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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, 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 a an 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 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.


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Table of contents

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Note

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



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  • HRES : spectral T1279 (16km grid) highest resolution 10 day deterministic forecast.
  • ENS :   spectral T639 (34km 31km grid) resolution ensemble forecast (50 members) is run for days 1-10 of the forecast, T319 (70km) is run for days 11-15.

In 2016, the ECMWF operational forecasts has been was upgraded compared to 2012 and consisted of:

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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 metviewMetview, please try increasing the memory assigned to the VM.

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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':

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This task will look at the synoptic development of Hurricane Nadine and the cutoff low up to 00Z, 20th September 2012. The forecasts in the next exercises start from this time and date.

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titleMetview icons in Analysis folder

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).

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Info

If the contour lines appear jagged, in the plot window, select the menu item 'Tools -> Antialias'.


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Q. What is unusual about Hurricane Nadine?


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titleClose 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.

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titlePlot PV at 320K

Change the value of "plot1" again to animate the PV at 320K.

Code Block
plot1=["pv320K"]

You might add the mslp or z500 fields to this plot e.g.

Code Block
plot1=["z500.s","pv320K","mslp"]

Note that the fields are plotted in the order specified in the list!

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titleQuestions
Q. When does the cut-off low form (see z500)?

Q. 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 the map geographical area

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For this exercise, you will use the metview Metview icons in the folder 'HRES_forecast' shown above.

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 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.

     : 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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  • "vo850", "mslp"               : vorticity at 850hPa and MSLP  -  low level signature of Nadine and disturbance associated with the cutoff low.
  • "r700", "mslp",         : MSLP + relative humidity at 700hPa  -  with mid-level humidity of the systems.
  • "pv320K", "mslp"     : 320K potential vorticity (PV) + MSLP  -   upper level conditions, upper level jet and the cutoff signature in PV, interaction between Nadine and the cut-off low.
  • "wind850", "w700"     : Winds at 850hPa + vertical velocity at 700hPa (+MSLP) : focus on moist and warm air in the lower levels and associated vertical motion.
  • "t2", "mslp"              : 2m-temperature and MSLP - low level signature of Nadine and temperature.
  • "mslp", "wind10"      : MSLP + 10m winds  -   interesting for Nadine's tracking and primary circulation.
  • "t500","z500"          : Geopotential + temperature at 500hPa  -  large scale patterns, mid-troposphere position of warm Nadine and the cold Atlantic cutoff.
  • "eqt850eqpt850", "z850"        : Geopotential + equivalent potential temperature at 850hPa  -  lower level conditions, detection of fronts.

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Q. 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 until the 30th Sept 00Z (step 240).

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fclen=5

to

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Changing cross-section location

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

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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. Use the cursor data icon Image Added to find the new position of the line.

Change the forecast time again to day+8 (28th Sept), or a different date if you are interested, relocate and plot the cross-section of Nadine and the low pressure system. Use the hres_1x1.mv icon from task 1 if you need to follow location of Nadine.

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Q. What changes are there to the vertical structure of Nadine during the forecast?
Q. What is the fate of the cut-off and Nadine?
Q. Does this kind of Hurricane landfall event over the Iberian peninsula happen often?

Cyclone phase space (CPS) diagrams

An objectively defined cyclone phase space (CPS) is described using the storm-motion-relative thickness asymmetry (symmetric/non-frontal versus asymmetric/frontal) and vertical derivative of horizontal height gradient (cold- versus warm-core structure via the thermal wind relationship). A cyclone's life cycle can then be analyzed within this phase space, providing insight into the cyclone structural evolution.

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this will plot (a) the mean of the ensemble forecast, (b) the ensemble spread, and (c) the HRES deterministic forecast and (d) the control forecast.


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. 


 this plots a vertical cross-section through the forecasts in the same way as the cross-section plots for the analyses.

 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.

Additional plots for further study

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.

this will plot the difference between the ensemble control, ensemble mean or an individual ensemble member and the HRES forecast for a given parameter.

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.

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.


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Q. What is different about SST between the two ensemble forecasts?

Cross-sections of

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an ensemble member

To show a cross-section of a particular ensemble member, use the macro ens_xs.mv.

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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"]

If your cluster definition file is has another name, e.g. ens_oper_cluster.fred.txt, then members_1=["cl.fred.1"].

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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 a 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 00 00Z 24/9/2012 is recommend (it 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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Q. What do the EOFs plotted by eof.mv show?
Q. Change the parameter used for the EOF (try the 'total precipitation' (tp) field). How does the cluster change?

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Q. What are the two scenarios proposed by the two clusters?
Q. How would you describe the interaction between Nadine and the cutoff cut-off low in the two clusters?
Q. How similar is the EOF computed clusters to your manual clustering?
Q. How useful is the cluster analysis as an aid to forecasting for HyMEX?

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Task 4: Forecast error curve

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Image Removed : this plots the root-mean-square-error growth curves for the operational HRES forecast compared to the ECMWF analyses.

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).

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Image Added : this plots the root-mean-square-error growth curves for the operational HRES forecast compared to the ECMWF analyses.

Right-click the hres_rmse.mv icon, select 'Edit' and plot the RMSE curve for z500.

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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?

Appendix

Further reading

For more information on the stochastic physics scheme in 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, 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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