한반도를 포함한 동아시아 지역에서는 물리적 방법에 의한 총가강수량 산출의 효율성이 큰 것으로 사료된다.

Keywords

References

  1. Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumly, 1998. Discriminating clear sky from clouds with MODIS, Journal of Geophysical Research, 103: 32,141-32,157
  2. Anding, D. and R. Kauth, 1970. Estimation of sea surface temperature from space, Remote Sensing of Environment, 1: 217-220 https://doi.org/10.1016/S0034-4257(70)80002-5
  3. Boukabara, S. A., J. L. Moncet, R. Lynch, and C. Prigent, 2000. Microwave remote sensing over land: Application to SSM/I, Proceedings of 10th Conference on Satellite Meteorology and Oceanography, Long Beach, CA, 9-14 January 2000, pp. 283-286
  4. Chesters, D., L. W. Uccellini, and W. D. Robinson, 1983. Low-level water vapor fields from the VISSR Atmospheric Sounder (VAS) "split window" channels, Journal of Climate and Applied Meteorology, 22: 725-743 https://doi.org/10.1175/1520-0450(1983)022<0725:LLWVFF>2.0.CO;2
  5. Eyre, J. R., 1989. Inversion of cloudy satellite sounding radiances by nonlinear optimal estimation. I : Theory and simulation for TOVS, Quarterly Journal of the Royal Meteorological Society, 115: 1001-1026 https://doi.org/10.1002/qj.49711548902
  6. Frey, R. A., B. A. Baum, W. P. Menzel, S. A. Ackerman, C. C. Moeller, and J. D. Spinhirne, 1999. A comparison of cloud top heights computed from airborne lidar and MAS radiance data using $CO_2$ slicing, Journal of Geophysical Research, 104: 24,547-24,555 https://doi.org/10.1029/1999JD900796
  7. Koenig, M., 2002. Atmospheric instability parameters derived from MSG SEVIRI observations, EUMETSAT Technical Memorandum No. 9, 28pp
  8. Labraga, J. C., O. Frumento, and M. Lopez, 2000. The atmospheric water vapor cycle in South America and the tropospheric circulation, Journal of Climate, 13: 1899-1915 https://doi.org/10.1175/1520-0442(2000)013<1899:TAWVCI>2.0.CO;2
  9. Li, J., C. C. Schmidt, J. P. Nelson III, T. J. Schmit, and W. P. Menzel, 2001. Estimation of total atmospheric ozone from GOES sounder radiances with high temporal resolution, Journal of Atmospheric and Oceanic Technology, 18: 157-168 https://doi.org/10.1175/1520-0426(2001)018<0157:EOTAOF>2.0.CO;2
  10. Ma, X. L., T. J. Schmit, and W. L. Smith, 1999. A nonlinear physical retrieval algorithm - its application to the GOES-8/9 sounder, Journal of Applied Meteorology, 38: 501-513 https://doi.org/10.1175/1520-0450(1999)038<0501:ANPRAI>2.0.CO;2
  11. McMillin, L. M. and D. S. Crosby, 1984. Theory and validation of the multiple window sea surface temperature technique, Journal of Geophysical Research, 89: 3655-3661 https://doi.org/10.1029/JC089iC03p03655
  12. Menzel, W. P., S. W. Seemann, J. Li, and L. E. Gumley, 2002. MODIS atmospheric profile retrieval algorithm theoretical basis document, EOS Project Science Office, NASA, Goddard Space Flight Center, 39pp
  13. Roberts, R. E., J. E. A. Selby, and L. M. Biberman, 1976. Infrared continuum absorption by atmospheric water vapor in the 8-12 micron window, Applied Optics, 15: 2085-2090 https://doi.org/10.1364/AO.15.002085
  14. Saunders, P. M., 1967. Aerial measurements of sea surface temperature in the infrared, Journal of Geophysical Research, 72: 4109-4117 https://doi.org/10.1029/JZ072i016p04109
  15. Saunders, R., M. Matricardi, and P. Brunel, 1999. An improved fast radiative transfer model for assimilation of satellite radiance observations, Quarterly Journal of the Royal Meteorological Society, 125: 1407-1425 https://doi.org/10.1256/smsqj.55614
  16. Seemann, S. W., J. Li, W. P. Menzel, and L. E. Gumley, 2003. Operational retrieval of atmospheric temperature, moisture, and ozone from MODIS infrared radiances, Journal of Applied Meteorology, 42: 1072-1091 https://doi.org/10.1175/1520-0450(2003)042<1072:OROATM>2.0.CO;2
  17. Smith, W. L., H. M. Woolf, and H. E. Revercomb, 1991. Linear simultaneous solution for temperature and observing constituent profiles from radiance spectra, Applied Optics, 30: 1117-1123 https://doi.org/10.1364/AO.30.001117
  18. Wentz, F. J., 1995. A well calibrated ocean algorithm for SSM/I, Remote Sensing Systems Technical Report 101395, Remote Sensing Systems, Santa Rosa, Ca, 34pp
  19. Yang, X., B. H. Sass, G. Elgered, J. M. Johansson, and T. R. Emardson, 1999. A comparison of precipitable water vapor estimates by an NWP simulation and GPS observations, Journal of Applied Meteorology, 38: 941-956 https://doi.org/10.1175/1520-0450(1999)038<0941:ACOPWV>2.0.CO;2