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Campaign data for parameterization tests: Examples from MAP‘99 VERTIKATOR’02, AWIATOR‘03 Hans Volkert, Thorsten Fehr, Christoph Kiemle, Oliver Reitebuch,

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Presentation on theme: "Campaign data for parameterization tests: Examples from MAP‘99 VERTIKATOR’02, AWIATOR‘03 Hans Volkert, Thorsten Fehr, Christoph Kiemle, Oliver Reitebuch,"— Presentation transcript:

1 Campaign data for parameterization tests: Examples from MAP‘99 VERTIKATOR’02, AWIATOR‘03 Hans Volkert, Thorsten Fehr, Christoph Kiemle, Oliver Reitebuch, Arnold Tafferner and Martin Weißmann DLR Oberpfaffenhofen, D Institut für Physik der Atmosphäre Evelyne Richard Laboratoire d‘Aérologie, CNRS & Université Paul Sabatier Toulouse, F

2 “Whenever possible, parameterizations should … be quantitatively validated against observations“ (Peixoto & Oort 1991) Textbook knowledge: High resolution … … simulation models need special data, e.g from dedicated field campaigns here:wind,boundary layer, precipitation microphysics Which variables and processes?

3 The campaigns MAP-SOP 1999Mesoscale Alpine Programme Special Observing Period „Weather and Alps“7 Sept. – 15 Nov 1999 cf.Bougeault et al. 2001, BAMS 82, 433–462 wind QJ, Jan. 2003 B (No. 588), 129, 341-895 VERTIKATOR-02Vertikaler Austausch, Trans- port und Orographie „Alpine Pumping“July 2002, north of the Alps wind & precipitation AWIATOR-03Wake Vortices at airports „Wake Vortex“Aug. 2003, north of Pyrénées wind

4 MAP-SOP 1999 IOP-15 8 Nov. north Föhn with waves

5 Rhone Aosta (model) orography (  x=1 km) 1.67–2.00 km aircraft triptych: B-A-C-D, 8 Nov. 1999

6 L North Föhn 8 Nov. 1999 ARPEGE ana. 12 UT 500 hPa: flow (max. 30 m/s) geopot. (  =40 gpm) model: Meso-NH with four nests (32, 8, 2, 0.5 km)

7 Drop-sounding: uniform wind direction ffddTrh  dry moist

8 Three drop-sondes in 2mins.: reproducibility 130355 130455 130555

9 vertical velocity: trans-Alpine sections RhoneAostaRhoneValtellina obs + sim (--) F C E +2 -2 18 legs4 legs10 legs

10 vertical velocity: 2 & 1/2 km simulation 2 km ½ km RhoneValtellinaRhoneAosta F C E +2 -2

11 water vapour triptych: lidar-obs. vs. simulation 100 350 100 350 w (C130) mean diff. backs retrieval (s.p.=0.7) sim. obs. 100 ppmv 200

12 VERTIKATOR 2002 8, 9, 19 July Alpine pumping & generation of thunderstorms

13 10 µm-System WIND DLR/CNRS/CNES/Meteo-France vert. res. 250 m hor. res. 3 - 15 km accuracy 0.5 - 2 m/s 2 µm-System DLR/CTI-MAG1 100 m 3 - 10 km 0.2 - 2 m/s Airborne Doppler Lidar at DLR

14 Flight Track Falcon 8 July 2002 13:05 - 15:34 LT Data processing with averaging 3 scanner revolutions: 10 - 11 km

15 east-west gradient in speed (> 4 km) southwest northerly winds along the Alps up to 2.3 km WIND July 8, 2002, 14:10 - 14:27 LT Track Bodensee  Chiemsee wind speed below 4 m/s (< 2.5 km)

16 MM5 by L. Gantner, Uni München WIND and MM5 on July 8, 2002 Track Bodensee  Chiemsee WIND: top, 14:10 - 14:27 LT; MM5: bottom, 14:00 LT

17 19 July: 2 µm Lidar and vertical velocity

18 shallow medium steep 40 km 9 July: Bistatic Doppler and polarimetric radar

19 steepshallow medium 12 min later...

20 shallow medium steep

21 12:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

22 13:00 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

23 13:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

24 14:00 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

25 14:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

26 15:00 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

27 15:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

28 16:00 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

29 16:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

30 17:00 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

31 17:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

32 18:00 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

33 18:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

34 19:00 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

35 19:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

36 20:00 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

37 20:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

38 21:00 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

39 21:30 UT Meso-NH 2 km res. surf. wind, all hydromet., accum. precip. storm gene- ration @ Alps

40 AWIATOR 2003 27 August Diurnal cycle of surface profiles RASS vs. LM & MM5

41 Tarbes Airport Airport Tarbes Pau The Pyrenees LM forecast domain (DWD) MM5 forecast domain 0 2 4 6 8 10 12 14 16 18 m/s 2000 1800 1600 1400 1200 1000 800 600 400 LM MM5 Wake Predictor P2P MM5 vertical grid Modelling chain: LM–MM5–P2P

42 00 UT virt. temperaturewind speed

43 01 UT virt. temperaturewind speed

44 02 UT virt. temperaturewind speed

45 03 UT virt. temperaturewind speed

46 04 UT virt. temperaturewind speed

47 05 UT virt. temperaturewind speed

48 06 UT virt. temperaturewind speed

49 07 UT virt. temperaturewind speed

50 08 UT virt. temperaturewind speed

51 09 UT virt. temperaturewind speed

52 10 UT virt. temperaturewind speed

53 11 UT virt. temperaturewind speed

54 12 UT virt. temperaturewind speed

55 13 UT virt. temperaturewind speed

56 14 UT virt. temperaturewind speed

57 15 UT virt. temperaturewind speed

58 16 UT virt. temperaturewind speed

59 17 UT virt. temperaturewind speed

60 18 UT virt. temperaturewind speed

61 19 UT virt. temperaturewind speed

62 20 UT virt. temperaturewind speed

63 21 UT virt. temperaturewind speed

64 22 UT virt. temperaturewind speed

65 23 UT virt. temperaturewind speed

66 24 UT virt. temperaturewind speed

67 Concluding messages Potentially useful, non-standard data are available for parameterization test of high-resolution models; here: remotely sensed wind and precipitation A suitable number of case-studies may be more revealing than standard statistics Modelling and measuring camps have to come closer together DANKE for listening to me !!!

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