DOI QR코드

DOI QR Code

Evaluation of GPM IMERG Applicability Using SPI based Satellite Precipitation

SPI를 활용한 GPM IMERG 자료의 적용성 평가

  • Jang, Sangmin (Climate Application Department, APEC Climate Center) ;
  • Rhee, Jinyoung (Climate Application Department, APEC Climate Center) ;
  • Yoon, Sunkwon (Climate Application Department, APEC Climate Center) ;
  • Lee, Taehwa (School of Agricultural Civil & Bio-Industrial Engineering, Kyungpook National University) ;
  • Park, Kyungwon (Climate Application Department, APEC Climate Center)
  • Received : 2017.03.03
  • Accepted : 2017.03.28
  • Published : 2017.05.31

Abstract

In this study, the GPM (Global Precipitation Mission) IMERG (Integrated Multi-satellitE retrievals for GPM) rainfall data was verified and evaluated using ground AWS (Automated Weather Station) and radar in order to investigate the availability of GPM IMERG rainfall data. The SPI (Standardized Precipitation Index) was calculated based on the GPM IMERG data and also compared with the results obtained from the ground observation data for the Hoengseong Dam and Yongdam Dam areas. For the radar data, 1.5 km CAPPI rainfall data with a resolution of 10 km and 30 minutes was generated by applying the Z-R relationship ($Z=200R^{1.6}$) and used for accuracy verification. In order to calculate the SPI, PERSIANN_CDR and TRMM 3B42 were used for the period prior to the GPM IMERG data availability range. As a result of latency verification, it was confirmed that the performance is relatively higher than that of the early run mode in the late run mode. The GPM IMERG rainfall data has a high accuracy for 20 mm/h or more rainfall as a result of the comparison with the ground rainfall data. The analysis of the time scale of the SPI based on GPM IMERG and changes in normal annual precipitation adequately showed the effect of short term rainfall cases on local drought relief. In addition, the correlation coefficient and the determination coefficient were 0.83, 0.914, 0.689 and 0.835, respectively, between the SPI based GPM IMERG and the ground observation data. Therefore, it can be used as a predictive factor through the time series prediction model. We confirmed the hydrological utilization and the possibility of real time drought monitoring using SPI based on GPM IMERG rainfall, even though results presented in this study were limited to some rainfall cases.

Keywords

References

  1. Baek, S. G., H. W. Jang, J. S. Kim, J. W. Lee, 2016. Agricultural drought monitoring using the satellite-based vegetation index. Journal of Korea Water Resource Association 49(4): 305-314. https://doi.org/10.3741/JKWRA.2016.49.4.305
  2. Case, J. L., 2016. From drought to flooding in less than a week over South Carolina. Results in Physics 6: 1183-1184. https://doi.org/10.1016/j.rinp.2016.11.012
  3. Cressman, G. W., 1959. An operational objective analysis system. Monthly Weather Review 87: 367-374. https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
  4. Ghulam, A., Q. Qin, T. Teyip, and Z. Li, 2007, Modified perpendicular drought index (MPDI): a real-time drought monitoring method, ISPRS Journal of Photogrammetry & Remote Sensing 62(2): 150-164. https://doi.org/10.1016/j.isprsjprs.2007.03.002
  5. Gu, Y., J. F. Brown, J. P. Verdin, and B. Wardlow, 2007, A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States, Geophysical Research Letters, 34: L06407.
  6. Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, and P. Xie, 2014. NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) Algorithm Theoretical Basis Document (ATBD) v4.4.
  7. Jeong, S. and S. C. Shin, 2006, The applicastion of satellite imagery in droughts analysis of large area. Journal of the Korean Society for Geo-spatial Information Science 14(2):55-62 (in Korean).
  8. Karnieli, A., N. Agam, R. T. Pinker, M. Anderson, M. L. Imhoff, G. G. Gutman, N. Panov, and A. Goldberg, 2010, Use of NDVI and land surface temperature for drought assessment: merits and limitations, Journal of Climate 23(3): 618-633. https://doi.org/10.1175/2009JCLI2900.1
  9. Kim, K. Y., J. M. Park, J. G. Baik, and M. H. Choi, 2017. Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia. Atmospheric Research 187:95-105. https://doi.org/10.1016/j.atmosres.2016.12.007
  10. Kwon, H. J. 2006. Development of semidistributed hydrological drought assessment method based on SWSI (Surface Water Supply Index). Ph.D. Thesis, Konkuk University.
  11. Kwon, H. J., H. J. Lim, and S. J. Kim. 2007. Drought assessment of agricultural district using modified SWSI. Journal of the Korean Association of Geographic Information Studies 10(1): 22-34 (in Korean).
  12. Liu, Z. 2016. Comparison of Integrated Multisatellite Retrievals for GPM (IMERG) and TRMM Multisatellite Precipitation Analysis (TMPA) Monthly Precipitation Products: Initial Results. Journal of Hydrometeorology 17:777-790. https://doi.org/10.1175/JHM-D-15-0068.1
  13. Marshall, J. S. and W. M. K. Palmer, 1948. The distribution of raindrops with size. Journal of Meteorology 5(4):165-166. https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2
  14. McKee, T. B., N. J. Doesken, and J. Kliest, 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference of Applied Climatology, 17-22 January, Anaheim, CA. American Meteorological Society, Boston, MA. 179-184.
  15. NDMI (National Disaster Management research Institute), 2014. Application Plan Research of National Satellite Images for Disaster Management, pp. 1-207 (in Korean).
  16. Park, M. J., H. J. Shin, Y. D. Choi, J. Y. Park, and S. J. Kim, 2011. Development of a hydrological drought index considering water availability. Journal of the Korean Society of Agricultural Engineers 53(6): 165-170 (in Korean). https://doi.org/10.5389/KSAE.2011.53.6.165
  17. Ray, P. S., B. C. Johnson, K. W. Johnson, J. S. Bradberry, J. J. Stephens, K. K. Wagner, R. B. Wilhelmson, and J. B. Klemp, 1981. The morphology of several tornadic storms on 20 May 1977. Journal of Atmospheric Science 38:1643-1663. https://doi.org/10.1175/1520-0469(1981)038<1643:TMOSTS>2.0.CO;2
  18. Sharifi, E. R. Steinacker, and B. Saghafian, 2016. Assessment of GPM-IMERG and Other Precipitation Products against Gauge Data under Different Topographic and Climatic Conditions in Iran: Preliminary Results, Remote Sensing 8(2): 135. https://doi.org/10.3390/rs8020135
  19. Shin, H. J., M. J. Park, E. H. Hwang, H. S. Chae, and S. J. Park, 2015. A Study of Spring Drought Using Terra MODIS Satellite Image. Journal of the Korean Association of Geographic Information Studies 18(4): 145-157 (in Korean). https://doi.org/10.11108/kagis.2015.18.4.145
  20. Son, K. H., D. H. Bae, and H. S. Cheong, 2015. Construction & Evaluation of GloSea5-Based Hydrological Drought Outlook System. Atmosphere 25(2): 271-281 (in Korean). https://doi.org/10.14191/Atmos.2015.25.2.271
  21. Wang, L. and J. J. Qu, 2007, NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing, Geophysical Research Letters, 34: L20405. https://doi.org/10.1029/2007GL031021