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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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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_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

Panel
borderColor lightgrey borderStyle solid title Metview icons in HRES_forecast folder
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. hres_2x2.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 4 frames per page. 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).

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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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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"]

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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.

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

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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

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

  clusterNum=2

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