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The WWRP Forecast Demonstration Project MAP D-PHASE on flood forecasting in the Alps

The WWRP Forecast Demonstration Project MAP D-PHASE on flood forecasting in the Alps. Authors: Arpagaus, Dorninger, Hegg, Montani, Ranzi, Rotach Presentation: Saskia Willemse, MeteoSwiss. Technical Conference preceding CAS XV 16-17 November 2009, Incheon, South Korea. Outline .

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The WWRP Forecast Demonstration Project MAP D-PHASE on flood forecasting in the Alps

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  1. The WWRP Forecast Demonstration Project MAP D-PHASE on flood forecasting in the Alps Authors: Arpagaus, Dorninger, Hegg, Montani, Ranzi, Rotach Presentation: Saskia Willemse, MeteoSwiss Technical Conference preceding CAS XV16-17 November 2009, Incheon, South Korea

  2. Outline • D-PHASE essentials • Key elements and characteristics • Achievements and outreach • Lessons learned & future developments

  3. D-PHASE essentials • Forecast Demonstration Project for MAP • 2nd WWRP Forecast Demonstration Project (FDP) • Focus on heavy precipitation and flood forecasting • D-PHASE:Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region • D-PHASE Operations Period (DOP): June to November 2007 (COPS & “MAP season”) • 9 countries involved • 30 atmospheric models / 7 hydrological models in over 40 catchments

  4. Distributed real-time end-to-end forecasting system Medium range Short range Now-casting

  5. Key elements of D-PHASE • Centralised Visualisation Platform (forecasts & alerts; in real-time); • Data archiving ( COPS); • Nowcasting tools; • Systematic integration of end users; • Evaluation, objective and subjective.

  6. Overview: Is there any alert? Where? 8.8.2007, ~ 15:00 local time

  7. More details: Which models alert? 8.8.2007, ~ 15:00 local time

  8. Key characteristics of D-PHASE Samelook and feel for all models (colour coding etc.) joint alert definitions  same script to determine alerts same programs for plots Full information is provided to the end user; the diversity of forecasts is visible Hierarchical layout allowing to zoom in –from an overview to the details

  9. Same plots (variables, colour coding, …) for all models 8.8.2007, ~ 15:00 local time

  10. JDC = Joint D-PHASE/COPS Verification data set Achievements • Unprecedented data set model intercomparison / validation process studies (with COPS) test beds (COST 731 for Data Assimilation, HEPEX) Integrated Mesoscale Research Environment (WG MWFR) • Demonstration of operational coupling of hydrological and meteorological models

  11. Achievements Participation of users 45 end users institutions (civil protection ...) workshops & questionnaires feedback exchange of needs Scientific results  advances in ensemble hydrological modelling radar ensemble, high-resolution EPS, high-res reforecasting, fuzzy verification, economic forecast value, …. 21 peer reviewed papers 72 reports and ext. abs. 165 presentations BAMS Paper, Sept 2009

  12. Achievements Findings high resolution atmospheric models prove better importance of ensemble modelling Single-model ensemble substantially gains from calibration value of ‘ensemble observations’ (radar ensemble) Relative value param. conv. resolved conv.

  13. Outreach D-PHASE/COPS data set as testbed for COST / HEPEX, IMRE D-PHASE as role model for Swiss natural hazards platform (GIN) VP still up and running until GIN goes life…. Many operational hydrological services started to use model-based input (I, CH, D) Funded research projectsPriority Programme QPF (several) FP7 Imprints Austrian Science Foundation (VERITA) FOEN, Switzerland Univ Hamburg funding

  14. Lesson learned 1 End users feedback • Resolution needs to further increase (and will) • Take end users early on board • EPS’s need careful support (for interpretation) • ‘Ensemble thinking’ for air pollution modelling, heat wave warnings, health impact (e.g., pollen), …

  15. Lesson learned 2 Interoperability • International agreements needed (procedures, thresholds) • Multi-model availability by far exceeds multi-model use… • In the transformation to operations the commercial value of meteorological data is a barrier

  16. Finances Ways of dissemination within WMO / member countries ‘increase involvement of users and NMHSs from developing countries in future FDP’s...’ Themes / activity: WWRP could also propose themes future directions Future FDP‘s

  17. Thanks! Meteo- Swiss AEMet ARPA Piemonte DLR ARPA- SIMC Uni Wien ZAMG APAT CNMCA ETHZ Uni Brescia WWA Kempten ARPA Liguria LUBW IMK-IFU Uni Hohenheim ARPA Veneto POLIMI Uni Paul Sabatier WSL Env Canada ARPA Lombardia DWD Met Office MétéoFrance ISAC- CNR BAFU SRNWP Steering Committee & WG chairmen Operational Service University Res. Inst. Data Provider

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