single-dr.php

JDR Vol.13 No.1 pp. 22-30
doi: 10.20965/jdr.2018.p0022
(2018)

Paper:

Preliminary Assessment of GPM Satellite Rainfall over Myanmar

Muhammad Mohsan*,†, Ralph Allen Acierto*, Akiyuki Kawasaki*, and Win Win Zin**

*The University of Tokyo
7 Chome-3-1 Hongo, Bunkyo, Tokyo 113-8654, Japan

Corresponding author

**Yangon Technological University, Yangon, Myanmar

Received:
September 2, 2017
Accepted:
January 31, 2018
Published:
February 20, 2018
Keywords:
satellite rainfall, GPM, Myanmar, rain gauge, categorical statistics
Abstract

Intensive and long-term rainfall in Myanmar causes floods and landslides that affect thousands of people every year. However, the rainfall observation network is still limited in number and extent, so satellite rainfall products have been shown to supplement observations over the ungauged areas. One example is the estimates from Global Precipitation Measurement (GPM) called Integrated Multi-satellite Retrievals for GPM (IMERG), which has high spatial (0.1 × 0.1 degree) and temporal (30 min) resolution. This has potential to be used for modeling streamflow, early warnings, and forecasting systems. This study investigates the utility of these GPM satellite estimates for representing the daily rainfall for 25 rain gauges over Myanmar. Statistical metrics were used to understand the characteristic performance of the GPM satellite estimates. Daily rainfall estimates from GPM show a range of 29.3% to 81.1% probability of detection (POD). The satellite estimates show a capability of detecting no-rain days between 61.4 and 93.5%. For different rainfall intensities, the satellite estimates have a 12.9 to 39.1% POD for light rain (1–10 mm/day), 11.1 to 49% POD for moderate rain (10–50 mm/day), a maximum of 36% for heavy rain (50–150 mm/day), and a maximum of 12.5% for extreme rain (=150 mm/day). However, the correlation coefficient (CC) only ranges from 0.064 to 0.581, which is considered low, and is not uniform for all the stations. The highest CC scores and POD scores tend to be located in the northern part and deltaic region extending to the southern coasts in Myanmar, indicating a dependency of the statistical metrics on rainfall magnitude. The high POD scores indicate the utility of the estimates without correction for early warning purposes, but the estimates have low reliability for rainfall intensity. The satellite estimates can be used for forecasting and modeling purposes in the region, but the estimates require bias-correction before application.

References
  1. [1] M. M. Bitew and M. Gebremichael, “Evaluation of satellite rainfall products through hydrologic simulation in a fully distributed hydrologic model,” Water Resources Research, Vol.47, No.6, pp. n/a–n/a, 2011, W06526, doi:10.1029/2010WR009917.
  2. [2] S. Jiang, L. Ren, B. Yong, Y. Hong, X. Yang, and F. Yuan, “Evaluation of latest TMPA and CMORPH precipitation products with independent rain gauge observation networks over high-latitude and low-latitude basins in China,” Chinese Geographical Science, Vol.26, No.4, pp. 439–455, Aug 2016, doi:10.1007/s11769-016-0818-x.
  3. [3] S. Sorooshian, K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, “Evaluation of PERSIANN system satellite–based estimates of tropical rainfall,” Bulletin of the American Meteorological Society, Vol.81, No.9, pp. 2035–2046, 2000.
  4. [4] G. J. Huffman, D. T. Bolvin, E. J. Nelkin, D. B. Wolff, R. F. Adler, G. Gu, Y. Hong, K. P. Bowman, and E. F. Stocker, “The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales,” Journal of Hydrometeorology, Vol.8, No.1, pp. 38–55, 2007, doi:10.1175/JHM560.1.
  5. [5] E. E. Ebert, J. E. Janowiak, and C. Kidd, “Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models,” Bulletin of the American Meteorological Society, Vol.88, No.1, pp. 47–64, 2007, doi:10.1175/BAMS-88-1-47.
  6. [6] T. Dinku, F. Ruiz, S. J. Connor, and P. Ceccato, “Validation and Intercomparison of Satellite Rainfall Estimates over Colombia,” Journal of Applied Meteorology and Climatology, Vol.49, No.5, pp. 1004–1014, 2010, doi:10.1175/2009JAMC2260.1.
  7. [7] J. Liu, Z. Duan, J. Jiang, and A.-X. Zhu, “Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China,” Advances in Meteorology, 2015, doi:10.1155/2015/151239.
  8. [8] A. Y. Hou, R. K. Kakar, S. Neeck, A. A. Azarbarzin, C. D. Kummerow, M. Kojima, R. Oki, K. Nakamura, and T. Iguchi, “The Global Precipitation Measurement Mission,” Bulletin of the American Meteorological Society, Vol.95, No.5, pp. 701–722, 2014, doi:10.1175/BAMS-D-13-00164.1.
  9. [9] M. Tan and Z. Duan, “Assessment of GPM and TRMM Precipitation Products over Singapore,” Remote Sensing, Vol.9, No.7, pp. 720, 2017, doi:10.3390/rs9070720, http://www.mdpi.com/2072-4292/9/7/720 [accessed Aug. 30, 2017]
  10. [10] Z. Liu, “Comparison of Integrated Multisatellite Retrievals for GPM (IMERG) and TRMM Multisatellite Precipitation Analysis (TMPA) Monthly Precipitation Products: Initial Results,” Journal of Hydrometeorology, Vol.17, No.3, pp. 777–790, 2016, doi:10.1175/JHM-D-15-0068.1, http://journals.ametsoc.org/doi/10.1175/JHM-D-15-0068.1 [accessed Jun. 14, 2017]
  11. [11] K. Kim, J. Park, J. Baik, and M. Choi, “Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia,” Atmospheric Research, Vol.187, pp. 95–105, 2017, doi:http://dx.doi.org/10.1016/j.atmosres.2016.12.007,http://www.sciencedirect.com/science/article/pii/S0169809516306901 [accessed Jun. 15, 2017]
  12. [12] J. Tan, W. A. Petersen, and A. Tokay, “A Novel Approach to Identify Sources of Errors in IMERG for GPM Ground Validation,” Journal of Hydrometeorology, Vol.17, No.9, pp. 2477–2491, 2016, doi:10.1175/JHM-D-16-0079.1.
  13. [13] E. Sharifi, R. Steinacker, and B. Saghafian, “Assessment of GPM-IMERG and Other Precipitation Products against Gauge Data under Different Topographic and Climatic Conditions in Iran: Preliminary Results,” Remote Sensing, Vol.8, No.2, 2016, doi:10.3390/rs8020135, http://www.mdpi.com/2072-4292/8/2/135 [accessed Jun. 15, 2017]
  14. [14] R. Xu, F. Tian, L. Yang, H. Hu, H. Lu, and A. Hou, “Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network,” Journal of Geophysical Research: Atmospheres, Vol.122, No.2, pp. 910–924, 2017, 2016JD025418, doi:10.1002/2016JD025418.
  15. [15] U. V. Murali Krishna, S. K. Das, S. M. Deshpande, S. L. Doiphode, and G. Pandithurai, “The Assessment of Global Precipitation Measurement estimates over the Indian Subcontinent,” Earth and Space Science, pp. 540–553, 2017, doi:10.1002/2017EA000285.
  16. [16] F. Yuan, L. Zhang, K. W. Wah Win, L. Ren, C. Zhao, Y. Zhu, S. Jiang, and Y. Liu, “Assessment of GPM and TRMM multi-satellite precipitation products in streamflow simulations in a data sparse mountainous watershed in Myanmar,” Remote Sensing, Vol.9, No.3, 2017, doi:10.3390/rs9030302.
  17. [17] Z. Chen, Y. Qin, Y. Shen, and S. Zhang, “Evaluation of Global Satellite Mapping of Precipitation Project Daily Precipitation Estimates over the Chinese Mainland,” Advances in Meteorology, 2016, doi:10.1155/2016/9365294.
  18. [18] W. McKinney and P. D. Team, “Pandas - Powerful Python Data Analysis Toolkit,” Pandas - Powerful Python Data Analysis Toolkit, p. 1625, 2015.
  19. [19] J. D. Hunter, “Matplotlib: A 2D graphics environment,” Computing in Science and Engineering, Vol.9, No.3, pp. 99–104, 2007, arXiv:0402594v3, doi:10.1109/MCSE.2007.55.
  20. [20] M. Waskom, O. Botvinnik, P. Hobson, J. B. Cole, Y. Halchenko, S. Hoyer, A. Miles, T. Augspurger, T. Yarkoni, T. Megies, L. P. Coelho, D. Wehner, cynddl, E. Ziegler, diego0020, Y. V. Zaytsev, T. Hoppe, S. Seabold, P. Cloud, M. Koskinen, K. Meyer, A. Qalieh, and D. Allan, “seaborn: v0.5.0,” https://github.com/mwaskom/seaborn/
Cite this article as:
Muhammad Mohsan, Ralph Allen Acierto, Akiyuki Kawasaki, and Win Win Zin, “Preliminary Assessment of GPM Satellite Rainfall over Myanmar,” J. Disaster Res., Vol.13, No.1, pp. 22-30, 2018
Muhammad Mohsan, Ralph Allen Acierto, Akiyuki Kawasaki, and Win Win Zin, J. Disaster Res., Vol.13, No.1, pp. 22-30, 2018

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Jun. 14, 2018