1. Observation operators for wind-profiler reflectivity, weather- radar dual-polarization observations, and weather-radar refractivity Olivier Caumont et al. CNRM-GAME IODA-MED meeting 31 January 2013

2. 2 IODA-MED deliverables

3. 3 Outline of talk 1. Reflectivity from wind profiler O. Caumont 1, K. Y. Nawanti 2, F. Saïd 3, B. Campistron 3, Y. Bezombes 3, S. Derrien 3, O. Bousquet 1, J.-M. Donier 1, T. Douffet 1, O. Garrouste 1, J. Van Baelen 4, J.-L. Caccia 5, H. Luce 5 1.CNRM-GAME 2.ENIT (Tunisia) 3.LA/CRA 4.LaMP 5.LSEET 2. Weather-radar dual-polarization observations 3. Weather-radar refractivity

4. 4 Reflectivity from wind profiler Observations = vertical profiles of:  Doppler velocity (V, already assimilated)  Reflectivity or C n ² (refractivity turbulence structure constant)  Doppler spectrum width (  eddy dissipation rate ε) Development of an observation operator (Stankov et al. 2003): © S. Derrien CRA’s UHF wind profiler L w /L  : ratio of the outer length scales for potential refractive index and shear (set to 4 here) p: pressure (hPa) T: temperature (K) q: specific humidity (kg kg -1 ) g: acceleration of gravity (9.81 m s -2 ) Γ: adiabatic lapse rate (9.8 10 -3 K m -1 ) (Brunt-Väisälä frequency)

5. 5 Reflectivity from wind profiler Comparison of C n 2 from UHF profiler and radiosonde  5 to 10 % error, with large variability Vertical profiles of mean relative error between C n 2 (dB) measured by LA’s UHF radar and simulated from radiosonde (solid, thick line), and their standard deviations (dashed lines). Low mode High mode (F. Saïd)

6. 6 a) b) Reflectivity from wind profiler Validation of observation operator:  Boundary Layer Late Afternoon and Sunset Turbulence (BLLAST, http://bllast.sedoo.fr/) field campaign in southwestern France (summer 2011) http://bllast.sedoo.fr/  CRA and CNRM-GAME UHF profilers  overall consistent; some discrepancies (black ellipses) but also some notable matching patterns (blue ellipses). Times series of log10(C n 2 ) vertical profiles (a) measured by CNRM-GAME’s UHF radar in high mode and (b) simulated from Arome analyses between 15 and 28 June 2011

7. 7 Reflectivity from wind profiler Future work:  Study each component of the observation operator separately oCompare Arome, wind profiler, and independent measurements (RS) oFor each component, determine which is the best estimate (Arome or wind profiler)  Investigate the sensitivity of C n 2 to each of these components  If needed, use alternative formulations (other than Stankov’s)

8. 8 Outline of talk 1. Reflectivity from wind profiler 2. Weather-radar dual-polarization observations O. Caumont 1, C. Augros 2, P. Tabary 2, V. Ducrocq 1 1.GAME 2.DSO/CMR (Météo-France) 3. Weather-radar refractivity

9. 9 Weather-radar dual-polarization data Observations:  Horizontal+vertical polarizations  additional information about hydrometeors (nature, size, shape, etc.)  Already 12 dual-pol radars (out of 26) in the French operational network (purple, blue, and green); more to come!  Widely-used technology on research radars  Complex observations  need for significant work on observation operator Work done:  On-going development of a versatile observation operator in Meso-NH’s post-processing

10. 10 Weather-radar dual-polarization data Status of observation operator in Meso-NH’s post-processing:  Gridpoint observations of ZHH, ZDR, KDP (J.-P. Pinty): oRayleigh and Jameson scattering oZDR and KDP only sensitive to rain

11. 11 Weather-radar dual-polarization data: Gridpoint observation operator Aaaaa  Bbbb oCccc  Ddddd  Reflectivity (ZHH in dBZ) Meso-NH simulation of 8 Oct. 2002, 21 UTC Horizontal cross sections at 2 km MSL Differential reflectivity (ZDR in dB)   Specific differential phase shift (KDP in ° km -1 )

12. 12 Weather-radar dual-polarization data Status of observation operator in Meso-NH’s post-processing:  Gridpoint observations of ZHH, ZDR, KDP (J.-P. Pinty): oRayleigh and Jameson scattering oZDR and KDP only sensitive to rain  PPI (cones) of ZHH, ZDR, KDP, and more (C. Augros et al.): oVarious scattering algorithms (Rayleigh(-for-spheroids), Mie, T-matrix) oPropagation ((differential) attenuation) and broadening effects oMove from T-matrix dynamic, burdensome scattering computations to lookup tables to increase numerical efficiency oAddition of ρHV

13. 13 Rayleigh Mie Rayleigh spheroids Rayleigh 6 th order T-matrix Meso-NH simulation of 8 Oct. 2002, 21 UTC S-band radar (Nîmes) Elevation: 2° T=15°C everywhere ZHH due to rain (dBZ) Weather-radar dual-polarization data: PPI simulations (C. Augros)

14. 14 ZDR (dB) Rayleigh Mie Rayleigh spheroids Rayleigh 6 th order T-matrix Weather-radar dual-polarization data: PPI simulations Meso-NH simulation of 8 Oct. 2002, 21 UTC S-band radar (Nîmes) Elevation: 2° T=15°C everywhere (C. Augros)

15. 15 KDP Rayleigh Mie Rayleigh spheroids Rayleigh 6 th order T-matrix KDP (° km -1 ) Weather-radar dual-polarization data: PPI simulations Meso-NH simulation of 8 Oct. 2002, 21 UTC S-band radar (Nîmes) Elevation: 2° T=15°C everywhere (C. Augros)

16. 16 Weather-radar dual-polarization data Status of observation operator in Meso-NH’s post-processing:  Gridpoint observations of ZHH, ZDR, KDP (J.-P. Pinty): oRayleigh and Jameson scattering oZDR and KDP only sensitive to rain  PPI (cones) of ZHH, ZDR, KDP, and more (C. Augros et al.): oVarious scattering algorithms (Rayleigh(-for-spheroids), Mie, T-matrix) oPropagation ((differential) attenuation) and broadening effects oMove from T-matrix dynamic, burdensome scattering computations to lookup tables to increase numerical efficiency oAddition of ρHV oShort-term plans:  Implement scattering models for icy hydrometeors  Validation for a HyMeX case  Add to official Meso-NH version & Write documentation

17. 17 Outline of talk 1. Reflectivity from wind profiler 2. Weather-radar dual-polarization observations 3. Weather-radar refractivity O. Caumont 1, A. Foray 2, L. Besson 3, J. Parent du Châtelet 3, C. Boudjabi 4 1.GAME 2.DIRIC (Météo-France) 3.DSO/CMI (Météo-France) 4.LATMOS

18. 18 Weather-radar refractivity radar r1r1 r2r2 target #1 target #2 radar beam Principle:  Based on radar pulse’s propagation time through the atmosphere, which depends on the index of refraction of air (n) or refractivity (N=(n-1)∙10 6 )  Phase change between radar and target and over time (δΔφ) depends on path- averaged refractivity change over time (δ  N  ).  Initially formulated for klystron transmitters by Fabry et al. (1997)  Refractivity related to pressure (p in hPa), temperature (T in K), and water vapour partial pressure (e in hPa) through N=77.6 p/T+3.73 10 5 e/T² Work done:  Formulation for magnetron transmitters (Parent du Châtelet et al. 2012)  Link between refractivity and atmospheric phenomena (Besson et al. 2012)  Observation operator for refractivity + sensitivity study  Long-term comparisons of radar observations vs. Arome  Real-time production of refractivity maps during HyMeX SOP1

19. 19 Link between N and (T,q) (Besson et al. 2012) Mediterranean surface rain rate refractivity temperature relative humidity warm cold moist altitude low level 20 Oct 2008

20. 20 Monitoring: Time evolution for Nîmes radar    N  integrated over time (  Δ  N  )  Circle corresponds to 40-km range  Filtering of observations Radar observations (PPI at 0.6°-elevation) Arome analyses (beam height at 2 m AGL)

21. 21 Sensitivity to beam height Different models  LVLXX: model level (~terrain-following)  RAYCT: 4/3-Earth-model path of beam centre  RAYGE: 4/3-Earth-model path that hits the ground (most accurate operator)  AWS: AWS- vs. model-derived refractivity  Best results when beam within ~60 m AGL  No significant improvement when using more accurate models (Caumont et al. in rev. BLM) 2 m AGL ~60 m AGL ~15 m AGL ~113 m AGL

22. 22 Long-term observation-vs.-model statistics  Good consistency between refractivity observations and model  Similar spatial variability Time series of domain-averaged observation (red) and model (green) refractivity difference since the beginning of the period. Corresponding standard deviations in blue and purple, respectively. (Caumont et al. in rev. BLM)

23. 23 Real-time refractivity maps during HyMeX SOP1  Fabry’s algorithm adapted to magnetrons  http://sop.hymex.org/, Home>Observations>Radars>Single operational radars>refractivity http://sop.hymex.org/ Humide Sec

24. 24 Weather-radar refractivity Future/on-going work:  Improvement of the raw data quality (use of dual-polarization and higher elevations, use of quality index)  Deeper understanding of measurement physics (including role of turbulence)  Use in process studies Further reading:  Besson, L., C. Boudjabi, O. Caumont, J. Parent du Châtelet, 2012: Links between refractivity characteristics and weather phenomena measured by precipitation radar. Bound.-Lay. Meteorol., 143(1), 77–95, DOI : 10.1007/s10546-011-9656-7.  Caumont, O., A. Foray, L. Besson, J. Parent du Châtelet: An observation operator for radar refractivity change: Comparison of observations and convective-scale simulations. Bound.-Lay. Meteorol., in revision.  Parent du Châtelet, J., C. Boudjabi, L. Besson, O. Caumont, 2012: Errors caused by long-term drifts of magnetron frequencies for refractivity measurement with a radar: Theoretical formulation and initial validation. J. Atmos. Oceanic Technol., 29(10), 1428–1434, DOI: 10.1175/JTECH-D-12-00070.1.

25. Thank you for your attention!