Evaluation of Microphysics and Cumulus Schemes of WRF for Forecasting of Heavy Monsoon Rainfall over the Southeastern Hilly Region of Bangladesh

Abstract

Accurate forecasting of heavy rainfall is crucial for the improvement of flood warning to prevent loss of life and property damage due to flash-flood-related landslides in the hilly region of Bangladesh. Forecasting heavy rainfall events is challenging where microphysics and cumulus parameterization schemes of Weather Research and Forecast (WRF) model play an important role. In this study, a comparison was made between observed and simulated rainfall using 19 different combinations of microphysics and cumulus schemes available in WRF over Bangladesh. Two severe rainfall events during 11th June 2007 and 24–27th June 2012, over the eastern hilly region of Bangladesh, were selected for performance evaluation using a number of indicators. A combination of the Stony Brook University microphysics scheme with Tiedtke cumulus scheme is found as the most suitable scheme for reproducing those events. Another combination of the single-moment 6-class microphysics scheme with New Grell 3D cumulus schemes also showed reasonable performance in forecasting heavy rainfall over this region. The sensitivity analysis confirms that cumulus schemes play a greater role than microphysics schemes for reproducing the heavy rainfall events using WRF.

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References

  1. Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P. P., Janowiak, J., et al. (2003). The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology, 4(6), 1147–1167.

    Article  Google Scholar 

  2. Ahasan, M. N., Chowdhury, M. A. M., & Quadir, D. A. (2011). Simulation of a heavy rainfall event on 14 September 2004 over Dhaka, Bangladesh using MM5 model. Journal of Scientific Research, 3(2), 261–270.

    Article  Google Scholar 

  3. Ahasan, M., Chowdhury, M., & Quadir, D. (2013a). Simulation of high impact rainfall events over southeastern hilly region of Bangladesh using MM5 model. Sci: International Journal of Atmospheric Sciences.

    Google Scholar 

  4. Ahasan, M. N., Rayhun, K. M. Z., Mannan. M. A., & Debsarma, S. K. (2013b). Synoptic analysis of a heavy rainfall event over southeast region of Bangladesh using WRF model. Journal of Scientific Research, 5(3), 421–434.

    Article  Google Scholar 

  5. Ahasan, M., Chowdhury, M., & Quadir, D. (2014). Sensitivity test of parameterization schemes of mm5 model for prediction of the high impact rainfall events over Bangladesh. Journal of Mechanical Engineering, 44(1), 33–42.

    Article  Google Scholar 

  6. Ahasan, M. N., & Debsarma, S. K. (2015). Impact of data assimilation in simulation of thunderstorm (squall line) event over Bangladesh using WRF model, during SAARC–STORM Pilot Field Experiment 2011. Natural Hazards, 75(2), 1009–1022.

    Article  Google Scholar 

  7. Ali, H., & Mishra, V. (2017). Contrasting response of rainfall extremes to increase in surface air and dewpoint temperatures at urban locations in India. Scientific Reports, 7(1), 1228.

    Article  Google Scholar 

  8. Bangladesh Meteorological Department (BMD) (2016). Numerical weather prediction (NWP) products - WRF forecast. http://www.bmd.gov.bd/?/nwp-products/&/wrf-model/. Accessed 24 Oct 2016.

  9. Bhaskaran, B., Ramachandran, A., Jones, R., & Moufouma-Okia, W. (2012). Regional climate model applications on sub-regional scales over the Indian monsoon region: The role of domain size on downscaling uncertainty. Journal of Geophysical Research: Atmospheres, 117(D10), 1–12.

    Article  Google Scholar 

  10. Bukovsky, M. S., & Karoly, D. J. (2009). Precipitation simulations using WRF as a nested regional climate model. Journal of Applied Meteorology and Climatology, 48(10), 2152–2159.

    Article  Google Scholar 

  11. Chang, C. P., & Krishnamurti, T. N. (1987). Monsoon meteorology. Oxford: Oxford University Press.

    Google Scholar 

  12. Chen, F., & Dudhia, J. (2001). Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review, 129(4), 569–585.

    Article  Google Scholar 

  13. Chowdhury, M. D., & Ward, N. (2004). Hydro-meteorological variability in the greater Ganges–Brahmaputra–Meghna basins. International Journal of Climatology, 24(12), 1495–1508.

    Article  Google Scholar 

  14. Cossu, F., & Hocke, K. (2014). Influence of microphysical schemes on atmospheric water in the Weather Research and Forecasting model. Geoscientific Model Development, 7(1), 147–160.

    Article  Google Scholar 

  15. Das, P. K. (1988). The monsoons. India: National Book Trust.

    Google Scholar 

  16. Das, M. K., Debsarma, S. K., Das, S., & Chowdhury, M. (2011). Tornadic storms of 2008 over Bangladesh: observed by radar and simulated by using WRF-ARW model 6th European Conference on Severe Storms (ECSS 2011), Balearic Islands, Spain.

  17. Das, et al. (2008). Skills of different mesoscale models over Indian region during monsoon season: forecast errors. Journal of Earth System Science, 117(5), 603–620.

    Article  Google Scholar 

  18. De Silva, G. T., Herath, S., Weerakoon, S. B., & Rathnayake, U. R. (2010). 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 (vol. 1, pp. 513–516).

  19. Deb, S., Srivastava, T., & Kishtawal, C. (2008). The WRF model performance for the simulation of heavy precipitating events over Ahmedabad during August 2006. Journal of Earth System Science, 117(5), 589–602.

    Article  Google Scholar 

  20. Done, J., Davis, C. A., & Weisman, M. (2004). The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecasting (WRF) model. Atmospheric Science Letters, 5(6), 110–117.

    Article  Google Scholar 

  21. Doswell, C. A., III. (2004). Weather forecasting by humans—heuristics and decision making. Weather Forecasting, 19(6), 1115–1126.

    Article  Google Scholar 

  22. Dudhia, J. (1989). Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of the Atmospheric Sciences, 46(20), 3077–3107.

    Article  Google Scholar 

  23. Ebert, E., & McBride, J. (2000). Verification of precipitation in weather systems: determination of systematic errors. Journal of Hydrology, 239(1), 179–202.

    Article  Google Scholar 

  24. Gilbert, G. F. (1884). Finley’s tornado predictions. American Meteorological Journal, 1, 166–172.

    Google Scholar 

  25. Grell, G. A., & Dévényi, D. (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysical Research Letters, 29(14), 38–41.

    Article  Google Scholar 

  26. Gruber, A., & Levizzani, V. (2008). Assessment of global precipitation products. WCRP-128, WMO Technical Document, 1430, 57.

    Google Scholar 

  27. Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2), 135–143.

    Article  Google Scholar 

  28. Haines, A., Kovats, R. S., Campbell-Lendrum, D., & Corvalán, C. (2006). Climate change and human health: Impacts, vulnerability and public health. Public Health, 120(7), 585–596.

    Article  Google Scholar 

  29. Hasan, M. A., & Islam, A. K. M. S. (2012). Changes of seasonal temperature extremes in future over Bangladesh using projections by a regional climate model. In Proceedings of the national seminar climate change impact and adaptation, Gazipur (pp. 116–120).

  30. Hasan, M.A, Islam, A.S, & Bhaskaran, B. (2013). Predicting future precipitation and temperature over Bangladesh using high resolution regional scenarios generated by multi-member ensemble climate simulations. In Proceedings of the 4th international conference on water & flood management (ICWFM-2013), Dhaka (vol. 1, pp. 575–582).

  31. Hong, S. Y., & Lim, J. O. J. (2006). The WRF single-moment 6-class microphysics scheme (WSM6). Journal of the Korean Meteorological Society, 42(2), 129–151.

    Google Scholar 

  32. Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman, K. P., et al. (2007). The TRMM multi-satellite precipitation analysis: Quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. Journal of Hydrometeorology, 8, 38–55.

    Article  Google Scholar 

  33. Im, E. S., In, S. R., & Han, S. O. (2013). 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, 69(3), 1681–1695.

    Article  Google Scholar 

  34. Islam, M. N., & Uyeda, H. (2007). Use of TRMM in determining the climatic characteristics of rainfall over Bangladesh. Remote Sensing of Environment, 108(3), 264–276.

    Article  Google Scholar 

  35. Jacobson, M. Z. (2005). Fundamentals of atmospheric modeling. Cambridge: Cambridge University Press.

    Google Scholar 

  36. Janjic, Z. I. (1994). The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Monthly Weather Reviews, 122(5), 927–945.

    Article  Google Scholar 

  37. Janjic, Z. I. (2000). Comments on “Development and evaluation of a convection scheme for use in climate models”. Journal of the Atmospheric Sciences, 57(21), 3686.

    Article  Google Scholar 

  38. Kain, J. S. (2004). The Kain-Fritsch convective parameterization: an update. Journal of Applied Meteorology, 43(1), 170–181.

    Article  Google Scholar 

  39. Kain, J. S., & Fritsch, J. M. (1993). Convective parameterization for mesoscale models: the Kain–Fritsch scheme. In The representation of cumulus convection in numerical models (pp. 165–170). Boston, MA: American Meteorological Society.

  40. Kessler, E. (1969). On the distribution and continuity of water substance in atmospheric circulation. In On the distribution and continuity of water substance in atmospheric circulations (pp. 1–84). Boston, MA: American Meteorological Society.

  41. Krishnamurthy, V., & Kinter, III J. L. (2003). The Indian monsoon and its relation to global climate variability. In Global Climate (pp. 186–236) Springer, Berlin.

  42. Kumar, A., Dudhia, J., & Bhowmik, S. (2010). Evaluation of Physics options of the Weather Research and Forecasting (WRF) Model to simulate high impact heavy rainfall events over Indian Monsoon region. Geofizika, 27(2), 101–125.

    Google Scholar 

  43. Kumar, A., Dudhia, J., Rotunno, R., Niyogi, D., & Mohanty, U. (2008). Analysis of the 26 July 2005 heavy rain event over Mumbai, India using the Weather Research and Forecasting (WRF) model. Quarterly Journal of the Royal Meteorological Society, 134(636), 1897–1910.

    Article  Google Scholar 

  44. Kumar, et al. (2012). Simulations over South Asia using the weather research and forecasting model with chemistry (WRF-Chem): chemistry evaluation and initial results. Geoscientific Model Development, 5(1), 1–66.

    Article  Google Scholar 

  45. Lin, Y., & Colle, B. A. (2011). A new bulk microphysical scheme that includes riming intensity and temperature-dependent ice characteristics. Monthly Weather Review, 139(3), 1013–1035.

    Article  Google Scholar 

  46. Lin, Y. L., Farley, R. D., & Orville, H. D. (1983). Bulk parameterization of the snow field in a cloud model. Journal of Climate and Applied Meteorology, 22(6), 1065–1092.

    Article  Google Scholar 

  47. Litta, A., & Mohanty, U. (2008). Simulation of a severe thunderstorm event during the field experiment of STORM programme 2006, using WRF-NMM model. Current Science, 95(2), 204–215.

    Google Scholar 

  48. Mannan, M. A., Chowdhury, M. A. M., & Karmakar, S. (2013). Application of NWP model in prediction of heavy rainfall in Bangladesh. Procedia Engineering, 56, 667–675.

    Article  Google Scholar 

  49. Maussion, et al. (2011). WRF simulation of a precipitation event over the Tibetan Plateau, China—an assessment using remote sensing and ground observations. Hydrology and Earth System Sciences, 15, 1795–1817.

    Article  Google Scholar 

  50. Mirza, M. M. Q. (2002). Global warming and changes in the probability of occurrence of floods in Bangladesh and implications. Global Environmental Change, 12(2), 127–138.

    Article  Google Scholar 

  51. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research: Atmospheres, 102(D14), 16663–16682.

    Article  Google Scholar 

  52. Mohanty, U. C., Routray, A., Osuri, K. K., & Prasad, S. K. (2012). A study on simulation of heavy rainfall events over Indian region with ARW-3DVAR modeling system. Pure and Applied Geophysics, 169(3), 381–399.

    Article  Google Scholar 

  53. Monin, A. S., & Obukhov, A. M. F. (1954). Basic laws of turbulent mixing in the surface layer of the atmosphere. Contribution of Geophysics Institute Academic Science USSR, 151(163), e187.

    Google Scholar 

  54. Mooney, P., Mulligan, F., & Fealy, R. (2013). 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, 26(3), 1002–1017.

    Article  Google Scholar 

  55. Mukhopadhyay, P., Taraphdar, S., & Goswami, B. N. (2011). Influence of moist processes on track and intensity forecast of cyclones over the north Indian Ocean. Journal of Geophysical Research: Atmospheres, 116(D5).

  56. Murshed, S. B., Islam, A. K. M. S., & Khan, M. S. A. (2011). Impact of climate change on rainfall intensity in Bangladesh. In Proceedings of the 3rd international conference on water and flood management, Dhaka (pp. 881–890).

  57. Pan, H. L., & Wu, W. S. (1995). Implementing a mass flux convection parameterization package for the NMC medium-range forecast model. NMC Office Note, 409(40), 20–233.

    Google Scholar 

  58. Pattanaik, D., & Rao, Y. R. (2009). Track prediction of very severe cyclone ‘Nargis’ using high resolution weather research forecasting (WRF) model. Journal of Earth System Science, 118(4), 309–329.

    Article  Google Scholar 

  59. Pattanayak, S., & Mohanty, U. (2008). A comparative study on performance of MM5 and WRF models in simulation of tropical cyclones over Indian seas. Current Science, 95(7), 923–936.

    Google Scholar 

  60. Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., et al. (2015). A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Reviews of Geophysics, 53(2), 323–361.

    Article  Google Scholar 

  61. Que, Z. P., Wu, F., Bi, C., Long, Y. L., & Li, C. Y. (2016). Impacts of monthly anomalies of intraseasonal oscillation over South China sea and South Asia on the activity of summer monsoon and rainfall in Eastern China. Journal of Tropical Meteorology, 22(2), 145.

    Google Scholar 

  62. Rafiuddin, M., Uyeda, H., & Islam, M. N. (2010). Characteristics of monsoon precipitation systems in and around Bangladesh. International Journal of Climatology, 30(7), 1042–1055.

    Google Scholar 

  63. Raghavan, K. (1973). Break-monsoon over India. Monthly Weather Reviews, 101(1), 33–43.

    Article  Google Scholar 

  64. Rajeevan, M., Kesarkar, A., Thampi, S. B., Rao, T. N., Radhakrishna, B., & Rajasekhar, M. (2010). Sensitivity of WRF cloud microphysics to simulations of a severe thunderstorm event over Southeast India, In Annales Geophysicae (vol. 28, no. 2, pp. 603–619).

  65. Ratnam, J. V., & Cox, E. (2006). Simulation of monsoon depressions using MM5: sensitivity to cumulus parameterization schemes. Meteorology and Atmospheric Physics, 93(1–2), 53–78.

    Article  Google Scholar 

  66. Routray, A., Mohanty, U., Niyogi, D., Rizvi, S., & Osuri, K. K. (2010). Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system. Meteorology and Atmospheric Physics, 106(1–2), 107–125.

    Article  Google Scholar 

  67. Sarker, A. A., & Rashid, A. M. (2013). Landslide and flashflood in Bangladesh, disaster risk reduction approaches in Bangladesh (pp. 165–189). Berlin: Springer.

    Google Scholar 

  68. Schneider, U., Fuchs, T., Meyer-Christoffer, A., & Rudolf, B. (2008). Global precipitation analysis products of the GPCC. In Global Precipitation Climatology Centre (GPCC), DWD, Internet Publikation (pp. 1–12).

  69. Shah, S., Rao, B., Kumar, P., & Pal, P. (2010). Verification of cloud cover forecast with INSAT observation over western Indian. Journal of Earth System Science, 119(6), 775–781.

    Article  Google Scholar 

  70. Shahid, S. (2010). Rainfall variability and the trends of wet and dry periods in Bangladesh. International Journal of Climatology, 30(15), 2299–2313.

    Article  Google Scholar 

  71. Smagorinsky, J. (1963). General circulation experiments with the primitive equations: I. The basic experiment. Monthly Weather Review, 91(3), 99–164.

    Article  Google Scholar 

  72. Sohrabinia, M., Rack, W., & Zawar-Reza, P. (2012). Analysis of MODIS LST compared with WRF model and in situ data over the Waimakariri River basin, Canterbury, New Zealand. Remote Sensing, 4(11), 3501–3527.

    Article  Google Scholar 

  73. Stergiou, I., Tagaris, E., & Sotiropoulou, R. E. P. (2017). Sensitivity assessment of WRF parameterizations over Europe. In Multidisciplinary digital publishing institute proceedings (vol. 1, no. 5, p. 119)

  74. Thapliyal, V. (1997). Preliminary and final long range forecast for seasonal monsoon rainfall over India. Journal of Arid Environments, 36(3), 385–403.

    Article  Google Scholar 

  75. Tiedtke, M. I. C. H. A. E. L. (1989). A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Monthly Weather Review, 117(8), 1779–1800.

    Article  Google Scholar 

  76. Unit UoEACR, Regions t, Project CIL. (1997). Climate impacts LINK Project: Applying results from the Hadley Centre’s climate change experiments for climate change impacts assessments. University of East Anglia, Climatic Research Unit.

  77. Wang, W., Bruyère, C., Duda, M., Dudhia, J., Gill, D., & Lin, H. C. (2009). ARW version 3 modelling system user’s guide.

  78. Wardah, T., Kamil, A. A., Sahol Hamid, A. B., & Maisarah, W. W. I. (2009). Quantitative precipitation forecast using MM5 and WRF models for Kelantan River basin. International Journal of Geological and Environmental Engineering, 5(11), 712–716.

    Google Scholar 

  79. Warner, T. T. (2011). Numerical weather and climate prediction, 526. Cambridge: Cambridge University Press.

    Google Scholar 

  80. Webster, P. J., Magana, V. O., Palmer, T. N., Shukla, J., Tomas, R. A., Yanai, M. U., et al. (1998). Monsoons: Processes, predictability, and the prospects for prediction. Journal of Geophysical Research: Oceans, 103(C7), 14451–14510.

    Article  Google Scholar 

  81. Yatagai, et al. (2012). APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bulletin of the American Meteorological Society, 93(9), 1401–1415.

    Article  Google Scholar 

  82. Yeh, H. C., & Chen, G. T. J. (2004). Case study of an unusually heavy rain event over eastern Taiwan during the Mei-yu season. Monthly Weather Review, 132(1), 320–337.

    Article  Google Scholar 

  83. Zeyaeyan, S., Fattahi, E., Ranjbar, A., Azadi, M., & Vazifedoust, M. (2017). Evaluating the effect of physics schemes in WRF simulations of summer rainfall in North West Iran. Climate, 5(3), 48.

    Article  Google Scholar 

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Hasan, M.A., Islam, A.K.M.S. Evaluation of Microphysics and Cumulus Schemes of WRF for Forecasting of Heavy Monsoon Rainfall over the Southeastern Hilly Region of Bangladesh. Pure Appl. Geophys. 175, 4537–4566 (2018). https://doi.org/10.1007/s00024-018-1876-z

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Keywords

  • Model evaluation
  • microphysics scheme
  • cumulus scheme
  • numerical weather prediction