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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.
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Pantillon, F., Chaboureau, J.-P. and Richard, E. (2015), 'Vortex-vortex interaction between Hurricane Nadine and an Atlantic cutoff dropping the predictability over the Mediterranean, http://onlinelibrary.wiley.com/doi/doi: 10.1002/qj.2635 /abstract. |
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. |
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The exercises described below are part of a complete training given at the Météo-France in 20183-day training. The Metview macros with the accompanying data are collected into 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. For the tutorial of the original training please visit the page Please follow this link to see the original tutorial of the ENM/OpenIFS workshop 2018.
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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 11–15.
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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:Please enter the folder 'openifs_training' to begin working.
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metview & | ||
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Please enter the folder 'openifs_2019' to begin working.& |
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If the plotting produces thick contour lines and large labels, ensure that the environment variable |
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Begin by entering the folder labelled 'Analysis':
Task 1: Mean-sea-level pressure and track
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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'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
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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.
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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.
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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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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.
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.
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To animate the difference in MSLP of an individual ensemble member 30 to the HRES forecast, edit the lines:
and visualise the plot. To compare the control forecast, change:
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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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Enter the folder 'Clusters' in the openifs_2018training folder to begin working.
Task 1: Create your own clusters
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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.
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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
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If your cluster definition file is called 'ens_oper_cluster.example.txt', then Edit cluster_to_anref.mv and set:
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#ENS members (use ["all"] or a list of members like [1,2,3] members_1=["cl.example.1"] members_2=["cl.example.2"] |
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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.
| Stamp maps: the stamp maps will be reordered such at the ensemble members will be grouped according to their cluster. This will make it easier to see the forecast scenarios according to your clustering. |
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.
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Use the stamp.mv icon and change it to plot the total precipitation over France with clusters enabled., e.g.
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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.
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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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Always use the If you change step or ensemble, recompute the EOFS EOFs and cluster definitions using Note that the EOF analyses analysis is run over the smaller domain over France. This may produce a different clustering to your manual cluster if you used a larger domain. |
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Plot ensemble and cluster maps
Use the cluster definition file computed by eof.mv to the plot ensembles and maps with clusters enabled (as above, but this time with the 'eof' cluster file).
The macro cluster_to_ref.mv can be used to plot maps of parameters as clusters and compared to the HRES forecast.
Use cluster_to_ref.mv to plot z500 and MSLP maps of the two clusters created by the EOF analysis.
Edit cluster_to_ref.mv and set:
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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"] |
Run the macro.
If time also look at other parameters such as PV/320KPV320K.
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Q. What are the two scenarios proposed by the two clusters? |
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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.
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The macros described in this tutorial can write PostScript and GIF image files to the To save the images, use the 'Execute' menu option on the icon, rather than 'Visualise'. The 'okular' command can be used to view the PDF & gif images. |
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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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.
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Additional plots for further study
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Additional tasks of exercise 4
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To change the number of clusters created by the EOF analysis, edit eof.mv. Change:
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clusterNum=2 |
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