Abstract
Although gridded precipitation products (GPPs) are essential when evaluating weather and climate systems, their reliability must be known before application for scientific works. This study assessed the correspondence of observed daily precipitation data over southwestern Iran and the corresponding precipitation values from two well-known GPPs, CHIRPS and CPC-Unified (CPC), from 1998 through 2018. The datasets were for the Jan-Mar period, which contributes to 30 to 80% of annual precipitation in northwestern Iran and Persian Gulf coastal areas. Principal component analysis was used to divide 218 rain-gauge stations in the study area into three main zones, the western and northwestern (Zone 1), south-central (zone 2), and coastal areas (zone 3). The area-averaged time series of zonal precipitation data were then compared with the corresponding zonal GPPs values. All precipitation data that was equal to or greater than 1 mm (wet day) and equaled or exceeded the 95th or 99th percentiles (very wet or extremely wet days, respectively) were compared for each zone. The comparison was repeated for 3-day cumulative precipitation data. The statistical tests demonstrated significant relationships between the observational-based and model-based GPPs. The results were more robust for the CPC and 3-day cumulative datasets than for CHIRPS and the daily records. The strongest results were for estimation of extremely wet days and were significant for CPC estimations of the three-day cumulative precipitation in zone 3.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
None.
Code availability
The authors used different software such as R statistic and Microsoft Excel for preparation or analyzing data and producing maps.
References
Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675. https://doi.org/10.1002/joc.3370060607
Alijani B (2008) Effect of the Zagros mountains on the spatial distribution of precipitation. J Mt Sci 5:218–231. https://doi.org/10.1007/s11629-008-0126-8
Alijani B, O’Brien J, Yarnal B (2008) Spatial analysis of precipitation intensity and concentration in Iran. Theor Appl Climatol 94:107–124. https://doi.org/10.1007/s00704-007-0344-y
Alijanian M, Rakhshandehroo GR, Mishra A, Dehghani M (2017) Evaluation of satellite rainfall climatology using CMORPH, PERSIANN-CDR, PERSIANN, TRMM, MSWEP over Iran. Int J Climatol 37:4896–4914. https://doi.org/10.1002/joc.5131
Bhakar SR, Bansal AK, Chhajed N, Purohit RC (2006) Frequency analysis of consecutive days maximum rainfall at Banswara, Rajasthan, India. JEAS 1:64–67
Bajracharya SR, Shrestha M, Shrestha AB (2014) Assessment of high-resolution satellite rainfall estimation products in a streamflow model for flood prediction in the Bagmati basin, Nepal. J Flood Risk Manag 10(1). https://doi.org/10.1111/jfr3.12133
Bai L, Shi C, Li L, Yang Y, Wu J (2018) Accuracy of CHIRPS satellite-rainfall products over mainland China. Remote Sens10(362). doi:https://doi.org/10.3390/rs10030362
Beck HE, van Dijk AIJM, Levizzani V, Schellekens J, Miralles DG, Martens B, de Roo A (2017) MSWEP: 3-hourly 0.25 degrees global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. HESS 21:589–615. https://doi.org/10.5194/hess-21-589-2017
Belay AS, Fenta AA, Beyene AY, Abera FN et al (2019). Evaluation and application of multi-source satellite rainfall product CHIRPS to assess spatio-temporal rainfall variability on data-sparse Western margins of Ethiopian Highlands. Remote Sens 11(22). https://doi.org/10.3390/rs11222688
Buishand TA (1982) Some methods for testing the homogeneity of rainfall records. J Hydrol 58:11–27. https://doi.org/10.1016/0022-1694(82)90066-X
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? – arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
Chen M, Shi W, Xie P, Silva VBS, Kousky VE, Higgins RW, Janowiak JE (2008) Assessing objective techniques for gauge-based analyses of global daily precipitation. J Geophys Res 113. https://doi.org/10.1029/2007JD009132
Dinh KD, Anh TN, Nguyen NY, Bui DD, Srinivasan R (2020) Evaluation of grid-based rainfall products and water balances over the Mekong river basin. Remote Sens 12:21–32. https://doi.org/10.3390/rs12111858
Dinku T, Funk C, Peterson P, Maidment R, Tadesse T, Gadain H, Ceccato P (2018) Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q J R Meteorol Soc 144:292–312. https://doi.org/10.1002/qj.3244
Du H, Alexander LV, Donat MG, Lippmann T et al (2019) Precipitation from persistent extremes is increasing in most regions and globally. Geophys Res Lett 46:6041–6049. https://doi.org/10.1029/2019GL081898
Duan Z, Liu J, Tuo Y, Chiogna G, Disse M (2016) Evaluation of eight high spatial resolution gridded precipitation products in Adige basin (Italy) at multiple temporal and spatial scales. Sci Total Environ 573:1536–1553. https://doi.org/10.1016/j.scitotenv.2016.08.213
Fallah A, Rakhshandehroo GR, Berg P, Sungmin O, Orth R (2019) Evaluation of precipitation datasets against local observations in southwestern Iran. Int J Climatol 40:4102–4116. https://doi.org/10.1002/joc.6445
Floodlist.com (2019) http://floodlist.com/tag/iran. Accessed 19 Nov 2020
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J (2015) The climate hazards infrared precipitation with stations - a new environmental record for monitoring extremes. Sci Data 2. https://doi.org/10.1038/sdata.2015.66
Gebremicael TG, Mohamed YA, Van der Zaag P, Gebremedhin A, Gebremeskel G, Yazew E, Kifle M (2019) Evaluation of multiple satellite rainfall products over the rugged topography of the Tekeze-Atbara basin in Ethiopia. Int J Remote Sens 40:4326–4345. https://doi.org/10.1080/01431161.2018.1562585
Hammond JC, Saavedra FA, Kampf SK (2018) Global snow zone maps and trends in snow persistence 2001–2016. Int J Climatol 38:4369–4383. https://doi.org/10.1002/joc.5674
Higgins RW, Shi W, Yarosh E, Joyce R (2000) Improved United States precipitation quality control system and analysis. NCEP/Climate Prediction Center ATLAS No. 7.
Katiraie-Boroujerdy PS, Akbari Asanjan A, Hsu K, Sorooshian S (2017) Intercomparison of PERSIANN-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales. Atmos Res 193:36–49. https://doi.org/10.1016/j.atmosres.2017.04.005
Katsanos D, Retalis A, Tymvios F, Michaelides S (2016) Analysis of precipitation extremes based on satellite (CHIRPS) and in situ dataset over Cyprus. Nat Hazards 83:53–63. https://doi.org/10.1007/s11069-016-2335-8
Kidd C, Becker A, Huffman GJ, Muller CL, Joe P, Skofronick-Jackson G, Kirschbaum DB (2017) So, how much of the Earth’s surface is covered by rain gauges? BAMS 98:69–78. https://doi.org/10.1175/bams-d-14-00283.1
Liu J, Shangguan D, Liu S, Ding Y, Wang S, Wang X (2019) Evaluation and comparison of CHIRPS and MSWEP daily-precipitation products in the Qinghai-Tibet plateau during the period of 1981–2015. Atmos Res. https://doi.org/10.1016/j.atmosres.2019.104634
Nazemosadat MJ, Shahgholian K (2017) Heavy precipitation in the southwest of Iran: association with the Madden–Julian oscillation and synoptic scale analysis. Clim Dyn 49:3091–3109. https://doi.org/10.1007/s00382-016-3496-6
Nazemosadat MJ, Shahgholian K, Ghaedamini H, Nazemosadat E (2021) Introducing new climate indices for identifying wet/dry spells within an Madden-Julian Oscillation phase. Int J Climatol 41:E1686–E1699. https://doi.org/10.1002/joc.6799
NCEP/Climate Prediction Center (2014) PRCP_CU_GAUGE_V1.0GLB_0.50deg_EOD.gif. https://ftp.cpc.ncep.noaa.gov/precip/CPC_UNI_PRCP/GAUGE_GLB/DOCU. Accessed 20 Sept 2020
Paredes-Trejo FJ, Barbosa HA, Kumar TVL (2020) Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil. J Arid Environ 139:26–40. https://doi.org/10.1016/j.jaridenv.2016.12.009
Peñarrocha D, Estrela MJ, Millán MM (2002) Classification of daily rainfall patterns in a Mediterranean area with extreme intensity levels: the Valencia region. Int J Climatol 22:677–695. https://doi.org/10.1002/joc.747
Peterson TC, Manton M (2008) Monitoring changes in climate extremes: a tale of international collaboration. Bull Am Meteorol Soc 89:1266–1271. https://doi.org/10.1175/2008BAMS2501.1
Pettitt AN (1979) A non-parametric approach to the change-point problem. J R Stat Soc. Series C (applied Statistics) 28:126–135. https://doi.org/10.2307/2346729
Romero R, Ramis C, Guijarro JA, Sumner G (1999) Daily rainfall affinity areas in Mediterranean Spain. Int J Climatol 19:557–578. https://doi.org/10.1002/(SICI)1097-0088(199904)19:5%3c557:AID-JOC377%3e3.0.CO;2-D
Rahimi M, Fatemi SS (2019) Mean versus extreme precipitation trends in Iran over the period 1960–2017. Pure Appl Geophys. https://doi.org/10.1007/s00024-019-02165-9
Raziei T, Mofidi A, Santos JA, Bordi B (2012) Spatial patterns and regimes of daily precipitation in Iran in relation to large-scale atmospheric circulation. Int J Climatol 32:1226–1237. https://doi.org/10.1002/joc.2347
Rivera JA, Hinrichs S, Marianetti G (2019) Using CHIRPS dataset to assess wet and dry conditions along the semi-arid Central-Western Argentina. Adv. Meteorol. https://doi.org/10.1155/2019/8413964
Saeidizand R, Sabetghadam S, Tarnavsky E, Pierleoni A (2018) Evaluation of CHIRPS rainfall estimates over Iran. Q J R Meteorol Soc 144:282–291. https://doi.org/10.1002/qj.3342
Shen Y, Xiong A (2015) Validation and comparison of a new gauge-based precipitation analysis over mainland China: new gauge-based precipitation over China. Int J Climatol 36. https://doi.org/10.1002/joc.4341
Shi W, Higgins RW, Yarosh E, Kousky VE (2001) The annual cycle and variability of precipitation in Brazil. NCEP/Climate Prediction Center ATLAS No. 9. NOAA National Weather Service. https://www.cpc.ncep.noaa.gov/research_papers/ncep_cpc_atlas/9/toc.html. Accessed 20 Dec 2020
Silva VBS, Kousky VE, Shi W, Higgins RW (2007) An improved gridded historical daily precipitation analysis for Brazil. J Hydrometeorol 8:847–861. https://doi.org/10.1175/JHM598.1
Sun Q, Miao C, Duan Q, Ashouri H, Sorooshian S, Hsu KL (2017) A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev Geophys 56:79–107. https://doi.org/10.1002/2017RG000574
Vaghefi SA, Keykhai M, Jahanbakhshi F, Sheikholeslami, J, Ahmadi A, Yang H, Abbaspour KC (2019) The future of extreme climate in Iran. Sci Rep 9. https://doi.org/10.1038/s41598-018-38071-8
Wilks DS (2019) Statistical methods in the atmospheric Sciences, 4rd eds. Ithaca, NY, USA
Wu W, Li Y, Luo X, Zhang Y, Ji X, Li X (2019) Performance evaluation of the CHIRPS precipitation dataset and its utility in drought monitoring over Yunnan province, China. Geomatics Nat Hazards Risk 10:2145–2162. https://doi.org/10.1080/19475705.2019.1683082
Xie P, Chen M, Yang S, Yatagai M, Hayasaka T, Fukushima Y, Liu C (2007) A gauge-based analysis of daily precipitation over East Asia. J Hydrometeorol 8:607–626. https://doi.org/10.1175/JHM583.1
Acknowledgements
The authors would like to thank the I.R of Iran Meteorology Organization (IRIMO) for providing the daily precipitation data. We are grateful to the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, developers for providing the daily CPC Global Unified Precipitation dataset. We also acknowledge the University of California Santa Barbara’s Climate Hazards Group (CHG) to make the daily CHIRPS precipitation data available to international users that made this study possible. It is a pleasure to acknowledge helpful comments from anonymous reviewers for their helpful comments and suggestions. A great thank you to Professor Dr. Hartmut Graßl from the journal editorial board for his useful comments and advice.
Author information
Authors and Affiliations
Contributions
All authors, including Habib Allah Ghaedamini, Saeed Morid, Mohammad Jafar Nazemosadat, Ali Shamsoddini, and Hossein Shafizadeh Moghadam, contributed the design and implementation of the research. Habib Allah Ghaedamini and Saeed Morid performed the analysis. Habib Ghaedamini wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. Habib Ghaedamini, Saeed Morid, and Mohammad Jafar Nazemosadat read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval
The paper is part of doctoral theses and is not currently being considered for publication elsewhere. It reflects the authors’ own research and analysis truthfully and completely credits the meaningful contributions of co-authors.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ghaedamini, H.A., Morid, S., Nazemosadat, M.J. et al. Validation of the CHIRPS and CPC-Unified products for estimating extreme daily precipitation over southwestern Iran. Theor Appl Climatol 146, 1207–1225 (2021). https://doi.org/10.1007/s00704-021-03790-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-021-03790-y