The Subseasonal to Seasonal (S2S) Prediction Project Database
Vitart, F. ; Ardilouze, Constantin ; Bonet, A. ; Brookshaw, A. ; Chen, M. ; Codorean, C. ; Déqué, Michel ; Ferranti, L. ; Fucile, E. ; Fuentes, M. ; Hendon, H. ; Hodgson, J. ; Kang, H.-S. ; Kumar, A. ; Lin, H. ; Liu, G. ; Liu, X. ; Malguzzi, P. ; Mallas, I. ; Manoussakis, M. ; Mastrangelo, D. ; MacLachlan, C. ; McLean, P. ; Minami, A. ; Mladek, R. ; Nakazawa, T. ; Najm, S. ; Nie, Y. ; Rixen, M. ; Robertson, A. W. ; Ruti, P. ; Sun, C. ; Takaya, Y. ; Tolstykh, M. ; Venuti, F. ; Waliser, D. ; Woolnough, S. ; Wu, T. ; Won, D.-J. ; Xiao, H. ; Zaripov, R. ; Zhang, L.
Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days). The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.
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