Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30576
A Comparative Analysis of the Performance of COSMO and WRF Models in Quantitative Rainfall Prediction

Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Mary Nsabagwa, Triphonia Jacob Ngailo, Joachim Reuder, Sch¨attler Ulrich, Musa Semujju


The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics.

Keywords: Extreme Rainfall Events, comparative performance, the COSMO model, light rainfall events, the WRF model

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 611


[1] M. Paras and M. Sanjay, “A simple weather forecasting model using mathematical regression.,” Indian Research Journal of Extension Education, pp. 161–168, 2012.
[2] F. G. Du, W., X. Y. Hou, and W. Zhu, “A case study in flood fatality: Beijing July 2012 flood,” 2013.
[3] D. J. Short Gianotti, B. T. Anderson, and G. D. Salvucci, “The potential predictability of precipitation occurrence, intensity, and seasonal totals over the continental United States.,” Journal of Climate, pp. 6904–6918, 2014.
[4] I. Mugume, C. Basalirwa, D. Waiswa, J. Reuder, M. d. S. Mesquita, S. Tao, and T. J. Ngailo, “Comparison of parametric and nonparametric methods for analyzing the bias of a numerical model,” Modelling and Simulation in Engineering, pp. 1–8, 2016.
[5] A. Grosjean and J. Kueny, “Statistical weather forecasting.,” Journal of Dynamics Systems, Measurement & Control., pp. 49–55, 1976.
[6] F. J. Opijah, “Application of the EMS-WRF Model in dekadal rainfall prediction over the GHA Region,” Africa Journal of Physical Sciences, vol. 1, no. 1, pp. 2313–3317, 2014.
[7] T. B¨ohme, S. Stapelberge, T. Akkermans, S. Crewell, J. Fischer, T. Reinhardt, A. Seifert, C. Selbach, and N. V. Lipzig, “Long–term evaluation of COSMO Forecasting using combined observational data of the GOP period.,” Meteorologische Zeitschrift, pp. 119–132, 2011.
[8] S. Dierer, A. Marco, S. Axel, A. Euripides, D. Rodica, G. Federico, M. Paola, M. Massimo, and S. Katarzyna, “Deficiencies in quantitative precipitation forecasts: Sensitivity studies using the COSMO model.,” Meteorologische Zeitschrift, vol. 18, no. 6, pp. 631–645, 2009.
[9] Z. Sokol and D. Rezacova, “Assimilation of radar reflectivity into the LM COSMO Model with a high horizontal resolution.,” Meteorological Applications, vol. 13, pp. 317–330, 2006.
[10] M. Baldauf, A. Seifert, J. F¨orstner, D. Majewski, and M. Raschendorfer, “Operational convective–scale numerical weather prediction with the COSMO Model: Description and sensitivities.,” Monthly Weather Review, vol. 139, pp. 3887–3905, 2011.
[11] T. T. Warner, Numerical weather and climate prediction. Cambridge University Press., 2010.
[12] S. Cai and H. Yu, “Analysis of different Weather Research and Forecasting Radiation schemes impact on the numerical simulation of a typical mesoscale convective weather in china.,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 80, pp. 68–72, 2012.
[13] B. Xie, J. C. Fung, A. Chan, and A. Lau, “Evaluation of nonlocal and local planetary boundary layer schemes in the WRF Model.,” Journal of Geophysical Research: Atmospheres,, vol. 117, 2012.
[14] G. Xu, Y. Xie, C. Cui, Z. Zhou, W. Li, and J. Xu, “Sensitivity of the summer precipitation simulated with WRF Model to Planetary Boundary Layer Parameterization over the Tibetan Plateau and its Downstream Areas.,” Journal of Geology and Geophysics, vol. 5, no. 4, pp. 1–11, 2012.
[15] W. Wang, C. Bruyere, M. Duda, J. Dudhia, D. Gill, H. C. Lin, and J. Mandel, “Arw version 3 modeling system users guide.,” tech. rep., National Center for Atmospheric Research: Mesoscale and Microscale Meteorology Division.,, 2015.
[16] C. Pennelly, G. Reuter, and T. Flesch, “Verification of the WRF Model for simulating heavy precipitation in Alberta.,” Atmospheric Research, vol. 135, no. 136, pp. 172–192, 2014.
[17] Y. G. Mayor and M. D. S. Mesquita, “Numerical simulations of the 1 May 2012 deep convection event over Cuba: sensitivity to cumulus and microphysical schemes in a high–resolution model.,” Advances in Meteorology, vol. 2015, pp. 1–11, 2015.
[18] M. Rajasekhar, T. Sreeshna, M. Rajeevan, and S. Ramakrishna, “Prediction of severe thunderstorms over Sriharikota Island by using the WRF–ARW Operational model,” SPIE 50Asia-Pacific Remote Sensing,, vol. 2016, pp. 988214–988214, 2016.
[19] O. Krogsæter and J. Reuder, “Validation of boundary layer parameterization schemes in the Weather Research and Forecasting model under the aspect of offshore wind energy applicationspart I: Average wind speed and wind shear,” Wind Energy, vol. 18, no. 5, pp. 769–782, 2015.
[20] E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, et al., “The ncep/ncar 40-year reanalysis project,” Bulletin of the American meteorological Society, vol. 77, no. 3, pp. 437–471, 1996.
[21] I. Mugume, D. Waiswa, M. Mesquita, J. Reuder, C. Basalirwa, Y. Bamutaze, R. Twinomuhangi, F. Tumwine, J. Sansa-Otim, T. Jacob Ngailo, and G. Ayesiga, “Assessing the Performance of WRF Model in Simulating Rainfall over Western Uganda.,” Journal of Climatology and Weather Forecasting, vol. 5, no. 1, pp. 1–9, 2017.
[22] M. Tiedtke, “A comprehensive mass flux scheme for cumulus parameterization in large–scale models.,” Monthly Weather Review, vol. 117, no. 8, pp. 1779–1800, 1989.
[23] Y. L. Lin, R. D. Farley, and H. D. Orville, “Bulk parameterization of the snow field in a cloud model.,” Journal of Climate and Applied Meteorology, vol. 22, no. 6, pp. 1065–1092, 1983.
[24] B. Ritter and J. F. Geleyn, “A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations.,” Monthly Weather Review,, vol. 120, no. 2, pp. 303–325, 1992.
[25] S. Brdar, M. Baldauf, A. Dedner, and R. Kl¨ofkorn, “Comparison of dynamical cores for NWP Models: Comparison of COSMO and Dune.,” vol. 27, no. 3–4, pp. 453–472, 2013.
[26] J. S. Kain, “The Kain–Fritsch Convective Parameterization: An update.,” Journal of Applied Meteorology,, vol. 43, pp. 170–181, 2003.
[27] S. Y. Hong, J. Dudhia, and S. H. Chen, “A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation.,” Monthly Weather Review,, vol. 132, no. 1, pp. 103–120, 2004.
[28] S. A. Clough, M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, ..., and P. D. Brown, “Atmospheric radiative transfer modeling: a summary of the aer codes.,” vol. 91, no. 2, pp. 233–244, 2005.
[29] J. Dudhia, “Numerical study of convection observed during the winter monsoon experiment using a mesoscale two–dimensional model.,” Journal of the Atmospheric Sciences, vol. 46, no. 20, pp. 3077–3107, 1989.
[30] G. Y. Niu, Z. L. Yang, K. E. Mitchell, F. Chen, M. B. Ek, M. Barlage, ..., and M. Tewari, “The Community Noah Land Surface Model with Multiparameterization Options (Noah MP): Model Description and Evaluation with Local–Scale Measurements.,” Journal of Geophysical Research: Atmospheres,, vol. 116, 2011.
[31] X. M. Hu, J. W. Nielsen-Gammon, and F. Zhang, “Evaluation of three Planetary Boundary Layer Schemes in The WRF Model.,” Journal of Applied Meteorology and Climatology, vol. 49, no. 9, pp. 1831–1844, 2010.
[32] J. Liu, M. Bray, and D. Han, “Sensitivity of the weather research and forecasting (wrf) model to downscaling ratios and storm types in rainfall simulation,” Hydrological Processes, vol. 26, no. 20, pp. 3012–3031, 2012.
[33] T. J. Ngailo, N. Shaban, J. Reuder, E. Rutalebwa, and I. Mugume, “Non Homogeneous Poisson Process Modelling of Seasonal Extreme Rainfall Events in Tanzania,” International Journal of Science and Research (IJSR), vol. 5, no. 10, pp. 2319–7064, 2016.
[34] S. Crewell, M. Mech, T. Reinhardt, C. Selbach, H.-D. Betz, E. Brocard, G. Dick, E. OConnors, J. Fischer, T. Hanisch, T. Hauf, A. Huenerbein, L. Delobbe, A. Mathes, H. Peters, H. Wernli, M. Weigner, and V. Wulfmeyer, “The General Observation Period 2007 within the Priority Program on Quantitative Precipitation Forecasting: Concepts and First Results.,” Meteorologische Zeitschrift, vol. 17, pp. 849–866., 2008.
[35] T.-H. Yang, S.-C. Yang, J.-Y. Ho, G.-F. Lin, G.-D. Hwang, and C.-S. Lee, “Flash flood warnings using the ensemble precipitation forecasting technique: a case study on forecasting floods in taiwan caused by typhoons.,” Journal of Hydrology, vol. 520, pp. 367–378, 2015.
[36] v. S. Hans and F. W. Zwiers, Statistical Analysis in Climate Research. Cambridge University Press, 2003.