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Exercise 1. Visualise the ECMWF analysis
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Handout
See map of observations of wind-gust during the storm and the timeseries of maximum gusts.
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For this task, use the metview icons in the row labelled 'Analysis' |
Getting started
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Task 1: Mean-sea-level pressure & wind gustRight-click the mouse button on the 'an_1x1.mv' icon and select the 'Visualise' menu item (see figure right) After a few seconds, this will generate a map showing two parameters: mean-sea-level pressure (MSLP) and 3hrly max wind-gust at 10m (wgust10). Use the play button to animate the map and follow the development and track of the storm. You can use the 'Speed' menu to change the animation speed (each frame is every 3 hours). Task 2: Geographical regionRight-click the mouse button on the 'an_1x1.mv' icon and select the 'Edit' menu item (see figure right). An edit window appears that shows a number of lines of 'Metview macro' code. During these exercises you can change some of these to alter the parameters and plot types.
Change, Animate the storm on this smaller geographical map. Task 3: Plot wind fieldsChange the fields plotted to include the wind arrows. Make sure you have the Edit window showing.
As above, click the play button and then animate the map that appears. You might also want to change the mapType back to 'mapType=1' to show the larger geographical area. Discuss the storm development and behaviour with your colleagues & team members.
That completes the first exercise. If time
If you prefer to see multiple plots per page rather than overlay them, please use the |
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How to plot in various layouts
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Exercise 2: Visualise operational HRES forecast
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Recap
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The ECMWF operational deterministic 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 operational HRES forecast. |
Available forecast dates
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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 dates: 24th, 25th, 26th and 27th. Some tasks will use all the lead times, others require only one. |
Available plot types
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For this exercise, you will Section | Column | use the metview icons in the row labelled 'Oper forecast' as shown above. 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 : 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 : 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 Column | |
Key questions
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Getting started
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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
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Task ??. CDF/RMSE at different locations
Recap
TO DO: RMSE & CDF (concepts need explanation)
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