Volume 4, Issue 5, October 2015, Page: 150-160
Validation of Satellite-Based PERSIANN Rainfall Estimates Using Surface-Based APHRODITE Data over Iran
Javad Bodagh Jamli, The Environmental Engineering Faculty, the University of Environment, Karaj, Iran; The Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran
Received: May 6, 2015;       Accepted: May 18, 2015;       Published: Sep. 6, 2015
DOI: 10.11648/      View  4883      Downloads  190
Surface-based precipitation measurements with high accuracy on different spatial-temporal scales have a crucial importance in different land-use planning sectors, especially in arid and semi-arid regions, such as Iran. Because the density of spatial distribution of rain-gauges is not uniform throughout the country, satellite sensor technology is considered useful for precipitation monitoring over the study area. In this study, PERSIANN satellite-based rainfall data were validated through comparison with the APHRODITE surface-based precipitation data. The validation was carried out for annual and seasonal precipitation, as well as an inter-annual comparison. Our analysis was based on a visual comparison and a statistical approach, including linear regression and spatial correlation between APHRODITE and PERSIANN datasets for each 0.25°×0.25° grid cell in the entire country, in the Caspian Sea region, and in the Zagros Mountains, indicating spatial correlation coefficients of 0.62, 0.62, 0.47, respectively. Both APHRODITE data and PERSIANN data showed that spatial distribution of mean annual and seasonal precipitation over Iran has two main patterns: along the Caspian Sea and along the Zagros Mountain chain. In general, PERSIANN underestimates high rainfall rates by 5.5 mm/day in winter but overestimates the low rainfalls in annual and seasonal scales by 0.9 mm/day in summer.
Precipitation Validation, Satellite Data, PERSIANN, APHRODITE, Iran, Gridded Data
To cite this article
Javad Bodagh Jamli, Validation of Satellite-Based PERSIANN Rainfall Estimates Using Surface-Based APHRODITE Data over Iran, Earth Sciences. Vol. 4, No. 5, 2015, pp. 150-160. doi: 10.11648/
Ebert, N. Kerle, and A. Stein, 2007: Remote sensing based assessment of social vulnerability, the space policy institute, George Washington University.
Jensen, N. E. and L. Pedersen, 2005: Spatial variability of rainfall: Variations within a single radar pixel, Atmos.Res.,77,269-277
Hong G., P. Yang, B.C. Gao, B. A. Baum, Y.X. Hu, M.D. King, and S. Platnicks, 2007: High cloud properties from three years of MODIS Terra and Aqua collection-4 data over the tropics, J. Appl. Meteor. Climatol., 46, 1840-1856.
Sorooshian S, K-L. Hsu, B. Imam, Y. Hong, 2006: Global precipitation estimation from satellite imagery using artificial neural networks. Chapter 2in Hydrological Modeling in Arid and Semi-arid Areas. UNESCO.
Javanmard S., A. Yatagai, M. I. Nodzu, J. Bodagh Jamali, and H. Kawamoto, 2010: Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM_3B42 over Iran, Adv. Geosci., 25, 119-125
Kamali Gh. A., A. Asgari, and K. Noohi, 2009: Applied Meteorology, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran, pp. 271 (in Persian language)
Hsu, K., X. Gao, S. Sorooshian, and H.V. Gupta, 1997: Precipitation estimation from remotely sensed information using artificial neural networks, J. Appl. Meteor., 36, 1176-1190.
Hsu, K., H. V. Gupta, X. Gao, and S. Sorooshian, 1999: Estimation of physical variables from multi-channel remotely sensed imagery using a neural network: Application to rainfall estimation, Water Resources Res., 35(5), 1605-1618.
Ferraro, R. R., and G. F. Marks, 1995: The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements, J. Atmos. Oceanic Technol., 12, 755-770.
Janowiak, J. E., R. J. Joyce, and Y. Yarosh, 2000: A real-time global half-hourly pixel resolution infrared dataset and its applications, Bull. Amer. Meteor. Soc., 82,205-217.
Sorooshian, S., K. Hsu, X. Gao, H.V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall, Bull. Amer. Meteor. Soc., 81, 2035-2046.
Weng, F., L. Zhao, R. Ferraro, G. Poe, X. Li, and N. Grody, 2003: Advanced microwave sounding unit cloud and precipitation algorithms, Radio Sci.,38, 8086-8096.
Sorooshian S., Lawford R., Try P., Rossow W., Roads J., Polcher J., Sommeria G., and Schiffer R., 2005: Water and energy cycles: investigating the links. WMO Bull.54, 58-64
Gruber A., and V. Levizzani, 2008: Assessment of global precipitation products, WCRP Report, 128, WMO/TD-No. 1430
Yatagai, A., O. Arakawa, K. Kamiguchi, H. Kawamoto, M. I. Nodzu, and A. Hamada, 2009: A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges, SOLA, 5, 137-140, DOI:10.2151.
Turk, F. J., P. Arkin, E. E. Ebert, and M. R. P. Sapiano, 2008: Evaluating high-resolution precipitation products, Bull. Amer. Meteor. Soc., 89, 1911-1916.
Ebert, E. E., and M. J. Manton, 1998: Performance of satellite rainfall estimation algorithms during TOGA COARE, J. Atmos. Sci., 55, 1537-1557.
Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res., 113, D04110, doi: 10.1029/2007 JD009132.
Xie, P., and P. A. Arkin, 1997: Global precipitation: A17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539-2558.
Adler, R. F., G. J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, and E. Nelkin, 2003: The Version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeor., 4, 1147-1167.
Yatagai, A., P. Xie, and A. Kitoh, 2005: Utilization of a new gauge-based daily precipitation dataset over monsoon Asia for validation of the daily precipitation climatology simulated by the MRI/JMA 20-km-mesh AGCM. SOLA, 1, 193-196, doi: 10.2151.
Huffman, G. J., R. F. Adler, M. Morrissey, D. Bolvin, S. Curtis, R. Joyce, B. Mc Gavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multi-satellite observations, J. Hydrometeor., 2, 36-50.
Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis, 2005: Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25, 1965-1978.
Shepard, D., 1968: A two-dimensional interpolation function for irregularly spaced data, Proc. 23 ACM Nat’l Conf., Princeton, N.J., Barandon/Systems Press, 517-524.
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