Representation and prediction of the Indian Ocean dipole in the POAMA seasonal forecast model

By Mei Zhao, H. Hendon. 2009

161 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/ec0578cd4aaa57afb2937841f533c0384fe4f860

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. M. Wheeler
2. O. Alves
3. Ravinesh C. Deo
4. Mehmet Sahin
5. H. Hendon
6. A. Marshall
7. H. Rashid
8. D. Hudson

In Global and Planetary Change

1. Ravinesh C. Deo
2. M. Wheeler
3. O. Alves
4. Mehmet Sahin
5. H. Hendon
6. Andrew Schepen
7. A. Marshall
8. H. Rashid

In Oceanography

1. Ravinesh C. Deo
2. Mehmet Sahin
3. M. Wheeler
4. O. Alves
5. H. Rashid
6. H. Hendon
7. A. Marshall
8. Kwok-Wing Chau

Citations in the corpus

• Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “Physically based models [6] were long used to predict hydrological events, such as storm [7,8], rainfall/runoff [9,10], shallow water condition [11], hydraulic models of flow [12,13], and further global circulation phenomena [14], including the coupled effects of atmosphere, ocean, and floods [15].”
• Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system (2011, methodology): “Details of the model components and the initialization and generation of the retrospective hindcasts are described in Alves et al. (2003), Wang et al. (2008), and Zhao and Hendon (2009) .”
• Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging (2012, background): “Skill arising from the Indian Ocean reaches the southeast region of Australia in SON and OND when the amplitude of the IOD tends to be at a maximum (Zhao and Hendon 2009).”
• Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia (2015, methodology): “DEP deviation of effective precipitation EDI Effective Drought Index EDIo observed Effective Drought Index EDIp predicted Effective Drought Index ELM extreme learning machine EP effective precipitation GCM global circulation model Hardlim hard limit IOD Indian Ocean Dipole JISAO Joint Institute of t”
• Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia (2011, background): “5 system for seasonal time-scales has been thoroughly assessed (e.g. Wang et al., 2008; Hendon et al., 2009; Lim et al., 2009; Spillman and Alves, 2009; Zhao and Hendon, 2009; Hudson et al., 2011).”