Accuracy assessment of GPM-IMERG satellite precipitation data on half-hourly and daily time scales (Case study: Gorganroud Basin)

Document Type : Complete scientific research article

Authors

1 M.Sc. Graduate, Dept. of Watershed Management, University of Birjand, Iran

2 Associate Prof., Dept. of Watershed Management, University of Birjand, Iran

3 Dept. of Desert Area Management, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad (FUM), Iran

4 Prof., Dept. of Water Science and Engineering, University of Birjand, Iran

Abstract

Background and Objectives: Precipitation is one of the most important factors affecting water and energy balance in the world and important meteorological variables. To accurately estimate precipitation, various methods are used, including the direct use of meteorological ground station data and direct observations, the use of remote sensing satellite data, or the use of interpolation methods based on geo-statistical methods. Therefore, developing innovative approaches for accurate estimation of precipitation in areas with inadequate or inadequate data is critical. The use of radar remote sensing technologies in the accurate estimation of precipitation is crucial as the most important factor affecting water and energy balance in areas with unsuitable and inadequate data. Therefore, this research was conducted to evaluate GPM-IMERG satellite precipitation data and compare it with the data of observatory stations in the Golestan province-Gorganroud basin.
Materials and Methods: To do this research, after obtaining the GPM satellite data and processing it, we performed a comparison between half-hourly daily satellite data set with the ground-based (stability and normal) observational data. Concerning the spatial (0.1*0.1) and temporal (daily and half-hourly) resolutions of GPM-IMERG satellite data, we employed enough and valid ground-based rainfall records dated 20/03/2014-20/03/2016 (for daily series) and 20/03/2014-21/09/2016 (for half-hourly series). To assess the accuracy of GPM data in rainfall estimation, some statistical indicators such as FAR , CSI , POD , RBias and some other validation indicators were used.
Results: The results showed that the half-hour rainfall IMERG records with CC values equal to 0.05-0.23 and CSI equal to 0.20-0.52 were relatively acceptable. Validation of GPM satellite rainfall data using MAE, RMSE, and MBE statistical indicators has also been relatively acceptable. Based on the validation analysis of daily records, the RBias index showed the highest level of accordance of GPM data with observational data at 0.74 , and the lowest level corresponding to 2.27, that belongs to Nodeh station. The POD index also showed that Nodeh and HagholKhajeh stations had the highest and lowest correspondence with ground stations with the values of 0.5 and 0.25, respectively. The values of the CSI index in all stations were calculated to be between 0.13 and 0.22, which were related to Zarrin Gol and Shirabad stations, respectively. Based on the values of the FAR index, it was observed that the lowest value of FAR in Bagh Salian and Zarringol stations was 0.64 and the highest value was 0.80 in Shirabad station. Therefore, to improve the data obtained from the IMERG algorithm, especially in arid regions with the extensive spatial distribution and temporal changes in precipitation, satellite precipitation products should be calibrated to improve their accuracy in measuring daily precipitation.
Conclusion: In this study, the calculation of statistical and matching indicators was performed for the first time to compare half-hour data of the GPM satellite with observational data. It was found that the IMERG algorithm of the GPM satellite is relatively consistent with the recorded values of ground stations daily, as well. Given the FAR values at all stations, it can be said that there is a relative correspondence between satellite data and observed data from ground stations. POD values also showed acceptable performance of this satellite's data. The results of this study also showed that there was a relative correlation between the data of ground stations and GPM satellite data. Therefore, considering the non-evaluation of precipitation data of the GPM satellite system with data of ground stations in many regions of Iran, including the study area of Gorganroud, the results of this study can be very useful for innovation and increasing the efficiency in water resources management.

Keywords


1.Abdollahi, B., Hosseini-Moghari, S.M., and Ebrahimi, K. 2017. Assessment of Satellite Precipitation Data from TRMM 3B42RT V7 and CMORPH in Order to Estimate Precipitation in Gorganroud Basin-Iran, J. Water. Manage. Sci. Eng. 11: 36. 55-68. (In Persian)
2.Akbari, M., Ownegh, M., Asgari, H.R., Sadoddin, A., and Khosravi, H. 2016. Drought Monitoring based on the SPI and RDI Indices under Climate Change Scenarios (Case Study: Semi-Arid
Areas of West Golestan Province). ECOPERSIA. 4: 1585-1602.
3.Akbari Yengehghaleh, M., Sanaeenejad, S.H., Faridhosseini, A., and Akbari, M. 2017. The Study of Spatial -Temporal Distribution of Rainfall, using TRMM data (Case study: Khorasan Razavi province), J. Clim. Res. 29: 1-18.(In Persian)
4.Alibakhshi, S.M., Farid Hossini, A.R., Davari, K., Alizadeh, A., and Munyka, H. 2017. Statistical comparison between IMERG and TMPA 3B42V7 products at the level of three GPM and TRMM precipitation data Case study: Kashafrood catchment, Razavi Khorasan province. Iranian J. Nat. Resour. 4: 69. 963-981. https://doi.org/10.22059/jrwm.2017.61194. (In Persian)
5.Anjum, M.N., Ding, Y., Shangguan, D., Ijaz, M.W., and Zhang, S. 2016. Evaluation of high-resolution satellite-based real-time and post-real-time precipitation estimates during 2010 extreme flood event in Swat River Basin, Hindukush region. Adv. Meteorol. 1-8. http://dx.doi.org/10.1155/2016/2604980.
6.Azari, M., Moradi, H.R., Saghafian, B., and Faramarzi, M. 2013. Assessment of Hydrological Effects of Climate Change in Gourganroud River Basin. J. Water Soil. 27: 3. 537-547. (In Persian)
7.Golestan province Regional Water Company. 2016. Integrated Water Resources Studies Update Update Report for Gharasu and Gorganrood River Basin. 247p. (In Persian)
8.Guo, H., Chen, S., Bao, A., and Hu, J. 2015. Inter-comparison of high-resolution satellite precipitation products over Central Asia,” Remote Sens. 7: 6. 7181-7211. https://doi.org/10.3390/rs70607181.
9.Guo, H., Chen, S., Bao, A., Behrangi, A., Hong, Y., Ndayisaba, F., and Stepanian, P.M. 2016. Early assessment of integrated multi-satellite retrievals for global precipitation measurement over China. Atmos. Res. 176: 121-133.
10.Hou, A.Y., Kakar, R.K., Neeck, S., Azarbarzin, A.A., Kummerow, C.D., Kojima, M., and Iguchi, T. 2014. The global precipitation measurement mission. B. AM. Meteorol. Soc.95: 5. 701-722.
11.Hsu, K. 1997. Precipitation estimation from remotely sensed information using artificial neural networks,” J. Appl. Meteorol. Clim. 36: 1176-1190. https:// doi.org/10.1175/1520-0450 http://trmm. gsfc.nasa.gov (1/06/2016 available access date).
12.Huffman, G.J., Adler, R.F., and Bolvin, D.T. 2007. The TRMM Multi-Satellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, J. Hydrometeorol. 8: 1. 38-55. https://doi.org/10.1175/JHM560.1.
13.Huffman, G.J., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., and Yoo, S.H. 2015. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm theoretical basis document, Nat. Aero. Space Admin. 4: 1-30.
14.Joyce, R.J., Janowiak, J.E., Arkin, P.A., and Xie, P. 2004. CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at the high spatial and temporal resolution, J. Hydrometeorol. 5: 3. 487-503. https://doi.org/ 10.1175/ 1525-7541.
15.Khwarazmi, S. 2013. Validation of microwave satellite rain rate algorithms based on observations. M.Sc. Thesis. The University of Hormozgan. Iran. 121p. (In Persian)
16.Kidd, C., and Huffman, G. 2011. Global precipitation measurement. Meteorological Applications. 18: 3. 334-353. https:// doi.org/10.1002/met.284. (In Persian)
17.Kim, K., Park, J., Baik, J., and Choi, M. 2017. Evaluation of topographical and seasonal features using GPM IMERG and TRMM 3B42 over Far-East Asia. Atmos. Res. 187: 95-105.
18.Kubota, T., Shige, S., Hashizume, H., and Aonashi, K. 2007. Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: production and validation,” IEEE T. Geosci. Remote Sens.45: 7. 2259-2275. https://doi.org/ 10.1109/TGRS.2007.895337.
19.Liechti, T., Matos, G.C., Pedro, J., Boillat, J.L., and Schleiss, A. 2012. Comparison and evaluation of satellite-derived precipitation products for hydrological modeling of the Zambezi River Basin. Hydrol. Earth Syst. Sci.16: 489-500.
20.Li, N., Tang, G., Zhao, P., Hong, Y., Gou, Y., and Yang, K. 2017. Statistical assessment and hydrological utility of the latest multi-satellite precipitation analysis IMERG in the Ganjiang River basin. Atmos. Res. 183: 212-223.
21.Liu, J., Zhang, W., and Nie, N. 2018. Spatial Downscaling of TRMM Precipitation Data Using an Optimal Subset Regression Model with NDVI and Terrain Factors in the Yarlung Zangbo River Basin, China, Adv. Meteorol. 1: 1-13. https://doi.org/ 10.1155/2018/3491960.
22.Modaresi, F., Araghinejad, S.H., Ebrahimi, K., and Kholghi, M. 2010. Regional Assessment of Climate Change Using Statistical Tests: Case Study of Gorganroud-Gharehsou Basin, J. Water Soil. 24: 3. 476-489.
23.Mohammadi, R., Dastorani, M.T., Akbari, M., and Ahani, H. 2019. The impacts of magnetized water treatment of different morphological and physiological factors of plant species
in the arid regions, Water Supply.19: 6. 1587-1596. https://doi.org/ 10.2166/ws.2019.027.
24.Mosaedi, A., Ghabaei Sough, M., Sadeghi, S.H., Mooshakhian, Y., and Bannayan, M. 2017. Sensitivity analysis of monthly reference crop evapotranspiration trends in Iran: a qualitative approach, Theor. Appl. Climatol. 128: 3. 857-873.
25.Ning, S., Wang, J., Jin, J., and Ishidaira, H. 2016. Assessment of the latest GPM-era high-resolution satellite precipitation products by comparison with observation gauge data over the Chinese Mainland. Water. 8: 11. 481.
26.O’h, S., and Kirstetter, P.E. 2018. Evaluation of diurnal variation of GPM IMERG‐derived summer precipitation over the contiguous US using MRMS data. Q. J. R. Meteorol. Soc. 144: 1.270-281. https://doi.org/10.1002/qj.3218.
27.Prakash, S., Mitra, A.K., AghaKouchak, A., Liu, Z., Norouzi, H., and Pai, D.S. 2016. A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region. J. Hydrol. 556: 865-876. https://10.1016/j.jhydrol.2016.01.029.
28.Sahlu, D., Nikolopoulos, E.I., Moges, S.A., Anagnostou, E.N., and Hailu, D. 2016. First evaluation of the Day-1 IMERG over the upper Blue Nile basin. J. Hydrometeorol. 17: 11. 2875-2882.
29.Sharifi, E., Saghafian, B., and Steinacker, R. 2016a. Performance evaluation of the latest generation of high temporal-spatial resolution satellite precipitation products. National Conference on Water Resources Management, University of Kurdistan. 10p.
30.Sharifi, E., Steinacker, R., and Saghafian, B. 2016b. Assessment of GPM-IMERG and Other Precipitation Products against Gauge Data under Different Topographic and Climatic Conditions in Iran: Preliminary Results”. Remote Sens. 8: 2. 1-25.
31.Sorooshian, S., Hsu, K.L., Gao, X., Gupta, H.V., Imam, B., and Braithwaite, D. 2000. Evaluation of PERSIAN system satellite-based estimates of tropical rainfall,” B. AM. Meteorol. Soc. 81: 2035-2046. https://doi.org/ 10.1175/ 1520-0477.
32.Tao, J., Hua, Y., Rui, L., Tairong, H., and Jianfeng, W. 2014. Applicability analysis of the TRMM precipitation data in the Sichuan-Chongqing region,”
Prog. Phys. Geog. 33: 10. 1375-1386. https://doi.org/10.11820/dlkxjz.2014.10.009.
33.Tan, M.L., and Duan, Z. 2017. Assessment of GPM and TRMM precipitation products over Singapore. Remote Sens. 9: 7. 720.
34.Tan, M.L., and Santo, H. 2018. Comparison of GPM IMERG, TMPA 3B42, and PERSIAN-CDR satellite precipitation products over Malaysia. Atmos. Res. 202: 63-76. http://dx.doi. org/10.1016/j.atmosres.2017.11.006.
35.Tang, G., Ma, Y., Long, D., Zhong, L., and Hong, Y. 2016a. Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. J. Hydrometeorol. 17:5.1407-1423. http://dx.doi.org/ 10. 1175/ JHM-D-15-0081.1.
36.Tang, G., Zeng, Z., Long, D., Guo, X., Yong, B., Zhang, W., and Hong, Y. 2016b. Statistical and hydrological comparisons between TRMM and GPM level-3 products over a mid-latitude basin: Is day-1 IMERG a good successor for TMPA 3B42V7? J. Hydrometeorol. 17: 1. 121-137.