Forecast Skill of the Madden-Julian Oscillation in Two Canadian Atmospheric Models

By Hai Lin, Gilbert Brunet, J. Derome. 2008

199 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d7d5106f67fa28963720631077a64d4676f3a18f

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. F. Vitart
2. Chidong Zhang
3. Duane Waliser
4. E. Maloney
5. Daehyun Kim
6. M. Wheeler
7. Hye-Mi Kim
8. Bin Wang

In Global and Planetary Change

1. F. Vitart
2. Chidong Zhang
3. Duane Waliser
4. E. Maloney
5. Daehyun Kim
6. M. Wheeler
7. Hye-Mi Kim
8. Bin Wang

Citations in the corpus

• Prediction of the Madden–Julian Oscillation: A Review (2018, background): “…forecast system, a common benchmark to measure the MJO performance is scalar metrics, such as the bivariate anomaly correlation coefficient (COR) or bivariate root-mean-square error (RMSE), which represents the skill as a function of forecast lead times (e.g., Lin et al. 2008; Rashid et al. 2011).”
• Predictability of the Madden–Julian Oscillation in the Intraseasonal Variability Hindcast Experiment (ISVHE)* (2014, methodology): “Lin et al. (2008) demonstrated such a dependence on the phase of the MJO at initiation in their estimates of prediction skill.”
• Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system (2011, methodology): “However, despite the relatively long timescale of the MJO, the upper limit of prediction skill for the MJO using global numerical weather prediction systems has been reported to be less than 2 weeks (Hendon et al. 2000; Vitart et al. 2007; Lin et al. 2008; Agudelo et al. 2009; Seo 2009).”
• Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment (2015, methodology): “The RMSE of ensemble-mean was obtained by the RMSE between the observed and ensemble-mean hindcast BSISO index (Lin et al. 2008).”
• MJO prediction in the NCEP Climate Forecast System version 2 (2014, background): “…MD 20740, USA e-mail: wanqiu.wang@noaa.gov X. Fu IPRC, SOEST, University of Hawaii at Manoa, Honolulu, HI, USA 2000; Jones et al. 2004; Jones and Schemm 2000; Lin and Brunet 2009; Lin et al. 2009), and high-latitude circulation (Zhou and Miller 2005; L’Heureux and Higgins 2008; Lin et al. 2009).”
• Cracking the MJO nut (2013, methodology): “[11] Madden-Julian oscillation prediction studies using operational dynamical models have increased in recent years [Savage and Milton, 2007; Vitart et al., 2007; Lin et al., 2008; Seo, 2009; Seo et al., 2009; Rashid et al. 2011].”
• Predictability and Prediction Skill of the MJO in Two Operational Forecasting Systems (2014, background): “The MJO prediction skill is distinctly better when the MJO is strong at the beginning of the forecast, irrespective of the phase, compared to those that are weak (Lin et al. 2008; Agudelo et al. 2009; Kang and Kim 2010; Rashid et al. 2011; Zhang and van den Dool 2012; Wang et al. 2014).”