Investigate the efficiency of the probabilistic forecasting model of seasonal precipitation variation over Iran

Document Type : Original Article

Authors

1 Department Member of Water Research Institute (WRI)

2 Research Expert of Water Research Institute (WRI)

Abstract

This study evaluates the performance of an ensemble framework using the Weather Research and Forecasting (WRF) model for probabilistic seasonal precipitation forecasts. In this study, two types of data were used: a) The meteorological initial and boundary conditions come from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) data. b) Precipitation data from the Global Precipitation Climatology Centre (GPCC) dataset used as observational data over Iran. The ensemble model was designed based on a one-way double-nested (60-parent domain and 20-nested Km resolutions) modeling system using Weather Research and Forecasting (WRF) version 4.2 customized over Iran to downscale the second version of the NCEP Climate Forecast System (CFSv2). The results showed that precipitation forecast at seasonal time scale in Iran has high uncertainty. Although probabilistic forecasts can increase the efficacy of seasonal forecasts more than deterministic, the uncertainty of these forecasts is still high. Additionally, the downscaling of the CFS.v2 model by WRF and using multiple initial conditions and model physics can increase the accuracy of seasonal forecasts. The spatial distribution of the forecast accuracy of the ensemble model is dependent on the spatial distribution of precipitation over Iran. Another factor that affects the model's accuracy is the forecast lead time dependent especially at 2-month and 3-month forecast lead times. The results showed that the model has high uncertainty in the east and southeast of Iran. The implementation of this model for an operational period showed that although the model can forecast the spatial variation of rainfall over Iran up to a three-month lead time, probabilistic forecasting cannot significantly reduce the uncertainty of the model in a seasonal time scale. The ensemble model tends to overestimate precipitation in the third lead time.

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References

  1. Agyeman, R. Y. K., Annor, T., Lamptey, B., Quansah, E., Agyekum, J., & Tieku, S.A. 2017. Optimal physics parameterization scheme combination of the weather research and forecasting model for seasonal precipitation simulation over Ghana: Advances in Meteorology, 2017, 1-15.
  2. Alizadeh‐Choobari, O. 2019. Dynamical downscaling of CSIRO‐Mk3. 6 seasonal forecasts over Iran with the regional climate model version 4: International Journal of Climatology, 39(7). 3313-3322.
  3. Bierkens, M.F.P., & Van Beek, L.P.H. 2009. Seasonal predictability of European discharge: NAO and hydrological response time. Journal of Hydrometeorology, 10(4), 953-968.
  4. Darand, M., & Khandu, K. 2020. Statistical evaluation of gridded precipitation datasets using rain gauge observations over Iran: Journal of Arid Environments, 178, 104172.
  5. Frnda, J., Durica, M., Rozhon, J., Vojtekova, M., Nedoma, J., & Martinek, R. 2022. ECMWF short-term prediction accuracy improvement by deep learning. Scientific Reports, 12(1), 7898.
  6. Infanti, J.M., & Kirtman, B.P. 2014. Southeastern US rainfall prediction in the North American multi-model ensemble. Journal of Hydrometeorology, 15(2), 529-550.
  7. Krishnamurti, T. N., Kishtawal, M., Zhang, Z., LaRow, T., Bachiochi, D., Williford, E., Gadgil, S. and Surendran, S. 1999. Improved weather and seasonal climate forecasts from multimodel superensemble, Science, 285, 1548–1550.
  8. Lang, Y., Ye, A., Gong, W., Miao, C., Di, Z., Xu, J., & Duan, Q. 2014. Evaluating skill of seasonal precipitation and temperature predictions of NCEP CFSv2 forecasts over 17 hydroclimatic regions in China. Journal of Hydrometeorology, 15(4), 1546-1559.
  9. Le, P. V., Randerson, J. T., Willett, R., Wright, S., Smyth, P., Guilloteau, C., & Foufoula-Georgiou, E. 2023. Climate-driven changes in the predictability of seasonal precipitation. Nature communications, 14(1), 3822.
  10. Miller, S., Mishra, V., Ellenburg, W. L., Adams, E., Roberts, J., Limaye, A., & Griffin, R. 2021. Analysis of a short-term and a seasonal precipitation forecast over Kenya. Atmosphere, 12(11), 1371.
  11. Neelin, J.D. 2011. Climate Change and Climate Modeling. Cambridge: Cambridge University Press p. 282.
  12. Phan-Van, T., Nguyen-Xuan, T., Van Nguyen, H., Laux, P., Pham-Thanh, H. and Ngo-Duc, T. 2018. Evaluation of the NCEP Climate Forecast System and Its Downscaling for Seasonal Rainfall Prediction over Vietnam. Wea. Forecasting, 33, 615–640.
  13. Pinheiro, E., & Ouarda, T. B. 2023. Short-lead seasonal precipitation forecast in northeastern Brazil using an ensemble of artificial neural networks. Scientific Reports, 13(1), 20429.
  14. Singh, J., Yeo, K., Liu, X., Hosseini, R., & Kalagnanam, J. R. 2015, Evaluation of WRF model seasonal forecasts for tropical region of Singapore: Advances in Science and Research, 12(1), 69-72.
  15. Wood, A. W., & Lettenmaier, D. P. 2008. An ensemble approach for attribution of hydrologic prediction uncertainty. Geophysical Research Letters, 35(14).
  16. Yun, W. T., Stefanova, L., & Krishnamurti, T. N. 2003. Improvement of the multimodel superensemble technique for seasonal forecasts. Journal of Climate, 16(22), 3834-3840.

 

 

 

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