The Subseasonal to Seasonal (S2S) Prediction Project Database

F. Vitart ECMWF, Reading, United Kingdom

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C. Ardilouze Météo-France/CNRM, Toulouse, France

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A. Bonet ECMWF, Reading, United Kingdom

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A. Brookshaw ECMWF, Reading, and Met Office Hadley Centre, Exeter, United Kingdom

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M. Chen NCEP, College Park, Maryland

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C. Codorean ECMWF, Reading, United Kingdom

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M. Déqué Météo-France/CNRM, Toulouse, France

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L. Ferranti ECMWF, Reading, United Kingdom

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E. Fucile ECMWF, Reading, United Kingdom

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M. Fuentes ECMWF, Reading, United Kingdom

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H. Hendon Bureau of Meteorology, Melbourne, Victoria, Australia

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J. Hodgson Environment and Climate Change Canada, Montreal, Quebec, Canada

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H.-S. Kang Korea Meteorological Agency, Seoul, South Korea

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A. Kumar NCEP, College Park, Maryland

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H. Lin Environment and Climate Change Canada, Montreal, Quebec, Canada

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G. Liu Bureau of Meteorology, Melbourne, Victoria, Australia

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X. Liu China Meteorological Administration, Beijing, China

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P. Malguzzi CNR-ISAC, Bologna, Italy

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I. Mallas ECMWF, Reading, United Kingdom

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M. Manoussakis ECMWF, Reading, United Kingdom

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D. Mastrangelo CNR-ISAC, Bologna, Italy

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C. MacLachlan Met Office Hadley Centre, Exeter, United Kingdom

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P. McLean Met Office Hadley Centre, Exeter, United Kingdom

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A. Minami Japan Meteorological Agency, Tokyo, Japan

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R. Mladek ECMWF, Reading, United Kingdom

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T. Nakazawa Korea Meteorological Agency, Seoul, South Korea

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S. Najm ECMWF, Reading, United Kingdom

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Y. Nie China Meteorological Administration, Beijing, China

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M. Rixen World Meteorological Organization, Geneva, Switzerland

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A. W. Robertson International Research Institute for Climate and Society, Columbia University, Palisades, New York

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P. Ruti World Meteorological Organization, Geneva, Switzerland

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C. Sun China Meteorological Administration, Beijing, China

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Y. Takaya Japan Meteorological Agency, Tokyo, Japan

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M. Tolstykh Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia

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F. Venuti ECMWF, Reading, United Kingdom

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D. Waliser Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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S. Woolnough National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, United Kingdom

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T. Wu China Meteorological Administration, Beijing, China

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D.-J. Won Korea Meteorological Agency, Seoul, South Korea

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H. Xiao China Meteorological Administration, Beijing, China

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R. Zaripov Hydrometeorological Research Center, Moscow, Russia

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L. Zhang China Meteorological Administration, Beijing, China

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Abstract

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.

CORRESPONDING AUTHOR E-MAIL: Frédéric Vitart, frederic.vitart@ecmwf.int

Abstract

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.

CORRESPONDING AUTHOR E-MAIL: Frédéric Vitart, frederic.vitart@ecmwf.int
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