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Observation operator for weather-radar refractivity

Observation operator for weather-radar refractivity Olivier Caumont 1 , Lucas Besson 2 , Laurent Goulet 3 , Sophie Bastin 2 , Jacques Parent du Châtelet 2,4 , Laurent Menut 5 , Frédéric Fabry 6

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Observation operator for weather-radar refractivity

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  1. Observation operator for weather-radar refractivity Olivier Caumont1, Lucas Besson2, Laurent Goulet3, Sophie Bastin2, Jacques Parent du Châtelet2,4, Laurent Menut5, Frédéric Fabry6 1 CNRM-GAME (Météo-France, CNRS) – 2 LATMOS– 3 DIRSE (Météo-France) – 4 Observing Systems Department (Météo-France) – 5 LMD – 6 McGill University IODA-MED meeting 16 May 2014

  2. IODA-MED deliverables No update since last year’s meeting Talk by Clotilde Augros

  3. What is refractivity? • Refractivity: N = (n-1) x 106, where n = index of refraction of air. • Refractivity may be expressed as (Smith and Weintraub 1953): P: pressure (hPa) e: partial pressure of water vapour (hPa) T: temperature (K) • Refractivity mainly depends on moisture when temperature is high (at constant pressure): 1 N unit ~ 1 % relative humidity at 20°C • At constant pressure: High N = moist and/or cold Low N = dry and/or warm (Fabry et al. 1997)

  4. target #1 r2 r1 target #2 radar radar beam Principle of refractivity measurement by weather radar • Measurement by radar based on radar pulse’s propagation time through the atmosphere, which depends on refractivity. • Phase changebetween radar and target or between 2 targets depends on refractivity averaged over radar ray path (Fabry et al. 1997), i.e. ~ less than a few hundred metres above ground. • In practice, measurement of time phase change. Need for initial values, usually interpolated from automatic weather stations (AWSs) in homogeneous situation. • Technique initially for klystron (= stable-frequency) transmitters. Adaptation for magnetron (= drifting-frequency) transmitters (Parent du Châtelet et al. 2012).

  5. Summary of endeavour related to radar refractivity • Work done so far: • Formulation for magnetron transmitters (Parent du Châtelet et al. 2012) • Link between refractivity and atmospheric phenomena (Besson et al. 2012) • Technical proposals for improved-quality refractivity retrievals (Besson and Parent du Châtelet 2013) • Definition of quality index for target selection • Investigation of the use of faster antenna rotation speeds, additional elevations and dual-polarization returns • Observation operator for refractivity (Caumont et al. 2013): • Sensitivity study to formulation of observation operator • Long-term comparisons of radar observations vs. Arome • Comparison of radar refractivity with automatic weather stations and numerical simulations during HyMeX SOP1 (Besson et al., in prep. for HyMeX special issue) • Use of refractivity retrievals produced in real time during HyMeX SOP1 • Cross-validation with independent observations and models • First attempt to relate real refractivity data with Mediterranean meteorological processes

  6. Available observations • 3 operational radars: • Nîmes, Bollène, Opoul • 7 automatic weather stations (AWS): • Nîmes-Garons, Nîmes-Courbessac, Tarascon (Nîmes radar) • Visan (Bollène radar) • Perpignan, Leucate, Durban-Corbière (Opoul radar)

  7. Available models • WRF: • Initial & boundary conditions: nudging from NCEP global model • 2 nested domains: 54- and 9-km horizontal resolutions • N at 2 m AGL from innermost domain • AROME-WMED: • Initial & boundary conditions: Arpege global model • Horizontal resolution: 2.5 km • 3-h forecasts from a 3DVar assimilation cycle • N at 10 m AGL Date, Time D-1, 00 UTC D-1, 12 UTC D+3, 18 UTC WRF simulation NCEP analysis NCEP forecasts

  8. Refractivity time series @ Nîmes-Courbessac 8 August – 30 November 2012 High correlation coefficients between radar refractivity and other data: Radar vs AWS = 0.89 Radar vs Arome-WMED analysis = 0.90 Radar vs Arome-WMED forecast = 0.84 Radar vs WRF analysis = 0.83 Radar vs WRF forecast = 0.79 Similar results at other AWS locations Large differences at times: - between WRF and other data on 18, 19, and 20 October - diurnal cycle poorly simulated on 8, 9, and 10 September (needs further investigation)

  9. IOP6 (24 September 2012) – Time series 1: Convection in the vicinity of Bollène: - precipitation - humidity increases while temperature decreases - refractivity increases 2: Convection in the vicinity of Nîmes: - precipitation - humidity alreeady close to 100% - refractivity remains constant 3: Convection in the vicinity of Bollène: - precipitation - humidity already close to 100% - refractivity remains constant 4: Front passage: - humidity decreases markedly - refractivity decreases markedly 1 4 3 2 4 4 2 4

  10. IOP6 (24 September 2012) – Front passage Refractivity from Nîmes and Bollène radars – Front passage

  11. IOP6 (24 September 2012) – Radars vs. models Good agreement between Nîmes radar and models Less agreement between Bollène radar and models: - correct magnitude near the radar - large discrepancy at far range Large discrepancies probably caused by mountains (Massif Central to the west and Alps to the east) which have a double impact on radar retrievals: - lower-quality targets - calibration of retrieval algorithm

  12. On-going and future activities • On-going work: • Investigate the relationship with near-ground turbulence (PhD thesis of R. Hallali @ LATMOS – off HyMeX), • Improve calibration • Perspectives: • Further assessment of usefulness in process studies (cold pool, valley effects, breeze, low-level flow feeding HPEs, etc.) • Model validation in AWS-sparse areas • Data assimilation (coordinate with ZAMG/University of Vienna effort to assimilate 3D GPS-tomography refractivity data?)

  13. References • Besson, L., J. Parent du Châtelet, 2013: Solutions for improving the radar refractivity measurement by taking operational constraints into account. J. Atmos. Oceanic Technol., 30, 1730–1742. DOI: 10.1175/JTECH-D-12-00167.1 • Besson, L., C. Boudjabi, O. Caumont, J. Parent du Châtelet, 2012: Links between weather phenomena and characteristics of refractivity measured by precipitation radar. Bound.-Lay. Meteor., 143(1), 77–95, DOI: 10.1007/s10546-011-9656-7. • Besson, L. et al.: Comparison of refractivity measurement by radar with automatic weather stations, AROME-WMED and WRF forecasts simulations during the SOP1 of HyMeX campaign. In prep. for HyMeX special issue of QJRMS. • Caumont, O., A. Foray, L. Besson, J. Parent du Châtelet, 2013: A radar refractivity change observation operator for convective-scale models: Comparison of observations and simulations. Bound.-Lay. Meteorol., 148(2), 379–397, DOI: 10.1007/s10546-013-9820-3. • 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.

  14. Thank you for your attention!

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