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.
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 current (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.
Table of contents
Caveat on use of ensembles for case studies
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.
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 (if installed). 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
At the time of this case study in 2012, ECMWF operational forecasts consisted of:
- HRES : spectral T1279 (16km grid) highest resolution 10 day deterministic forecast.
- ENS : spectral T639 (34km grid) resolution ensemble forecast (50 members) is run for days 1-10 of the forecast, T319 (70km) is run for days 11-15.
At the time of this workshop (2016), the ECMWF operational forecasts has been upgraded compared to 2012 and consisted of:
- HRES/2016 : spectral T1279 with an octahedral grid configuration providing highest resolution of 9km.
- ENS/2016 : spectral T639 with an octahedral grid configuration providing highest resolution of 18km for all 15 days of the forecast.
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
Please enter the folder 'openifs_2016' to begin working.
Saving images and printing
To save 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. This will also allow animations to be saved into postscript.
or use the following command to take a 'snapshot' of the screen:
ksnapshot
Exercise 1. The ECMWF analysis
Learning objectives
- Study synoptic conditions over the N.Atlantic and Europe during the approach of Hurricane Nadine.
Hurricane Nadine and the cut-off low
For these tasks, use the metview icons in the row labelled 'Analysis'
an_1x1.mv : this plots horizontal maps of parameters from the ECMWF analyses overlaid on one plot.
an_2x2.mv : this plots horizontal maps of parameters from the ECMWF analyses four plots to a page (two by two).
an_xs.mv : this plots vertical cross-sections of parameters from the ECMWF analyses.
Task 1: Mean-sea-level pressure and track
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).
Now drag the mv_track.mv icon onto the map. This will add the track of 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.
You can use the 'Speed' menu to change the animation speed (each frame is every 6 hours).
If the contour lines appear jagged, in the plot window, select the menu item 'Tools -> Antialias'.
Close unused plot windows!
Please close any unused plot windows if using a virtual machine. This case study uses high resolution data over a relatively large domain. Multiple plot windows can therefore require significant amounts of computer memory which can be a problem for virtual machines with restricted memory.
Task 2: MSLP and 500hPa geopotential height
This task creates Figure 2. from Pantillon et al.
Right-click the mouse button on the 'an_1x1.mv' icon and select the 'Edit' menu item.
An edit window appears that shows the Metview macro code used to generate the plot. During these exercises you can change the top lines of these macros to alter the choice of parameters and plot types.
#Available parameters: # mslp,t2,wind10,speed10,sst # t,z,pt,eqpt [850,700,500,200] # wind,speed,r[925,850,700,500,200] # w700, vo850, pv320K
The surface fields (single level) are: MSLP (mean-sea-level-pressure), 2-metre temperature (t2), 10-metre wind arrows (wind10), wind-speed at 10m (sqrt(u^2+v^2): speed10), sea-surface temperature (sst).
The upper level fields are: temperature (t), geopotential (z), potential temperature (pt), equivalent potential temperature (eqpt), wind arrows (wind), wind-speed (speed), relative humidity (r).
These 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.
Some extra fields are also provided: vertical velocity at 700hPa (w700), relative vorticity at 850hPa (vo850) and potential vorticity at 320K.
Wind fields are normally plotted as coloured arrows. To plot them as wind barbs add the suffix '.flag'. e.g. "wind10.flag" will plot 10m wind as barbs.
With the edit window open, find the line that defines 'plot1':
#Define plot list (min 1- max 4) plot1=["mslp"] # use square brackets when overlaying multiple fields per plot
Change this line to:
plot1=["z500.s","mslp"]
The suffix '.s' means plot the 500hPa geopotential as a shaded plot instead of using contours (this style is not available for all fields)
As above, click the play button and then animate the map that appears.
Compare an animation of the z500 and mslp fields with Figure 1. from Pantillon et al.
1. When does the cut-off low form (see z500)?
2. How close do Nadine and the cut-off low get in the analyses?
3. Instead of z500, plot the PV at 320K (similar to Fig.13 in Pantillon). 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 'an_1x1.mv' icon and select the 'Edit' menu item.
In the edit window that appears
#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.
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.
Task 4: Wind fields, sea-surface temperature (SST)
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.
#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 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".
Animating. If only one field on the 2x2 plot animates, make sure the menu item 'Animation -> Animate all scenes' is selected.
Plotting may be slow depending on the computer used. This reads a lot of data files.
What do you notice about the SST field?
Task 5: Satellite images
Open the folder 'satellite' by doubling clicking (scroll the window if it is not visible).
This folder contains satellite images (water vapour, infra-red, false colour) for 00Z on 20-09-2012 and animations of the infra-red and water vapour images.
Double click the images to display them.
Use the an_1x1.mv and/or the an_2x2.mv macros to compare the ECMWF analyses with the satellite images.
Task 6: Cross-sections
The last task in this exercise is to look at cross-sections through Hurricane Nadine and the cut-off low.
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) 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.
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]
Changing fields
A smaller set 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))
Changing cross-section location
#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.
Look at the PV field, how do the vertical structures of Nadine and the cut-off low differ?
This completes the first exercise.
You have learnt how to use the macros, alter fields for plotting and animate fields. The next exercises use similar macros.
Exercise 2: The operational HRES forecast
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 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 performance of the operational HRES forecast of the time.
Available forecast
Data is provided for a single 5 day forecast starting from 20th Sept 2012, as used in the paper by Pantillon et al.
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.
Fields available
A new field is total precipitation : tp.
The fields (parameters) available in the analyses are available in the forecast data.
Questions to consider
- How does the HRES forecast compare to analysis and satellite images?
- Was it a good or bad forecast? Why?
Available plot types
For this exercise, you will use the metview icons in the row labelled 'HRES forecast' as shown above.
hres_rmse.mv : this plots the root-mean-square-error growth curves for the operational HRES forecast compared to the ECMWF analyses.
hres_1x1.mv & hres_2x2.mv : these work in a similar way to the same icons used in the previous task where parameters from a single lead time can be plotted either in a single frame or 4 frame per page.
hres_to_an_diff.mv : this plots a single parameter as a difference map between the operational HRES forecast and the ECMWF analysis. Use this to understand the forecast errors.
Forecast performance
Task 1: Forecast error
In this task, we'll look at the difference between the forecast and the analysis by using "root-mean-square error" (RMSE) curves as a way of summarising the performance of the forecast.
Root-mean square error curves are a standard measure to determine forecast error compared to the analysis and several of the exercises will use them. The RMSE is computed by taking the square-root of the mean of the forecast difference between the HRES and analyses. RMSE of the 500hPa geopotential is a standard measure for assessing forecast model performance at ECMWF (for more information see: http://www.ecmwf.int/en/forecasts/quality-our-forecasts).
Right-click the hres_rmse.mv icon, select 'Edit' and plot the RMSE curve for z500.
Repeat for the mean-sea-level pressure mslp.
Repeat for both geographical regions: mapType=1 (Atlantic) and mapType=2 (France).
1. What do the RMSE curves show?
2. Why are the curves different between the two regions?
Task 2: Compare forecast to analysis
Use the hres_to_an_diff.mv icon and plot the difference map between the HRES forecast and the analysis for z500 and mslp.
1. What differences can be seen?
2. How well did the forecast position the Hurricane and cut-off N.Atlantic low?
If time: look at other fields to study the forecast. For example, jet position, total precipitation (tp), PV (320K)
Task 3: Precipitation over France
This task produces a plot similar to Figure 2 in Pantillon et al.
Choose a hres macro to use, plot the total precipitation (parameter: tp), near surface wind field, relative humidity (and any other parameters of interest).
As for the analyses, the macros hres_1x1.mv, hres_2x2.mv and hres_xs.mv can be used to plot and animate fields or overlays of fields from the HRES forecast.
How does the timing and distribution of the precipitation from the forecast compare to the observations shown in the paper by Pantillon?
Suggested plots for discussion
The following is a list of parameters and plots that might be useful to produce for later group discussion. Choose a few plots and use both the HRES forecast and the analyses.
For help on how to save images, see the beginning of this tutorial.
- 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.
This completes the second exercise.
You have seen how the ECMWF operational HRES forecast of 2012-09-20 00Z performed compared to the analysis. The next exercises look at the ECMWF ensemble.
Exercise 3 : The 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 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.
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 (you should have seen this in the earlier tasks!).
2016 Operational ensemble
ens_2016: This dataset is a reforecast of the 2012 event using the ECMWF operational ensemble from 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 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 EDA members were in use at that time, whereas 25 would be used in the 2016 operational ensemble, so each EDA member will be used multiple times. This will impact on the spread and clustering seen in the tasks in this exercise.
Ensemble exercise tasks
Visualising ensemble forecasts can be done in various ways. During this exercise, in order to understand the errors and uncertainties in the forecast, we will use a number of visualisation techniques.
Key parameters: MSLP and z500. We suggest concentrating on viewing these fields. If time, visualize other parameters (e.g. PV320K).
Available plot types
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 an ensemble forecast member and the analysis 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' and so on.
Choose your ensemble dataset by setting the value of 'expId':
#The experiment. Possible values are: # ens_oper = operational ENS # ens_2016 = 2016 operational ENS # ens_both = OpenIFS (EDA+SV+SPPT+SKEB) # ens_initial = OpenIFS (EDA+SV) # ens_model = OpenIFS (SPPT+SKEB) expId="ens_oper"
Ensemble forecast performance
In these tasks, the performance of the ensemble forecast is studied.
- How does the ensemble mean MSLP and Z500 fields compare to the HRES forecast and analysis?
- Examine the initial diversity in the ensemble and how the ensemble spread and error growth develops. What do the extreme forecasts look like?
- Are there any members that consistently provide a better forecast?
Task 1: RMSE "plumes"
This is similar to task 1 in exercise 2, except now the RMSE curves for all the ensemble members from a particular forecast will be plotted.
Using the ens_rmse.mv icon, right-click, select 'Edit' and plot the curves for 'mslp' and 'z500'.
useClusters="off"
- How do the HRES, ensemble control forecast and ensemble mean compare?
- How do the ensemble members behave, do they give better or worse forecasts?
There might be some evidence of clustering in the ensemble plumes.
There might be some individual forecasts that give a lower RMS error than the control forecast.
If time:
- Explore the plumes from other variables.
- Do you see the same amount of spread in RMSE from other pressure levels in the atmosphere?
Task 2: Ensemble spread
In the previous task, we have seen that introducing uncertainty into the forecast by starting from different initial conditions and enabling the stochastic parameterizations in IFS can result in significant differences in the RMSE (for this particular case and geographical region).
The purpose of this task is to explore the difference in more detail and look in particular at the 'ensemble spread'.
Use the ens_to_an.mv icon and plot the MSLP and wind fields. 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.
This macro can be used to look at collections of ensemble members. It will be used later in the clustering exercises. For this task, make sure all the members of the ensemble are used.
#ENS members (use ["all"] or a list of members like [1,2,3] members=["all"] #[1,2,3,4,5] #["all"] ["cl1"]
- How does the mean of the ensemble forecasts compare to the HRES & analysis?
- Does the ensemble spread capture the error in the forecast?
- What other comments can you make about the ensemble spread?
If time:
- set the 'members=' option to change the number of members in the spread plots.
e.g. try a "reduced" ensemble by only using the first 5 ensemble members: "members=[1,2,3,4,5]".
Task 3: Spaghetti plots - another way to visualise spread
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 field using the default value for the contour level. This will indicate the low pressure centre. Note that not all members may reach the low pressure set by the contour.
Note that this macro may animate slowly because of the computations required.
Experiment with changing the contour value and (if time) plotting other fields.
Task 4: Visualise ensemble members and difference
So far we have been looking at reducing the information in some way to visualise the ensemble.
To visualise all the ensemble members as normal maps, we can use stamp maps. These are small, stamp sized contour maps plotted for each ensemble member using a small set of contours.
There are two icons to use, stamp.mv and stamp_diff.mv. Plot the MSLP parameter for the ensemble. Repeat for wind field.
Q. Using the stamp and stamp difference maps, study the ensemble. Identify which ensembles produce "better" forecasts.
Q. Can you see any distinctive patterns in the difference maps? Are the differences similar in some way?
If time:
Use the macros to see how the perturbations are evolving; use ens_to_an_diff.mv to compare individual members to the analyses.
Find ensemble members that appear to produce a better forecast and look to see how the initial development in these members differs. Start by using a single lead time and examine the forecast on the 28th.
- 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 in the formation of the storm?
- use the pf_to_cf_diff macro to take the difference between these perturbed ensemble member forecasts from the control to also look at this.
Use 'mapType=1' to see the larger geographical area (please note that due to data volume restrictions, this mapType only works for the MSLP parameter).
Task 5: Cumulative distribution function at different locations
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 |
|---|
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.
The CDF gives the probability that a value on the curve will be found to have a value less than or equal to the corresponding value on the x-axis. For example, in the figure, the probability for values less than or equal to X=0 is 50%.
The shape of the CDF curve is related to the shape of the normal distribution. The width of the CDF curve is directly related to the value of the standard deviation of the probability distribution function. For our ensemble, the width is then related to the 'ensemble spread'.
For a forecast ensemble where all values were the same, the CDF would be a vertical straight line.
Plot the CDF for 3 locations
This exercise uses the cdf.mv icon. Right-click, select 'Edit' and then:
- Plot the CDF of MSLP for the 3 locations listed in the macro.e.g. Reading, Amsterdam, Copenhagen.
- If time, change the forecast run date and compare the CDF for the different forecasts.
Q. What is the difference between the different stations and why? (refer to the ensemble spread maps to answer this)
Q. How does the CDF for Reading change with different forecast lead (run) dates?
Forecasting an event using an ensemble : Work in teams for group discussion
Ensemble forecasts can be used to give probabilities to a forecast issued to the public.
Notes from Frederic: email 7/4/16
day 2
See above. My preference after talking with people here is to use the comparison between 2012 operational ensemble and 2016 operational ensemble. The lower res (T319) ensembles; control (EDA+SV), (SPPT_SKEB) ensembles for this case are running now and we can include the data (as long as filesize does not become an issue). But honestly, I do not think there will be time. I will leave it to you to decide!
For the SCM we thought that it might be interesting to use the SCM for a point near Toulouse that experienced very heavy rainfall during HYMEX. Then we get the students to adjust the entrainment rates (similar to the convection exercises here) to see what impact it has on the precipitation?
Exercise 2.
Extended deterministic forecast : 20-28 September just for MSLP : Etienne told me that the ECMWF model of the 20 000UTC proposed a very extreme situation on the 28th, with a storm over Gibraltar. This would be a way to illustrate the limits of a deterministic approach.
.
As I am writing I am beginning to wonder if we should not make 2 groups : one for task 4 and one for task 5. Tasks 1-3 would be for all students. This would allow to keep the CDO task. What do you think ?
On 04/05/16 15:25, FERRY Frédéric wrote:
*T1279 Analysis* : 20121020 0000UTC to 20121025 0000UTC --> Only the 20
september analysis will be looked at but I assume you need to get the
other analysis to compute the RMSE in day 2 ?
*Extended analysis* : 15-20 September just for MSLP and T2m (or better
the SST) --> Nadine tracking before the 20th
15-20th ok. We'll produce all the variables for this date range as the students can animate the fields if they have them. If data size is an issue we'll restrict 15-19th to just T2m & MSLP.
The SST is a good point as this will be interesting to compare between the analysis and the forecasts. I'll add it to the list of fields.
I assumed the students would look at the analyses on all days. The first task would be to look at the track of Nadine & the cut-off low and animate different fields across the date range. This is very easy to do with the metview macros. We can also get them to plot difference maps between the forecasts and the analyses (and yes we need all the days to compute RMSE) if you think that's instructive.
As the data is time-consuming to extract (it will take ~ 7 days), I would rather get more than we think we might use in the exercises to allow the students to explore their own ideas during the workshop.
> *T1279 Deterministic forecast* : 20 000UTC analysis and 20121025 0000
> UTC deterministic forecast (t+120).
Ok. We'll use 6hrly data throughout.
*Extended deterministic forecast* : 20-28 September just for MSLP :
Etienne told me that the ECMWF model of the 20 000UTC proposed a very
extreme situation on the 28th, with a storm over Gibraltar. This would
be a way to illustrate the limits of a deterministic approach.
I'd rather not add another 3 days of data (26-28) to show a single forecast. If this is just a short example to the students during the workshop, can we plot these and include just the figures? (ie. not an interactive exercise)
All ok apart from:
3 : Equivalent potential temperature at 850 hPa + winds at 850 hPa +
vertical velocity at 600hPa + MSLP in background --> focussing on the
Can we use 700hPa VV instead of 600, to be consistent with data on other levels? We will need VV on multiple levels in order to plot the x-sections (see below), though these will only be available 00Z on each day. The horiz. maps will have VV available 6hrly but on selected levels only (we're proposing 200, 500, 700, 850).
Proposed tasks for Day 1 :
5 : Beyond D+5 deterministic scenario : MSLP only
See above. I think this will be too much data for us to include beyond day 5.
Concerning the ensemble runs, 6 hourly data is OK. If you have space on
the VM it would be interesting to go up to D+10 (or D+15). This would
allow to try and look at the extreme member over Gibraltar on the 28
September.
Task 1 : forecast error
Task 2 : compare forecast to analysis
we think task 2 fits better on day 1 when the students visualize the analyses & HRES forecast. If time, they look at HRES forecast error though that could be day 2. Day 2 then becomes mostly about the ensemble.
Task 3 : visualize ensembles (plumes, ensemble spread, spaghetti, stamp,
CDO)
--> These 3 tasks from last year are very interesting. To gain time
maybe that we should put a group on each item for task 3 or suppress
task 2 ? The CDO adds a "statistical" taint to the workshop do you think
we can adapt it to our case ?
Maybe we can put the students into the role of the forecaster during HyMEX; given the CDF plots & clusters, what would the students have decided to do and why?
We did this last year asking the question what would the participants do if the Queen had a birthday garden party and the forecast showed a risk of severe weather It was a good discussion.
Perhaps this could be led by Etienne based on Hymex experience?
Task 4 : PCA and clustering
Thanks for this. It will also be interesting to see what the latest operational ensemble does with this case (we do not yet know!). Comparing the two will be interesting.
So day 2 « menu » would be : Looking at the ensemble products and the cluster products and making a decision for Hymex field campaign —> They will have Etienne's forecaster feedback the day after.
I’ll ask Etienne his ideas for the workshop tasks on this topic. Looking at the impact of ocean coupling on the ensemble prediction. Tell me if you manage to redo the clustering and the composites in Metview, I hope it will work. If you manage to redo figures 5 6 7 8 and 10 I think I’ll have to tell Jean-Pierre to focus more during his presentation on the vortex-vortex interaction and the CRM sensitivity experiments he made. This will leave the cluster analysis for the students to discover.
Hello Glenn, Here are a few comments concerning your previous emails : 2- Véronique Ducrocq could play the role of an HyMeX operation director being the client of the students' forecast. This forecasting exercise could be done by the 8 students following the forecasting option (with me as their "teacher"), whereas the 18 others (informatic or statistic options) could keep doing more sensitivity tests while manipulating the code of the model (with Frédéric and you). 3- It would be very interesting to briefly tackle with the ECMWF Data Targeting System which was one of the observation strategies used during HyMeX SOP1. I precisely asked Véronique Ducrocq to speak about DTS during her presentation on Day 1. 4- ARPEGE and IFS deterministic charts are available at the French Met School between 18th and 24th sept (except the 20th runs unfortunately !). As far as I was the HyMeX forecaster myself before the 24th sept. event, I would be very interesting in the MSLP fields from the 20th 00UTC run between 25th and 28th sept. , in order to be able to illustrate (in my own Day 3 presentation) the propagation of this impressive "Gibraltar storm" I mentionned into my daily meeting report. A 6h step would be perfect, even if it is only a paper-scanned version...
Appendix
Datasets available
The following datasets are available on the Virtual Machine for this workshop:
Further reading
For more information on the stochastic physics scheme in (Open)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, 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.