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Obtaining the exercises

The exercises described below are available as a set of Metview macros with the accompanying data. This is available as a downloadable tarfile for use with Metview. It is also available as part of the OpenIFS/Metview virtual machine, which can be run on different operating systems.

For more details of the OpenIFS virtual machine and how to get the workshop files, please contact: openifs-support@ecmwf.int.

ECMWF operational forecasts

In 2012, at the time of this case study, ECMWF operational forecasts consisted of:

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Please follow this link to see more details on changes to the ECMWF IFS forecast system (http://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model)

Virtual machine

If using the OpenIFS/Metview virtual machine with these exercises the recommended memory is at least 6Gb, the minimum is 4Gb. If using 4Gb, do not use more than 2 parameters per plot.

These exercises use a relatively large domain with high resolution data. Some of the plotting options can therefore require significant amounts of memory. If the virtual machine freezes when running Metview, please try increasing the memory assigned to the VM.

Starting up Metview

To begin:

metview &

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Exercise 1: Hurricane Nadine and the cut-off low

ECMWF analyses to the 20th September 2012

In this exercise, the development of Hurricane Nadine and the cut-off flow up to the 20th September 2012 is studied.

Begin by entering the folder labelled 'Analysis':

Task 1: Mean-sea-level pressure and track

This task will look at the synoptic development of Hurricane Nadine and the cutoff cut-off low up to 00Z, 20th September 2012. The forecasts in the next exercises start from this time and date.

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Task 2: MSLP and 500hPa geopotential height

Right-click the mouse button on the an_1x1.mv icon and select the 'Edit' menu item.

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Exercise 2: Operational ECMWF HRES forecast

HRES performance

Exercise 1 looked at the synoptic development up to 20-Sept-2012. This exercise looks at the ECMWF HRES forecast from this date and how the IFS model developed the interaction between Hurricane Nadine and the cut-off low.

Enter the folder 'HRES_forecast' in the 'openifs_2018' folder to begin.

Recap

The ECMWF operational deterministic forecast is called HRES. At the time of this case study, the model ran with a spectral resolution of T1279, equivalent to 18km grid spacing.

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Before looking at the ensemble forecasts, first understand the behaviour of the operational HRES forecast of the time.

Available forecast

Data is provided for a single 10 day forecast starting from 20th September 2012.

Data is provided at the same resolution as the operational model, in order to give the best representation of the Hurricane and cut-off low iterations. This may mean that some plotting will be slow.

Available parameters

A new parameter is total precipitation: tp.

The parameters available in the analyses are also available in the forecast data.

Available plot types

Task 1: Synoptic development

Study the forecast scenario to day+10, focus on:

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To add the forecast track of Hurricane Nadine drag and drop the mv_track.mv icon onto any map.

Precipitation over France

Choose a hres macro (hres_1x1 or hres_2x2) and plot the total precipitation (parameter: tp), near surface wind field (parameter: wind10), relative humidity (parameter: r).

Change the area to France by setting 'maptype=2' in the macro script.

Other suggested isobaric maps

Using either the hres_1x1.mv or hres_2x2.mv macro plot some of these other maps to study the synoptic development.

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Exercise 3: Operational ensemble forecasts

Recap

• ECMWF operational ensemble forecasts treat uncertainty in both the initial data and the model.
• Initial analysis uncertainty: sampled by use of Singular Vectors (SV) and Ensemble Data Assimilation (EDA) methods. Singular Vectors are a way of representing the fastest growing modes in the initial state.
• Model uncertainty: sampled by use of stochastic parametrizations. In IFS this means the 'stochastically perturbed physical tendencies' (SPPT) and the 'spectral backscatter scheme' (SKEB)
• Ensemble mean : 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 : the standard deviation of the ensemble members, represents how different the members are from the ensemble mean.

The ensemble forecasts

In this case study, there are two operational ensemble datasets, one from the original 2012 operational forecast, the other from a reforecast of the event using the 2016 operational ensemble.

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• Control forecast (unperturbed)
• Perturbed ensemble members. Each member will use slightly different initial data conditions and include model uncertainty pertubations.

2012 Operational ensemble

ens_oper: This dataset is the operational ensemble from 2012 and was used in the Pantillon et al. publication. A key feature of this ensemble is use of a climatological SST field (seen in the earlier tasks).

2016 Operational ensemble

ens_2016: This dataset is a reforecast of the 2012 event using the ECMWF operational ensemble of March 2016.

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The analysis was not rerun for 20-Sept-2012. This means the reforecast using the 2016 ensemble will be using the original 2012 analyses. Also only 10 ensemble data assimilation (EDA) members were used in 2012, whereas 25 are in use for 2016 operational ensembles, so each EDA member will be used multiple times for this reforecast. This will impact on the spread and clustering seen in the tasks in this exercise.

Ensemble exercise tasks

Key parameters: MSLP and z500.  We suggest concentrating on viewing these fields. If time, visualize other parameters (e.g. PV320K).

Available plot types

Group working

If working in groups, each group could follow the tasks below with a different ensemble forecast. e.g. one group uses the 'ens_oper', another group uses 'ens_2016'.

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#The experiment. Possible values are:
# ens_oper = operational ENS
# ens_2016 = 2016 operational ENS

expId="ens_oper"

Ensemble forecast uncertainty

In these tasks, the performance of the ensemble forecast is studied.

Task 1: Ensemble spread

Use the ens_maps.mv icon and plot the the MSLP and z500. This will produce plots showing: the mean of all the ensemble forecasts, the spread of the ensemble forecasts and the operational HRES deterministic forecast.

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Task 2: Visualise ensemble members

Stamp maps are used to visualise all the ensemble members as normal maps. These are small, stamp sized contour maps plotted for each ensemble member using a small set of contours.

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Make sure clustersId="off" for this task. Clustering will be used later.

Compare ensemble members to the deterministic and control forecast

After visualizing the stamp maps, it can be useful to animate a comparison of individual ensemble members to the HRES and ensemble control deterministic forecasts.

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Sea-surface temperature

Compare the SST parameter used for the ens_oper and ens_2016 ensemble forecasts. The 2016 reforecast of this case study used a coupled ocean model unlike the 2012 ensemble and HRES forecast that used climatology for the first 5 days.

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Exercise 4: Cluster analysis

The paper by Pantillon et al, . describes the use of clustering to identify the main scenarios among the ensemble members.

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• Construct your own qualitative clusters by choosing members for two clusters.,
• Generate clusters using principal component analysis.

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Enter the folder 'Clusters' in the openifs_2018 folder to begin working.

Task 1: Create your own clusters

Clusters can be created manually from lists of the ensemble members.

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You can choose any parameter to construct the clusters from, if you think another parameter shows a clear clustering behaviour.

How to create your own cluster

Right-click 'ens_oper_cluster.example.txt' and select Edit (or make a duplicate)

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The filename is important !
The first part of the name 'ens_oper' refers to the ensemble dataset and must match the name used in the plotting macro. 
The 'example' part of the filename can be changed to your choice and should match the 'clustersId'.
As an example a filename of: ens_both_cluster.fred.txt would require 'expId=ens_both', 'clustersId=fred' in the macro

Plot maps of parameters as clusters

The macro cluster_to_ref.mv can be used to plot maps of parameters as clusters and compared to the ensemble control forecast and the HRES forecast.

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If your cluster definition file has another name, e.g. ens_oper_cluster.fred.txt, then members_1=["cl.fred.1"].

Plot ensembles with clusters

In this part of the task, redo the plots from the previous exercise which looked at ways of plotting ensemble data, but this time with clustering enabled.

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If time, also try the ens_part_to_all.mv icon. This compares the spread and mean of part of the ensemble to the full ensemble.

Plot other parameters

Use the stamp.mv icon and change it to plot the total precipitation over France with clusters enabled.e.g.

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Task 2: Empirical orthogonal functions / Principal component analysis

A quantitative way of clustering an ensemble uses empirical orthogonal functions from the differences between the ensemble members and the control forecast and then using an algorithm to determine the clusters from each ensemble as projected in EOF space (mathematically).

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#ENS members (use ["all"] or a list of members like [1,2,3]
members_1=["cl.eof.1"]
members_2=["cl.eof.2"]

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If time, change the date/time used to compute the clusters. How does the variance explained by the first two clusters change? Is geopotential the best parameter to use?

Changing the number of clusters

To change the number of clusters created by the EOF analysis, edit eof.mv.

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Exercise 5: Assessment of forecast errors

In this exercise, the analyses covering the forecast period are now available to see how Nadine and the cut-off low actually behaved.

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Enter the 'Forecast errors' folder in the openifs_2018 folder to start work on this exercise.

Task 1: Analyses from 20th September

Now look at the analyses from 20th September to observe what actually happened.

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If time, use the other icons such as an_2x2.mv and an_xs.mv to look at the cross-section through the analyses and compare to the forecast cross-sections from the previous exercises.

Task 2: RMSE "plumes" for the ensemble

This is similar to the previous exercise, except the RMSE curves for all the ensemble members from a particular forecast will be plotted.

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Change 'expID' for your choice of ensemble (either ens_oper or ens_2016).

Clusters

First plot the plumes with clustering off:

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Task 3: Difference stamp maps

Use the stamp_diff.mv plot to look at the differences between the ensemble members and the analysis. It can be easier to understand the difference in the ensembles by using difference stamp maps.

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Appendix

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saveimage saveimage

Saving images and animations

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convert -delay 75 -rotate "90<" in.ps out.gif

Back to the tutorial

Additional tasks of exercise 1

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changemap changemap

Changing the map geographical area

Right-click on an_1x1.mv icon and select 'Edit'.

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Animate the storm on this smaller geographical map.

Back to the tutorial

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multiplemap multiplemap

Plotting multiple variables on multiple maps

The an_2x2.mv icon plots up to 4 separate figures on a single frame.

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Back to the tutorial

Additional tasks of exercise 2

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precipitation precipitation

Precipitation over France

Choose a hres macro (hres_1x1 or hres_2x2) and plot the total precipitation (parameter: tp), near surface wind field (parameter: wind10), relative humidity (parameter: r).

Change the area to France by setting 'maptype=2' in the macro script.

Back to the tutorial

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vertical vertical

Vertical structure and forecast evolution

This task focuses on the fate of Nadine and examines vertical PV cross-sections of Nadine and the cut-off low at different forecast times.

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Changing forecast time

Cross-section data is only available every 24hrs until the 30th Sept 00Z (step 240).

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steps=[2012-09-26 00:00]

Changing cross-section location

#Cross section line [ South, West, North, East ]
line = [30,-29,45,-15]

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Suggestions for other vertical cross-sections

A reduced number of fields is available for cross-sections compared to the isobaric maps: temperature (t), potential temperature (pt), relative humidity (r), potential vorticity (pv), vertical velocity (w), wind-speed (speed; sqrt(u*u+v*v)) and wind vectors (wind3).

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Back to the tutorial

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furtherplots furtherplots

Additional tasks of Exercise 3

Additional plots for further study

Back to the tutorial

Further reading

For more information on the stochastic physics scheme in IFS, see the article:

Shutts et al, 2011, ECMWF Newsletter 129.

Acknowledgements

We gratefully acknowledge the following for their contributions in preparing these exercises. From ECMWF: Glenn Carver, Gabriella Szepszo, Sandor Kertesz, Linus Magnusson, Iain Russell, Simon Lang, Filip Vana. From ENM/Meteo-France: Frédéric Ferry, Etienne Chabot, David Pollack and Thierry Barthet for IT support at ENM.

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