A Comparative Analysis of the Performance of COSMO and WRF Models in Quantitative Rainfall Prediction
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.
[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., www2.mmm.ucar.edu, 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.
[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., www2.mmm.ucar.edu, 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.
@article{"International Journal of Earth, Energy and Environmental Sciences:77059", author = "Isaac Mugume and Charles Basalirwa and Daniel Waiswa and Mary Nsabagwa and Triphonia Jacob Ngailo and Joachim Reuder and Sch¨attler Ulrich and Musa Semujju", title = "A Comparative Analysis of the Performance of COSMO and WRF Models in Quantitative Rainfall Prediction", abstract = "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 = "Comparative performance, the COSMO model, the
WRF model, light rainfall events, extreme rainfall events.", volume = "12", number = "2", pages = "130-9", }
