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 (MétéoFrance) – 5 LMD – 6 McGill University
IODA-MED meeting
16 May 2014
IODA-MED deliverables
No update since last year’s meeting
Talk by Clotilde Augros
2
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
e
N = 77.6 + 373000
T
T²
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
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(Fabry et al. 1997)
Principle of refractivity measurement by weather
radar
r1
target #1
r2
target #2
radar
radar
beam
 Measurement by radar based on radar pulse’s propagation time through the
atmosphere, which depends on refractivity.
 Phase change between 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.
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 Technique initially for klystron (= stable-frequency) transmitters. Adaptation
for magnetron (= drifting-frequency) transmitters (Parent du Châtelet et al.
2012).
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)
o Definition of quality index for target selection
o Investigation of the use of faster antenna rotation speeds, additional elevations and dualpolarization returns
 Observation operator for refractivity (Caumont et al. 2013):
o Sensitivity study to formulation of observation operator
o 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)
o Use of refractivity retrievals produced in real time during HyMeX SOP1
o Cross-validation with independent observations and models
o First attempt to relate real refractivity data with Mediterranean meteorological processes
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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)
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Available models
WRF:
 Initial & boundary conditions: nudging from NCEP global model
Date, Time
D-1, 00 UTC
D-1, 12 UTC
D+3, 18 UTC
WRF simulation
NCEP analysis
NCEP forecasts
 2 nested domains: 54- and 9-km horizontal resolutions
 N at 2 m AGL from innermost domain
AROME-WMED:




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Initial & boundary conditions: Arpege global model
Horizontal resolution: 2.5 km
3-h forecasts from a 3DVar assimilation cycle
N at 10 m AGL
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)
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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
1
4
3
2
3: Convection in the vicinity of Bollène:
- precipitation
- humidity already close to
100%
- refractivity remains constant
2
4
4
4: Front passage:
- humidity decreases markedly
- refractivity decreases
markedly
4
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IOP6 (24 September 2012) – Front passage
Refractivity from Nîmes and Bollène radars – Front passage
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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
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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?)
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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.
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Thank you for
your attention!