Evaluating the Performance of Era-5 Re-Analysis Data in Estimating Daily and Monthly Precipitation, Case Study; Ardabil Province

Document Type : Research Paper

Authors

1 Assistant Professor. Department of Water Engineering Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.

2 Associate Professor. Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.

3 Former Graduate Student, MSc of water resource engineering, College of Aburaihan, Tehran University, Karaj, Iran.

4 Former Graduate Student, MSc of water resource engineering, Department of Water Engineering Faculty of Agriculture and Natural Resources IKIU University, Qazvin, Iran.

5 Former Graduate Student, MSc of water resource engineering, Department of Water Faculty of Agriculture University of Uremia, Uremia,Iran

Abstract

Inappropriate distribution of precipitation measurement stations has led to the use of gridded precipitation datasets, consisting of satellites, reanalysis and ground-based precipitation datasets in recent years. In this study, one of the important precipitation products named Era5 has been evaluated in Ardabil province. The observation data were first interpolated during the 2004–2014 statistical period and compared with Era5 data based on daily, monthly and annual time scales. Evaluations were performed using RMSE, correlation coefficient and contingency table indices consisting of POD, FAR, CSI and POFD. The results showed that the correlation coefficient for Era5 at the daily time scale was above 0.75 for most of the cells and RMSE was below 3 mm. Also, the correlation coefficient for monthly time scale was above 0.8 and the RMSE was below 20 mm in most of the cells. Evaluation using contingency table indices showed that POD index in the studied cells ranged from 0.7 to 0.85, FAR ranged from 0 to 0.25, POFD ranged from 0.1 to 0.2 and the CSI was in the range of 0.4 to 0.5. Precipitation values of both precipitation sources were classified into 6 classes using Ward cluster analysis method. The results of k-means method and Wilkes-Lambda model confirmed the classification accuracy and the difference between the means of the clusters. In general, it can be concluded that the Era5 precipitation product at both daily and monthly time scales can be used as an appropriate alternative to data scarce regions after bias correction.

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References

Amjad M, Yilmaz MT, Yucel I and Yilmaz KK (2020) Performance evaluation of satellite- and model-based precipitation products over varying climate and complex topography. Journal of Hydrology 584:124707. Available at: http://www.sciencedirect.com/science/article/pii/S0022169420301670
Azizian A, Shayeghi A, Bruca L. (2019). Evaluation of reanalysis rainfall product based on remote sensing techniques for hydrological modeling using the large-scale VIC-3L model.  Journal of Water Resources Research, 15(2):57-72 (In Farsi).
Azizian A, Ramezani H. (2019). Evaluation of the performance of Era-Interim re-analysis data in estimating daily and monthly rainfall. Iranian Journal of Soil and Water Research, 50(4): 791-779 (In Farsi).
De Leeuw J, Methven J and Blackburn M (2015) Evaluation of ERA‐Interim reanalysis precipitation products using England and Wales observations. Quarterly Journal of the Royal Meteorological Society. Wiley Online Library 141(688):798–806
Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G and Bauer  d P (2011) The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society. Wiley Online Library 137(656):553–597
Derin Y, Anagnostou E, Berne A, Borga M, Boudevillain B, Buytaert W, Chang C-H, Delrieu G, Hong Y, Hsu YC, … Yilmaz KK (2016) Multiregional Satellite Precipitation Products Evaluation over Complex Terrain. Journal of Hydrometeorology. American Meteorological Society 17(6):1817–1836
F. Lobligeois, V. Andréassian, C. Perrin, P. Tabary, C. Loumagne. (2014). When does higher spatial resolution rainfall information improve stream flow simulation? An evaluation using 3620 flood events. Hydrology and Earth System Sciences, European Geosciences Union, 18 (2), p. 575 - p. 594. 
Gorjizadeh A, Akhundali A, Shahbazi A, Moridi A. (2019). Comparison of rainfall estimated by ERA-Interim, PERSIANN-CDR and CHIRPS models above Maron Dam. Journal of Water Resources Research, 15(1):267-279 (In Farsi).
Hosseini-Moghari S-M, Araghinejad S and Ebrahimi K (2018) Spatio-temporal evaluation of global gridded precipitation datasets across Iran. Hydrological Sciences Journal. Taylor & Francis 63(11):1669–1688
Hosseini-Moghari S-M and Tang Q (2020) Validation of GPM IMERG V05 and V06 Precipitation Products over Iran. Journal of Hydrometeorology 21(5):1011–1037
Khorshiddoust, A., Shirzad, A. (2014). 'The Study of Precipitation in North of Iran Using Cluster and Discriminative Function Analyses', Geography and Planning, 18(49), pp. 101-118. (In Farsi)
Khosravi, H., Moradi, E., Darabi, H. (2015). 'Identification of Homogeneous Groundwater Quality Regions Using Factor and Cluster Analysis;A case study Ghir Plain of Fars Province', Irrigation and Water Engineering, 6(1), pp. 119-133. (In Farsi)
Ma L, Zhang T, Frauenfeld OW, Ye B, Yang D and Qin D (2009) Evaluation of precipitation from the ERA-40, NCEP-1, and NCEP-2 Reanalyses and CMAP-1, CMAP-2, and GPCP-2 with ground-based measurements in China. Journal of Geophysical Research: Atmospheres. John Wiley & Sons, Ltd 114(D9). Available at: https://doi.org/10.1029/2008JD011178.
Ochoa-Rodriguez S, Wang L P, Gires A, Pina R D, Reinoso-Rondinel R, Bruni G, ... & Kroll S. (2015). Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation. Journal of Hydrology, 531, 389-407.
Peña-Arancibia J. L, van Dijk A. I, Renzullo L. J, & Mulligan M. (2013). Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an ensemble method for regions in Australia and South and East Asia. Journal of Hydrometeorology, 14(4), 1323-1333.
Sharifi E, Steinacker R and Saghafian B (2016) Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: Preliminary results. Remote Sensing. Multidisciplinary Digital Publishing Institute 8(2):135.
Stanski HR, Wilson LJ and Burrows WR (1989) Survey of common verification methods in meteorology. World Meteorological Organization Geneva.
 Rahmati A, Massah bavani A. (2019). Evaluation of Global Precipitation products for Use in Physical Models, Case Study: Karun Basin, Journal of Water Resources Research, 15(1):178-192 (In Farsi).
Taghavi F, Neiestani A and Sarmad gh. (2012). WRF numerical model forecasts to assess short-term rainfall during a month in Iran. Journal of Earth and Space Physics, 39(2): 145-170. (In Farsi)
Tavousi T, Delara GH. (2010). Climate zoning of Ardabil province, Nivar journal, 34(70-71): 47-52 (In Farsi).
Xu X, Frey S. K , Boluwade A, Erler A. R, Khader O, Lapen D. R, & Sudicky  E. (2019). Evaluation of variability among different precipitation products in the Northern Great Plains. Journal of Hydrology, Regional Studies, 24, 100608.
Iranian Journal of Soil and Water Research
Volume 51, Issue 11
February 2021
Pages 2937-2951

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