LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Batté, L.; Déqué, M. (2011)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:
ENSEMBLES stream 2 single-model and multimodel ensemble seasonal precipitation forecasts are evaluated over the African continent with respect to Global Precipitation Climatology Centre (GPCC) precipitation data for the 1960–2005 time period using deterministic and probabilistic skill scores. Focus is set on three regions of Africa during main precipitation seasons: West Africa during boreal summer, southern Africa during austral summer and the Greater Horn of Africa during the ‘long rains’ and ‘short rains’ transition seasons. The 45-member multimodel improves the ensemble spread-skill ratio over all regions, which translates into enhanced skill in terms of anomaly correlation and ranked probability skill scores for climatological precipitation deciles over West Africa and southern Africa. Results are contrasted depending on the region and probabilistic formulations of the ensemble predictions after a quantile–quantile calibration give valuable information essentially over areas where deterministic skill is found. Probabilistic skill scores illustrate the range of possibilities formore user-related applications of ensemble seasonal forecasts. A simple illustration using a cost-loss model shows that model potential economic values can reach over 10% depending on the regions and occurrences studied.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Berner, J., Doblas-Reyes, F., Palmer, T., Shutts, G. and Weisheimer, A. 2008. Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model. Philos. Trans. R. Soc. London, Ser. A 366(1875), 2559-2577. doi:10.1098/rsta.2008.0033.
    • Bouali, L., Philippon, N., Fontaine, B. and Lemond, J. 2008. Performance of DEMETER calibration for rainfall forecasting purposes: application to the July - August Sahelian rainfall. J. Geophys. Res. 113(D15),doi:10.1029/2007JD009403.
    • Bowden, J. and Semazzi, F. 2007. Empirical analysis of intraseasonal climate variability over the Greater Horn of Africa. J. Clim. 20(23), 5715-5731.
    • Brier, G. 1950. Verification of forecasts expressed in terms of probability. Mon. Wea. Rev. 78, 1-3.
    • Camberlin, P., Janicot, S. and Poccard, I. 2001. Seasonality and atmospheric dynamics of the teleconnection between African rainfall and tropical sea-surface temperature: Atlantic VS. ENSO. Int. J. Climatol. 21(8), 973-1005.
    • Camberlin, P. and Philippon, N. 2002. The East-African March-May rainy season: Associated atmospheric dynamics and predictability over the 1968-1997 period. J. Clim. 15(9), 1002-1019.
    • Collins, M., Booth, B., Harris, G., Murphy, J., Sexton, D. and co-authors. 2006. Towards quantifying uncertainty in transient climate change. Clim. Dyn. 27, 127-147, doi:10.1007/s00382-006-0121-0.
    • De´que´, M. and Royer, J.-F. 1992. The skill of extended-range extratopical winter dynamical forecasts. J. Clim. 5, 1346-1356.
    • Doblas-Reyes, F., Weisheimer, A., De´que´, M., Keenlyside, N., MacVean, M. and co-authors. 2009. Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts. Q. J. R. Meteorol. Soc. 135, 1538-1559, doi:10.1002/qj.464.
    • Epstein, E. 1969. A scoring system for probability forecasts of ranked categories. J. Appl. Meteorol. 8, 985-987.
    • Garric, G., Douville, H. and De´que´, M. 2002. Prospects for improved seasonal predictions of monsoon precipitation over Sahel. Int. J. Climatol. 22, 331-345, doi:10.1002/joc.736.
    • Giannini, A., Saravanan, R. and Chang, P. 2005. Dynamics of the boreal summer African monsoon in the NSIPP1 atmospheric model. Clim. Dyn. 25, 517-535. doi:10.1007/s00382-005-0056-x.
    • Gue´re´my, J.-F., De´que´, M., Braun, A. and Piedelie`vre, J.-P. 2005. Actual and potential skill of seasonal predictions using the CNRM contribution to DEMETER: coupled versus uncoupled model. Tellus 57A, 308-319.
    • Murphy, A. 1973. A new vector partition of the probability score. J. Appl. Meteorol. 12, 595-600.
    • Palmer, T. 2002. The economic value of ensemble forecasts as a tool for risk assessment : from days to decades. Q. J. R. Meteorolog. Soc. 128, 747-774.
    • Palmer, T., Alessandri, A., Andersen, U., Cantelaube, P., Davey, M. and co-authors. 2004. Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull. Am. Meteorol. Soc. 85, 853-872.
    • Philippon, N., Doblas-Reyes, F. and Ruti, P. 2010. Skill, reproducibility and potential predictability of the West African monsoon in coupled GCMs. Clim. Dyn. 35, 53-74, doi:10.1007/s00382-010- 0856-5.
    • Richardson, D. 2003. Economic value and skill. In: Forecast Verification: A Practitioner's Guide in Atmospheric Science, (eds. I. Joliffe and D. Stephenson), John Wiley & Sons Ltd, Chichester, UK, pp. 165-187.
    • Rouault, M. and Richard, Y. 2005. Intensity and spatial extent of droughts in southern Africa. Geophys. Res. Lett. 32, doi:10.1029/2005GL022436.
    • Schneider, U., Fuchs, T., Meyer-Christoffer, A. and Rudolf, B. 2008. Global precipitation analysis products of the GPCC. Global Precipitation Climatology Centre (GPCC) Internet Publication, Offenbach, Germany, pp. 1-12.
    • Shukla, J. 1998. Predictability in the midst of chaos: a scientific basis for climate forecasting. Science 282, 728-731, doi:10.1126/science.282.5389.728.
    • Sultan, B., Janicot, S. and Diedhiou, A. 2003. The West African Monsoon dynamics. Part I: documentation of intraseasonal variability. J. Clim. 16(21), 3389-3406.
    • Toth, Z., Talagrand, O., Candille, G. and Zhu, Y. 2003. Probability and ensemble forecasts. In: Forecast Verification, A Practitioner's Guide in Atmospheric Science, (eds. I. Joliffe and D. Stephenson), John Wiley & Sons Ltd, Chichester, UK, pp. 137-163.
    • Uppala, S. M., Kallberg, P., Simmons, A., Andrae, U., Da Costa Bechtold, V. and co-authors. 2005. The ERA-40 reanalysis. Q. J. R. Meteorol. Soc. 131, 2961-3012, doi:10.1256/qj.04.176.
    • Vizy, E. and Cook, K. 2001. Mechanisms by which Gulf of Guinea and Eastern North Atlantic sea surface temperature anomalies can influence African Rainfall. J. Clim. 14(5), 795-821.
    • Wang, B., Lee, J.-Y., Kang, I.-S., Shukla, J., Park, C.-K. and co-authors. 2009. Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004). Clim. Dyn. 33, 93-117, doi:10.1007/s00382-008- 0460-0.
    • Weisheimer, A., Doblas-Reyes, F., Palmer, T., Alessandri, A., Arribas, A. and co-authors. 2009. ENSEMBLES: a new multi-model ensemble for seasonal-to-annual predictions-skill and progress beyond DEMETER in forecasting tropical Pacific SSTs. Geophys. Res. Lett. 36, doi:10.1029/2009GL040896.
    • Wilks, D. 2006. Statistical Methods in the Atmospheric Sciences. In: Chapter 7: Forecast Verification, 2nd Edition, Academic Press, London, UK, pp. 255-335.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from