Abstract
Models participating in the North American Multi Model Ensemble project were calibrated and combined to produce reliable precipitation probabilistic forecast over South America. Ensemble Regression method (EREG) was chosen as it is computationally affordable and uses all the information from the ensemble. Two different approaches based on EREG were applied to combine forecasts while different ways to weight the relative contribution of each model to the ensemble were used. All the consolidated forecast obtained were confronted against the simple multi-model ensemble. This work assessed the performance of the predictions initialized in November to forecast the austral summer (December–January–February) for the period 1982–2010 using different probabilistic measures. Results show that the consolidated forecasts produce more skillful forecast than the simple multi-model ensemble, although no major differences were found between the combination and weighting approaches considered. The regions that presented better results are well-known to be impacted by El Niño Southern Oscillation.
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Acknowledgements
The research was supported by UBACyT20020170100428BA, PDE_46_2019 and the CLIMAX Project funded by Belmont Forum/ANR-15-JCL/-0002-01. We acknowledge the agencies that support the NMME-Phase II system, and we thank the climate modeling groups (Environment Canada, NASA, NCAR, NOAA/GFDL, NOAA/NCEP, and University of Miami) for producing and making available their model output. NOAA/NCEP, NOAA/CTB, and NOAA/CPO jointly provided coordinating support and led development of the NMME-Phase II system. MO thanks Dan Collins from Climate Prediction Center for helpful discussions and suggestions throughout the investigation. CASC thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), process 305206/2019-2, and Fundação de Amparo à Pesquisa do Estado de S ao Paulo (FAPESP), process 2015/50687-8 (CLIMAX Project) for the support received.
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Osman, M., Coelho, C.A.S. & Vera, C.S. Calibration and combination of seasonal precipitation forecasts over South America using Ensemble Regression. Clim Dyn 57, 2889–2904 (2021). https://doi.org/10.1007/s00382-021-05845-2
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DOI: https://doi.org/10.1007/s00382-021-05845-2