Spatial Variation of WRF Model Rainfall Prediction over Uganda
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.
[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.
[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.
@article{"International Journal of Earth, Energy and Environmental Sciences:75980", author = "Isaac Mugume and Charles Basalirwa and Daniel Waiswa and Triphonia Ngailo", title = "Spatial Variation of WRF Model Rainfall Prediction over Uganda", 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.", volume = "11", number = "7", pages = "630-5", }
