A comparison of cloud microphysics in deep tropical convection forming over the continent and over the ocean Emmanuel Fontaine 1, Elise Drigeard 1, Wolfram Wobrock 1, Alfons Schwarzenböck 1, Julien Delanoë 2, Alain Protat 3 1 Laboratoire de Météorologie-Physique, CNRS, Aubière, France 2 Laboratoire ATmosphère, Milieux, Observations Spatiales, Velizy, France 3 Center for Australian and Weather Climate Research, Melbourne, Australia Introduction: Significant differences in the dynamics and microphysics of tropical MCS occurring over the continent and over the ocean are often reported. In this study deep convective systems over West Africa (Niger) are compared to those developing over the Indian Ocean next to the Maldives. The study uses the observations collected during the airborne experiments of MEGHA- TROPIQUES in August 2010 over the western part of the Niger and in Nov. /Dec over the Maldives'. The aircraft was equipped with the latest microphysical measuring techniques (2DS, CIP, PIP) allowing to resolve droplet and ice crystal size spectra from 25 μm to 6 mm. In addition cloud radar measurements onboard of the aircraft and ground based C and X band radar observations allow characterizing the horizontal and vertical structure of the observed MCS. Observational results presented here restrict to the stratiform regions of the MCS where radar reflectivity was less then 35 dBZ. Acknowledgements : The authors are particularly grateful to : (1) CNES for funding the aircraft measurement campaigns within the Megha-Tropiques project and (2) both CNRS and CNES for financing the PhD thesis of E. Fontaine. We would also like to thank SAFIRE for operating the French Falcon 20 research aircraft during the two campaigns. Laboratoire de Météorologie Physique, DACA 2013 Fig.1 in Aug Niamey (Niger) (13°N, 3°E) in Nov./Dec over Gan-Island (Maldives, 0°, Example for number distribution averaged during 10s during the flight #20 Observational sites: Airborne and surface measurements : Particle Imagers CIP: µm 2DS: µm PIP: 100µm – 6mm Fig1: composite cloud particle spectrum and surface Rain Radars C-band MIT Xport LTHE, IRD Fig.1 Important differences occur in ice crystal shape between continental and oceanic convection NIGER MT1 (Africa): flight 18 at T ~ – 26°C INDIAN OCEAN MT2 (Maldives): flight < T < -30°C Findings: Ice hydrometeors over the Niger have more aggregate and graupel- like shapes over the ocean pristine dendrite and stellar crystal shapes dominate The cloud particle Imager recorded the largest ice hydrometeors in the African convection The estimated total cloud ice mass in the African MCS exceeds significantly the ice mass in convective clouds over the ocean Ice particle morphology from 2D images: 3 mm Estimation of the crystal mass and the total Cloud Water Content CWC: Method: using the mass–diameter relationship: m=  D max  applying from cloud imagers: particle shape parameters as L max, particle width and surface and the number concentration fitting them to the observed reflectivity Z 94GHz measured by the airborne cloud radar ‘RASTA’ by means of detailed T matrix calculations and supposing that crystal shapes are prolate spheroid (Fontaine et al., 2013) the resulting values for  and  are given in Fig.2 number of individual β   α observations observed reflectivity calculated CWC (g/m3) number conc. (#/l) at 94GHz (dBZ) Fig.2 Niger Maldives’ Niger Size dependent properties resulting from the mass – diameter approach m=  D max  Niger Maldives’ Fig.3 Ice density (g cm -3 ) (supposing a sphere with equivalent D max ) Ice mass distribution (g m -3 µm -1 ) Simulated 94 GHz reflectivity (dBZ) - cloud radar - Simulated 9 GHz reflectivity (dBZ) - rain radar - Results Ice density for large particles is slightly higher in African MCS also the ice mass is higher for African MCS the retrieved microphysics allows to simulate cloud and rain radar reflectivity: values for the African clouds are stronger especially in the upper levels Validation of the retrieved microphysics by surface rain radar 2 volumetric scanning radars were available during MT1: MIT C-band with 15 scans every 10 minutes Xport with 12 scans every 12 minutes 25 flight hours collocated with the MIT radar are available PPIs of MIT C-band during MT1, Niger Technique of co-localization between radar and aircraft: - use of all scans collected during one observational period of 10 min - steady state hypothesis of the reflectivity field during this period - spatial interpolation (by inverse distance) using 8 observation points Results Fig.4 shows that the retrieval of the reflectivity for precipitation radars is in fact possible when airborne microphysical observations are available. Regions below the melting level were excluded from the analysis as cloud radar measurements with RASTA (needed for the retrieval of  and  ) are strongly attenuated. Results are best when the distance between ground radar and aircraft is smaller then 80 km. For longer ranges, the cloud voxels scanned by the radar increase strongly, but sampling time and volume by the aircraft remain small and difference between observed and retrieved reflectivity become stronger. Discussion and Conclusion: By means of a mass-diameter approach the microphysical properties in stratiform glaciated regions of deep convective systems can be well described. Radar reflectivity observed by ground radars could be quite accurately recalculated by means of the cloud in-situ observations and this mass-diameter approach. Differences between continental and oceanic convection are obvious. Hydrometeors in the African MCS are larger and more dense leading to a higher total water content. Future objective: can this technique also be applied to mixed phase cloud region in MCS? Fig.4 radar reflectivity retrieved from in-situ microphysics vs. radar observations: upper plot aircraft altitude and distances aircraft-radar during flight 20 (MT1, Niger); yellow shaded zone: aircraft in layers > 0°C lower plot aircraft collocated reflectivity (blue) Xport 9.4 GHz radar (black) MIT 5.5 GHz (red) retrieved reflectivity Z e using Rayleigh approximation Z e   N (D equi ) D equi 6 d D equi with