Spatial Variation of WRF Model Rainfall Prediction over Uganda

Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Triphonia Ngailo

Abstract:

Rainfall is a major climatic parameter affecting many sectors such as health, agriculture and water resources. Its quantitative prediction remains a challenge to weather forecasters although numerical weather prediction models are increasingly being used for rainfall prediction. The performance of six convective parameterization schemes, namely the Kain-Fritsch scheme, the Betts-Miller-Janjic scheme, the Grell-Deveny scheme, the Grell-3D scheme, the Grell-Fretas scheme, the New Tiedke scheme of the weather research and forecast (WRF) model regarding quantitative rainfall prediction over Uganda is investigated using the root mean square error for the March-May (MAM) 2013 season. The MAM 2013 seasonal rainfall amount ranged from 200 mm to 900 mm over Uganda with northern region receiving comparatively lower rainfall amount (200–500 mm); western Uganda (270–550 mm); eastern Uganda (400–900 mm) and the lake Victoria basin (400–650 mm). A spatial variation in simulated rainfall amount by different convective parameterization schemes was noted with the Kain-Fritsch scheme over estimating the rainfall amount over northern Uganda (300–750 mm) but also presented comparable rainfall amounts over the eastern Uganda (400–900 mm). The Betts-Miller-Janjic, the Grell-Deveny, and the Grell-3D underestimated the rainfall amount over most parts of the country especially the eastern region (300–600 mm). The Grell-Fretas captured rainfall amount over the northern region (250–450 mm) but also underestimated rainfall over the lake Victoria Basin (150–300 mm) while the New Tiedke generally underestimated rainfall amount over many areas of Uganda. For deterministic rainfall prediction, the Grell-Fretas is recommended for rainfall prediction over northern Uganda while the Kain-Fritsch scheme is recommended over eastern region.

Keywords: Convective parameterization schemes, March-May 2013 rainfall season, spatial variation of parameterization schemes over Uganda, WRF model.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131463

References:

[1] I. Mugume, M. D. Mesquita, C. Basalirwa, Y. Bamutaze, J. Reuder, A. Nimusiima, D. Waiswa, G. Mujuni, S. Tao, and T. Jacob Ngailo, “Patterns of dekadal rainfall variation over a selected region in lake victoria basin, uganda,” Atmosphere, vol. 7, no. 11, p. 150, 2016.
[2] B. A. Ogwang, H. Chen, X. Li, and C. Gao, “The influence of topography on east african october to december climate: sensitivity experiments with regcm4,” Advances in Meteorology, vol. 2014, 2014.
[3] S.W. Karuri and R.W. Snow, “Forecasting paediatric malaria admissions on the kenya coast using rainfall,” Global health action, vol. 9, 2016.
[4] A. T. Kabo-Bah, C. J. Diji, K. Nokoe, Y. Mulugetta, D. Obeng-Ofori, and K. Akpoti, “Multiyear rainfall and temperature trends in the volta river basin and their potential impact on hydropower generation in ghana,” Climate, vol. 4, no. 4, p. 49, 2016.
[5] S. He, S. V. Raghavan, N. S. Nguyen, and S.-Y. Liong, “Ensemble rainfall forecasting with numerical weather prediction and radar-based nowcasting models,” Hydrological Processes, vol. 27, no. 11, pp. 1560–1571, 2013.
[6] D. Ntwali, B. A. Ogwang, and V. Ongoma, “The impacts of topography on spatial and temporal rainfall distribution over rwanda based on wrf model,” Atmospheric and Climate Sciences, vol. 6, no. 02, p. 145, 2016.
[7] J. Awange, R. Anyah, N. Agola, E. Forootan, and P. Omondi, “Potential impacts of climate and environmental change on the stored water of lake victoria basin and economic implications,” Water Resources Research, vol. 49, no. 12, pp. 8160–8173, 2013.
[8] T. 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. 1858–1868, 2016.
[9] W. Jie, T. Wu, J. Wang, W. Li, and T. Polivka, “Using a deterministic time-lagged ensemble forecast with a probabilistic threshold for improving 6–15day summer precipitation prediction in china,” Atmospheric Research, vol. 156, pp. 142–159, 2015.
[10] J. Zhu, F. Kong, L. Ran, and H. Lei, “Bayesian model averaging with stratified sampling for probabilistic quantitative precipitation forecasting in northern china during summer 2010,” Monthly Weather Review, vol. 143, no. 9, pp. 3628–3641, 2015.
[11] A. E. Raftery, T. Gneiting, F. Balabdaoui, and M. Polakowski, “Using bayesian model averaging to calibrate forecast ensembles,” Monthly Weather Review, vol. 133, no. 5, pp. 1155–1174, 2005.
[12] G. Redmond, K. I. Hodges, C. Mcsweeney, R. Jones, and D. Hein, “Projected changes in tropical cyclones over vietnam and the south china sea using a 25 km regional climate model perturbed physics ensemble,” Climate Dynamics, vol. 45, no. 7-8, pp. 1983–2000, 2015.
[13] J. M. Fritsch and R. Carbone, “Improving quantitative precipitation forecasts in the warm season: A uswrp research and development strategy,” Bulletin of the American Meteorological Society, vol. 85, no. 7, pp. 955–965, 2004.
[14] 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.
[15] C. Funk, A. Hoell, S. Shukla, G. Husak, and J. Michaelsen, “The east african monsoon system: Seasonal climatologies and recent variations,” in The Monsoons and Climate Change, pp. 163–185, Springer, 2016.
[16] W. Yang, R. Seager, M. A. Cane, and B. Lyon, “The annual cycle of East African precipitation,” Journal of Climate, vol. 28, no. 6, pp. 2385–2404, 2015.
[17] R. Pizarro, P. Garcia-Chevesich, R. Valdes, F. Dominguez, F. Hossain, P. Ffolliott, C. Olivares, C. Morales, F. Balocchi, and P. Bro, “Inland water bodies in chile can locally increase rainfall intensity,” Journal of hydrology, vol. 481, pp. 56–63, 2013.
[18] Y. G. Mayor and M. D. 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, 2015.
[19] 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, vol. 2016, 2016.
[20] R. Franke, “Scattered data interpolation: tests of some methods,” Mathematics of computation, vol. 38, no. 157, pp. 181–200, 1982.