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Introduction

The ECMWF operational ensemble forecasts for the western Mediterranean region exhibited high uncertainty while Hurricane Nadine was slowly moving over the eastern North Atlantic in September 2012. Interaction with an Atlantic cut-off low produced a bifurcation in the ensemble and significant spread, influencing both the track of Hurricane Nadine and the synoptic conditions downstream.

The HyMEX (Hydrological cycle in Mediterranean eXperiment) field campaign was also underway and forecast uncertainty was a major issue for planning observations during the first special observations period of the campaign.

This interesting case study examines the forecasts in the context of the interaction between Nadine and the Atlantic cut-off low in the context of ensemble forecasting. It will explore the scientific rationale for using ensemble forecasts, why they are necessary and how they can be interpreted, particularly in a "real world" situation of forecasting for an observational field campaign.

In this case study

In the exercises for this interesting case study we will:

• Study the development of Hurricane Nadine and the interaction with the Atlantic cut-off low using the ECMWF analyses.
• Study the performance of the ECMWF high resolution (HRES) deterministic forecast of the time.
• Use the operational ensemble forecast to look at the forecast spread and understand the uncertainty downstream of the interaction.
• Compare a reforecast using the

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• March 2016 ECMWF operational ensemble with the 2012 ensemble forecasts.
• Use manual clustering to characterize the behaviour of the ensembles and compare the results with clustering based on principal component analysis (PCA; see Pantillon et al.).
• Study the performance of the ECMWF ensemble forecasts trough RMSE curves.

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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 individual events, like the one in this exercise, to get a more complete picture of IFS performance and identify weaker aspects that need further exploration.

If the plotting produces thick contour lines and large labels, ensure that the environment variable LC_NUMERIC="C" is set before starting metview.

Obtaining the exercises

The exercises described below are available as a set of part of a 3-day training. The Metview macros with the accompanying data . This is available as a downloadable tarfile for use with Metvieware collected into a tarfile. 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. Please follow this link to see the original tutorial of the ENM/OpenIFS workshop 2018.

ECMWF operational forecasts

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

• HRES : spectral T1279 (18km 16km grid) highest resolution 10 day deterministic forecast.
• ENS    :    spectral T639 (36km 31km grid) resolution ensemble forecast (50 members) is run for days 1-10 of the forecast, T319 (70km) is run for days 11-1511–15.

In 2016, the ECMWF operational forecasts was upgraded compared to 2012 and 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 6GB, the minimum is 4Gb4GB. If using 4Gb4GB, 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:Please enter the folder 'openifs_training' to begin working.

metview &

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 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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The upper level fields have a list of available pressure levels in square brackets. To plot upper level fields, specify the pressure level after the name, e.g. z500 would plot geopotential at 500hPa.

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Exercise 2: Operational ECMWF HRES forecast

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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'training 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 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 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

Image Removed

Panel
borderColor lightgrey borderStyle solid title	Metview icons in HRES_forecast folder
Image Added Image Added Image Added hres_1x1.mv: it works in a similar way to the same icon used in the previous task where parameters from a single lead time can be plotted in a single frame the folder 'HRES_forecast' shown above. hres_1x1.mv & hres_2x2.mv: these work it works in a similar way to the same icons icon used in the previous task where parameters from a single lead time can be plotted either in a single frame or 4 frames per page.hres_xs.mv                                        : this plots a vertical cross section and can be used to compare the vertical structure of Hurricane Nadine and the cut-off low. Image Removed      track.mv: for this exercise, this icon can be used to overlay the forecast track of Nadine (and not the track from the analyses as in Exercise 1).

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.

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 2016 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

Enter the folder 'ENS' in the openifs_training folder to begin.

Image Added

Panel

Image Modified this will plot (a) the mean of the ensemble forecast, (b) the ensemble spread, (c) the HRES deterministic forecast and (d) the control forecast. Image Removed Image Added  this plots all of the ensemble forecasts for a particular field and lead time. Each forecast is shown in a stamp sized map. Very useful for a quick visual inspection of each ensemble forecast. Image Removed Image Added this useful macro allows two individual ensemble forecasts to be compared to the control forecast. As well as plotting the forecasts from the members, it also shows a difference map for each. Image Removed Image Added this will plot the difference between the ensemble control, ensemble mean or an individual ensemble member and the HRES forecast for a given parameter.

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 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.This can help in identifying individual ensemble members that produce a different forecast than the control or HRES forecast.
to the HRES and ensemble control deterministic forecasts.

This can help in identifying individual ensemble members that produce a different forecast than the control or HRES Use the ens_to_ref_diff.mv icon to compare an ensemble member to the HRES forecast. Use pf_to_cf_diff.mv to compare ensemble members to the control forecast.

param="mslp"
ensType="pf30"

ensType="cf"

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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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Enter the folder 'Clusters' in the openifs_2018training 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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It is usual to create clusters from z500 as it represents the large-scale flow and is not a noisy field. However, for this particular case study, the stamp map of 'tp' (total precipitation) over France is also very indicative of the distinct forecast scenarios. . To change the map geographical area see the Appendix.

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 second line defines the list of members for 'Cluster 2': in this example, members 10, 11, 12, 31, 49.

Change these two lines!.
Put your choice of ensemble member numbers for cluster 1 and 2 (lines 1 and 2 respectively).

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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 of 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 is called 'ens_oper_cluster.example.txt', then Edit cluster_to_anref.mv and set:

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

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.

Use the clusters of ensemble members you have created in ens_oper_cluster.example.txt.

Set clustersId='example' in the stamp.mv to enable cluster highlighting.

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.

param="tp"
expId="ens_oper"
mapType=2
clustersId="example"

If you your choice of clustering is accurate, you should see a clear separation of precipitation over France between the two clusters.

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).

As a smooth dynamical field, geopotential height at 500hPa at 00Z 24/9/2012 is recommend recommended (it is used in the paper by Pantillon et al.), but the steps described below can be used for any parameter at any step.

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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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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.

Various methods for presenting the forecast error are used in the tasks below.  The clusters created in the previous exercise can also be used.

Enter the 'Forecast errors' folder in the openifs_2018training 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

To save any other images during these exercises for discussion later, you can either use : "Export" button in Metview's display window under the 'File' menu to save to PNG image format

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(this will also allow animations to be saved into postscript

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) or use the ksnapshot command to take a 'snapshot' of the screen and save it to a file.

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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'.

In the edit window that appears you can see the map types available covering a different area:

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With mapType=0 , the map covers a large area centred on the Atlantic suitable for plotting the analyses and track of the storm (this area is only available for the analyses).

With mapType=1 , the map also covers the Atlantic but a smaller area than for the analyses. This is because the forecast data in the following exercises does not cover as large a geographical area as the analyses.

With mapType=2 , the map covers a much smaller region centred over France.

Change, mapType=0   to   mapType=1   then click the play button at the top of the window.

Repeat using mapType=2 to see the smaller region over France.These different regions will be used in the following exercises.

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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Wind parameters can be shown either as arrows or as wind flags ('barbs') by adding '.flag' to the end of variable name e.g. "wind10.flag".

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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

Panel

Image Modified this plots a 'spaghetti map' for a given parameter for the ensemble forecasts compared to the reference HRES forecast. Another way of visualizing ensemble spread. Image Modified  this plots a vertical cross-section through the forecasts in the same way as the cross-section plots for the analyses. Image Removed Image Added this comprehensive macro produces a single map for a given parameter. The map can be either: i/ the ensemble mean, ii/ the ensemble spread, iii/ the control forecast, iv/ a specific perturbed forecast, v/ map of the ensemble probability subject to a threshold, vi/ ensemble percentile map for a given percentile value. For example, it is possible to plot of a map showing the probability that MSLP would be below 995hPa. Image Removed Image Added this macro can be used to plot the difference for two ensemble members against the HRES forecasts. As ensemble perturbations are applied in +/- pairs, using this macro it's possible to see the nonlinear development of the members and their difference to the HRES forecast.

Back to the tutorial

Additional tasks of exercise 4

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clusternumber clusternumber

Changing the number of clusters

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

  clusterNum=2

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You can have as many clusters as you like but it does not make sense to go beyond 3 or 4 clusters.

Back to the tutorial

Further reading

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

Shutts, G., Leutbecher, M., Weisheimer, A., Stockdale, T., Isaksen, L., Bonavita, M., 2011: Representing model uncertainty: stochastic parametrizations at ECMWF. ECMWF Newsletter 129, 19--24.

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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