Open Access
Issue |
Climatologie
Volume 19, 2022
|
|
---|---|---|
Article Number | 2 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/climat/202219002 | |
Published online | 31 January 2023 |
- Batté L., 2013. Prévision d’ensemble à l’échelle saisonnière : mise en place d’une dynamique stochastique. Thèse de doctorat, Université Paris-Est. [Google Scholar]
- Bjerknes J., 1969. Atmospheric teleconnections from the Equatorial Pacific. Monthly Weather Review, 97(3), 163–172, doi: 10.1175/1520-0493(1969)097<0163:atftep>2.3.co;2. [CrossRef] [Google Scholar]
- Beck H. E. et al., 2017. MSWEP: 3-hourly 0.25◦ global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrology and Earth System Sciences, 21(1), 589–615, doi: 10.5194/hess-21-589-2017. [CrossRef] [Google Scholar]
- Cowan T. et al., 2019. Forecasting the extreme rainfall, low temperatures, and strong winds associated with the northern Queensland floods of February 2019. Weather and Climate Extremes, 26, 100232, doi: 10.1016/j.wace.2019.100232. [CrossRef] [Google Scholar]
- de Andrade F. M. et al., 2019. Global precipitation hindcast quality assessment of the Subseasonal-to-Seasonal (S2S) prediction project models. Climate Dynamics, 52, 5451–5475. doi: 10.1007/s00382-018-4457-z. [CrossRef] [Google Scholar]
- Hagedorn R. et al., 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concept. Tellus A: Dynamic Meteorology and Oceanography, 57(3), 219–233, doi: 10.1111/j.1600-0870.2005.00103.x. [Google Scholar]
- Hogan R. J. et Mason I. B., 2012. Deterministic forecasts of binary events. In Forecast Verification, 31–59, John Wiley & Sons Ltd, doi: 10.1002/9781119960003.ch3. [Google Scholar]
- Kalnay E. et Dalcher A., 1987. Forecasting forecast skill. Monthly Weather Review, 115(2), 349–356, doi: 10.1175/1520-0493(1987)115<0349:ffs>2.0.co;2. [CrossRef] [Google Scholar]
- Mariotti A. et al., 2020. Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond. Bulletin of the American Meteorological Society, 101(5), E608–E625, doi: 10.1175/bams-d-18-0326.1. [CrossRef] [Google Scholar]
- McGree S. et al., 2014. An updated assessment of trends and variability in total and extreme rainfall in the western Pacific. International Journal of Climatology, 34(8), 2775–2791, doi: 10.1002/joc.3874. [CrossRef] [Google Scholar]
- Merryfield W. J. et al., 2020. Current and emerging developments in subseasonal to decadal prediction. Bulletin of the American Meteorological Society, 101(6), E869–E896, doi: 10.1175/BAMS-D-19-0037.1. [CrossRef] [Google Scholar]
- Moron V. et Robertson A. W., 2020. Tropical rainfall subseasonal-to-seasonal predictability types. npj Climate and Atmospheric. Science, 3(1), 1–8, doi: 10.1038/s41612-020-0107-3. [CrossRef] [Google Scholar]
- Pariyar S. K. et al., 2020. Factors affecting extreme rainfall events in the South Pacific. Weather and Climate Extremes, 29, 100262, doi: 10.1016/j.wace.2020.100262. [CrossRef] [Google Scholar]
- Specq D. et al., 2020. Multimodel forecasting of precipitation at subseasonal timescales over the southwest tropical Pacific. Earth and Space Science, 7(9), doi:10.1029/2019EA001003. [CrossRef] [Google Scholar]
- Specq D. et Batté L., 2020. Improving subseasonal precipitation forecasts through a statistical-dynamical approach: application to the southwest tropical Pacific. Climate Dynamics, 55, 1913–1927, doi: 10.1007/s00382-020-05355-7. [CrossRef] [Google Scholar]
- Vitart F. et al., 2017. The Subseasonal to Seasonal (S2S) prediction project database. Bulletin of the American Meteorological Society, 98(1), 163–173, doi: 10.1175/bams-d-16-0017.1. [CrossRef] [Google Scholar]
- Vitart F., 2017. Madden-Julian Oscillation prediction and teleconnections in the S2S database. Quarterly Journal of the Royal Meteorological Society, 143(706), 2210–2220, doi: 10.1002/qj.3079. [CrossRef] [Google Scholar]
- White C. J. et al., 2017. Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteorological Applications, 24(3), 315–325, doi: 10.1002/met.1654. [CrossRef] [Google Scholar]
- Zhang C., 2005. Madden-Julian Oscillation. Reviews of Geophysics, 43(2), doi: 10.1029/2004RG000158. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.