OpenIFS user workshop 2015

Preface

In these exercises we will look at a case study using a forecast ensemble.The case study will look at severe wind-storms that hit Europe in late 2013 (see separate ECMWF article by Hewson et al, ECMWF Newsletter 139).

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

Recap

Key points

• Sources of uncertainty: initial analysis and model error.
• Initial analysis uncertainty: sampled by use of Singular Vectors (SV) and Ensemble Data Assimilation (EDA).
• Model uncertainty: sampled by use of stochastic processes. In IFS this means Stochastically Perturbed Physical Tendencies (SPPT) and the spectral backscatter scheme (SKEB)

ECMWF operational forecasts consist of:

• HRES : T1279 (16km grid) highest resolution 10 day forecast
• ENS : Ensemble (50 members), T639 for days 1-10, T319 days 11-15.

Exercise 1. Evaluating the ECMWF forecast

Objective: Understanding the error in the forecast by comparing the ECMWF forecast with analysis & observations

Starting up metview

metview

Task 1: Visualise operational forecast

Dates 24th - 29th October 2013.

Suggested fields to plot: MSL, Z200, 10m wind and visualize the storm track.

Use the metview macros to plot different days and compare to analysis and plot forecast differences.

  differences and layout 4 plots per frame (e.g.  3 forecasts & the analysis)

Task 2 : Visualize the ensemble forecasts and ensemble spread

Again using the ECMWF operational forecast,  look now at the 50 ensemble forecasts. These are at a lower resolution (T639) than the HIRES (T1279).

Objective: understand the forecast uncertainty

PlotsSuggested plots:

• 4 per frame: analysis, ensemble mean, spread + and 1 other?
• single frame spaghetti
• .
• spaghetti plots (multiple plots per frame)
• stamp plots.4 frame; 3 spaghetti + analysis?
• Observations of wind gust + analysis (faked): 1 day only.

Note that as only 6hrly wind gust data is available from the operational forecasts, we have supplemented the 3hrly fields using forecast data.

Exercise 2. CDF/RMSE at different locations

Concepts to introduce

Recap

TO DO: RMSE & CDF (needs concepts need explanation)Discuss concept of

Objective: Understand ensemble reliability.

• Choose 3 locations.e.g. Reading, Amsterdam, Copenhagen.
• Using 10m wind and/or wind gust data plot CDF & RMSE curves using one of the OpenIFS forecasts and the ECMWF analysis data.
• What is the difference and why?
• Repeat for one of the other OpenIFS runs. Are there any differences, if so why?

Exercise 3. Creating an ensemble forecast using OpenIFS

(see See separate handout?)

OpenIFS running at T319 (resolution of second leg of ECMWF's forecast ensemble).

Each participant runs one ensemble.

(possibly including Filip's coding exercise here).

At the end of this, participants will have a single member ensemble run with SPPT+SKEB enabled (model error only).

.

In this exercise, OpenIFS will be run on the ECMWF Cray XC30 to create  a forecast for the storm at T319 resolution using only the stochastic schemes in the model. All forecasts are started from the same initial conditions based on the analysis.Need steps to process the data for metview - macro or grib tools?

Aim is to understand the impact of these different methods on the ensemble

(point out this is a case study and the correct approach would be to use more cases to get better statistics)

Exercise 4. Verifying / Quantifying OpenIFS forecasts

Experiments available:

• EDA+SV+SPPT+SKEB : nagc/gbzl in MARS: Includes initial data and model uncertainty (experiment id : gbzl)
• EDA+SV only              : nagc/gc11 in MARSonly               : Includes only initial data uncertainty (experiment id: gc11)
• SPPT+SKEB only       : only        : Includes model uncertainty only (run by participants (available in MARS as gc1d, experiment id: oifs)

These are at T319 with start dates: 24/25/26/27 Oct 00Z for 5 days with 3hrly output.

Plots:

• as above
• 4 frame: fc-an, fc-fc, pert.fc, ctl-an? *(compare the fc-fc maps with fc-an maps - can we see the uncertainty in the difference?)
• PV maps

Task 1.

Objective: Understand the impact of changing the ensemble uncertainty

Look at ensemble mean and spread for all 3 cases.

• How does it vary?
• Which gives the better spread?
• How does the forecast change with reducing lead time?

Task 2.

Look at different ensemble sizes?

Task 3.

• For this case only, does the forecast improve by including model uncertainty?
• Find an ensemble member that gives the best 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.  How does this differ between the 3 OpenIFS runs? (with model uncertainty only, each initial state is identical so differences will develop from from

Task 4.

Ensemble perturbations are applied in positive and negative pairs. For each perturbation computed, the initial fields are CNTL +/- PERT. (need a diagram here)

• Choose an odd & even ensemble member from one of the 3 OpenIFS forecasts (e.g. members 9 and 10). For different forecast steps, compute difference of each member from the control forecast and then subtract those differences.  (i.e. centre the differences about the control forecast).
• What is the result? Do you get zero? If not why not? Use Z200 & Z500? MSLP?
• Repeat looking at one of the other forecasts. How does it vary between the different forecasts?

If time:

• Plot PV at 330K. What are the differences between the forecast? Upper tropospheric differences played a role in the development of this shallow fast moving cyclone.

Further reading

For more information on the stochastic physics scheme in (Open)IFS, see the article:

Shutts et al, 2011, ECMWF Newsletter 129.

Acknowledgements

Many people have contributed to this exercise and their contributions are acknowledged, in particular from ECMWF: Glenn Carver, Linus Magnusson, Martin Leutbecher, Sandor Kertesz, Iain Russell, Erland Kallen. From University of Oxford: Aneesh Subramanian, Peter Dueben, Peter Watson, Helen Christensen.