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.

 To save images during these exercises for discussion later, you can use:

or

ksnapshot

Printing. If hardcopy prints are desired please use the printer at the rear of the classroom and only print if necessary.

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).

• On the 28th October 2013 a small, severe wind-storm named St Jude in the UK, hit the UK & north-western Europe.
• A total of 19 people were killed across Europe, 5 in the UK.
• The return period of the event based on wind-gust observations show the 10yr return period was exceeded along the North Sea coast.

• From the 23rd October, the ECMWF forecast predicted a greater than 70% probability of a severe wind event (greater than 60kt, 31m/s, at 1km) over southern England. A signal for the storm was evident from the 21st October.
• On the 24th October, the UK MetOffice issued an amber alert for wind-speed across southern England placing the potential impact in the highest category.
  
  
• The cyclone first appeared as a cold front wave, over the Atlantic, late on 25th October.
• It deepened and moved rapidly east then northeast, with the storm centre reaching southern Sweden late afternoon on the 28th.
• The most rapid deepening occurred between 06-12UTC on the 28th when the strongest wind gusts were observed.

1. Examine the map of wind-gust observations in the handout.
   Note the observed area of strongest windgusts and their intensity.
2. How does the analyses compare with the observations?
3. Understand the storm development and behaviour from the ECMWF analyses.

metview

Please enter the folder 'openifs_2015' to begin working.

For this task, use the metview icons in the row labelled 'Analysis'

Task 1: Mean-sea-level pressure & wind gust

Right-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 region

Right-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, mapType=0   to   mapType=1   then click the play button at the top of the window (please ask if you are not sure).

Animate the storm on this smaller geographical map.

Task 3: Plot wind fields

Change the fields plotted to include the wind arrows.

Make sure you have the Edit window showing.

#Define plot list (min 1 - max 4)
plot1=["mslp","wgust10","wind10"]

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

• Explore the storm development and passage using the other parameters available on other pressure levels.
• More explanation of how to use the Metview macro icons to alter the fields plotted are shown below.

If you prefer to see multiple plots per page rather than overlay them, please use the an_2x2.mv macro.

Icon 'an_1x1.mv' produces a single plot on the page.

Icon 'an_2x2.mv' can produce up to 4 plots per page.

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.

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.

For this exercise, you will 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_1x1.mv & oper_2x2.mv    : these work in a similar way to the same icons used in the previous task where parameters from a single lead time can be plotted.

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 mapType, either the Atlantic sector or over Europe.

1. How does the HRES forecast compare to analysis and observations?
2. Was it a good or bad forecast? Why?
3. How does the forecast change with the different lead times?

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 oper_rmse.mv icon for this.

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.

Key questions:

1. Visualise the ensemble mean - how does it compare to the HIRES forecast and analysis?
2. Visualize stamp map - are there any members that provide a better forecast?
3. Visualize spaghetti map - see how members spread over the duration of the forecast. How does the spread grow?

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?