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| dc.contributor | Barcelona Supercomputing Center |
|---|---|
| dc.contributor.author | Mishra, Niti |
| dc.contributor.author | Prodhomme, Chloé |
| dc.contributor.author | Guemas, Virginie |
| dc.date | 2018-08-31 |
| dc.identifier.citation | Mishra, N.; Prodhomme, C.; Guemas, V. Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe. "Climate Dynamics", 31 Agost 2018. |
| dc.identifier.citation | 0930-7575 |
| dc.identifier.citation | 10.1007/s00382-018-4404-z |
| dc.identifier.uri | http://hdl.handle.net/2117/122724 |
| dc.description.abstract | There is now a wide range of forecasts and observations of seasonal climatic conditions that can be used across a range of application sectors, including hydrological risk forecasting, planning and management. As we rely more on seasonal climate forecasts, it becomes essential to also assess its quality to ensure its intended use. In this study, we provide the most comprehensive assessment of seasonal temperature and precipitation ensemble forecasts of the EUROSIP multi-model forecasting system over Europe. The forecasts from the four individual climate models within the EUROSIP are assessed using both deterministic and probabilistic approaches. One equally and two unequally Weighted Multi-Models (WMMs) are also constructed from the individual models, for both climate variables, and their respective forecasts are also assessed. Consistent with existing literature, we find limited seasonal climate prediction skill over Europe. A simple equally WMM system performs better than both unequally WMM combination systems. However, the equally WMM system does not always outperform the single best model within the EUROSIP multi-model. Based on the results, it is recommended to assess seasonal temperature and precipitation forecast of individual climate models as well as their multi-model mean for a comprehensive overview of the forecast skill. |
| dc.description.abstract | The authors thank Prof. Francisco J. Doblas Reyes, Alicia Sanchez Lorente, Veronica Torralba and Louis-Philippe Caron for discussions and suggestions during the forming of this paper. Thanks to Nicolau Manubens, whose incredible technical support allowed steady implementation of the experiments in this study. Thanks are also due to the anonymous reviewers and their critical reviews. Their comments and suggestions improved the content of this paper. The research leading to these results has received funding from the EU H2020 Framework Programme under grant agreement #641811 (IMPREX). |
| dc.description.abstract | Peer Reviewed |
| dc.language.iso | eng |
| dc.publisher | Springer Verlag |
| dc.relation | https://link.springer.com/article/10.1007%2Fs00382-018-4404-z |
| dc.relation | info:eu-repo/grantAgreement/EC/H2020/641811/EU/IMproving PRedictions and management of hydrological EXtremes/IMPREX |
| dc.rights | info:eu-repo/semantics/openAccess |
| dc.subject | Àrees temàtiques de la UPC::Energies |
| dc.subject | Seasonal prediction (Meteorology) |
| dc.subject | Seasonal climate forecast |
| dc.subject | Probability Ensemble Forecast |
| dc.subject | Weighted Multi-Model |
| dc.subject | Forecast Verification |
| dc.subject | Previsió del temps |
| dc.title | Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe |
| dc.type | info:eu-repo/semantics/submittedVersion |
| dc.type | info:eu-repo/semantics/article |
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