Quantitative Precipitation Forecast using MM5 and WRF models for Kelantan River Basin
Quantitative precipitation forecast (QPF) from
atmospheric model as input to hydrological model in an integrated
hydro-meteorological flood forecasting system has been operational
in many countries worldwide. High-resolution numerical weather
prediction (NWP) models with grid cell sizes between 2 and 14 km
have great potential in contributing towards reasonably accurate QPF.
In this study the potential of two NWP models to forecast
precipitation for a flood-prone area in a tropical region is examined.
The precipitation forecasts produced from the Fifth Generation Penn
State/NCAR Mesoscale (MM5) and Weather Research and
Forecasting (WRF) models are statistically verified with the observed
rain in Kelantan River Basin, Malaysia. The statistical verification
indicates that the models have performed quite satisfactorily for low
and moderate rainfall but not very satisfactory for heavy rainfall.
[1] DID (2009) Laporan Banjir 2009-2010
[2] Ducrocq, V., Lafore, J.-P .,Redelsperger, J.-L.& Orain, F.(2000)
Initialization of a fine-scale model for convective system prediction: a
case study. Q.J. Roy. Meteor. Soc. 126:3041-3065.
[3] Grell, G. A., Dudhia, J., and Stauffer, D. R.: A description of the fifthgeneration
Penn State/NCAR mesoscale model (MM5), NCAR
Technical Note, NCAR/TN-398+STR, 1994.
[4] Habets, F., LeMoigne, P., and Noilhan, J. (2004) On the utility of
operational precipitation forecasts to served as input for streamflow
forecasting, Journal of Hydrology , 293 , pp. 270-288
[5] Jasper, K., Gurtz, J., and Lang, H. (2002) Advanced flood forecasting in
Alpine watersheds by coupling meteorological observations and
forecasts with a distributed hydrological model. Journal of Hydrology.
267, pp. 40-52
[6] Kim, G. and Barros, A.P. (2001) Quantitative flood forecasting using
multisensor data and neural networks. Journal of Hydrology, 246, pp.
45-62.
[7] Low, K. C., 2006: Application of Nowcasting Techniques Towards
Strengthening National Warning Capabilities on Hydrometeorological
and Landslides Hazards.Sydney, Australia.
[8] Meneguzzoa, F., Pasquia, M., Mendunib, G., Messeric, G., Gozzinia,
B., Grifonia, D., Rossic, M., and Maracchi, G. (2004). "Sensitivity of
meteorological high-resolution numerical simulations of the biggest
floods occurred over the Arno river basin, Italy, in the 20th century",
Journal of Hydrology .288 pp. 37-56
[9] Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W.
Wang, and J. G. Powers (2005): "A Description of the Advanced
Research WRF Version 2". NCAR Technical Note
NCAR/TND468+STR URL http://box.mmm.ucar.edu/wrf/
users/docs/arw_v2.pdf.
[10] Snook, J. S. & Pielke, R. A. (1995) "Diagnosing a Colorado heavy snow
event with a nonhydrostatic mesoscale numerical model structured for
operational use.", Wea. Forecasting 10: 262-285.
[11] Toth, A., Brath, E. and Montanari .(2000). "Comparison of short-term
rainfall prediction models for real-time flood forecasting", Journal of
Hydrology. pp. 239
[12] Wardah, T., Abu Bakar, S.H.,Bardossy, A., Maznorizan, M., 2008. "Use
of geostationary meteorological satellite images in convective rain
estimation for flash-flood forecasting". Journal of Hydrology Journal of
Hydrology 356 (3-4), pp. 283-298.
[13] Warner, T. T., Brandes, E. A., Sun, J., Yates, D. N. & Mueller, C. K.
(2000) Prediction of a flash flood in complex terrain. Part I: A
comparison of rainfall estimates from radar, and very short range
rainfall simulations from a dynamic model and an automated
algorithmic system. J. Appl.Meteorol. 39: 797-814.
[14] Wilmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M.,
Legates, D.R., O'Donnell, J., Rowe, M.C., 1985. Statistics for the
evaluation and comparison of models. J. Geo. Res. 90, 8995-9005.
[1] DID (2009) Laporan Banjir 2009-2010
[2] Ducrocq, V., Lafore, J.-P .,Redelsperger, J.-L.& Orain, F.(2000)
Initialization of a fine-scale model for convective system prediction: a
case study. Q.J. Roy. Meteor. Soc. 126:3041-3065.
[3] Grell, G. A., Dudhia, J., and Stauffer, D. R.: A description of the fifthgeneration
Penn State/NCAR mesoscale model (MM5), NCAR
Technical Note, NCAR/TN-398+STR, 1994.
[4] Habets, F., LeMoigne, P., and Noilhan, J. (2004) On the utility of
operational precipitation forecasts to served as input for streamflow
forecasting, Journal of Hydrology , 293 , pp. 270-288
[5] Jasper, K., Gurtz, J., and Lang, H. (2002) Advanced flood forecasting in
Alpine watersheds by coupling meteorological observations and
forecasts with a distributed hydrological model. Journal of Hydrology.
267, pp. 40-52
[6] Kim, G. and Barros, A.P. (2001) Quantitative flood forecasting using
multisensor data and neural networks. Journal of Hydrology, 246, pp.
45-62.
[7] Low, K. C., 2006: Application of Nowcasting Techniques Towards
Strengthening National Warning Capabilities on Hydrometeorological
and Landslides Hazards.Sydney, Australia.
[8] Meneguzzoa, F., Pasquia, M., Mendunib, G., Messeric, G., Gozzinia,
B., Grifonia, D., Rossic, M., and Maracchi, G. (2004). "Sensitivity of
meteorological high-resolution numerical simulations of the biggest
floods occurred over the Arno river basin, Italy, in the 20th century",
Journal of Hydrology .288 pp. 37-56
[9] Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W.
Wang, and J. G. Powers (2005): "A Description of the Advanced
Research WRF Version 2". NCAR Technical Note
NCAR/TND468+STR URL http://box.mmm.ucar.edu/wrf/
users/docs/arw_v2.pdf.
[10] Snook, J. S. & Pielke, R. A. (1995) "Diagnosing a Colorado heavy snow
event with a nonhydrostatic mesoscale numerical model structured for
operational use.", Wea. Forecasting 10: 262-285.
[11] Toth, A., Brath, E. and Montanari .(2000). "Comparison of short-term
rainfall prediction models for real-time flood forecasting", Journal of
Hydrology. pp. 239
[12] Wardah, T., Abu Bakar, S.H.,Bardossy, A., Maznorizan, M., 2008. "Use
of geostationary meteorological satellite images in convective rain
estimation for flash-flood forecasting". Journal of Hydrology Journal of
Hydrology 356 (3-4), pp. 283-298.
[13] Warner, T. T., Brandes, E. A., Sun, J., Yates, D. N. & Mueller, C. K.
(2000) Prediction of a flash flood in complex terrain. Part I: A
comparison of rainfall estimates from radar, and very short range
rainfall simulations from a dynamic model and an automated
algorithmic system. J. Appl.Meteorol. 39: 797-814.
[14] Wilmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M.,
Legates, D.R., O'Donnell, J., Rowe, M.C., 1985. Statistics for the
evaluation and comparison of models. J. Geo. Res. 90, 8995-9005.
@article{"International Journal of Earth, Energy and Environmental Sciences:56911", author = "Wardah and T. and Kamil and A.A. and Sahol Hamid and A.B. and Maisarah and W.W.I", title = "Quantitative Precipitation Forecast using MM5 and WRF models for Kelantan River Basin", abstract = "Quantitative precipitation forecast (QPF) from
atmospheric model as input to hydrological model in an integrated
hydro-meteorological flood forecasting system has been operational
in many countries worldwide. High-resolution numerical weather
prediction (NWP) models with grid cell sizes between 2 and 14 km
have great potential in contributing towards reasonably accurate QPF.
In this study the potential of two NWP models to forecast
precipitation for a flood-prone area in a tropical region is examined.
The precipitation forecasts produced from the Fifth Generation Penn
State/NCAR Mesoscale (MM5) and Weather Research and
Forecasting (WRF) models are statistically verified with the observed
rain in Kelantan River Basin, Malaysia. The statistical verification
indicates that the models have performed quite satisfactorily for low
and moderate rainfall but not very satisfactory for heavy rainfall.", keywords = "MM5, Numerical weather prediction (NWP),
quantitative precipitation forecast (QPF), WRF", volume = "5", number = "11", pages = "688-5", }