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Data is provided for multiple forecasts starting from different dates, known as different lead times. Available lead times for the St Judes storm are forecasts starting from these October 2013 aredates: 24th, 25th, 26th and 27th. Some tasks will use all the lead times, others require only one. |
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Each team should look at the forecast from all 4 starting dates and each team member should see the RMSE curves. Start by looking at the RMS error curves for the 4 different starting dates using MSLP (mean-sea-level pressure) and wind parameters (wind gust at 10m: WGUST10 and wind-speed at 850hPa : SPEED850) and the two geographical regions. Use the As a team, discuss what plots & parameters to use to address the questions above given what you see in the error growth curves. Then look at the difference between forecast and analysis to understand the error in the forecast, particularly the starting formation and final error. Team members can look at particular dates and choose particular variables for team discussion. |
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Exercise 3 : Visualize the ensemble forecasts and ensemble spread
Recap
Key points
- Sources of forecast uncertainty: initial analysis and model error.
- Initial analysis uncertainty: sampled by use of Singular Vectors (SV) and Ensemble Data Assimilation (EDA).
- Model uncertainty: sampled by use of stochastic processes. In IFS this means Stochastically Perturbed Physical Tendencies (SPPT) and the spectral backscatter scheme (SKEB)
- Singular Vectors: a way of representing the fastest growing modes.
- Ensemble mean : this gives the average of all the ensemble members. Where the spread is high, small scale features can be smoothed out in the ensemble mean.
- Ensemble spread: this gives the standard deviation of the ensemble members and represents how different the members are from the ensemble mean
Objective: understand the forecast uncertainty
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Key questions:
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Note that as only 6hrly wind gust data is available from the operational forecasts, we have supplemented the 3hrly fields using forecast data.
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Task ??. CDF/RMSE at different locations
Recap
TO DO: RMSE & CDF (concepts need explanation)
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Group A: The Swedish weather centre is interested in giving out useful weather warnings. They want to know the maximum precipitation and wind gusts over Scandinavia and their likelihood (more than XX m/s wind and XX mm precipitation). Would you give out weather warnings and if yes for which days and areas (1 day lead time)? Group B: The organiser of a garden party at Windsor Castle at a particular date needs to prepare for weather. The Queen will join the party and she is not amused if there are too many tents that are not necessary. However, it will take some time to prepare shelters for rain and wind or to cancel the event (two days lead time). Would you prepare for rain? Group C: The operator of a wind farm in the northern see needs to know the highest possible wind speeds to shut down turbines (at XX m/s). If the turbines are shut down too early, power production will be reduced. If they are shut down too late, they will break (6 hours lead time). Would you shut down the wind farm? |
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Task ?? Creating an ensemble forecast using OpenIFS
See separate handout.
In this exercise, OpenIFS will be run on the ECMWF Cray XC30 to create a forecast for the storm at T319 resolution using only the stochastic schemes in the model. All forecasts are started from the same initial conditions based on the analysis.
Aim is to understand the impact of these different methods on the ensemble
Exercise 4.
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Exploring the role of uncertainty using OpenIFS forecasts
Experiments available:
- EDA+SV+SPPT+SKEB : Includes initial data and model uncertainty (experiment id : gbzl)
- EDA+SV only : Includes only initial data uncertainty (experiment id: gc11)
- SPPT+SKEB only : Includes model uncertainty only (run by participants, experiment id: oifs)
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Shutts et al, 2011, ECMWF Newsletter 129.
Acknowledgements
Many people have contributed to this exercise and their contributions are acknowledgedWe gratefully acknowledge the following for their contributions in preparing these exercises, in particular from ECMWF: Glenn Carver, Sandor Kertesz, Linus Magnusson, Sandor Kertesz, Martin Leutbecher, Iain Russell, Filip Vana, Erland Kallen. From University of Oxford: Aneesh Subramanian, Peter Dueben, Peter Watson, Hannah Christensen, Antje Weisheimer.
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