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titleSt Jude wind-storm key highlights

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, south of Nova Scotia 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 between eastern England and the North Sea where the strongest wind gusts were observed.

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)

ECMWF operational forecasts consist of:

  • HRES : T1279 (16km grid) highest resolution 10 day forecast
  • ENS : Ensemble (50 members), T639 for days 1-10, T319 days 11-15.

 

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titleTeams

 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.

 

Exercise 1. Evaluating the ECMWF analyses and forecasts

 

Objective: Understanding (a) formation of the storm (b) the error in the forecast by comparing the ECMWF forecast with analysis & observations

Starting up metview
Code Block
titleType the following command in a terminal window
metview
Info

Please enter the folder 'OpenIFS workshop 2015' to begin working.

Task 1: Visualise observations and ECMWF analyses

 

 

ECMWF operational forecasts consist of:

  • HRES : T1279 (16km grid) highest resolution 10 day forecast
  • ENS : Ensemble (50 members), T639 for days 1-10, T319 days 11-15.

 

Panel
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titleTeams

 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.

 

Exercise 1. Evaluating the ECMWF analyses and forecasts

 

Objective: Understanding (a) formation of the storm (b) the error in the forecast by comparing the ECMWF forecast with analysis & observations

Starting up metview
Code Block
titleType the following command in a terminal window
metview
Info

Please enter the folder 'OpenIFS workshop 2015' to begin working.

Task 1: Visualise observations and ECMWF analyses

 

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titleMetview icons

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

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titlePlot wind gust observations

 1. Right-click on the icon labelled 'wgust_obs.mv' and select 'Visualise' (or Control-I on the keyboard)

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titleMetview icons

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

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titlePlot wind gust observations

 1. Right-click on the icon labelled 'wgust_obs.mv' and select 'Visualise' (or Control-I on the keyboard)

Plot analyses in various layouts

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

Icon 'an_2x2.mv' can produce

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titlePlot analyses in various layouts

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

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

 

Info
titleChange plot fields

For the 'an_1x1.mv' icon, the plot contents can be changed by editing the plot1 variable in the macro. By default the 10m wind is shown.

To alter the plotted field, right-click and choose 'Edit'.

It is possible to overlay multiple fields like this:

You will find a list of available parameters in the macro.

After editing the macro text, you can optionally save using the 'File' menu and 'Save'.

Display the plot by clicking:

Animate the plots in the display window by clicking

Info
titleChange plot appearance

 The number of maps appearing in the plot layout can be 1, 2, 3 or 4.

Macro an_2x2.mv demonstrates how to plot a four-map layout in a similar fashion to the one-map layout. The only difference here is that you need to define four plots instead of one.

Right-click on the icon and select 'Edit'. Change the plot layout like this:

Two map types are available covering a different area.

With mapType=0, the map will cover a smaller geographical area centred on the UK.

With mapType=1, the map will cover most of the North Atlantic

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titleKey points
  1. Examine windgust observations. Note the observed area of strongest windgusts and their intensity. How does the analyses compare with the observations?
  2. Understand the storm development from the ECMWF analyses.
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titleTeam : Getting started
  1. Start with an_1x1.mv with mapType=1 to see the large scale.
    Everyone should plot the mean-sea-level-pressure (mslp) and 10m wind-gust, over each 3hr period, (wgust10) plots. Remember you can overlay two or more variables as shown above and use animation.
  2. Each team member can then choose their own parameters; look at the fields on different pressure levels; use the smaller geographical area (mapType=0) to compare with the observations.
  3. Discuss the storm characteristics and development.

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

 

Task 2: Visualise operational HIRES forecast

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Data is provided for multiple forecasts starting form different dates, known as different lead times.

Available lead times for October 2013 are: 24th, 25th, 26th and 27th.
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titleThe HIRES forecast

The ECMWF operational forecast is called HIRES. 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 HIRES forecast.

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titleAvailable forecast dates

.

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 HIRES forecast.

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titleAvailable forecast dates

Data is provided for multiple forecasts starting from different dates, known as different lead times.

Available lead times for October 2013 are: 24th, 25th, 26th and 27th.

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titlePlot HIRES forecast

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

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oper_2x2.mv              works in a similar way to the an_2x2.mv icon used in the previous task where parameters from a single lead time can be plotted.

oper_to_an_runs.mv   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     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.

Use the metview macros to plot different days and compare to analysis and plot forecast differences.

 

Note
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titleKey questions
  1. How does HIRES 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?
Notepanel
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titlePlot HIRES forecast

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

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oper_2x2.mv  works in a similar way to the an_2x2.mv icon used in the previous task.

Suggested fields to plot: MSL, Z200, 10m wind and visualize the storm track.

Use the metview macros to plot different days and compare to analysis and plot forecast differences.

 

 

...

titleTeam: getting started

 Each team should look at the forecast from all 4 starting dates.

As a team, discuss what plots & parameters to use to address the questions above.

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.

 

 

Task 2 : 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)

Again using the ECMWF operational forecast,  look now at the 50 ensemble forecasts. These are at a lower resolution (T639) than the HIRES (T1279).

...

Exercise 2. CDF/RMSE at different locations

Recap

TO DO: RMSE & CDF (concepts need explanation)

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