1. Comparisons between polarimetric radar observations and convective-scale simulations of HyMeX first special observing period PhD student under the supervision of Olivier Caumont (CNRM/GMME/MICADO), Véronique Ducrocq (CNRM/GMME), Pierre Tabary (DSO/CMR) and Nicolas Gaussiat (DSO/CMR/DEP) IODA-MED / HyMeX ST WV Meeting 16 May 2014 Clotilde Augros

2. Polarimetric radar data Principle and French radar network 13 operational polarimetric radars 11 C-band 2 S-band 3 X-band polarimetric radars « RHYTMME » + data from Mont Vial radar  All new/upgraded radars will be polarimetric  Dual polarization  Simultaneous emission of 2 waves with horizontal and vertical polarization 2 Ø 4Ø 3.68Ø 2.9 Big drops are more oblate Ø 2.65Ø 1.75Ø 1.35

3. Polarimetric data What new information do they provide ? 3 26/10/2012

4. Polarimetric data and convective-scale NWP models 4 Objectives of the study: Develop a forward polarimetric radar observation operator: direct comparisons between radar and model Evaluate the potential of polarimetric data for assimilation in Arome Convective-scale NWP models operating at a horizontal kilometric resolution, with explicit description of convection, rich microphysics, enhanced data assimilation capabilities (e.g. the French NWP system AROME) Polarimetric radars the new standard for operational weather radars (S / C / X) in the world Dual-pol radars provide additional variables (Z DR,  DP, K DP,  HV, …) which help unveiling the cold & warm microphysics inside precipitation systems

5. Plan  Description of the polarimetric radar forward operator  Radar/model subjective comparisons Montclar C-band radar, IOP6 HyMeX: 24/09/2012 Nîmes S-band radar, IOP6 HyMeX: 24/09/2012  Radar/model comparisons : membership functions  Radar/model comparisons : CFAD  Conclusions and outlook 5

6. Description of the polarimetric radar forward operator 6 Input : model prognostic variables (T°, q v, q r, q s, q g, q c, q i …) Output : model and radar variables (reflectivity and radial velocity) interpolated in the radar projection (PPI) + polarimetric radar variables (Z hh, Z dr,  hv,  dp, K dp …)  From the radar simulator from Caumont et al 2006 in Meso-NH research model  Simulates beam propagation and backscattering  Simulates Signal-to-Noise Ratio (SNR) diagnosis of extinct areas (important at X-band)  Parameters fixed by the microphysics scheme ICE3 : PSD (gamma laws), density of snow/graupel/ice  « Free » parameters: dielectric constant, hydrometeor shape, orientation => Defined after a sensitivity study

7. Radar/model subjective comparisons 24/09/2012 (IOP 6 HyMeX) C band 7

8. Radar/model subjective comparisons 24/09/2012 (IOP 6 HyMeX) 8 C band

9. Radar/model subjective comparisons 24/09/2012 (IOP 6 HyMeX) 9 C band

10. Radar/model subjective comparisons 24/09/2012 (IOP 6 HyMeX) 10 S band

11. Radar/model subjective comparisons S band 24/09/2012 (IOP 6 HyMeX) 11

12. Radar/model subjective comparisons 24/09/2012 (IOP 6 HyMeX) 12 S band

13. Radar/model subjective comparisons 24/09/2012 (IOP 6 HyMeX) 13 S band

14. Radar/model subjective comparisons 24/09/2012 (IOP 6 HyMeX) 14 S band

15. 24/09/2012 C-band Montclar S- band Nimes Rain Snow Radar/model comparisons : membership functions Distribution of Zdr as a function of Zhh 15

16. Radar/model comparisons : membership functions 24/09/2012 Distribution of Kdp as a function of Zhh 16 C-band Montclar S- band Nimes Rain Snow

17. Radar/model comparisons : CFAD Montclar (C-band) – 24/09/2012 Radar Model Distribution of Zhh, Zdr and Kdp as a function of temperature in convective areas ZhhZdr Kdp 17

18. Radar/model comparisons : CFAD Nîmes (S-band) – 24/09/2012 Radar Model ZhhZdr Kdp 18 Distribution of Zhh, Zdr and Kdp as a function of temperature in convective areas

19. Conclusions and outlook 19  Main conclusions of radar/model comparisons for 24/09/2012 and 26/10/2012 Membership functions : good consistency between median Zdr and Kdp radar/model for a given Zhh. But high dispersion in radar data (natural variability of PSD + noise) CFAD of Zhh, Kdp and Zdr rather good consistency but varying with the case/radar Overestimation of snow/ice/graupel contents in some cases by the model?Underestimation of the maximum Zhh/Kdp in low levels (rain) Sharp transition between rain and snow in model But : uncertainties due to the methodology : all radar scans are not simultaneous => can impact vertical profiles + comparison of convective cells that do not necessarily have the same temporal evolution  Paper in preparation for HyMeX special issue in QJRMS + presentation of results at ERAD and HyMeX conferences (September 2014)  Outlook : toward the assimilation of polarimetric variables in Arome Literature review of the use of dual-pol variables for assimilation in NWP models Design of a methodology for the selection of polarimetric observations « useful » for assimilation Development of a new assimilation methodology using polarimetric data: to be defined this summer

20. Merci ! Vos questions sont bienvenues !