Evaluation of Spatio-Temporal Accuracy of Precipitation of European Center for Medium-Range Weather Forecasts (ECMWF) over Iran

Document Type : Full length article


1 Assistant Professor of Climatology, Faculty of Natural Resources, University of Kurdistan, Iran

2 MSc Student in Climatology, Faculty of Natural Resources, University of Kurdistan, Iran.


Precipitation is one of the most important meteorological variables in comparison with other climatic parameters. It varies extremely over time and space. The occurrence of this climate phenomenon requires specific circumstances in environment. Its accurate measurement is important to a wide range of decision makers including hydrologists, agriculturalists, industrialists and etc. The density of rain gauges and meteorological radars is often too poor to satisfactorily capture rainfall characteristics at fine spatial resolutions. To overcome this problem gridded precipitation data base was developed based on interpolation of the daily precipitation data. ERA-Interim is the latest gridded global atmospheric reanalysis produced by the European Center for Medium-Range Weather Forecasts (ECMWF). This data covers the period from 1 January 1979 to the present. The precipitation analysis and estimation is based on obtained precipitation data from rain gauge stations, meteorological radars and satellite sensors. The forecasted precipitation is available at 3-hourly based on applying different models. The horizontal spatial resolution is available at 3, 2.5, 1.5, 1.125, 1, 0.75, 0.5, 0.25 and 125 Gaussian degrees over globe. Information about the current status of ERA-Interim production, availability of data online, and near-real-time updates of various climate indicators derived from ERA-Interim data, can be found at http://www.ecmwf.int/research/era. The purpose of this research is to evaluate temporal-spatial accuracy of gridded precipitation data of ERA-Interim version from European Center for Medium-Range Weather Forecasts (ECMWF) data base over Iran country.
Material and Methods
In order to conduct this research the 3-hourly gridded precipitation data from ECMWF version ERA-Interim over Iran country has been extracted during 1/1/1979 to 31/12/2013. The high spatial resolution data with 0.125 degree has been selected. By accumulation of 3-hourly data the daily, monthly and yearly time series have been created. A Matrix with dimension 12784×9965 has been created that located time (Days) on the rows and location (Pixels in Iran’s country political boundary) on the columns. During the same period, Iran’s daily precipitation data of the synoptic stations have been extracted from Iranian Meteorological Organization. The national precipitation gridded data base of ASFEZARI with 15 km spatial resolution was also prepared. By nearest neighbor algorithm and conversion of high density to low density approach, the spatial resolutions were even. To evaluate temporal-spatial accuracy of the estimated ECMWF precipitation we applied different indices. 
Results and Discussion
The results of this research indicate that not only there is a high temporal correlation between estimated ECMWF precipitations and two national data bases but also there is high correlation between amounts of precipitations. At spatial view, the high correlation observed over Zagros Mountains is covered over southwestern and northeastern parts of country. Over these regions correlation coefficient (R) and Index of Agreement (IA) are over 0.94 and close to 1, respectively. The Bias index rate (Bias) of estimated precipitation relevant to this data base is negative over very rainy regions at southern parts of Caspian Sea and northern parts of Persian Gulf. While the Bias rate on the other regions is positive. The bias and Root Mean Square Error (RMSE) rate are considerable rather than other regions but the estimated precipitation error is very low to total observation precipitation. Thus, the Relatively Root Mean Square Error (RRMSE) show low rate over these regions rather than other regions. In other words, it can be said that the bias and error rate of estimated precipitation over dry regions including southeastern, some regions in northwestern and central parts is higher than very rainy regions cores in southwestern parts of Caspian Sea and Zagros mountain ranges. The results of probabilities of detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI) indices imply high ability of ECMWF data base to isolation of these days over Zagros mountain ranges, southern parts of Caspian Sea and northeastern parts of country. The results show that the accuracy of this data base is higher during rainy months rather than dry and low rainy months.
This research has provided further evidence of the capability of ECMWF precipitation data base to capture precipitation characteristics at fine temporal-spatial resolutions. Agreement between ECMWF precipitation data base and synoptic stations data (Stations) is as good as with gridded national ASFEZARI data base. This provides confidence in the quality of ECMWF precipitation data base and confirm the findings of other researches.


Main Subjects

1. Balsamo, G., Boussetta, S., Lopez, P. and Ferranti, L. (2010). "Evaluation of ERA-Interim and ERA-Interim-GPCP-rescaled precipitation over the U.S.A.''. ERA Report Series. No. 5. ECMWF. Reading. UK. 10 pp.
2. BeloPereira, M., Dutra, E. and Viterbo, P. (2011). ''Evaluation of global precipitation data sets over the Iberian Peninsula''. Journal of Geophysical Research: Atmospheres (1984–2012). 116 (D20).
3. Bosilovich, M.G., Chen, J., Robertson, F.R. and Adler, R.F. (2008). ''Evaluation of global precipitation in reanalysis''. Journal of applied meteorology and climatology. No. 47 (9): 2279-2299.
4. Cohen Liechti, T., Matos, J.P., Boillat J.L. and Schleiss, A.J. (2012). ''Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin''. Hydrol. Earth Syst. Sci.. No. 16: 489–500.
5. Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A., Van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kallberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge-Sanz, B.M., Morcrette, J.J., Park, B.K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N. and Vitart, F. (2011). ''The ERA-Interim reanalysis: configuration and performance of the data assimilation system''. Q. J. Roy. Meteorol. Soc.. No. 137: 553–597.
6. Dee, D.P. and Uppala, S.M. (2009). ''Variational bias correction of satellite radiance data in the ERA-Interim reanalysis''. Q. J. R. Meteorol. Soc. No. 135: 1830–1841.
7. Diro, G.T., Grimes, D.I.F., Black, E., O'Neill, A. and Pardo-Iguzquiza, E. (2009). ''Evaluation of reanalysis rainfall estimates over Ethiopia''. International Journal of Climatology. No. 29 (1): 67-78.
8. Ebert, E.E., Janowiak, J.E. and Kidd, C. (2007). ''Comparison of near real time precipitation estimates from satellite observations and numerical models''. Bull. Amer. Meteor. Soc.. No. 88: 47–64.
9. Habib, E., Tamiru Haile, A., Tian, Y and Joyce, R.J. (2012). ''Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates''. Journal of hydrometeorology. No. 13: 1784-1798.
10. Huffman, G.J., Adler, R.T., Bolvin, D.T., Gu,G., Nelkin, E.J., Bowman, K., Hong, Y., Stocker, E.F. and Wolff, D.B. (2007). ''The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales''. J. Hydrometeor. No. 8: 38–55.
11. Joyce, R.J., Janowiak, J.E., Arkin, P.A. and Xie, P.P. (2004). ''CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution''. J. Hydrometeor. No. 5: 487–503.
12. Khan, A., Richards, K.S., Parker, G.T., McRobie, A., Booij, M.J., Duan, Z. and Khan, M. (2015). ''Spatial and altitudinal variation of precipitation and the correction of gridded precipitation datasets for the Upper Indus Basin and the Hindukush-Karakoram-Himalaya''.Geophysical Research Abstracts. Vol. 17. EGU 2015-7770-2.
13. Kishore, P., Jyothi, S., Basha, G., Rao, S.V. B., Rajeevan, M., Velicogna, I. and Sutterley, T.C. (2015). ''Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends''. Climate Dynamics: 1-16.
14. Layberry, R., Kniveton, D.R., Todd, M.C., Kidd, C. and Bellerby, T.J. (2006). ''Daily precipitation over Southern Africa: A new resource for climate studies''. J. Hydrometeorol. No. 7: 149–159.
15. Poli, P., Healy, S.B. and Dee, D.P. (2010). ''Assimilation of Global Positioning System radio occultation data in the ECMWFERA-Interim reanalysis''. Q. J. R. Meteorol. Soc.. No. 136: 1972–1990.
16. Simmons, A.J., Willett, K.M., Jones, P.D., Thorne, P.W. and Dee, D.P. (2010). ''Low-frequency variations in surface atmospheric humidity, temperature and precipitation: Inferences from reanalysis and monthly gridded observational datasets''. J. Geophys. Res.. 115. 1–21. doi: 10.1029/2009JD012442.
17. Sorooshian, S., Hsu, K., Gao, X., Gupta, H.V., Imam, B. and Braithwaite, D. (2000). ''Evaluation of PERSIANN systemsatellite-based estimates of tropical rainfall''. Bull. Amer. Meteor. Soc.. No. 81: 2035–2046.
18. Stanski, H.R., Wilson, L.J. and Burrows, W.R. (1989). ''Survey of common verification methods in meteorology''. World weather watch tech. Rep. 8. WMO/TD. No. 358. Geneva. Switzerland.
19. Szczypta, C., Calvet, J.C., Albergel, C., Balsamo, G., Boussetta, S., Carrer, D., Lafont, S. and Meurey, C. (2011). ''Verification of the new ECMWF ERA-Interim reanalysis over France''. Hydrol. Earth Syst. Sci.. No. 15: 647–666.
20. Uppala SM, Kallberg, P.W., Simmons, A.J., Andrae, U., Da Costa Bechtold, V., Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A., Beljaars, A.C.M., Van De Berg, L., Bidlot. J,, Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., H´olm, E., Hoskins, B.J., Isaksen, L., Janssen, PAEM, Jenne, R., McNally, A.P., Mahfouf, J.F., Morcrette, J.J., Rayner, N.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E., Untch, A., Vasiljevic, D., Viterbo, P. and Woollen, J. (2005). ''The ERA‐40 reanalysis''. Q. J. R. Meteorol. Soc.. No. 131: 2961–3012.
21. Willmott, C.J. (1981). ''On the validation of models''. Phys. Geogr. No. 2: 184-194.
22. Zhao, T. and Fu, C. (2006). ''Comparison of products from ERA-40, NCEP-2, and CRU with station data for summer precipitation over China''. Advances in Atmospheric sciences. No. 23: 593-604.