Articles | Volume 20, issue 1
https://doi.org/10.5194/nhess-20-107-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-20-107-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2020
Research article |
| 14 Jan 2020
| 14 Jan 2020
Improving sub-seasonal forecast skill of meteorological drought: a weather pattern approach
CSIRO Oceans and Atmosphere, Hobart, 7001, Australia
School of Engineering, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, UK
Hayley J. Fowler
School of Engineering, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, UK
Christopher G. Kilsby
School of Engineering, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, UK
Robert Neal
Weather Science, Met Office, Exeter, EX1 3PB, UK
Rutger Dankers
Weather Science, Met Office, Exeter, EX1 3PB, UK
Wageningen Environmental Research, Wageningen University & Research, Wageningen, 6708 PB, the Netherlands
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Cited
15 citations as recorded by crossref.
- Forecasting extreme precipitation in the central Mediterranean: Changes in predictors' strength with prediction lead time N. Mastrantonas et al. 10.1002/met.2101
- Weather regimes and patterns associated with temperature-related excess mortality in the UK: a pathway to sub-seasonal risk forecasting W. Huang et al. 10.1088/1748-9326/abcbba
- What do large‐scale patterns teach us about extreme precipitation over the Mediterranean at medium‐ and extended‐range forecasts? N. Mastrantonas et al. 10.1002/qj.4236
- Preface: Recent advances in drought and water scarcity monitoring, modelling, and forecasting B. Bonaccorso et al. 10.5194/nhess-22-1857-2022
- Catchment memory explains hydrological drought forecast performance S. Sutanto & H. Van Lanen 10.1038/s41598-022-06553-5
- Seasonal predictability of Mediterranean weather regimes in the Copernicus C3S systems I. Giuntoli et al. 10.1007/s00382-021-05681-4
- Dynamic-LSTM hybrid models to improve seasonal drought predictions over China Z. Wu et al. 10.1016/j.jhydrol.2022.128706
- Forecasting seasonal to sub-seasonal rainfall in Great Britain using convolutional-neural networks A. Barnes et al. 10.1007/s00704-022-04242-x
- Generating weather pattern definitions over South Africa suitable for future use in impact‐orientated medium‐range forecasting L. Ireland et al. 10.1002/joc.8396
- A Markov chain approach to the predictability of surface temperature over the northeastern part of India S. Ray et al. 10.1007/s00704-020-03458-z
- The application of predefined weather patterns over India within probabilistic medium‐range forecasting tools for high‐impact weather R. Neal et al. 10.1002/met.2083
- Added value of seasonal hindcasts to create UK hydrological drought storylines W. Chan et al. 10.5194/nhess-24-1065-2024
- Seasonal climate forecast can inform the European agricultural sector well in advance of harvesting A. Ceglar & A. Toreti 10.1038/s41612-021-00198-3
- Video-Based Convolutional Neural Networks Forecasting for Rainfall Forecasting A. Barnes et al. 10.1109/LGRS.2022.3167456
- Climate influence on compound solar and wind droughts in Australia D. Richardson et al. 10.1038/s41612-023-00507-y
15 citations as recorded by crossref.
- Forecasting extreme precipitation in the central Mediterranean: Changes in predictors' strength with prediction lead time N. Mastrantonas et al. 10.1002/met.2101
- Weather regimes and patterns associated with temperature-related excess mortality in the UK: a pathway to sub-seasonal risk forecasting W. Huang et al. 10.1088/1748-9326/abcbba
- What do large‐scale patterns teach us about extreme precipitation over the Mediterranean at medium‐ and extended‐range forecasts? N. Mastrantonas et al. 10.1002/qj.4236
- Preface: Recent advances in drought and water scarcity monitoring, modelling, and forecasting B. Bonaccorso et al. 10.5194/nhess-22-1857-2022
- Catchment memory explains hydrological drought forecast performance S. Sutanto & H. Van Lanen 10.1038/s41598-022-06553-5
- Seasonal predictability of Mediterranean weather regimes in the Copernicus C3S systems I. Giuntoli et al. 10.1007/s00382-021-05681-4
- Dynamic-LSTM hybrid models to improve seasonal drought predictions over China Z. Wu et al. 10.1016/j.jhydrol.2022.128706
- Forecasting seasonal to sub-seasonal rainfall in Great Britain using convolutional-neural networks A. Barnes et al. 10.1007/s00704-022-04242-x
- Generating weather pattern definitions over South Africa suitable for future use in impact‐orientated medium‐range forecasting L. Ireland et al. 10.1002/joc.8396
- A Markov chain approach to the predictability of surface temperature over the northeastern part of India S. Ray et al. 10.1007/s00704-020-03458-z
- The application of predefined weather patterns over India within probabilistic medium‐range forecasting tools for high‐impact weather R. Neal et al. 10.1002/met.2083
- Added value of seasonal hindcasts to create UK hydrological drought storylines W. Chan et al. 10.5194/nhess-24-1065-2024
- Seasonal climate forecast can inform the European agricultural sector well in advance of harvesting A. Ceglar & A. Toreti 10.1038/s41612-021-00198-3
- Video-Based Convolutional Neural Networks Forecasting for Rainfall Forecasting A. Barnes et al. 10.1109/LGRS.2022.3167456
- Climate influence on compound solar and wind droughts in Australia D. Richardson et al. 10.1038/s41612-023-00507-y
Latest update: 25 Apr 2024
Short summary
Models are not particularly skilful at forecasting rainfall more than 15 d in advance. However, they are often better at predicting atmospheric variables such as mean sea-level pressure (MSLP). Comparing a range of models, we show that UK winter and autumn rainfall and drought prediction skill can be improved by utilising forecasts of MSLP-based weather patterns (WPs) and subsequently estimating rainfall using the historical WP–precipitation relationships.
Models are not particularly skilful at forecasting rainfall more than 15 d in advance. However,...
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