Categorical Climate Forecasts through Regularization and Optimal Combination of Multiple GCM Ensembles

By Balaji Rajagopalan, Upmanu Lall, S. Zebiak. 2002

182 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/147792ebe5e94d85ad95f2fd296935b09c1d9db7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept (2005, methodology): “However, more complex methods of optimally combining the single-model output have been described (Krishnamurti et al., 1999; Pavan and Doblas-Reyes, 2000; Rajagopalan et al., 2002).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, methodology): “Some efforts in this direction have been described in Rajagopalan et al. (2002) and Stefanova and Krishnamurti (2002), while the method described in Coelho et al. (2004) provides an estimate of the uncertainty around ensemblemean predictions that can be used to estimated probabilities.”
Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, background): “For that reason, this approach appears to have wider applicability in probabilistic forecasting than deterministic regression-based averaging (Rajagopalan et al., 2002).”
Skill of Multimodel ENSO Probability Forecasts (2008, methodology): “Similarly, in MM-bow weights for the ensemble probabilities of each model and the climatological probabilities are found to optimize the log-likelihood of the observations, which is proportional to the average ignorance (Rajagopalan et al. 2002).”
Estimation of Seasonal Precipitation Tercile-Based Categorical Probabilities from Ensembles (2007, methodology): “To see if reducing sampling error still has a noticeable impact after calibration, we use a simple version of Bayesian weighting (Rajagopalan et al. 2002; Robertson et al. 2004).”
Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction (2004, methodology): “scheme’s skill is compared against the individual model ensembles (with 9‐24 members), simple pooled ensembles of three and six models, as well as the original version of the Bayesian weighting scheme devised by Rajagopalan et al. (2002) .”
Multimodel Ensembling in Seasonal Climate Forecasting at IRI (2003, methodology): “One is Bayesian in nature (Rajagopalan et al. 2002; A. W. Robertson et al. 2003, unpublished manuscript, hereafter RLZG), and the other is a canonical variate technique (Mason and Mimmack 2002).”