Comparison of Some Statistical Methods of Probabilistic Forecasting of ENSO.

By Simon Mason, Gillian M. Mimmack. 2002

103 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/7083bd767951001303341f0468bb90d264a2e88d

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

1. L. Goddard
2. A. Barnston
3. Andrew Robertson
4. Michael Ghil
5. Yuqiong Liu
6. H. Seim
7. Gregory Dusek
8. Jiang Zhu

In Global and Planetary Change

1. L. Goddard
2. Michael Ghil
3. Yuqiong Liu
4. A. Barnston
5. D. Kondrashov
6. Andrew Robertson
7. Albrecht H. Weerts
8. H. Seim

Citations in the corpus

• Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, methodology): “Statistically-based approaches such as linear regression (Krishnamurti et al., 1999) and canonical variate analysis (Mason and Mimmack, 2002) can also be employed to improve the predictive skill of multi-model ensembles.”
• A Probabilistic Rip Current Forecast Model (2013, methodology): “This probabilistic output makes logistic regression a good model for weather-related forecasts (Leroy and Wheeler; 2008; Lo et al., 2007; Mason and Mimmack, 2002) and lends itself to hazardous rip current prediction.”
• A Hierarchy of Data-Based ENSO Models (2005, background): “Ghil and Jiang (1998) and Mason and Mimmack (2001) reviewed a variety of statistical models that possess useful ENSO forecast skill; most of these models are still linear.”
• 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at IRI (2003, methodology): “One is Bayesian in nature (Rajagopalan et al. 2002; A. W. Robertson et al. 2003, unpublished manuscript, hereafter RLZG), and the other is a canonical variate technique (Mason and Mimmack 2002).”
• Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model (2016, background): “These authors indicated that ENSO forecasts should be probabilistic and that ensemble forecasts must necessarily include some quantitative assessment of the model uncertainties (e.g., Mason and Mimmack 2002; Tippett and Barnston 2008).”