Abstract
Evaluating the adaptability of precipitation forecasting is of great importance for regional flood control and drought warnings. This study conducted evaluations using the 1–9 days cumulative precipitation forecast data of five typical operational global ensemble prediction systems (EPSs) from TIGGE (i.e., The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble) and the observed daily precipitation data of 40 meteorological stations over the Huaihe River basin (HB). A series of verification metrics is used to evaluate the performances of quantitative precipitation forecasts (QPFs) and probabilistic quantitative precipitation forecasts (PQPFs) from the five EPSs from April to December 2015 in terms of overall performance, different precipitation thresholds, different lead times and the spatial distribution over the HB. The adaptability of the multimodel superensemble integrated from the five EPSs by the Bayesian model average is also examined during the main flood season. The results show that (1) the forecast quality of the China Meteorological Administration EPS is the worst for all lead times, which may relate to its having the fewest ensemble members. The European Centre for Medium-Range Weather Forecasts (ECMWF) EPS performs the best in terms of QPF and PQPF qualities for longer lead times because ECMWF has the largest ensemble members. (2) All EPSs have better discrimination at low thresholds, indicating the reference value for drought warnings. ECMWF is expected to obtain the best PQPF skill for a large threshold through postprocessing; (3) due to the differences in climates in the North and South of the basin, QPF and PQPF qualities are better in the northern HB than in the southern HB; (4) except for climate, the PQPF skill is also influenced by precipitation type, while the QPF accuracy is affected by terrain. The PQPF is good at forecasting the precipitation caused by ocean effects but not by mountain topography. The QPF accuracy decreases in mountainous areas; and (5) the multimodel superensemble has little effect on PQPF skill improvement but can improve QPF accuracy when raw EPSs have significantly different QPF accuracies.
Similar content being viewed by others
Availability of data and materials
All data used during the study are available from the first author by request.
Code availability
Codes used during the study are available from the first author by request.
References
Aminyavari S, Saghafian B (2019) Probabilistic streamflow forecast based on spatial post-processing of TIGGE precipitation forecasts. Stoch Environ Res Risk Assess 33:1939–1950. https://doi.org/10.1007/s00477-019-01737-4
Aminyavari S, Saghafian B, Delavar M (2018) Evaluation of TIGGE ensemble forecasts of precipitation in distinct climate regions in Iran. Adv Atmos Sci 35(4):457–468
Bhomia S, Jaiswal N, Kishtawal CM (2017) Accuracy assessment of rainfall prediction by global models during the landfall of tropical cyclones in the North Indian Ocean. Meteorol Appl 24:503–511
Bischiniotis K, van den Hurk B, Zsoter E, Coughlan de Perez E, Grillakis M, Aerts JCJH (2019) Evaluation of a global ensemble flood prediction system in Peru. Hydrol Sci J 64:1171–1189. https://doi.org/10.1080/02626667.2019.1617868
Bo Qu, Xingnan Z, Florian P, Tao Z, Yuanhao F (2017) Multi-model grand ensemble hydrologic forecasting in the Fu river basin using Bayesian model averaging. Water 9:74. https://doi.org/10.3390/w9020074
Bonnardot F, Quetelard H, Jumaux G, Leroux MD, Bessafi M (2018) Probabilistic forecasts of tropical cyclone tracks and intensities in the southwest Indian Ocean basin. Q J R Meteorol Soc 145:675–686
Buizza R, Miller M, Palmer TN (1999) Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q J R Meteorol Soc 125:2887–2908
Chen X, Yuan H, Xue M (2018) Spatial spread-skill relationship in terms of agreement scales for precipitation forecasts in a convection-allowing ensemble. Q J R Meteorol Soc 144:85–98. https://doi.org/10.1002/qj.3186
Clark AJ, Kain JS, Stensrud DJ, Xue M, Kong F, Coniglio MC, Thomas KW, Wang Y, Brewster K, Gao J, Wang X, Weiss SJ, Du J (2011) Precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon Weather Rev 139:1410–1418
Cloke HL, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375:613–626. https://doi.org/10.1016/j.jhydrol.2009.06.005
Dempster AP (1977) Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc Series B: Methodologic 39:1–38
Demargne J, Brown J, Liu Y, Seo DJ, Wu L, Toth Z, Zhu Y (2010) Diagnostic verification of hydrometeorological and hydrologic ensembles. Atmos Sci Lett 11:114–122
Duan Y, Gong J, Du J, Charron M, Chen J, Deng G, DiMego G, Hara M, Kunii M, Li X, Li Y, Saito K, Seko H, Wang Y, Wittmann C (2012) An overview of the Beijing 2008 olympics research and development project (B08RDP). Bull Am Meteorol Soc 93:381–403
Hagedorn R, Buizza R, Hamill TM, Leutbecher M, Palme TN (2012) Comparing TIGGE multimodel forecasts with reforecast-calibrated ECMWF ensemble forecasts. Q J R Meteorol Soc 138:1814–1827
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
Hemri S, Scheuerer M, Pappenberger F, Bogner K, Haiden T (2014) Trends in the predictive performance of raw ensemble weather forecasts. Geophys Res Lett 41:9197–9205
Hersbach H (2000) Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather Forecast 15:559–570
Huo W, Li Z, Wang J et al (2019) Multiple hydrological models comparison and an improved Bayesian model averaging approach for ensemble prediction over semi-humid regions. Stoch Environ Res Risk Assess 33:217–238
Jeffrey SW, Thomas MH (2002) Ensemble data assimilation without perturbed observations. Mon Weather Rev 130:1913–1924
Jeong DI, Kim Y-O (2009) Combining single-value streamflow forecasts—A review and guidelines for selecting techniques. J Hydrol 377:284–299
Ji L, Zhi X, Zhu S, Fraedrich K (2019) Probabilistic precipitation forecasting over East Asia using Bayesian model averaging. Weather Forecast 34:377–392. https://doi.org/10.1175/WAF-D-18-0093.1
Jianguo XZL (2014) BMA probabilistic quantitative precipitation forecasting over the Huaihe Basin using TIGGE multimodel ensemble forecasts. Mon Weather Rev 142:1542–1555. https://doi.org/10.1175/MWR-D-13-00031.1
Karuna Sagar S, Rajeevan M, Vijaya Bhaskara Rao S, Mitra AK (2017) Prediction skill of rainstorm events over India in the TIGGE weather prediction models. Atmos Res 198:194–204. https://doi.org/10.1016/j.atmosres.2017.08.017
Kaufmann P, Schubiger F, Binder P (2003) Precipitation forecasting by a mesoscale numerical weather prediction (NWP) model: 8 years of experience. Hydrol Earth Syst Sci 7:812–832
Kirtman BP et al (2014) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteor Soc 95:585–601
Krishnamurti TN, Kishtawal CM, LaRow TE, Bachiochi DR, Zhang Z, Williford CE, Gadgil S, Surendran S (1999) Improved weather and seasonal climate forecasts from multimodel superensemble. Science 285:1548–1550
Krishnamurti TN, Kumar V, Simon A, Bhardwaj A, Ghosh T, Ross R (2016) A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes. Rev Geophys 54:336–377
Lan C, Pagano Thomas C, Wang QJ (2011) A Review of quantitative precipitation forecasts and their use in short- to medium-range streamflow forecasting. J Hydrometeorol 12:713–728. https://doi.org/10.1175/2011JHM1347.1
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
McLachlan GJ, Krishnan T (1998) The EM algorithm and extensions. Stat Med 17:1187
Meng Zhiyong ZF (2011) Limited-area ensemble-based data assimilation. Mon Weather Rev 139:2025–2045. https://doi.org/10.1175/2011MWR3418.1
Molteni F, Buizza R, Palmer TN, Petroliagis T (1996) The ECMWF ensemble prediction system: methodology and validation. Q J R Meteorol Soc 122:73–119
Olson DA, Junker NW, Korty B (1995) Evaluation of 33 years of quantitative precipitation forecasting at the NMC. Weather Forecast 10:498–511
Osinski R, Bouttier F (2018) Short-range probabilistic forecasting of convective risks for aviation based on a lagged-average-forecast ensemble approach. Meteorol Appl 25:105–118
Pappenberger F, Beven KJ, Hunter NM, Bates PD, Gouweleeuw BT, Thielen J, de Roo APJ (2005) Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS). Hydrol Earth Syst Sci 9:381–393
Park YY, Buizza R, Leutbecher M (2008) TIGGE: preliminary results on comparing and combining ensembles. Q J R Meteorol Soc 134:2029–2050
Price DT, McKenney DW, Nalder IA, Hutchinson MF, Kesteven JL (2000) A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric Forest Meteorol 101:81–94
Qingyun D, Florian P, Andy W, Cloke Hannah L, Schaake John C (2019) Handbook of hydrometeorological ensemble forecasting. Springer, Berlin Heidelberg
Raftery AE, Tilmann G, Balabdaoui F, Polakowski M (2005) Using bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev133 (5):1155–1174. https://doi.org/10.1175/MWR2906.1
Richardson DS (2001) Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size. Q J R Meteorol Soc 127:2473–2489
Roberto B (2019) Introduction to the special issue on 25 years of ensemble forecasting. Q J R Meteorol Soc 145:1–11. https://doi.org/10.1002/qj.3370
Roebber Paul J, Schultz David M, Colle Brian A, Stensrud David J (2004) Toward improved prediction: high-resolution and ensemble modeling systems in operations. Weather Forecast 19:936–949
Saedi A, Saghafian B, Moazami S, Aminyavari S (2020) Performance evaluation of sub-daily ensemble precipitation forecasts. Meteorol Appl 27:6. https://doi.org/10.1002/met.1872
Scheuerer M, Hamill TM (2015) Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Mon Weather Rev 143:4578–4596
Shin Y, Lee Y, Choi J, Park J-S (2019) Integration of max-stable processes and Bayesian model averaging to predict extreme climatic events in multi-model ensembles. Stoch Environ Res Risk Assess 33:47–57
Sloughter JML, Raftery AE, Gneiting T, Fraley C (2007) Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon Weather Rev 135:3209–3220. https://doi.org/10.1175/MWR3441.1
Song L, Chen M, Gao F, Cheng C, Chen M, Yang L, Wang Y (2019) Elevation influence on rainfall and a parameterization algorithm in the Beijing area. J Meteorologic Res 33(6):1143–1156
Tao Y, Duan Q, Ye A, Gong W, Di Z, Xiao M, Hsu K (2014) An evaluation of post-processed TIGGE multimodel ensemble precipitation forecast in the Huai river basin. J Hydrol 519:2890–2905
Taylor JW, Buizza R (2003) Using weather ensemble predictions in electricity demand forecasting. Int J Forecast 19:57–70
Thomas MH, Josip J (2006) Measuring forecast skill: is it real skill or is it the varying climatology? Q J R Meteorol Soc 132:2905–2923. https://doi.org/10.1256/qj.06.25
Tilmann G, Raftery EA (2007) Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc 102:359–378. https://doi.org/10.1198/016214506000001437
Trenberth KEE (1992) Climate system modeling. Cambridge University Press, Cambridge
Verlinden KLB (2017) Using the second-generation GEFS reforecasts to predict ceiling, visibility, and aviation flight category. Weather Forecast 32:1765–1780
Vogel P, Knippertz P, Fink AH, Schlueter A, Gneiting T (2017) Skill of global raw and postprocessed ensemble predictions of rainfall over northern tropical Africa. Statistics. https://doi.org/10.1175/WAF-D-17-0127.s1
Wang H (2017) Preface to the special issue on the forecast and evaluation of meteorological disasters (FEMD). Adv Atmos Sci 34(2):127
Wang B, Ding QH, Liu J (2011) Concept of global monsoon. In: Chang C-P, Ding Y, Lau N-C, Johnson RH, Wang B, Yasunari T (eds) The global monsoon system: research and forecast. World Scientific, Singapore, pp 3–14
Wilks DS (2009) Statistical methods in the atmospheric sciences, 2nd edition. International geophysics series, vol 91. Elsevier: Amsterdam
Winter CL, Nychka D (2010) Forecasting skill of model averages. Stoch Environ Res Risk Assess 24:633–638
WMO (2012) Guidelines on ensemble prediction systems and forecasting, Switzerland
Wu Juan Lu, Zhiyong GW (2014) Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system. Nat Hazards 74:325–340. https://doi.org/10.1007/s11069-014-1204-6
Xiang Su, Huiling Y, Yuejian Z, Yan L, Yuan W (2014) Evaluation of TIGGE ensemble predictions of Northern Hemisphere summer precipitation during 2008–2012. J Geophys Res Atmos 119:7292–7310
Ye J, He Y, Pappenberger F, Cloke HL, Manful DY, Li Z (2014) Evaluation of ECMWF medium-range ensemble forecasts of precipitation for river basins. Q J R Meteorol Soc 140:1615–1628
Ying H, Yuan W, Hao W (2019) Evaluation of Multi-NWPs rainstorm forecasting performance in different time scales in Huaihe River basin and discussion on flood predictability. Meteorol Mon 45:989–1000
Zhang Xu, Qianjin D, Chen J (2019) Comparison of ensemble models for drought prediction based on climate indexes. Stoch Environ Res Risk Assess 33:593–606
Funding
This study is supported by the National Key Technologies R&D Program of China (Grant No. 2017YFC0405606), the Fundamental Research Funds for the Central Universities (Grant No. B200202032), the China Postdoctoral Science Foundation (Grant No. 2019M661715), National Natural Science Foundation of China, China (Grant No. 51909062), National Natural Science Foundation of Jiangsu Province, China (Grant No. BK20180509).
Author information
Authors and Affiliations
Contributions
Methodology, WH; conceptualization, ZPA; software, WH; validation, ZFL; formal analysis, WH, SX and MYF.; writing—original draft, WH; writing—review and editing, WH and ZFL; visualization, ZFL.; supervision, ZPA.; funding acquisition, ZPA.
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest regarding the publication of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, H., Zhong, Pa., Zhu, Fl. et al. The adaptability of typical precipitation ensemble prediction systems in the Huaihe River basin, China. Stoch Environ Res Risk Assess 35, 515–529 (2021). https://doi.org/10.1007/s00477-020-01923-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-020-01923-9