EurEPS

1. EurEPS The Realization of an Operational LAM-EPS in Europe A SRNWP Project under EUMETNET Proposed to the EUMETNET Council at its 30th meeting by Norwegian Meteorological Institute

2. Redaction Team • Prof. Trond Iversen (Norway – HIRLAM); Leader • Andras Horanyi (Hungary – ALADIN) • Chiara Marsigli (Italy - COSMO) • Ken Mylne (UK – Met Office UM) • Yong Wang (Austria – LACE)

3. The Overall Aim of EurEPS • is to understand and quantify the benefits of the combination of a multi-centre ensemble of ensemble systems (grand ensemble), • and to investigate the practical and technological feasibilities for real-time combination of several decentralized ensemble forecast systems, • for the purpose of guiding real-time operational short-range probabilistic weather forecasting over Europe.

4. Ensemble forecasts Complete description of weather prediction in terms of aProbability Density Function (PDF) Ensemble members Temperature Ensemble mean Deterministic control Initial condition Forecast Forecast time

5. Why EPS and Probabilistic forecasting? • Valuable information on weather components with rudimentary predictability – thus impossible to give deterministically – can be forecasted • Smaller scales of free flows lose predictability first • these scales are frequently associated with high-impact weather • due to the associated potential loss, even small forecasted probabilities can be highly valueable. Hence EurEPS is proposed to help met services to protect life and property from weather-related hazards byexploiting existing technology and established methods in a cost-efficient way.

6. Forecast uncertainty- i.e. Predictability -varies from day to day Forecast uncertainty- i.e. Predictability -varies with spatial scale beyond Day 10 Day 7 High predictability Day 5 Day 3.5 planetary scales Small scales Source: ECMWF low predictability

7. Deterministic forecasts provides single solutions. The simplest way to provide probability information is to add an error distribution to a deterministic forecast, e.g.gaussian or some observed distribution. Ensemble forecasts provide case-dependent probability distributions accounting for the meteorological information available to the ensemble and model uncertainty.

8. Schematic illustration of the effect of non-linearity on an ensemble forecast.In the early stage of a forecast, ensemble members diverge quasi-linearly. In later stages, even when one member predicts severe weather, most members can be expected to be drawn towards model climatology. Forecasting high-impact weather requires a probabilistic approach

9. The Purpose • in a two-year perspective, is to establish potentials and identify practical obstacles for a pan-European grand short-range ensemble system, • compared to the alterative of using several sub-European and less populated ensemble systems which single NMSs could run on their own. • before taking decisions for an operational pan-European grand ensemble system, both the implied operational challenges and the benefits for operational probabilistic forecasting need to be investigated

10. The Multi-centre approach • The distributed multi-centre approach permits to • increase the population of the ensemble • account for a larger diversity of uncertainties • reduce each centre’s computational effort and the time required to produce a probabilistic forecasts. • The intention is to enable increased benefits for similar total cost, since resources are used to produce complementary information in parallel.

11. Pro and cons of a Multi-centre approach frequent and swift real-time exchange of large data amounts reduce computational effort and time required large common domain increase population and diversity cons pros

12. Objectives • To co-ordinate LAM-EPS within SRNWP to avoid duplication of efforts • To develop a prototype setup for a multi-centre European grand LAM-EPS based on existing developments in the consortia. • To establish the practical feasibility of real-time collection of LAM-EPS data for combination into grand LAM-EPS forecasts to be disseminated to all EurEPS participants. • To demonstrate the skill and value of the grand LAM-EPS for probabilistic prediction of high-impact events, • compared to smaller, single-centre LAM-EPS

13. Deliverables • a document defining a set of verification methods, weather parameters and thresholds to be verified, and a set of cases or periods for verification to allow the objectives to be answered • a database of a range of ensemble forecasts and observations, some of which to be combined to a grand ensemble. Population of this database is likely to be completed after the project • a report on the benefits of a grand LAM-EPS for the prediction of high-impact weather events, as opposed to benefits of running simpler distributed systems at single NMSs • definition of a European contribution to TIGGE-LAM

14. EurEPS data centre • The LAM-EPS planned in EurEPS basically combines existing systems in European NWP consortia and countries; • A EurEPS data centre should be established. • ECMWF with one fully dedicated person is proposed (128'000 euro per year)

15. EurEPS Status • Positive moral support from Eumetnet Council • No financial support from Eumetnet Statements: • Interoperability regarded so far as more urgent • EurEPS may depend on progress in Interoperability

16. EurEPS StatusCan we follow up, and how? • The GLAMEPS work continues as a HIRLAM-ALADIN co-operative effort • RT data-exchange in particular • The full EurEPS project will need support from Eumetnet. • Co-operation with SRNWP • and the Interoperability project to ensure that EurEPS needs are urgently considered • TIGGE-LAM link • EurEPS should follow guidelines from and work in co-operation with TIGGE-LAM.