OpenIFS user workshop 2015
Preface
In these exercises we will look at a case study using a forecast ensemble. You will study the evolution of the ECMWF main (HIRES) forecast and the ECMWF ensemble forecast for this event, as well as running your own OpenIFS forecast at lower resolutions and working with
Starting up metview
- Type the following command in a terminal window:
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metview |
Recap
Case study
St. Judes storm..... (see separate sheet?)
Key points
- sources of uncertainty: initial analysis and model error.
- .... etc....
Exercise 1. Evaluating the ECMWF forecast
(following metview training course ensemble forecast)
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Task 1: Visualise operational forecast
Dates 24th - 29th.
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Task 2 : Visualize the ensemble
- Visualize ensemble mean
How does the mean compare to HIRES & analyses?
Task 3 : Visualize ensemble spread
Ensemble spread is ....
Exercise 2. Creating an ensemble forecast using OpenIFS
(see separate handout?)
At 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).
Need steps to process the data for metview - macro or grib tools?
Exercise 3. Verifying / Quantifying OpenIFS forecasts
Experiments available:
- EDA+SV+SPPT+SKEB : nagc/gbzl in MARS
- EDA+SV only : nagc/gc11 in MARS
- SPPT+SKEB only : run by participants
Question. How best to organise the experiments? Each user has an account or use one account with multiple directories?
Tasks
- 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?
- Compute mean of -ve ensemble members and +ve ensemble members & compare with analysis. If you take the difference, is it zero? If not, why not?