نیوار

نیوار

Evaluation of the Accuracy of Satellite Precipitation Data Compared to Station Observations in Northeastern Iran

نوع مقاله : مقاله پژوهشی

نویسندگان
Bromand Salahi 1
Ali Shahi 2
1 Professor of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
2 PhD of climatology, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil,
10.30467/nivar.2026.573075.1368
چکیده
This study aimed to evaluate the performance of three satellite precipitation products—CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TRMM (Tropical Rainfall Measuring Mission)—in representing precipitation patterns across northeastern Iran during the 2001–2010 period. For this purpose, data from six selected synoptic meteorological stations obtained from the Iranian Meteorological Organization were compared with satellite-derived datasets accessed through the Google Earth Engine platform. The products were evaluated at daily, monthly, and annual time scales. At the daily scale, statistical indicators including the correlation coefficient (R), root mean square error (RMSE), and bias were calculated. Additionally, independent-samples t-tests were applied to examine differences in monthly mean precipitation, and annual precipitation zoning during dry and wet years was conducted within a Geographic Information System (GIS) framework. All computations related to the Taylor diagram and statistical metrics were carried out using R, while independent-sample t-test analyses were performed in SPSS. The results demonstrated that the performance of satellite products is strongly dependent on temporal scale and climatic conditions. At the daily scale, TRMM showed the highest agreement with ground observations and outperformed CHIRPS and PERSIANN in terms of accuracy. Despite its higher spatial resolution, CHIRPS exhibited weak performance in estimating daily precipitation over the study area. At the annual scale, TRMM performed more reliably, particularly during dry years, whereas all three products tended to underestimate precipitation during wet years. Furthermore, the highest estimation accuracy for all three satellite products was jointly observed at the Torbat-Heydariye station. At the monthly scale, although TRMM occasionally demonstrated relatively better performance, statistically significant differences between satellite-derived and station data were detected in 83% of the stations. Overall, the findings indicate that the selection of satellite precipitation products should be tailored to temporal scale, topographic characteristics, and regional climatic conditions. Regional validation is therefore essential prior to application in water resource management and climatological studies in dry and semi-arid regions.
کلیدواژه‌ها
CHIRPS
PERSIAN
Precipitation
TRMM
Northeast Iran
موضوعات

عنوان مقاله English

Evaluation of the Accuracy of Satellite Precipitation Data Compared to Station Observations in Northeastern Iran

نویسندگان English

Bromand Salahi 1
Ali Shahi 2
1 Professor of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
2 PhD of climatology, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil,
چکیده English

This study aimed to evaluate the performance of three satellite precipitation products—CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TRMM (Tropical Rainfall Measuring Mission)—in representing precipitation patterns across northeastern Iran during the 2001–2010 period. For this purpose, data from six selected synoptic meteorological stations obtained from the Iranian Meteorological Organization were compared with satellite-derived datasets accessed through the Google Earth Engine platform. The products were evaluated at daily, monthly, and annual time scales. At the daily scale, statistical indicators including the correlation coefficient (R), root mean square error (RMSE), and bias were calculated. Additionally, independent-samples t-tests were applied to examine differences in monthly mean precipitation, and annual precipitation zoning during dry and wet years was conducted within a Geographic Information System (GIS) framework. All computations related to the Taylor diagram and statistical metrics were carried out using R, while independent-sample t-test analyses were performed in SPSS. The results demonstrated that the performance of satellite products is strongly dependent on temporal scale and climatic conditions. At the daily scale, TRMM showed the highest agreement with ground observations and outperformed CHIRPS and PERSIANN in terms of accuracy. Despite its higher spatial resolution, CHIRPS exhibited weak performance in estimating daily precipitation over the study area. At the annual scale, TRMM performed more reliably, particularly during dry years, whereas all three products tended to underestimate precipitation during wet years. Furthermore, the highest estimation accuracy for all three satellite products was jointly observed at the Torbat-Heydariye station. At the monthly scale, although TRMM occasionally demonstrated relatively better performance, statistically significant differences between satellite-derived and station data were detected in 83% of the stations. Overall, the findings indicate that the selection of satellite precipitation products should be tailored to temporal scale, topographic characteristics, and regional climatic conditions. Regional validation is therefore essential prior to application in water resource management and climatological studies in dry and semi-arid regions.

کلیدواژه‌ها English

CHIRPS
PERSIAN
Precipitation
TRMM
Northeast Iran
1.    Acosta-Reyes, M.-Á., Hernández-Anguiano, J.-H., Carrión-Viramontes, F.-J., Li, Y., Quintana-Rodríguez, J.-A., Rojo-Hernández, J.-D., Navarro-Céspedes, J.-M., Piña-González, V., Zha, X., & Liao, X. (2024). Analysis of IMERG precipitation product in Mexican states to evaluate the spatial and temporal variation in precipitation. Sustainable Water Resources Management, 10(6), 190. https://doi.org/10.1007/s40899-024-01171-7
2.    Akinyemi, D. F., Ayanlade, O. S., Nwaezeigwe, J. O., & Ayanlade, A. (2020). A comparison of the accuracy of multi-satellite precipitation estimation and ground meteorological records over Southwestern Nigeria. Remote Sensing in Earth Systems Sciences, 3(1), 1-12.‏ https://doi.org/10.1007/s41976-019-00029-3                    
3.    Ali, A. F., Xiao, C., Anjum, M. N., Adnan, M., Nawaz, Z., Ijaz, M. W., Sajid, M., & Farid, H. U. (2017). Evaluation and Comparison of TRMM Multi-Satellite Precipitation Products With Reference to Rain Gauge Observations in Hunza River Basin, Karakoram Range, Northern Pakistan. Sustainability, 9(11), 1954. https://doi.org/10.3390/su9111954                             
4.         Asadi Oskouei,E. , Mohamad Bygi Salkhori,M. , Kouhi,M. , Nourozi,H. , َAbbasi,F. and Mohammadian,A. (2025). Estimation of Soil Water Stress During the Growth Period of Rainfed Wheat Based on SMAP Satellite Data. Nivar, 49(128-129), 195-219. doi: 10.30467/nivar.2025.502222.1320. [In Persian]
5.       Awatade, S., Kashiwar, S. R., Ghosh, S., Singhandhupe, R. B., Dongarwar, U. R., & Dhakre, D. S. (2018). Satellite Based Estimation and Validation of Rainfall Distribution in Monsoon over Washim (Maharashtra), India. Int. J. Curr. Microbiol. App. Sci, 7(8), 1694-1704.‏ https://doi.org/10.20546/ijcmas.2018.708.194
6.       Babaeian, I., Modirian, R., Khazanedari, L., Kouhi, M., Kouzegaran, S., Falamrazi, Y., Karimian, M., & Malbusi, Sh. (2021). Precipitation forecasting in Iran using downscaled statistical methods based on CMIP6 model outputs. Internal Project of the Climatology Research Institute, Mashhad. [In Persian]
7.       Bahrami, A. & Darand, M. (2025). Evaluation of spatio-temporal accuracy of precipitation estimation of ERA-5 database over Iran. Iranian Journal of Geophysics, 19(1), 27-46. Doi: 10.30499/ijg.2024.455625.1599. [In Persian]
8.       Bayissa, Y., Tadesse, T., Demisse, G., & Shiferaw, A. (2017). Evaluation of satellite-based rainfall estimates and application to monitor meteorological drought for the Upper Blue Nile Basin, Ethiopia. Remote Sensing, 9(7), 669. https://doi.org/10.3390/rs9070669.
9.       Da Silva, L. d. D. d. J., Mahmoud, M., González-Rodríguez, L., Mohammed, S., Rodríguez-López, L., & Arias, M. I. A. (2023). Assessment of the IMERG Early-Run Precipitation Estimates over South American Country of Chile. Remote Sensing, 15(3), 573. https://doi.org/10.3390/rs15030573
10.   Das, P., Zhang, Z., & Ren, H. (2022). Evaluating the accuracy of two satellite-based Quantitative Precipitation Estimation products and their application for meteorological drought monitoring over the Lake Victoria Basin, East Africa. Geo-Spatial Information Science, 25(3), 500–518. https://doi.org/10.1080/10095020.2022.2054731
11.   Funk, C., Verdin, A., Michaelsen, J., Peterson, P., Pedreros, D., & Husak, G. (2015). A global satellite-assisted precipitation climatology. Earth System Science Data, 7(2), 275-287.‏ https://doi.org/10.5194/essd-7-275-2015
12.   Gebrechorkos, S. H., Leyland, J., Dadson, S. J., Cohen, S., Slater, L., Wortmann, M., Ashworth, P. J., Bennett, G. L., Boothroyd, R., Cloke, H., Delorme, P., Griffith, H., Hardy, R., Hawker, L., McLelland, S., Neal, J., Nicholas, A., Tatem, A. J., Vahidi, E., Liu, Y., Sheffield, J., Parsons, D. R., & Darby, S. E. (2024). Global-scale evaluation of precipitation datasets for hydrological modelling. Hydrology and Earth System Sciences, 28, 3099–3118. https://doi.org/10.5194/hess-28-3099-2024
13.   Gholami, S., Farajzadeh, M., & Ghavidel, Y. (2023). A Comparative Study of the Performance of CHIRPS and ERA5-Land Precipitation Datasets in Detecting Droughts in Iran. Physical Geography Quarterly, 61 (), 1-22. https://dorl.net/dor/20.1001.1.20085656.1402.16.61.1.2 [In Persian]
14.   Jiang, S., Ren, L., Yong, B., Hong, Y., Yang, X., & Yuan, F. (2016). 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, 26, 439-455.‏ https://doi.org/10.1007/s11769-016-0818-x
15.   Katiraie Boroujerdy, P. S. (2016). Comparison of high-resolution gridded monthly satellite and ground-based precipitation data over Iran. Iranian Journal of Geophysics, 7(4), 149-160. https://dor.isc.ac/dor/20.1001.1.20080336.1392.7.4.10.0. [In Persian]
16.   Khanmohammadi, Z., Mahjoobi, E., Gharachelou, S. & Banikhedmat, A. (2022). Statistical assessment of satellite rainfall products in daily and monthly gauge spatial scales. Watershed Engineering and Management, 14(4), 512-527. doi: 10.22092/ijwmse.2022.355269.1908. [In Persian]
17.   Kim, K., Park, J., Baik, J., & Choi, M. (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
18.   Kumar, D. N., & Reshmidevi, T. V. (2013). Remote Sensing Applications in Water Resources. Journal of the Indian Institute of Science, 93(2), 163–188. https://journal.iisc.ac.in/index.php/iisc/article/view/1088
19.   Masoodian, S. A., Rayatpishe, F. & Keykhosravi Kiani, M. S. (2016). Introducing the TRMM and Asfezariprecipitation database: A comparative study. Iranian Journal of Geophysics, 8(4), -. https://dor.isc.ac/dor/20.1001.1.20080336.1393.8.4.2.9. [In Persian]
20.   Miri, M. (2016). Evaluation and statistically comparison of TRMM and GPCC datasets with observed precipitation in Iran. Journal of the Earth and Space Physics, 42(3), 657-672. doi: 10.22059/jesphys.2016.56102. [In Persian]
21.   Mianabadi, A., Salari, K. & Pourmohamad, Y. (2022). Evaluation of CHIRPS Satellite Product for Trend Analysis of Precipitation in the Southeast of Iran. Iranian Journal of Irrigation & Drainage, 16(3), 670-684. https://dor.isc.ac/dor/20.1001.1.20087942.1401.16.3.15.6. [In Persian]
22.   Morales-Velázquez, M. I., Herrera, G. D. S., Aparicio, J., Rafieeinasab, A., & Lobato-Sánchez, R. (2021). Evaluating reanalysis and satellite-based precipitation at regional scale: A case study in southern Mexico. Atmósfera, 34(2), 189-206.‏ https://doi.org/10.20937/ATM.52789
23.   Ordooni, M., Memarian, H., Akbari, M., & Pourreza, M. (2021). Evaluation and Comparison of GPM Satellite Precipitation Data with Meteorological Station using Kolmogorov-Smirnov Test. Iranian Journal of Rainwater Catchment Systems, 9(2), 11-24.‏ http://dorl.net/dor/20.1001.1.24235970.1400.9.2.5.9. [In Persian]
24.   Payfeshordeh, F., Hafezparast, M., & Fatemi, S. E. (2022). Evaluation of precipitation data of Gamasiab basin synoptic stations in comparison with satellite data. Advanced Technologies in Water Efficiency, 2(3), 92-113. Doi: 10.22126/atwe.2022.7915.1021. [In Persian]
25.   Rodríguez, E., García-Echeverri, C., González, A., Sandoval, J., Patarroyo-González, M., Agudelo-Duque, D. E., & Roldán N, M. A. (2024). Valuating the IMERG precipitation satellite product to derive intensity-duration-frequency curves in Colombia. Revista Facultad de Ingeniería Universidad de Antioquia, (110), 31-47.‏ https://doi.org/10.17533/udea.redin.20230212
26.   Serrat-Capdevila, A., Merino, M., Valdes, J. B., & Durcik, M. (2016). Evaluation of the Performance of Three Satellite Precipitation Products over Africa. Remote Sensing, 8(10), 836. https://doi.org/10.3390/rs8100836
27.   Shahi, A. & Salahi, B. (2025). Spatio-Temporal Evaluation of Satellite Precipitation Products to Estimate Heavy Rainfall Events in Northwest Iran in the Last Two Decades. Journal of the Earth and Space Physics, 51(1), 133-150. Doi: 10.22059/jesphys.2024.377656.1007612. [In Persian]
28.   Sharafati, A. & Shobeiri, S. (2022). Evaluation of CHIRPS precipitation data in the analysis of precipitation characteristics trends in different climatic regions of Iran. Journal of Climate Research, 1400(48), 111-124. https://clima.irimo.ir/article_147867.html?lang=fa. [In Persian]
29.   Satgé, F., Ruelland, D., Bonnet, M. P., Molina, J., & Pillco, R. (2019). Consistency of satellite-based precipitation products in space and over time compared with gauge observations and snow-hydrological modelling in the Lake Titicaca region. Hydrology and Earth System Sciences, 23(1), 595-619.‏
30.   Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of geophysical research: atmospheres, 106(D7), 7183-7192.‏ https://doi.org/10.1029/2000JD900719
31.   Tedla, A. A., Gebremichael, M., & Berhe, F. (2024). Evaluation of satellite precipitation products for real-time extreme river flow modeling in data scarce regions. Proceedings of the International Association of Hydrological Sciences, 386, 223–230. https://doi.org/10.5194/piahs-386-223-2024              
32.   Toté, C., Patricio, D., Boogaard, H., Van der Wijngaart, R., Tarnavsky, E., & Funk, C. (2015). Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique. Remote Sensing, 7(2), 1758-1776. https://doi.org/10.3390/rs70201758 
33.   Zambrano-Bigiarini, M., Nauditt, A., Birkel, C., Verbist, K., & Ribbe, L. (2017). Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile. Hydrology and Earth System Sciences, 21, 1295–1320. https://doi.org/10.5194/hess-21-1295-2017
 

  • تاریخ دریافت 11 بهمن 1404
  • تاریخ بازنگری 29 بهمن 1404
  • تاریخ پذیرش 06 اسفند 1404
  • تاریخ انتشار 01 مهر 1404