1. A comparison of airborne in-situ cloud microphysical measurements with ground C and X band radar observations in African squall lines E. Drigeard 1, E. Fontaine 1, W. Wobrock 1, A. Schwarzenböck 1, E.R. Williams 2, F. Cazenave 3, M. Gosset 4, A. Protat 5 and J. Delanoë 6 ICCP 2012, July 30 – August 03, Leipzig, Germany 1 2 3 4 56

2. Introduction : The Megha-Tropiques mission French-Indian satellite (launched on the 11/10/12) –To improve our knowledge of the processes linked to the tropical convection and precipitation 2 ground validation campaigns (Niger & Maldives) –Aircraft measurements with the French Falcon 20 (CIP, PIP, 2DS probes, cloud radar RASTA)

3. Introduction : The Megha-Tropiques mission French-Indian satellite (launched on the 11/10/12) –To improve our knowledge of the processes linked to the tropical convection and precipitation 2 ground validation campaigns (Niger & Maldives) –Aircraft measurements with the French Falcon 20 (CIP, PIP, 2DS probes, cloud radar RASTA) –2 ground radars : MIT & Xport Objective : comparing ground based radar reflectivity with those calculated from in-situ microphysical observations

4. MIT & Xport radar : Data description Volumetric protocol : –3D spatial distribution of the reflectivity every 12 minutes Elevations : - Xport : 12 angles from 2 to 45° - MIT : 15 angles from 2 to 24°

5. MIT & Xport radar : Data description MIT radar : –On the Niamey airport –C-band (5.5 GHz) –Range of 150km Xport radar : –30 km SE of the airport –X-band (9.4 GHz) –Range of 135km To compare radar data and in-situ observations : Co-localization of the 2 ground radars data and the aircraft position Δ Xport radar + MIT radar 90 km

6. MIT & aircraft trajectory

7. Co-localization radar-aircraft : Method Use of all scans collected during a observationnal period Steady state hypothesis of the reflectivity field during this period (increasing the vertical resolution) Spatial interpolation (Inverse Distance Weighting) using 8 observation points 2 3 1 4 5 6 7 8 250 m 1° 1- 7° 250 m Radar 

8. Co-localization : Validation Comparison of observed and calculated RHI scans for the MIT radar –Differences increase with distance (deterioration of the vertical resolution of the volumetric data) –Statistical analysis : standard deviation = 3dBZ Calculated RHI (15 scans) Measured RHI (300 scans) ± 3dBZ

9. Co-localization : Validation Good agreement between co-localized MIT reflectivity and airborne radar RASTA Very similar pattern for the airborne and the ground observation 5.5 GHz 95 GHz

10. Calculation of reflectivity from in-situ microphysics In-situ probes (PIP, CIP, 2DS) show cloud particles from 50µm to 5mm. The cloud particles have irregular shapes (graupel, aggregate) m=αD βTo calculate the equivalent reflectivity Z e, a power mass law m=αD β is applied: Example for number distribution averaged during 10s during the flight #20

11. Calculation of reflectivity from in-situ microphysics In-situ probes (PIP, CIP, 2DS) show cloud particles from 50µm to 5mm. The cloud particles have irregular shapes (graupel, aggregate) m=αD βTo calculate the equivalent reflectivity Z e, a power mass law m=αD β is applied: α is determined by matching the reflectivity calculated by Mie theory with measurements of the cloud radar RASTA at 95GHz  0.001 < α < 0.1; and β = 2.1 The mass law obtained in this way is applied again to calculate the reflectivity of the precipitation radars MIT and Xport (using Rayleigh approximation)

12. Co-localization radar-aircraft : Results - Calculated reflectivity is in good agreement with observations of both ground radars - Best results in regions where aircraft < 8000 m and range < 80 km

13. Co-localization radar-aircraft : Results Some periods with differences between signals Statistically : MIT - microphysicsXport - microphysics Mean1.44 dBZ-0.96 dBZ Standard deviation4.76 dBZ5.51 dBZ

14. Conclusions Reflectivity observed by precipitation radar can be recalculated from in-situ cloud microphysical measurements, if a mass-diameter relationship in a form of m=αD β is applied (instead of m~D 3 ) Limits : –mixte phase clouds and predominantly cold clouds (in the levels from -5 to -30°C) –where reflectivity prevails from 15 to 35 dBZ.

16. DYNAMO

17. Mesures microphysiques : enregistrement d’images 2D. Tailles des hydrométéores mesurés [50 6400]µm

18. déduction de la distribution en tailles des hydrométéores (et surface)

19. Numerical simulations to retrieve β =f°(σ) relation Projection 2D V(Dmax)  A(Dmax) Estimation de la masse, densité, et loi masse-diamètre

21. Résultats pour MT2010

23. Pour MT2 ?

24. T > 0 T<0

25. MT-DYNAMO 2011

30. DYNAMO - Meilleure journée pour les données microphysiques : 27/11/2011:Vols #45 et #46 - Radars présents : - RASTA (95 GHz) - SPol (2.80 GHz) - SMART-R (5.63 GHz)

31. DYNAMO Vol #45

32. DYNAMO Radar SPol : - Protocole volumique de 5 minutes toutes les 15 minutes - 8 élévations (entre 0.5 et 11°)

33. DYNAMO Vol #46

34. DYNAMO

35. Travail en cours : Radar SMART-R –Protocole volumique de 7.5min toutes les 10 minutes –26 élévations (entre 0.5 à 33°) –Protocole difficile à décoder

36. Vertical Structure Niamey (Niger)Gan-Island (Maldives) strong wind shear in 850 hPa significant instability at the surface strong wind shear in 300 hPa weaker instability 800 900 1000 500 600 700 200 300 400 100 150 0° 10° 20° 30° 4 5 10 20 (g/kg) 20 m/s

37. Megha-Tropiques, Niger 2010 Ice and water field after 7 h 250 km

38. Ice and water field after 7 h Fields of ice supersaturation and water supersaturation Fields of ice supersaturation and water supersaturation and LWC Megha-Tropiques, Niger 2010

39. Microphysics Microphysical instrumentation onboard the French F-20: - 2DS, CIP, PIP, 2D-C+P and a cloud radar (see poster P.12.29 by Fontaine et al.) 18 Aug. 2010, Niger

40. Microphysics Explanation for the second mode in the hydrometeor spectra:

41. model Dynamics - Niger Frequency analysis of the vertical wind field in cloudy air measurements max.35%max.73% all cloudy points TWC >0.5 gm -3

42. Maldives (MT2 – Dynamo) Data processing not completed Nov./ Dec. 2011 – only few MCS encountered

43. Measurements in convective clouds Africa versus Maldives g/m 3 Condensed water content during 3 hours of flight Niger Maldives flight #20 18 aug ’10 flight #46 27 nov ‘11

44. (km) 17.5 14 10.5 7 3.5 Water and Ice field Model set-up: Maldives Identical with the African set-up – however: stronger latent heat fluxes and weaker sensible heat fluxes (km) 17.5 14 10.5 7 3.5 Water field 350 km

45. Dynamics and Microphysics Frequency analysis of vertical wind Cloud particle spectra

51. « Pristine » range fit (80 µm,300 µm) « pré-precipitation » range fit (300 µm,1000 µm) Precipitation range fit (1000 µm,3000 µm) Tail of big hydrometeors (D>3000 µm) (not fitted in log-log) (fitted with exponential decrease law) Statistical studies of the shape of PSD using different in-situ imaging probes (2DS,CIP,PIP) Three ranges of hydrometeore size are used to fit the PSD shape in log-log unit ( i.e. looking for the best power law fit in each diameter ranges) The largest size range (D>5 mm) is fit in lin-log unit (exponnential decrease) This mean description of PSD shape is estimated at small scale (200 metres) and is used: 1- To compare the different probes in common range (wathever exact concentration measurements) 2- To quantify the variability of PSD shapes in MCS, compare this variability with mesoscale model results and test some normalisation approach to fit PSD. Pente « d’équilibre » P=-3 Mode d’accumulation Transition Pré, précipitation Fit log-log