Evaluation of global ensemble prediction models for forecasting medium to heavy precipitations


In this study, 24-h forecasts of CMA, ECCC, ECMWF, KMA, NCEP and UKMO models were extracted from the TIGGE database and evaluated over selected stations in Iran within the 2010–2018 period. Daily forecast data were interpolated using the inverse distance method to the location of selected synoptic stations, while the frequency bias were further corrected using quantile mapping. The accumulation interval for precipitation was 24 h. Moreover, both raw and frequency-bias-corrected data were evaluated in deterministic and dichotomous modes at precipitation depth thresholds of 5, 15, 25, 35, and 45 mm. Forecasts were further partitioned and evaluated in different seasons in selected stations located in the margin of Alborz and Zagros Mountains. Best performing precipitation models did well over October–December season along the Alborz Mountains and over April–June along the Zagros Mountains. Results indicated that the forecasts were quite improved especially in high threshold after the frequency bias correction. Compared to other models, the ECMWF model provided best results in most stations. In contrast, NCEP performed poorly and could not provide a reliable medium to heavy precipitation forecasts. In general, none of the forecast models provided accurate estimates for precipitation depths over the 35-mm threshold. In fact, at higher precipitation thresholds, all models underpredicted the number of precipitation events.

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  1. Aminyavari S, Saghafian B, Delavar M (2018) Evaluation of TIGGE ensemble forecasts of precipitation in distinct climate regions in Iran. Adv Atmos Sci 35:457–468. https://doi.org/10.1007/s00376-017-7082-6

    Article  Google Scholar 

  2. Boé J, Terray L, Habets F, Martin E (2007) Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. Int J Climatol 27:1643–1655. https://doi.org/10.1002/joc.1602

    Article  Google Scholar 

  3. Coiffer J (2011) Fundamentals of numerical weather prediction. Cambridge University Press, Cambridge

    Google Scholar 

  4. George BA, Adams R, Ryu D, Western AW, Simon P, Nawarathna B (2011) An assessment of potential operational benefits of short-term stream flow forecasting in the Broken Catchment, Victoria. In: Proceedings of the 34th IAHR World Congress, Brisbane, Australia

  5. Gudmundsson L (2014) qmap: Statistical transformations for post-processing climate model output. R package version 1.0–3

  6. Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Downscaling RCM precipitation to the station scale using statistical transformations—a comparison of methods. Hydrol Earth Syst Sci 16:3383–3390. https://doi.org/10.5194/hess-16-3383-2012

    Article  Google Scholar 

  7. Hamill TM (2012) Verification of TIGGE multimodel and ECMWF reforecast-calibrated probabilistic precipitation forecasts over the contiguous United States. Mon Weather Rev 140:2232–2252. https://doi.org/10.1175/MWR-D-11-00220.1

    Article  Google Scholar 

  8. He Y, Wetterhall F, Bao H, Cloke H, Li Z, Pappenberger F, Hu Y, Manful D, Huang Y (2010) Ensemble forecasting using TIGGE for the July–September 2008 floods in the Upper Huai catchment: a case study. Atmos Sci Lett 11:132–138. https://doi.org/10.1002/asl.270

    Article  Google Scholar 

  9. Huang L, Luo Y (2017) Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season. J Geophys Res Atmos 122:8494–8516. https://doi.org/10.1002/2017JD026512

    Article  Google Scholar 

  10. Khan MM, Shamseldin AY, Melville BW (2014) Impact of ensemble size on forecasting occurrence of rainfall using TIGGE precipitation forecasts. J Hydrol Eng 19:732–738. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000864

    Article  Google Scholar 

  11. Kim KB, Kwon HH, Han D (2016) Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme. Hydrol Earth Syst Sci 20:2019–2034. https://doi.org/10.5194/hess-20-2019-2016

    Article  Google Scholar 

  12. Louvet S, Sultan B, Janicot S, Kamsu-Tamo PH, Ndiaye O (2016) Evaluation of TIGGE precipitation forecasts over West Africa at intraseasonal timescale. Clim Dyn 47:31–47. https://doi.org/10.1007/s00382-015-2820-x

    Article  Google Scholar 

  13. Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, Haerter JO (2010) Statistical bias correction of global simulated daily precipitation and temperature for application of hydrological models. J Hydrol 395:199–215. https://doi.org/10.1016/j.jhydrol.2010.10.024

    Article  Google Scholar 

  14. Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies. Geogr Compass 4:834–860. https://doi.org/10.1111/j.1749-8198.2010.00357.x

    Article  Google Scholar 

  15. Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456:12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052

    Article  Google Scholar 

  16. Thielen J, Bartholmes J, Ramos MH, Roo AD (2009) The European flood alert system—part 1: concept and development. Hydrol Earth Syst Sci 13:125–140. https://doi.org/10.5194/hess-13-125-2009

    Article  Google Scholar 

  17. Thiemig V, Bisselink B, Pappenberger F, Thielen J (2015) A Pan-African medium-range ensemble flood forecast system. Hydrol Earth Syst Sci 19:3365–3385. https://doi.org/10.5194/hess-19-3365-2015

    Article  Google Scholar 

  18. WCRP (2017) 7th International verification methods workshop: forecast verification methods across time and space scales. https://www.cawcr.gov.au/projects/verification/

  19. Wu L, Seo DJ, Demargne J, Brown JD, Cong S, Schaake J (2011) Generation of ensemble precipitation forecast from single-valued quantitative precipitation forecast for hydrologic ensemble prediction. J Hydrol 399:281–298. https://doi.org/10.1016/j.jhydrol.2011.01.013

    Article  Google Scholar 

  20. Zollo AL, Rianna G, Mercogliano P, Tommasi P, Comegna L (2014) Validation of a simulation chain to assess climate change impact on precipitation induced landslides. Landslide Sci Safer Geoenviron 1:287–292. https://doi.org/10.1007/978-3-319-04999-1_39

    Article  Google Scholar 

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Abdolmanafi, A., Saghafian, B. & Aminyavari, S. Evaluation of global ensemble prediction models for forecasting medium to heavy precipitations. Meteorol Atmos Phys 133, 15–26 (2021). https://doi.org/10.1007/s00703-020-00731-8

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