@article{(Open Science Index):https://publications.waset.org/pdf/10008642,
	  title     = {A Comparative Analysis of the Performance of COSMO and WRF Models in Quantitative Rainfall Prediction},
	  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},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Marine and Environmental Sciences},
	  volume    = {12},
	  number    = {2},
	  year      = {2018},
	  pages     = {130 - 138},
	  ee        = {https://publications.waset.org/pdf/10008642},
	  url   	= {https://publications.waset.org/vol/134},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 134, 2018},
	}