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

Image RemovedImage Added

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.

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Available plot types

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Available plot types

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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.  TODO; what parameters and what location(s)?
• If time, change the forecast run date and compare the CDF for the different forecasts.

• Toulouse for your choice of ensemble
• Find a latitude/longitude point in the area of intense precipitation on 12Z 24/9/2012 (see Figure 2(c) Pantillon et al) and plot the CDF for MSLP (set station=[lat,lon] in the macro cdf.mv)

Note that only MSLP, 2m temperature (t2) and 10m wind-speed (speed10) are available for the CDF.

Make sure useClusters='off'.

 Forecasting during HyMEX : Work in teams for group discussion

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Exercise 4:

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Cluster analysis

The paper by Pantillon et al, describes the use of clustering to identify the main scenarios among the ensemble members.

This exercise repeats some of the plots from the previous one but this time with clustering enabled.

In this exercise you will:

• Construct your own qualitative clusters by choosing members for two clusters
• Generate clusters using principal component analysis (similar to Pantillon et al).

Task 1: Create own clusters

Clusters can be created by hand from lists of the ensemble members. Refer back to the plots from the previous exercise to choose members for two clusters.

The stamp maps are useful for this task.

From the stamp.map of total precipitation (tp) over France, identify ensemble members that represent the two most likely forecast scenarios.

Create two clusters by:

(TO BE DONE)

With this text file in place, now replot the:

RMSE curves

Stamp maps

with clusters enabled.

To enable clusters in the macros:

(TO BE DONE)

Use clusters_to_an.mv with user defined clusters.

Task 2: Empirical orthogonal functions / Principal component analysis

This task provides a quantitative way of clustering an ensemble by computing empirical orthogonal functions from the differences between the ensemble members and the control forecast. Although geopotential height at 500hPa at 00 24/9/2012 is used in the paper by Pantillon et al., the steps described below can be used for any parameter at any step.

Image Added

To use the principal component analysis (PCA), the eof.mv macro computes the EOFs and the clustering:

param="z500"
expId="ens_oper"
steps=[2012-09-24 00:00]

Exercise 5. Exploring the role of uncertainty using OpenIFS forecasts

To further understand the impact of the different types of uncertainty (initial and model), some forecasts with OpenIFS have been made in which the uncertainty has been selectively disabled. These experiments use a 40 member ensemble and are at T319 resolution, lower than the operational ensemble.

As part of this exercise you may have run OpenIFS yourself in the class to generate another ensemble; one participant per ensemble member.

Recap

• • EDA is the  Ensemble Data Assimilation.
  • SV is the use of Singular Vectors to perturb the initial conditions.
  • SPPT is the stochastic physics parametrisation scheme.
  • SKEB is the stochastic backscatter scheme applied to the model dynamics.

Experiments available:

• Experiment id: ens_both.  EDA+SV+SPPT+SKEB  : Includes initial data uncertainty (EDA, SV) and model uncertainty (SPPT, SKEB)
  
• Experiment id: ens_initial.  EDA+SV only  : Includes only initial data uncertainty
• Experiment id: ens_model. SPPT+SKEB only : Includes model uncertainty only

The aim of this exercise is to use the same visualisation and investigation as in the previous exercises to understand the impact the different types of uncertainty make on the forecast.

A key difference between this exercise and the previous one is that these forecasts have been run at a lower horizontal resolution. In the exercises below, it will be instructive to compare with the operational ensemble plots from the previous exercise.

Task 1. RMSE plumes

Use the ens_exps_rmse.mv icon and plot the RMSE curves for the different OpenIFS experiments.

Compare the spread from the different experiments.

Task 2. Ensemble spread and spaghetti plots

Use the ens_exps_to_an.mv icon and plot the ensemble spread for the different OpenIFS experiments.

Also use the ens_exps_to_an_spag.mv icon to view the spaghetti plots for MSLP for the different OpenIFS experiments.

If time:

• use the ens_part_to_all.mv icon to compare a subset of the ensemble members to that of the whole ensemble. Use the stamp_map.mv icon to determine a set of ensemble members you wish to consider (note that the stamp_map icons can be used with these OpenIFS experiments. See the comments in the files).

Task 3.  What initial perturbations are important

The objective of this task is to identify what areas of initial perturbation appeared to be important for an improved forecast in the ensemble.

Using the macros provided:

• find an ensemble member(s) that gave a consistently improved forecast and take the difference from the control.
• Step back to the beginning of the forecast and look to see where the difference originates from. 

Use the large geographical area for this task. Use the MSLP and z500 fields (and any others you think are useful).

Task 4. Non-linear development

Ensemble perturbations are applied in positive and negative pairs. This is done to centre the perturbations about the control forecast.

So, for each computed perturbation, two perturbed initial fields are created e.g. ensemble members 1 & 2 are a pair, where number 1 is a positive difference compared to the control and 2 is a negative difference.

• Choose an odd & even ensemble pair (use the stamp plots). Use the appropriate icon to compute the difference of the members from the ensemble control forecast.
• Study the development of these differences using the MSLP and wind fields. If the error growth is linear the differences will be the same but of opposite sign. Non-linearity will result in different patterns in the difference maps.
• Repeat looking at one of the other forecasts. How does it vary between the different forecasts?

If time:

• Plot PV at 320K. What are the differences between the forecast? Upper tropospheric differences played a role in the interaction of Hurricane Nadine and the cut-off low.

Notes from Frederic

TO BE DONE

Exercise 4. Exploring the role of uncertainty using OpenIFS forecasts

To further understand the impact of the different types of uncertainty (initial and model), some forecasts with OpenIFS have been made in which the uncertainty has been selectively disabled. These experiments use a 40 member ensemble and are at T319 resolution, lower than the operational ensemble.

As part of this exercise you may have run OpenIFS yourself in the class to generate another ensemble; one participant per ensemble member.

Recap

• • EDA is the  Ensemble Data Assimilation.
  • SV is the use of Singular Vectors to perturb the initial conditions.
  • SPPT is the stochastic physics parametrisation scheme.
  • SKEB is the stochastic backscatter scheme applied to the model dynamics.

Experiments available:

• Experiment id: ens_both.  EDA+SV+SPPT+SKEB  : Includes initial data uncertainty (EDA, SV) and model uncertainty (SPPT, SKEB)
  
• Experiment id: ens_initial.  EDA+SV only  : Includes only initial data uncertainty
• Experiment id: ens_model. SPPT+SKEB only : Includes model uncertainty only
• Experiment id: ens_oifs. SPPT+SKEB only, class ensemble  : this is the result of the previous task using the ensemble run by the class

The aim of this exercise is to use the same visualisation and investigation as in the previous exercises to understand the impact the different types of uncertainty make on the forecast.

A key difference between this exercise and the previous one is that these forecasts have been run at a lower horizontal resolution. In the exercises below, it will be instructive to compare with the operational ensemble plots from the previous exercise.

Task 1. RMSE plumes

Use the ens_exps_rmse.mv icon and plot the RMSE curves for the different OpenIFS experiments.

Compare the spread from the different experiments.

Task 2. Ensemble spread and spaghetti plots

Use the ens_exps_to_an.mv icon and plot the ensemble spread for the different OpenIFS experiments.

Also use the ens_exps_to_an_spag.mv icon to view the spaghetti plots for MSLP for the different OpenIFS experiments.

If time:

• use the ens_part_to_all.mv icon to compare a subset of the ensemble members to that of the whole ensemble. Use the stamp_map.mv icon to determine a set of ensemble members you wish to consider (note that the stamp_map icons can be used with these OpenIFS experiments. See the comments in the files).

Task 3.  What initial perturbations are important

The objective of this task is to identify what areas of initial perturbation appeared to be important for an improved forecast in the ensemble.

Using the macros provided, find an ensemble member(s) that gave a consistently improved forecast and take the difference from the control. Step back to the beginning of the forecast and look to see where the difference originates from. 

Use the large geographical area for this task. Use the MSLP and z500 fields (and any others you think are useful).

Task 4. Non-linear development

Ensemble perturbations are applied in positive and negative pairs. This is done to centre the perturbations about the control forecast.

So, for each computed perturbation, two perturbed initial fields are created e.g. ensemble members 1 & 2 are a pair, where number 1 is a positive difference compared to the control and 2 is a negative difference.

• Choose an odd & even ensemble pair (use the stamp plots). Use the appropriate icon to compute the difference of the members from the ensemble control forecast.
• Study the development of these differences using the MSLP and wind fields. If the error growth is linear the differences will be the same but of opposite sign. Non-linearity will result in different patterns in the difference maps.
• Repeat looking at one of the other forecasts. How does it vary between the different forecasts?

If time:

• Plot PV at 320K. What are the differences between the forecast? Upper tropospheric differences played a role in the interaction of Hurricane Nadine and the cut-off low.

day 2

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.

 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.

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. 

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...