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You will start by studying the evolution of the ECMWF analyses and forecasts for this event. You will then run your own OpenIFS forecast for a single ensemble member at lower resolutions and work in groups to study the OpenIFS ensemble forecasts.
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In practise many cases are aggregated in order to evaluate the forecast behaviour of the ensemble. However, it is always useful to complement such assessments with case studies of extreme events, like the one in this exercise, to get a more complete picture of IFS performance and identify weaker aspects that need further exploration. |
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To save images during these exercises for discussion later, you can use:
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In practise many cases are aggregated in order to evaluate the forecast behaviour of the ensemble. However, it is always useful to complement such assessments with case studies of extreme events, like the one in this exercise, to get a more complete picture of IFS performance and identify weaker aspects that need further exploration. |
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The case study will look at one of several severe wind-storms that hit Europe in late 2013 (see handout of ECMWF article by Hewson et al, ECMWF Newsletter 139).
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ECMWF operational forecasts consist of:
- HRES : T1279 (16km grid) highest resolution 10 day forecast
- ENS : Ensemble (50 members), T639 (34km grid) resolution is run for days 1-10 of the forecast, T319 (70km) is run for days 11-15.
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We suggest these exercises are best done by small groups working in teams. Suggestions are made in the exercises for how each team can work on different data. |
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The ECMWF operational forecast is called HRES. The model runs at a spectral resolution of T1279, equivalent to 16km grid spacing. Only a single forecast is run at this resolution as the computational resources required are demanding. The ensemble forecasts are run at a lower resolution. Before looking at the ensemble forecasts, first understand the performance of the HRES forecast. |
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For this task, use the metview icons in the row labelled 'Oper forecast'
oper_rmse.mv : this plots the root-mean-square-error growth curves for the operational HRES forecast for the different lead times.
oper_to_an_runs.mv mv : this plots the same parameter from the different forecasts for the same verifying time. Use this to understand how the forecasts differed, particularly for the later forecasts closer to the event. oper_to_an_diff.mv mv : this plots a single parameter as a difference between the operational HIRES forecast and the ECMWF analysis. Use this to understand the forecast errors from the different lead times.
Parameters & map appearance. These macros have the same choice of parameters to plot and same choice of |
Use the metview macros to plot different days and compare to analysis and plot forecast differences.
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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 WGUST10 (wind gust at 10m) 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. A starting point is to 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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Task 2 : Visualize the ensemble forecasts and ensemble spread
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