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Section
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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 N.Atlantic in Sept. 2012. Interaction with an Atlantic cut-off low produced a bifurcation in the ensemble and significant spread, which controls 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 they can be interpreted, particularly in a "real world" situation of forecasting for a observational field campaign.

 

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titleThis case study is based on the following paper which is recommended reading

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/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  the May/2016 ECMWF operational ensemble with the 2012 ensemble forecasts.
  • Use principal component analysis (PCA) with clustering techniques (see Pantillon et al) to characterize the behaviour of the ensembles.
  • See how forecast products were used during the HyMEX field campaign.
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Table of contents

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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 (if installed). It is also available as part of the OpenIFS/Metview virtual machine, which can be run on different operating systems.

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At the time of this case study in 2012, ECMWF operational forecasts consisted of:

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At the time of this workshop (in 2016), the ECMWF operational forecasts has been upgraded compared to 2012 and consisted of:

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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:

Code Block
titleType the following command in a terminal window
metview &
Info

Please enter the folder 'openifs_2016' to begin working.

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or use the following command to take a 'snapshot' of the screen:

Code Block
languagebash
titleCommand for screen snapshot
ksnapshot

Exercise 1. The ECMWF analysis

 

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titleLearning objectives

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Hurricane Nadine and the cut-off low

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Task 1: Mean-sea-level pressure and track

Plot. Right-click the mouse button on the 'an_1x1.mv' icon and select the 'Visualise' menu item (see figure right)

After a pause, this will generate a map showing mean-sea-level pressure (MSLP).

Plot track of Nadine. Drag the mv_track.mv icon onto the map.

This will add the track of Hurricane Nadine. Although the full track of the tropical storm is shown from the 10-09-2012 to 04-10-2016, the ECMWF analyses (for the purpose of this study) only show 15-09-2012 to 25-09-2012.

In the plot window, use the play button in the animation controls  to animate the map and follow the development and track of Hurricane Nadine.

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borderColorred
titleQuestions

Compare the animation of the z500 and mslp fields with Figure 1. from Pantillon et al.1.

  1. When does the cut-off low form (see z500)
?2. How close do Nadine and the cut-off low get in the analyses
  1. ?
3.
  1. From the PV at 320K (and z500), what is different about the upper level structure of Nadine and the cut-off low?

Task 3: Changing geographical area

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

In the edit window that appears

Panel
bgColorwhite
titleBGColorlightlightgrey
titleTwo map types are available covering a different area
Code Block
languagebash
titleChanging geographical area
#Map type: 0=Atl-an, 1: Atl-fc, 2: France 
mapType=0

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.

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The 'an_2x2.mv' icon allows for plotting up to 4 separate figures on a single frame. This task uses this icon to plot multiple fields.

Right-click on the 'an_2x2.mv' icon and select the 'Edit' menu item.

Code Block
languagebash
titleMultiple plots per page
#Define plot list (min 1- max 4)
plot1=["mslp"]
plot2=["wind10"]
plot3=["speed500","z500"]
plot4=["sst"]

Click the play button at the top of the window to run this macro with the existing plots as shown above.

Note that each Each plot can be a single field or overlays of different fields as in the an_1x1.mv macro.

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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Right click on the icon 'an_xs.mv', select 'Edit' and push the play button.This generates a plot with a map of MSLP, a red line and underneath a cross-section plot along that red-line.

The default plot shows potential vorticity (PV), wind vectors and potential temperature roughly through the centre of the Hurricane and the cut-off low.

Changing forecast time

Cross-section data is only available every 24hrs.

The red line on the map of MSLP shows the location of the cross-section.

 

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titleQuestions

Look at the PV field, how do the vertical structures of Nadine and the cut-off low differ?

Changing forecast time

Cross-section data is only available every 24hrs.

This means the 'This means the 'steps' value in the macros is only valid for the times:  [2012-09-20 00:00], [2012-09-21 00:00], [2012-09-22 00:00], [2012-09-23 00:00], [2012-09-24 00:00], [2012-09-25 00:00]

To change the date/time of the plot, edit the macro and change the line:

Code Block
languagebash
steps=[2012-09-22 00:00]
Changing fields

A smaller set reduced number of fields is available for cross-sections: 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).

Changing cross-section location
Code Block
languagebash
#Cross section line [ South, West, North, East ]
line = [30,-29,45,-15]

The cross-section location (red line) can be changed in this macro by defining the end points of the line as shown above.

Remember that if the forecast time is changed, the storm centres will move and the cross-section line will need to be repositioned to follow specific features. This is not computed automatically, but must be changed by altering the coordinates above.

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titleQuestions

Look at the PV field, how do the vertical structures of Nadine and the cut-off low differ?

 

 

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iconfalse

 (tick)  This completes the first exercise.

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Exercise 2: The operational HRES forecast

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HRES 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 interations. This may mean that some plotting will be slow.

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Available parameters

A new fieldparameter is total precipitation : tp.

The fields ( parameters ) available in the analyses are available in the forecast data.

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titleBGColorlightblue
titleIsobaric maps
  • Geopotential at 500hPa + MSLP :                                                       primary circulation, Figure 1 from Pantillon et al.
  • MSLP + 10m winds :                                                            interesting for Nadine's tracking and primary circulation
  • MSLP + relative humidity at 700hPa + vorticity at 850hPa  : low level signature of Nadine and disturbance associated with the cutoff low, with mid-level humidity of the systems.
  • Geopotential + temperature at 500hPa :                         large scale patterns, mid-troposphere position of warm Nadine and the cold Atlantic cutoff
  • Geopotential + temperature at 850hPa :                                     lower level conditions, detection of fronts
  • 320K potential vorticity (PV) + MSLP,
  • 500hPa relative vorticity (see Fig. 14 in Pantillon) :            upper level conditions, upper level jet and the cutoff signature in PV, interaction between Nadine and the cut-off low.
  • Winds at 850hPa + vertical velocity at 700hPa (+MSLP) : focus on moist and warm air in the lower levels and associated vertical motion. Should not be a strong horizontal temperature gradient around Nadine, the winds should be stronger for Nadine than for the cutoff.
  • 10m winds + total precipitation (+MSLP) :                       compare with Pantillon Fig.2., impact on rainfall over France.
Panel
bgColorlightlightblue
titleBGColorlightblue
titleVertical cross-sections
Potential temperature + potential vorticity, &
Humidity + vertical motion :                                  to characterize the cold core and warm core structures of Hurricane Nadine and the cut-off low.
PV + vertical velocity (+ relative humidity) :         a classical cross-section to see if a PV anomaly is accompanied with vertical motion or not.

 

false
Info
icon
 (tick)

 

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Exercise 3 : The operational ensemble forecasts

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In this case study, there are two operational ensemble datasets and additional datasets created with the OpenIFS model, running at lower resolution, where the initial and model uncertainty are switched off in turn. The OpenIFS ensembles are discussed in more detail in later exercises. Please see below for more details.

An ensemble forecast consists of:

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

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ens_2016: This dataset is a reforecast of the 2012 event using the ECMWF operational ensemble from of March 2016. Two key differences between the 2016 and 2012 operational ensembles are: higher horizontal resolution, and coupling of NEMO ocean model to provide SST from the start of the forecast.

Note that the 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 use at that time2012, whereas 25 would be used in the are in use for 2016 operational ensembleensembles, 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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Visualising ensemble forecasts can be done in various ways. During this exercise we will use a number of visualisation techniques in order to understand the errors and uncertainties in the forecast,

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

Image Added

Available plot types

Panel

For these exercises please use the Metview icons in the row labelled 'ENS'.

ens_rmse.mv : this is similar to the hres_rmse.mv in the previous exercise. It will plot the root-mean-square-error growth for the ensemble forecasts.

ens_to_an.mv : this will plot (a) the mean of the ensemble forecast, (b) the ensemble spread, (c) the HRES deterministic forecast and (d) the analysis for the same date.

ens_to_an_runs_spag.mv : this plots a 'spaghetti map' for a given parameter for the ensemble forecasts compared to the analysis. Another way of visualizing ensemble spread.

stamp.mv : 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.

stamp_diff.mv : similar to stamp.mv except that for each forecast it plots a difference map from the analysis. Very useful for quick visual inspection of the forecast differences of each ensemble forecast.

 

Additional plots for further analysis:

pf_to_cf_diff.mv : 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.

ens_to_an_diff.mv : this will plot the difference between the ensemble control, ensemble mean or an individual ensemble member and the analysis for a given parameter.

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Choose your ensemble dataset by setting the value of 'expId', either 'ens_oper' or 'ens_2016' for this exercise. One of the OpenIFS ensembles could also be used but it's recommended one of the operational ensembles is studied first.

Code Block
languagebash
titleEnsemble forecast datasets available in the macros
#The experiment. Possible values are:
# ens_oper = operational ENS
# ens_2016 = 2016 operational ENS

expId="ens_oper"

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Right-click the ens_rmse.mv icon, select 'Edit' and plot the curves for 'mslp' and 'z500'.

Change 'expID' for your choice of ensemble.

Code Block
languagebash
titleMake sure 'clustering' is off for this task!
useClustersclustersId="off"

Clustering will be used in later tasks.

Panel
borderColorred
titleQuestions
  1. How do the HRES, ensemble control forecast and ensemble mean compare?
  2. How do the ensemble members behave, do they give better or worse forecasts?

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This task will explore the difference in another way by looking at the 'ensemble spread'.

Use the ens_to_an.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, the operational HRES deterministic forecast and the analysis.

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This macro can also be used to look at collections clusters of ensemble members. It will be used later in the clustering tasks. For this task, make sure all the members of the ensemble are used.

Code Block
languagebash
titleUse all ensemble members in this task:
#ENS members (use ["all"] or a list of members like [1,2,3]
members=["all"]        #[1,2,3,4,5] or #["all"] or ["cl1cl.example.1"]
Panel
borderColorred
titleQuestions
  1. How does the mean of the ensemble forecasts compare to the HRES & analysis?
  2. Does the ensemble spread capture the error in the forecast?
  3. What other comments can you make about the ensemble spread?

If time:

  • The 'members=' option is used to change the number of members in the spread plots.  Try creating your own cluster: e.g. "members=[1,3,4,5,7,8,9]".

Task 3: Spaghetti plots - another way to visualise spread

Task 3: Spaghetti plots - another way to visualise spread

A "spaghetti" plot is where A "spaghetti" plot is where a single contour of a parameter is plotted for all ensemble members. It is another way of visualizing the differences between the ensemble members and focussing on features.

Use the ens_to_an_runs_spag.mv icon. Plot and animate the MSLP and z500 fields using your suitable choice for the contour level. Find a value that highlights the low pressure centres. Note that not all members may reach the low pressure set by the contour.

The red contour line shows the control forecast of the ensemble.

Note that this macro may animate slowly because of the computations required.

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Code Block
languagebash
titleSet date/time to 24-09-2012 00Z
#Define forecast steps
steps=[2012-09-24 00:00,"to",2012-09-24 00:00,"by",6]

Make sure useClustersclustersId="off" for this task.

Precipitation over France

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Panel
borderColorred
titleQuestions
  1. Using the stamp and stamp difference maps, study the ensemble. Identify which ensembles produce "better" forecasts.
  2. Can you see any distinctive patterns in the difference maps? Are the differences similar in some way?
Compare ensemble members to analysis

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Panel

Find ensemble members that appear to produce a better forecast and look to see how the initial development in these members differs.

  • Select 'better' forecasts using the stamp plots and use ens_to_an.mv to modify the list of ensembles plots. Can you tell which area is more sensitive for the forecast?
  • Use pf_to_cf_diff and ens_to_an_diff to take the difference between these perturbed ensemble member forecasts from the control and analyses to also look at this.

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Task 5:  Cumulative distribution function

Recap

The probability distribution function of the normal distribution
or Gaussian distribution. The probabilities expressed as a
percentage for various widths of standard deviations (σ)
represent the area under the curve.

Figure from Wikipedia.

Cumulative distribution function for a normal
distribution with varying standard deviation (σ)

Figure from Wikipedia.

Cumulative distribution function (CDF)

The figures above illustrate the relationship between a normal distribution and its associated cumulative distribution function.The CDF is constructed from the area under the probability density function.

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For a forecast ensemble where all values were the same, the CDF would be a vertical straight line.

Plot the CDFs


This exercise uses the cdf.mv icon. Right-click, select 'Edit' and then:

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Make sure useClusters='off'. 

Panel
borderColorred
titleQuestions
  1. Compare the CDF from the different forecast ensembles; what can you say about the spread?

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 Forecasting during HyMEX : Work in teams for group discussion

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The stamp maps are useful for this task.

From the stamp.map of total precipitation (tp) over Francez500 at 24/9/2012 (t+96), identify ensemble members that represent the two most likely forecast scenarios.

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From Figure 7 we see that cluster 1 corresponds to a cutoff moving eastward over Europe and cluster 2 to a weak ridge over western Europe.
It would be great if we could also do the cluster composite of rainfall from Figure 8 : cluster 1 shows impact on precipitation over The Cévènes whereas cluster 2 shows weak precipitation over the Cévènes.
T We  We see more clearly that cluster 1 exhibits a weak interaction between cutoff and low and cutoff over Europe. In cluster 2, there is a strong interaction between the cutoff and Nadine and Nadine makes landfall over the Iberian penisula (in model world, is it realistic ?).

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Excerpt Include
Credits
Credits
nopaneltrue

Introduction

In these exercises we will look at a case study of a severe storm using a forecast ensemble. During the course of the exercise, we will explore the scientific rationale for using ensembles, how they are constructed and how ensemble forecasts can be visualised. A key question is how uncertainty from the initial data and the model parametrizations impact the forecast.

You will start by studying the evolution of the ECMWF analyses and forecasts for this event. You will then run your own OpenIFS forecast for a single ensemble member at lower resolutions and work in group to study the OpenIFS ensemble forecasts.