Statistical Post-Processing of the Precipitation Output of RegCM4 Model, Northwest Iran

Document Type : Full length article


1 Associate Professor, Department of Irrigation and Reclamation Engineering, University of Tehran

2 Assistant Professor, Climate Change Department, Climatological Research Institute of Mashhad

3 Associate Professor, Atmospheric Science and Meteorological Research Center

4 PhD Candidate of Agricultural Climatology, Department of Irrigation and Reclamation Engineering, University of Tehran


The main perspective in seasonal prediction of precipitation is presentation of a qualitative prediction for upcoming seasons. The information gained from such predictions can be used for decision making in various disciplines such as agriculture, water management and hydropower production. Besides, it can help us reduce the adverse effects of climatic changes like drought and flood. But General Ciculation Models (GCMs) outputs have coarse resolution (>100 km). Dynamic downscaling is a method to obtain high-resolution climate data from relatively coarse resolution global climate models which do not capture the effects of local and regional forcing in areas of complex surface physiography. GCMs outputs at spatial resolution of 150-300 km are unable to resolve important sub-grid scale features such as clouds and topography. Many impact models require information at scales of 50 km or less. Several statistical and dynamical methods are developed to estimate the smaller-scale information. Dynamical downscaling uses a limited area, high resolution model (a regional climate model: RCM) driven by boundary conditions from a GCM to derive smaller scale information.
The aim of this study was application of RegCM4 dynamic model (Reginal climate model) in forecasting rainfall and improving the outputs using post processing techniques in northwest Iran during period 1982-2011. The recorded data of precipitation were collected from Urmia, Tabriz, Ardebil and Khuy Stations. The data required for running the regional climate model RegCM4 were obtained from center ICTP (International Centre for Theoretical Physics), in the format of NetCDF including three sets of weather data, NNRP1 with a 6-hour-scale and a horizontal resolution of 2.5×2.5 on the reanalysis databases of National Center of environmental prediction of United States, sea surface temperature, (SST) with a horizontal resolution of 1×1 from the type of SST belonged to America and National Oceanic and Atmospheric surface SURFACE, which were consisted of three topographic data of GTOPO, the vegetation or land use, GLCC, and the soil type data GLZB, with a horizontal resolution of 30×30 seconds from United States Geological Survey. These data were organized for the period 1982 to 2011. In order to execute the dynamic model, a test was conducted to determine the Convective Precipitation scheme and the amount of horizontal resolution for the year 2009 (as a normal year). Accordingly, Kuo scheme with minimum mean bias error (MBE), in comparison with the observed precipitation in 36 synoptic stations of the region, was implemented as the main scheme with horizontal resolution of 30 × 30 square kilometers. The number of grid points including 152 in longitude (iy) and 168 in latitude (ix) was conducted during the statistical period of 1982 to 2011. Geographical area center implemented in the intended period was located in 30.5 degrees north latitude and 50 degrees east longitude. The output of the model included atmospheric data (ATM), surface cover (SRF) and radiation cover with the format of NetCDF, each containing a large number of meteorological variables among which, except precipitation from the Model (tpr), 9 variables that were associated more with precipitation were extracted. These variables are including q2m and t2m, ps, v1000, v500, u1000, u500, omega1000, omega500. For post-processing the output of the model we used the Multi-Layer Perceptron (MLP) and Moving Average (MA) methods. For MLP Entering variables were those 10 aforementioned and the target variable was observatory precipitation in the stationary point. At any one time, 80% of the data at the beginning of the series were for train and final 20 percent of data was used for test.
Results and Discussion
The results of the study demonstrated that, in the study area, the mean bias error of raw annual precipitation outputs of the RegCM4 model was 124.3 mm in the validation period, which by conducting Post Processing it was reduced to 8.9 mm. In the seasonal and monthly time scales, also, mean bias error were 31.1 and 10.4 mm, respectively, which were reduced to -0.3 and zero mm, respectively, after post processing. The MA model was the preferred post processing method, in all time scales.
In conclusion, it can be stated that the RegCM4 regional climate model with the implementing conditions and in the study area, contained mainly overestimate in precipitation forecasting. However, the application of post-processing will optimally reduce bias. The appropriate method is also the simple moving average (MA) method.


Main Subjects

  1. آزادی، م.، شیرغلامی، م.ر. و حجام، س. (1389). «پس‌پردازش برونداد مدل WRF برای بارندگی در ایران». مجموعه مقالات چهاردهمین کنفرانس ژئوفیزیک ایران، 21-23 اردیبهشت. مؤسسة ژئوفیزیک. ص91-94.
  2. بابائیان، ا.، مدیریان، ر.، کریمیان، م. و حبیبی نوخندان، م. (1386). «شبیه‌سازی بارش ماه‌های سرد سال‌های 1376 و 1379 با استفاده از مدل اقلیمی RegCM3». مجلة جغرافیا و توسعه. ش10. ص55-72.
  3. محمدی، ف. و زرین، آ. (1392). «پیش‌بینی فصلی بارش استان فارس با مدل RegCM». پایان‌نامة کارشناسی ارشد رشتة اقلیم‌شناسی. مشهد: دانشگاه فردوسی مشهد. گروه جغرافیا.
  4. Adeniyi, M.,O. (2013). "Sensitivity of different convection schemes in RegCM4.0 for simulation of precipitation during the Septembers of 1989 and 1998 over West Africa". Theor Appl Climatol.
  5. Afzaal, M. and Hussain, A. (2006). "Numerical Simulation of Summer Monsoon Precipitation of 1992 Over Pakistan". Pakistan Journal of Meteorology. Vol. 3. No. 5.
  6. Azadi, M., Shirqolami, M.R. and Hajjam, S. (2010). "Post processing of WRF model output for Iran precipitation". 14 th geophysic conference. pp. 91-94. (In Persian).
  7. Babaeian, I., Modirian, R., Karimian, M., and Habibi Nokhandan, M. (2007). "Simulation of precipitation on cold months for 1997 and 2000 with RegCM3 climate model". Journal of Geography and Development. No. 10. pp. 55-72. (In Persian).
  8. Boroneant, C., Potop, V. and Caian, M. (2011). "Validation of RegCM precipitation simulation over Republic of Moldova, Application for Standard Precipitation Indices calculated for the period 1960-1997". Source and Limit of Social Development. International Scientific Conference. 6th–9th September 2011. Topolcianky. Slovakia.
  9. Elguindi, N. and Giorgi, F. (2006). "Simulating Multi-decadal Variability of Caspian Sea Level Changes Using Regional Climate Model Outputs". Climate Dynamics. Vol. 26. pp. 167-181.
  10. Francisco, R. (2003). "Some Experiments in Running the RegCM over the Philippines, ICTP Workshop on the Theory and Use of Regional Climate Models". Trieste Italy.
  11. Fuentes-Franco, R., and Coppola, E. (2013). "Assessment of RegCM4 simulated inter-annual variability and daily-scale statistics of temperature and precipitation over Mexico". Clim Dyn. Vol. 42. pp. 629-647.
  12. ICTP/RegCM4 Homepage
  13. Islam, N., Rahman, M., Uddin Ahmed, A.U. and Afroz, R. (2007). "Comparison of RegCM3 simulated meteorological parameters in Bangladesh". Part I-preliminary result for rainfall. Sri Lankan Journal of Physics. Vol. 8. pp. 1-9.
  14. McCOLLER D. and STULL R. (2008). "Hydrometeorological Accuracy Enhancement via Postprocessing of Numerical Weather Forecasts in Complex Terrain". American Meteorological Society. pp. 131-144
  15. Miksovsky, J.; Skalak, P. and Stepanek, P. (2010). "Intercomparison of statistical techniques for postprocessing the RCM-generated data". 10th EMS Annual Meeting. 10th European Conference on Applications of Meteorology (ECAM) Abstracts.
  16. Mohammadi, F. and Zarrin, A. (2013). "Prediction of seasonal precipitation on Fars with RegCM model". Thesis for degree of climatology. Mashhad: Ferdowsi University. (In Persian).
  17. Nandozi, C.S. Majaliwa, J.G.M., Omondi, P., Komutunga, E., Aribo, L., Isubikalu, P., Tenywa, M.M and Massa-Makuma, H. (2012). "Reginal Climate Model Performance and of seasonal rainfall and surface tempreture of Uganda". African Crop Science Journal. Vol. 20. pp. 213-225.
  18. Paeth, H. (2011). "Postprocessing of simulated precipitation for impact research in West Africa, Part I: model output statistics for monthly data". Climate Dynamics. DOI. Vol. 36. No. 7. pp. 1321-1336.
  19. Pal, J., Giorgi, F., BiX., Elguindi, N.,Salmon, F., Gao X., Rauscher, S. A., Francisco, R., Zakey, A., Winter, J., Ashfagh, M., Syed, F.S., Bell, J., Diffenbaugh, J.K., Konare, A., Martinez, D., Rocha, R., Sloan, L. and Steiner, A. (2007). "Regional Climate modeling for the Developing World, the ICTP and RegCNET". Bulletin of American meteorological society. pp. 1396-1409.
  20. Schmidli, J., Goodess, C. M., Frei, C., Haylock, M. R., Hundecha, Y., Ribalaygua, J. and Schmith, T. (2007). "Statistical and dynamical downscaling of precipitation". An evaluation and comparison of scenarios for the european Alps. Journal of Geophysical Reserch. Vol. 112.
  21. Turco, M., Zollo, A., Rianna, G., Cattaneo, L., Vezzoli, R. and Mercogliano, P. (2013)."Post-processing methods for COSMO-CLMprecipitation over Italy, Center Euro-Mediterraneo". Research Papers. Issue RP0171.
  22. Wallach, D., Makowski, D. and Jones, J.W. (2006). working with dynamic crop models. Evaluation, analysis, parameterization and applications. ELSEVIER. e-book.
  23. Zong, P. and Wang, H. (2011). "Evaluation and analysis of RegCM3 simulated summer rainfall over the Huaihe river of China". Acta Meteorologica Sinica. Vol. 25. pp. 386-394.
Volume 47, Issue 3 - Serial Number 3
October 2015
Pages 385-398
  • Receive Date: 28 January 2015
  • Revise Date: 04 April 2015
  • Accept Date: 06 May 2015
  • First Publish Date: 23 September 2015