OpenIFS user workshop 2015

Preface

In these exercises we will look at a case study using a forecast ensemble. You will start by studying the evolution of the ECMWF HIRES forecast and the ECMWF 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.

Starting up metview

• Type the following command in a terminal window:

metview

Recap

Case study

St. Judes storm..... (see separate sheet?)

Key points

• sources of uncertainty: initial analysis and model error.
• Initial analysis uncertainty: accounted for by use of Singular Vectors (SV) and Ensemble Data Assimilation (EDA).
• Model uncertainty: accounted for 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

(following approach in metview training course ensemble forecast)

Task 1: Visualise operational forecast

Dates 24th - 29th.

1. How does HIRES forecast compare to analysis?
2. How does HIRES forecast compare to observations?

Task 2 : Visualize the ensemble

1. Visualize ensemble mean
2. How does the ensemble mean compare to HIRES & analyses?

Task 3 : Visualize ensemble spread

Ensemble spread is ....

1. Visualize stamp map - are there any members that provide a better forecast?
2. Visualize spaghetti map - see how members spread over the duration of the forecast.

Exercise 2. Creating an ensemble forecast using OpenIFS

(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).

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 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?
• For this case only, does the forecast improve by including model uncertainty?
• 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?

Concepts to introduce

RMSE & CDF

What to look at for RMSE & CDF?

Exercise 4. Forecast for Reading.

Introduce Brier score?

Given HIRES & OpenIFS ensembles - what would forecast be for Reading? (or something similar).