Forecast calibration and combination: A simple Bayesian approach for ENSO

111 citations recorded by the source corpus.

How researchers see this paper

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

A comparison of two statistical estimators in inverse prediction (2012, background): “Inverse prediction has also found successful applications in atmospheric forecasting (Coelho et al., 2004), instrument calibration (Chen et al.”
Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging (2012, background): “Various studies have shown the advantage of merging forecasts from multiple models for climate forecasting, be they statistical or dynamical models (e.g., Casey 1995; Coelho et al. 2004; Luo et al. 2007; Stephenson et al. 2005).”
Comparison of ensemble‐MOS methods in the Lorenz '96 setting (2006, methodology): “Coelho et al. (2004) define the likelihood distributions by inverting a weighted regression, although that method yielded less accurate forecasts for the data considered here.”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, methodology): “The combination of single-model predictions to create a multi-model forecast by fitting different weights has already been addressed in long-range forecasting (Fraedrich and Smith, 1989; Sarda et al., 1996; Krishnamurti et al. 1999, 2000; Pavan and Doblas-Reyes 2000; Derome et al., 2001; Kharin and ”