Skip to main content
SpringerLink
Log in

Application to Post-processing of Meteorological Seasonal Forecasting

  • Living reference work entry
  • First Online:
Handbook of Hydrometeorological Ensemble Forecasting

Abstract

Seasonal hydrological forecasting relies on accurate and reliable ensemble climate forecasts. A calibration, bridging, and merging (CBaM) method has been developed to statistically postprocess seasonal climate forecasts from general circulation models (GCMs). Postprocessing corrects conditional biases in raw GCM outputs and produces forecasts that are reliable in ensemble spread. The CBaM method is designed to extract as much skill as possible from the GCM. This is achieved by firstly producing multiple forecasts using different GCM output fields, such as rainfall, temperature, and sea surface temperatures, as predictors. These forecasts are then combined based on evidence of skill in hindcasts. Calibration refers to direct postprocessing of the target variable – rainfall for example. Bridging refers to indirect forecasting of the target variable – forecasting rainfall with the GCM’s Nino3.4 forecast for example. Merging is designed to optimally combine calibration and bridging forecasts. Merging includes connecting forecast ensemble members across forecast time periods by using the “Schaake Shuffle,” which creates time series forecasts with appropriate temporal correlation structure. CBaM incorporates parameter and model uncertainty, leading to reliable forecasts in most applications. Here, CBaM is applied to produce monthly catchment rainfall forecasts out to 12 months for a catchment in northeastern Australia. Bridging is shown to improve forecast skill in several seasons, and the ensemble time series forecasts are shown to be reliable for both monthly and seasonal totals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • K. Ashok, S. Behera, S. Rao, H. Weng, T. Yamagata, El Niño Modoki and its possible teleconnection. J. Geophys. Res. 112, C11007 (2007)

    Article  Google Scholar 

  • J. Barnard, R. McCulloch, X.-L. Meng, Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Stat. Sin. 10, 1281–1312 (2000)

    Google Scholar 

  • J. Cheng, J. Yang, Y. Zhou, Y. Cui, Flexible background mixture models for foreground segmentation. Image Vis. Comput. 24, 473–482 (2006)

    Article  Google Scholar 

  • M. Clark, S. Gangopadhyay, L. Hay, B. Rajagopalan, R. Wilby, The Schaake shuffle: a method for reconstructing space–time variability in forecasted precipitation and temperature fields. J. Hydrometeorol. 5, 243–262 (2004)

    Article  Google Scholar 

  • J. Eklund, S. Karlsson, Forecast combination and model averaging using predictive measures. Econ. Rev. 26, 329–363 (2007)

    Article  Google Scholar 

  • A. Gelman, J.B. Carlin, H.S. Stern, D.B. Rubin, Bayesian data analysis (Chapman and Hall, New York, 1995). 526 pp

    Google Scholar 

  • J. Geweke, C. Whiteman, Chapter 1, Bayesian forecasting, in Handbook of Economic Forecasting, ed. by G. Elliott, C.W.J. Granger, A. Timmermann, vol. 1 (Elsevier B.V., Amsterdam, 2006), pp. 3–80. ISSN 1574-0706, ISBN 9780444513953, http://dx.doi.org/10.1016/S1574-0706(05)01001-3

  • S. Hawthorne, Q.J. Wang, A. Schepen, D. Robertson, Effective use of general circulation model outputs for forecasting monthly rainfalls to long lead times. Water Resour. Res. 49(9), 5427–5436 (2013)

    Google Scholar 

  • W.-R. Hsu, A.H. Murphy, The attributes diagram. A geometrical framework for assessing the quality of probability forecasts. Int. J. Forecast. 2, 285–293 (1986)

    Article  Google Scholar 

  • C.H. Jackson, S.G. Thompson, L.D. Sharples, Accounting for uncertainty in health economic decision models by using model averaging. J. R. Stat. Soc. A. Stat. Soc. 172, 383–404 (2009)

    Article  Google Scholar 

  • D.A. Jones, W. Wang, R. Fawcett, High-quality spatial climate data-sets for Australia. Aust. Meteorol. Oceanogr. J. 58, 233–248 (2009)

    Google Scholar 

  • F. Laio, S. Tamea, Verification tools for probabilistic forecasts of continuous hydrological variables. Hydrol. Earth Syst. Sci. 11, 1267–1277 (2007)

    Article  Google Scholar 

  • E.-P. Lim, H.H. Hendon, D.L.T. Anderson, A. Charles, O. Alves, Dynamical, statistical-dynamical, and multimodel ensemble forecasts of Australian spring season rainfall. Mon. Weather Rev. 139, 958–975 (2011)

    Article  Google Scholar 

  • L. Luo, E.F. Wood, M. Pan, Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions. J. Geophys. Res. 112, D10102 (2007)

    Article  Google Scholar 

  • A.G. Marshall, D. Hudson, M.C. Wheeler, H.H. Hendon, O. Alves, Evaluating key drivers of Australian intra-seasonal climate variability in POAMA-2: a progress report. CAWCR Res. Lett. 7, 10–16 (2012)

    Google Scholar 

  • J.E. Matheson, R.L. Winkler, Scoring rules for continuous probability distributions. Manag. Sci. 22, 1087–1096 (1976)

    Article  Google Scholar 

  • J.S. Risbey, M.J. Pook, P.C. McIntosh, M.C. Wheeler, H.H. Hendon, On the remote drivers of rainfall variability in Australia. Mon. Weather Rev. 137, 3233–3253 (2009)

    Article  Google Scholar 

  • D. Robertson, D. Shrestha, Q. Wang, Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting. Hydrol. Earth Syst. Sci. Discuss. 10, 6765–6806 (2013)

    Article  Google Scholar 

  • R.T. Rust, D.C. Schmittlein, A Bayesian cross-validated likelihood method for comparing alternative specifications of quantitative models. Market. Sci. 4(1), 20–40 (1985)

    Google Scholar 

  • N.H. Saji, B.N. Goswami, P.N. Vinayachandran, T. Yamagata, A dipole mode in the tropical Indian Ocean. Nature 401, 360–363 (1999)

    Google Scholar 

  • A. Schepen, Q.J. Wang, Ensemble forecasts of monthly catchment rainfall out to long lead times by post-processing coupled general circulation model output. J. Hydrol. 519, 2920–2931 (2014)

    Google Scholar 

  • A. Schepen, Q.J. Wang, Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia. Water Resour. Res. 51(3), 1797–1812 (2015)

    Google Scholar 

  • A. Schepen, Q.J. Wang, D. Robertson, Evidence for using lagged climate indices to forecast Australian seasonal rainfall. J. Climate 25, 1230–1246 (2012)

    Article  Google Scholar 

  • Q. Shao, M. Li, An improved statistical analogue downscaling procedure for seasonal precipitation forecast. Stoch. Env. Res. Risk Assess. 27(4), 819–830 (2013)

    Google Scholar 

  • T. Shinozaki, S. Furui, T. Kawahara, Gaussian mixture optimization based on efficient cross-validation. IEEE J. Sel. Top. Sign. Process 4, 540–547 (2010)

    Article  Google Scholar 

  • P. Smyth, Clustering using Monte Carlo cross-validation. KDD, 126–133 (1996). http://www.aaai.org/Papers/KDD/1996/KDD96-021.pdf

  • P. Smyth, Model selection for probabilistic clustering using cross-validated likelihood. Stat. Comput. 10(1), 63–72 (2000)

    Article  Google Scholar 

  • M. Stone, An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion. J. R. Stat. Soc. Ser. B Methodol. 44–47 (1977)

    Google Scholar 

  • M. Thyer, G. Kuczera, Q.J. Wang, Quantifying parameter uncertainty in stochastic models using the Box-Cox transformation. J. Hydrol. 265, 246–257 (2002)

    Article  Google Scholar 

  • M. Thyer, B. Renard, D. Kavetski, G. Kuczera, S.W. Franks, S. Srikanthan, Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: a case study using Bayesian total error analysis. Water Resour. Res. 45, W00B14 (2009)

    Article  Google Scholar 

  • B. Timbal, D. Jones, Future projections of winter rainfall in southeast Australia using a statistical downscaling technique. Clim. Change 86, 165–187 (2008)

    Article  Google Scholar 

  • N.K. Tuteja, D. Shin, R. Laugesen, U. Khan, Q. Shao, E. Wang, M. Li et al., Experimental evaluation of the dynamic seasonal streamflow forecasting approach (2012). Report published by the Bureau of Meteorology, Melbourne. Available online http://www.bom.gov.au/water/about/publications/document/dynamic_seasonal_streamflow_forecasting.pdf

  • D.C. Verdon, S.W. Franks, Indian Ocean sea surface temperature variability and winter rainfall: Eastern Australia. Water Resour. Res. 41 W09413 (2005). doi:10.1029/2004WR003845.

    Google Scholar 

  • Q.J. Wang, D.E. Robertson, Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences. Water Resour. Res. 47, W02546 (2011)

    Google Scholar 

  • Q.J. Wang, D.E. Robertson, F.H.S. Chiew, A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour. Res. 45 W05407 (2009), doi:10.1029/2008WR007355

  • G. Wang et al., POAMA-2 SST skill assessment and beyond. CAWCR Res. Lett. 6, 40–46 (2011a)

    Google Scholar 

  • Q. Wang, T. Pagano, S. Zhou, H. Hapuarachchi, L. Zhang, D. Robertson, Monthly versus daily water balance models in simulating monthly runoff. J. Hydrol. 404, 166–175 (2011b)

    Article  Google Scholar 

  • Q. Wang, D. Shrestha, D. Robertson, P. Pokhrel, A log-sinh transformation for data normalization and variance stabilization. Water Resour. Res. 48, W05514 (2012a)

    Google Scholar 

  • Q.J. Wang, A. Schepen, D.E. Robertson, Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging. J. Climate 25, 5524–5537 (2012b)

    Article  Google Scholar 

  • I.K. Yeo, R.A. Johnson, A new family of power transformations to improve normality or symmetry. Biometrika 87, 954–959 (2000)

    Article  Google Scholar 

  • D. Zhu, A.O. Hero, Bayesian hierarchical model for large-scale covariance matrix estimation. J. Comput. Biol. 14, 1311–1326 (2007)

    Article  Google Scholar 

  • Z. Zivkovic, F. van der Heijden, Recursive unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 26, 651–656 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSIRO Land and Water, 56, 3190, Highett, VIC, Australia

    Q. J. Wang

  2. CSIRO Land and Water, Dutton Park, Queensland, Australia

    Andrew Schepen

  3. CSIRO Land and Water, Clayton, Victoria, Australia

    David E. Robertson

Authors
  1. Andrew Schepen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Q. J. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David E. Robertson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Q. J. Wang .

Editor information

Editors and Affiliations

  1. Beijing Normal University, Beijing, China

    Qingyun Duan

  2. European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

    Florian Pappenberger

  3. European Commission DG Joint Research Ce, Ispra, Italy

    Jutta Thielen

  4. National Center for Atmospheric Research, Boulder, Colorado, USA

    Andy Wood

  5. University of Reading, Reading, Berkshire, United Kingdom

    Hannah L. Cloke

  6. Consulting Hydrologist, Annapolis, USA

    John C. Schaake

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this entry

Cite this entry

Schepen, A., Wang, Q.J., Robertson, D.E. (2016). Application to Post-processing of Meteorological Seasonal Forecasting. In: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40457-3_18-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40457-3_18-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Online ISBN: 978-3-642-40457-3

  • eBook Packages: Springer Reference Earth and Environm. ScienceReference Module Physical and Materials ScienceReference Module Earth and Environmental Sciences

Publish with us

Policies and ethics

Search

Navigation