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Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs
 

Summary: Improved categorical winter precipitation forecasts
through multimodel combinations of coupled GCMs
Naresh Devineni1
and A. Sankarasubramanian2
Received 4 August 2010; revised 28 September 2010; accepted 4 October 2010; published 22 December 2010.
[1] A new approach to combine precipitation forecasts from
multiple models is evaluated by analyzing the skill of the
candidate models contingent on the forecasted predictor(s)
state. Using five leading coupled GCMs (CGCMs) from the
ENSEMBLES project, we develop multimodel precipitation
forecasts over the continental United States (U.S) by
considering the forecasted Nino3.4 from each CGCM as the
conditioning variable. The performance of multimodel
forecasts is compared with individual models based on rank
probability skill score and reliability diagram. The study
clearly shows that multimodel forecasts perform better
than individual models and among all multimodels,
multimodel combination conditional on Nino3.4 perform
better with more grid points having the highest rank
probability skill score. The proposed algorithm also

  

Source: Arumugam, Sankar - Department of Civil, Construction, and Environmental Engineering, North Carolina State University

 

Collections: Environmental Sciences and Ecology; Engineering