Calibrating Subseasonal to Seasonal Precipitation Forecasts to Improve Predictive Performance
Publication: Geo-Risk 2023
ABSTRACT
Subseasonal to seasonal (S2S) precipitation forecasts encompassing lead times ranging from two weeks to three months can bridge the gap between weather and seasonal forecasts. For practical applications, calibration is a necessary step to improve predictive performances of raw forecasts from S2S models. This paper illustrates the calibration of the S2S precipitation forecasts by a case study of the European Centre for Medium-Range Weather Forecasts. The Bernoulli-Gamma-Gaussian model and quantile mapping are used to calibrate raw S2S forecasts. The results of three catchments in China show that raw forecasts exhibit reasonable correlations with observed precipitation but suffer from considerable biases and unreliable spreads, leading to negative skill. The Bernoulli-Gamma-Gaussian model overall outperforms the quantile mapping in correcting biases and improving reliability and skill. For S2S precipitation, the Bernoulli-Gamma-Gaussian model can serve as an effective tool to generate skillful ensemble time series forecasts for the early warning of rainfall-induced geohazards.
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REFERENCES
Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., and Wilby, R. 2004. “The Schaake Shuffle: A Method for Reconstructing Space–Time Variability in Forecasted Precipitation and Temperature Fields.” J. Hydrometeor 5, 243–262.
de Andrade, F. M., Coelho, C. A. S., and Cavalcanti, I. F. A. 2019. “Global precipitation hindcast quality assessment of the Subseasonal to Seasonal (S2S) prediction project models.” Clim Dyn 52, 5451–5475.
Funk, C., et al. 2015. “The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes.” Sci Data 2, 150066.
Gneiting, T., Balabdaoui, F., and Raftery, A. E. 2007. “Probabilistic forecasts, calibration and sharpness.” J Royal Statistical Soc B 69, 243–268.
Huang, Z., Zhao, T., Zhang, Y., Cai, H., Hou, A., and Chen, X. 2021. “A five-parameter Gamma-Gaussian model to calibrate monthly and seasonal GCM precipitation forecasts.” J. Hydrol. 603, 126893.
Huang, Z., and Zhao, T. 2022. “Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes.” WIREs Water 9, e1580.
Huang, Z., Zhao, T., Xu, W., Cai, H., Wang, J., Zhang, Y., Liu, Z., Tian, Y., Yan, D., and Chen, X. 2022. “A seven-parameter Bernoulli-Gamma-Gaussian model to calibrate subseasonal to seasonal precipitation forecasts.” J. Hydrol. 610, 127896.
Kelly, K. S., and Krzysztofowicz, R. 1997. “A bivariate meta-Gaussian density for use in hydrology.” Stochastic Hydrol Hydraul 11, 17–31.
Krzysztofowicz, R. 1999. “Bayesian theory of probabilistic forecasting via deterministic hydrologic model.” Water Resour. Res. 35, 2739–2750.
Li, W., Duan, Q., Miao, C., Ye, A., Gong, W., and Di, Z. 2017. “A review on statistical postprocessing methods for hydrometeorological ensemble forecasting.” WIREs Water 4, e1246.
Li, W., Chen, J., Li, L., Chen, H., Liu, B., Xu, C.-Y., and Li, X. 2019. “Evaluation and Bias Correction of S2S Precipitation for Hydrological Extremes.” J. Hydrometeorol. 20, 1887–1906.
McInerney, D., Thyer, M., Kavetski, D., Laugesen, R., Tuteja, N., and Kuczera, G. 2020. “Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting.” Water Resour. Res. 56. e2019WR026979.
Monhart, S., Spirig, C., Bhend, J., Bogner, K., Schär, C., and Liniger, M. A. 2018. “Skill of Subseasonal Forecasts in Europe: Effect of Bias Correction and Downscaling Using Surface Observations.” J. Geophys. Res. Atmos. 123, 7999–8016.
Piciullo, L., Calvello, M., and, Cepeda, J. M. 2018. “Territorial early warning systems for rainfall-induced landslides.” Earth Sci. Rev. 179, 228–247.
Robertson, A. W., Kumar, A., Peña, M., and Vitart, F. 2015. “Improving and Promoting Subseasonal to Seasonal Prediction.” Bull. Am. Meteorol. Soc. 96, ES49–ES53.
Saha, S., et al. 2014. “The NCEP Climate Forecast System Version 2.” J. Clim. 27, 2185–2208.
Schepen, A., Zhao, T., Wang, Q. J., and Robertson, D. E. 2018. “A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments.” Hydrol. Earth Syst. Sci. 22, 1615–1628.
Scheuerer, M., and Hamill, T. M. 2015. “Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions*.” Mon. Weather Rev. 143, 4578–4596.
Tadesse, T., Haigh, T., Wall, N., Shiferaw, A., Zaitchik, B., Beyene, S., Berhan, G., and Petr, J. 2016. “Linking Seasonal Predictions to Decision-Making and Disaster Management in the Greater Horn of Africa.” Bull. Am. Meteorol. Soc. 97, ES89–ES92.
Vigaud, N., Tippett, M. K., and Robertson, A. W. 2019. “Deterministic Skill of Subseasonal Precipitation Forecasts for the East Africa‐West Asia Sector from September to May.” J. Geophys. Res. Atmos. 124, 11887–11896.
Vitart, F., et al. 2017. “The Subseasonal to Seasonal (S2S) Prediction Project Database.” Bull. Am. Meteorol. Soc. 98, 163–173.
Vitart, F., Robertson, A., and Anderson, D. 2012. “Subseasonal to Seasonal Prediction Project: Bridging the gap between weather and climate.” WMO Bulletin 61, 23.
Wetterhall, F., and Di Giuseppe, F. 2018. “The benefit of seamless forecasts for hydrological predictions over Europe.” Hydrol. Earth Syst. Sci. 22, 3409–3420.
White, C. J., et al. 2017. “Potential applications of subseasonal-to-seasonal (S2S) predictions.” Met. Apps 24, 315–325.
White, C. J., et al. 2021. “Advances in the application and utility of subseasonal-to-seasonal predictions.” Bull. Am. Meteorol. Soc. 1, 1–57.
Zhang, L., Kim, T., Yang, T., Hong, Y., and Zhu, Q. 2021. “Evaluation of Subseasonal-to-Seasonal (S2S) Precipitation Forecast from the North American Multi-Model Ensemble Phase II (NMME-2) over the contiguous U.S.” J. Hydrol. 127058.
Zhao, T., Bennett, J. C., Wang, Q. J., Schepen, A., Wood, A. W., Robertson, D. E., and Ramos, M.-H. 2017. “How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts?” J. Climate 30, 3185–3196.
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Published online: Jul 20, 2023
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