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

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)

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

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°

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

MIT & aircraft trajectory

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 m 1° 1- 7° 250 m Radar 

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

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

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

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  < 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)

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

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

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.

DYNAMO

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

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

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

Résultats pour MT2010

Pour MT2 ?

T > 0 T<0

MT-DYNAMO 2011

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)

DYNAMO Vol #45

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

DYNAMO Vol #46

DYNAMO

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

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 ° 10° 20° 30° (g/kg) 20 m/s

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

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

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

Microphysics Explanation for the second mode in the hydrometeor spectra:

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

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

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

(km) 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) Water field 350 km

Dynamics and Microphysics Frequency analysis of vertical wind Cloud particle spectra

« 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