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Gliffy Diagram
nameexercise flowchart

 

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. 

Note
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titleObjectives
  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.

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titleMetview icons

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 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.

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titleTwo 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

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.

Code Block
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titleAdd wind arrows to the plot:
#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

  • You are encouraged to 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.

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How to plot in various layouts

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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 as shown in the above first exercise.

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

It is possible to overlay multiple fields by putting them in square brackets 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

 For the an_2x2.mv icon the number of maps appearing in the plot layout can be 1, 2, 3 or 4.  This is true of all the icons labelled '2x2'.

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:

Note how some plots can be single parameters whilst others can be overlays of two (or more) fields.

Wind parameters can be shown either as arrows or as feather by adding '_f' to the variable name.

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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Exercise 2: Visualise operational HRES forecast

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Recap

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titleThe HRES forecast

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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titleBGColorlightlightgreyAvailable forecast dates

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

For this task,

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.

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titlePlot HRES forecast

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For this exercise, you will

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Key questions

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titleKey questions
  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?

Getting started

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titleTeam: Getting started

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.

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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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