1. High-resolution modelling in mountainous areas: MAP results Evelyne Richard Laboratoire d’Aérologie CNRS / Univ. Paul Sabatier Toulouse, France

2. What are the skills of high-resolution models to forecast orographically influenced precipitation? Does explicit (versus parameterized) convection lead to a gain in predictability? IOP 2a – 17 September 1999 A short, intense, isolated, convective event 70 mm within 12 hours Sensitivity experiments performed with Meso-NH

3. MAP IOP 2a: IR Meteosat

4. MAP target area

5. 18:00 UT19:00 UT20:00 UT MAP – IOP 2AComposite radar reflectivity @ z = 2km 250 km 21:00 UT 22:00 UT23:00 UT

6. Toce Ticino watershed ObservationSimulation (Δx =2km) 20:00 UT 23:00 UT Reflectivity @ 2000m

7. ECMWF: OP. ANA 1999 RADAR 12 hour accumulated precipitation SIMULATION

8. 17:00 UT18:00 UT 19:00 UT20:00 UT Composite radar reflectivity @ z = 2km Z > 60 dBz 250 km

9. Radar Retrieval (S-Pol) Simulation (Meso-NH) (x) hail + graupel (o) hail graupel hail 18:00 UT 19:00 UT 20:00 UT rain 12 km 100 km

10. Great ! My model is doing a good job

11. ECMWF: OP. ANA 1999 ARPEGE: OP. ANA. 1999 ECMWF: OP. ANA. 2002 ECMWF: REANALYSIS MAP - IOP2A: Intense Convection Strong sensitivity to initial state Low predictability RADAR OBSERVATIONS

12. ANA. OP. 1999 REANA: E9MI 850hPa water vapor mixing ratio : 17 September 1999 12UTC IOP 2a

13. ANA. OP. 1999 REANALYSIS 850hPa water vapor mixing ratio : 17 September 1999 12UTC IOP 2a REANA (NO MAP DATA)

14. ECMWF ANALYSIS MAP ECMWF REANALYSIS Low-level convergence between the Ligurian and Adriatic flows Increase in the model resolution -> higher mountains -> the Ligurian flow is blocked Streamlines at 1000 m, 17/09/99 12 UTC MAP IOP 2a Lascaux et al., 2004

15. Prectitability ? Still a long way to go !

16. IOP2b 20/21 September 1999: Orographic enhancement of a frontal system 200 mm within 30h Model intercomparison : MC2, MM5, MOLOCH, Meso-NH How do the models compare with each other ?

17. 19 Sept. 1999 12:0020 Sept. 1999 12:00 METEOSAT infrared

18. Sensitivity to the analysis ECMWF Op. Analysis MAP Reanalysis Max: 512 mmMax: 482 mm Mean: 78 mmMean: 87 mm

19. The different models: MESO-NH10 KM + 2.5 KM MOLOCH10 KM + 2 KM MM5-RE27 KM + 9 KM + 3 KM MM5-E118 KM + 6 KM + 2 KM MC240 KM + 10 KM +2 KM Initial and boundary conditions from ECMWF operational analyses From 19 Sep. 12 UTC to 20 Sep. 18 UTC (30 hours)

20. MAP - IOP2B - 19-20 Sep. 1999 Intercomparison exercise 4 non-hydrostatic models with horizontal resolution of 2 to 3 km Initialization based upon ECMWF operational analysis Accumulated precipitation from the 19th 15 UTC to the 20th 18UTC Toce-Ticino watershed

22. Rain gauges Time evolution of the mean hourly precipitation rate Radar

23. Time evolution of the correlation coeffecient (wrt rain gauges)

24. Heidke skill scores as a function of precip. class 1h precip.27h precip.

25. Comparison with rain gauge measurements (121 points)

26. Toce watershed 1532 km 2 Hydrological response

27. Grossi et al., 2004

29. How does the flow over complex terrain modify the growth mechanisms of precipitation particles? Three Doppler radars Monte Lema – Ronsard – S Pol Dual Doppler analysis 3D wind fields rerievals Microphysical retrievals Monte Lema S Pol Ronsard

30. Medina and Houze, 2003 U/Nh < 1 U/Nh > 1 dry snow wet snow light rain graupel riming heavy rain coalescence Blocked and stable case Unblocked and unstable case

31. To what extend the models able to reproduce this contrasted behaviour in the microphysics ?

32. Snow Graupel Hail Cloud Rain Ice IOP2A IOP2a ( Strong convection) - Deep system -Large amount of hail and graupel Mean vertical distribution of the hydrometeors IOP8 ( Stratiform event) - Shallow system - Large amount of snow IOP8 Snow

33. Dominant microphysical processes: DEPOSITION on ice (and sublimation) Growth of graupel by RIMING AUTOCONVERSION of pristine ice MELTING-CONVERSION of the snow (into graupel) ACCRETION of cloud droplets by raindrops Depletion of graupel by WET GROWTH of hail IOP2aIOP8

34. Conclusion: IOP 2A (Predictability) The use of non-hydrostatic high-resolution models will improve the precipitation forecast but only to some extend. Further improvement is tied to the improvement of the model initial state Adding mesoscale data in a global assimilation system is insufficient Mesoscale data assimilation system Limited area ensemble forecast (MAP D-PHASE)

35. IOP 2b (Model Intercomparison) –Very good consistency of the accumulated precipitation pattern –Model results over/under estimate the total precipitation by a factor ranging from +30% to -30% –The accuracy of the model precipitation is rather weak for the hourly rainfall but fairly reasonable for the precipitation accumulated over the 30h time period of the event –However model results are not yet accurate enough to be used for hydrological forecast on small watersheds

36. Explicit microphysical schemes do provide fairly realistic results … The contrasted microphysical behaviour between different IOPs is reasonably reproduced Convective – flow over ----> Strong riming and coalescence Stratiform – blocked flow ----> Melting of snow

37. http://www.aero.obs-mip.fr/map/MAP_wgnum ( 1) LA CNRS/UPS, Toulouse, France (2) CNRM, Météo-France, Toulouse, France (3) RPN, Montréal, Canada (4) ISAC, CNR, Bologna, Italy (5) University of L ’Aquila, Italy (6) University of Milano, Italy (7) University of Munich, Germany (8) University of Brescia, Italy (9) University of Waterloo, Canada N. Asencio (2), R. Benoit (3), A. Buzzi (4), R. Ferretti (5), F. Lascaux (1), P. Malguzzi (4), S. Serafin (6), G. Zängl (7), J-F. Georgis (1), R. Ranzi (8), G. Grossi (8), N. Kouwen (9)