Open Access
Issue |
MATEC Web of Conferences
Volume 150, 2018
Malaysia Technical Universities Conference on Engineering and Technology (MUCET 2017)
|
|
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Article Number | 03007 | |
Number of page(s) | 4 | |
Section | Civil Engineering | |
DOI | https://doi.org/10.1051/matecconf/201815003007 | |
Published online | 23 February 2018 |
- Michalakes, J., et al. The weather research and forecast model: software architecture and performance. in Proceedings of the Eleventh ECMWF Workshop on the Use of High Performance Computing in Meteorology. 2005. World Scientific: Singapore. [Google Scholar]
- Xue, Y., et al., A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability. Atmospheric research, 2014. 147: p. 68-85. [CrossRef] [Google Scholar]
- Bukovsky, M.S. and D.J. Karoly, Precipitation simulations using WRF as a nested regional climate model. Journal of applied Meteorology and Climatology, 2009. 48(10): p. 2152-2159. [CrossRef] [Google Scholar]
- De Silva, G., et al. Application of WRF with different cumulus parameterization schemes for precipitation forecasting in a tropical river basin. in Proceedings of the 13th Asian Congress of fluid Mechanics. 2010. [Google Scholar]
- Maussion, F., et al., WRF simulation of a precipitation event over the Tibetan Plateau, China-an assessment using remote sensing and ground observations. Hydrology and Earth System Sciences, 2011. 15(6): p. 1795. [CrossRef] [Google Scholar]
- Sohrabinia, M., W. Rack, and P. Zawar-Reza, Analysis of MODIS LST compared with WRF model and in situ data over the Waimakariri River basin, Canterbury, New Zealand. Remote Sensing, 2012. 4(11): p. 3501-3527. [CrossRef] [Google Scholar]
- Im, E.-S., S.-R. In, and S.-O. Han, Numerical simulation of the heavy rainfall caused by a convection band over Korea: a case study on the comparison of WRF and CReSS. Natural Hazards, 2013. 69(3): p. 1681-1695. [CrossRef] [Google Scholar]
- Mooney, P., F. Mulligan, and R. Fealy, Evaluation of the sensitivity of the weather research and forecasting model to parameterization schemes for regional climates of Europe over the period 1990–95. Journal of Climate, 2013. 26(3): p. 1002-1017. [CrossRef] [Google Scholar]
- Awan, N.K., H. Truhetz, and A. Gobiet, Parameterization-induced error characteristics of MM5 and WRF operated in climate mode over the Alpine region: an ensemble-based analysis. Journal of Climate, 2011. 24(12): p. 3107-3123. [CrossRef] [Google Scholar]
- Hong, S.-Y., J. Dudhia, and S.-H. Chen, A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly Weather Review, 2004. 132(1): p. 103-120. [CrossRef] [Google Scholar]
- Dudhia, J., WRF modeling system overview. www2.mmm.ucar.edu/wrf/users/tutorial/201201/WRF_Overview_ Dudhia.ppt.pdf, 2014. [Google Scholar]
- Skamarock, W. Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note. NCAR/TN-475+ STR. [Google Scholar]
- Dudhia, J., Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of the Atmospheric Sciences, 1989. 46(20): p. 3077-3107. [Google Scholar]
- Hong, S.-Y. and J.-O.J. Lim, The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc, 2006. 42(2): p. 129-151. [Google Scholar]
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