Development of a polarimetric radar simulator
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Development of a polarimetric radar simulator. Clotilde Augros , Olivier Caumont, Pierre Tabary and Véronique Ducrocq Météo France Centre de Météorologie Radar ( CMR ) & Centre National de Recherches Météorologiques ( CNRM ). Context and motivation. NWP models

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Development of a polarimetric radar simulator

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Development of a polarimetric radar simulator

Development of a polarimetric radar simulator

Clotilde Augros, Olivier Caumont, Pierre Tabary and Véronique Ducrocq

Météo France

Centre de Météorologie Radar (CMR) & Centre National de Recherches Météorologiques (CNRM)


Context and motivation

Context and motivation

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 (ZDR, DP, KDP, HV, …) which help unveiling the cold & warm microphysics inside precipitation systems


Context and motivation1

Context and motivation

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 (ZDR, DP, KDP, HV, …) which help unveiling the cold & warm microphysics inside precipitation systems

Development of a polarimetric radar simulator

  • Verify NWP models / Improve microphysical parametrization schemes

  • Pave the way towards assimilation of dual-pol variables into NWP

  • Help interpreting / understanding observed polarimetric signatures

  • Perform “laboratory experiments” on QPE, using the model as the reference – Test on wavelength, # radars, # tilts, …


The french operational radar network at the end of 2013

The French operational radar network at the end of 2013

27 radars overall

17 polarimetric

12 C-band

2 S-band

3 X-band

(still under test)

Dual-polarization is the new standard for operational radars in France and elsewhere


The m t o france polarimetric s c x processing chain

The Météo France polarimetric (S/C/X) processing chain

Processed Polarimetric Variables

(Zh, Zhcorr, Zdrcorrdp, Kdp, hv) + Type

Polarimetric PPIs

(ZH, ZDR, DP, HV) + Z

(Static) calibration of ZH & ZDR

Non meteorological echo identification

HV-based bright band identification

DP offset removal and filtering

KDP estimation

DP-based attenuation correction

Figueras i Ventura, J., and P. Tabary, 2013: The New French Operational Polarimetric Radar Rainfall Rate Product. J. Appl. Meteor. Climatol. doi:10.1175/JAMC-D-12-0179.1, in press.

Hydrometeor classification


Atmospheric model

Atmospheric model

  • Meso-NH model

    • Convective-scale atmospheric model developed by CNRM-GAME and LA

    • Nonhydrostatic dynamical model core, detailed moist physics, including a 1-moment bulk microphysics scheme (ICE 3)

    • 6 water species :

      • water vapor

      • rain

      • (dry) snow

      • graupel

      • cloud droplets

      • ice crystals

No representation of hail / melting snow currently …

Simulation domain

Nîmes radar (S-band)

256 km range circle


Polarimetric radar simulator main characteristics

Polarimetric radar simulator - Main characteristics

  • Simulates beam bending and antenna’s radiation pattern

  • Simulates beam propagation (attenuation, differential attenuation, differential phase) and backscattering (reflectivity, differential reflectivity, backscatter differential phase)

  • Simulates Signal-to-Noise Ratio (SNR) diagnosis of extinct areas (important at X-band)

Input : model prognostic variables (T°, qv, qr, qs, qg, qc, qi …) on a 3D grid

Output : radar variables (VR, ZH, ZDR, HV, AH, ADP, DP, HV , KDP, …) interpolated onto radar PPIs

Caumont, O., and Coauthors, 2006: A radar simulator for high resolution nonhydrostatic models. J. Atmos. Oceanic Technol., 23, 1049–1067.


Polarimetric radar simulator hydrometeors representation

Polarimetric radar simulator – Hydrometeors representation

  • Particule size distribution : gamma particle size distributions consistent with MesoNH. N(D) is parametrized by the hydrometeor’s content M

  • Scattering :

    • Rayleigh or Mie scattering for spheres

    • T-matrix method for spheroids

  • No melting snow at the moment

  • Fixed fraction of liquid water for the graupel

  • No representation of hail (dry / melting, small / large)


Case study hymex case 24 september 2012

Case study – HyMeX Case – 24 September 2012

  • A bow echo in south-eastern France observed during IOP6 of HYMEX

  • MesoNH simulation at 2.5 km horizontal resolution

  • Radar simulator:

    • T-matrix scattering for rain, graupel and snow and Mie for ice

    • Nimes radar : S-band

  • Extra measurements :

  • In-Situ Aircrafts measurements

  • Disdrometers, MRR

  • radiosoundings

  • balloons

  • research radar/lidar data …

Reflectivity composite from 00 to 10 UTC


Observations vs simulations z h z dr

Observations vs. Simulations : ZH & ZDR

Reflectivity and differential reflectivity

Observed variables (corrected for attenuation)

Nîmes radar

(S-band)

Elevation=0.6°

24/09/2012

0300 UTC

ZDR (dB)

ZH (dBZ)

100 km

Simulated variables

ZDR (dB)

ZH (dBZ)


Observations vs simulations dp k dp

Observations vs. Simulations : DP & KDP

Differential phase and specific differential phase

Observed variables (corrected from attenuation)

Nîmes radar

(S-band)

Elevation=0.6°

24/09/2012

0300 UTC

KDP (° km-1)

DP (°)

Simulated variables

100 km

KDP (° km-1)

DP (°)


Observations vs simulations hv

Observations vs. Simulations : HV

Nîmes radar (S-band)

Elevation=0.6°

24/09/2012 - 0300 UTC

Correlation coefficient

100 km

Observed hv

Simulated hv


Observations vs simulations zhh and kdp as a function of temperature

Observations vs. Simulations : Zhh and Kdp as a function of temperature

15 dBZ bias in snow/graupel/ice

=> model overestimation ?

0.5°/km bias for all temperatures

=> model overestimation ?

=>underestimation of radar retrieved Kdp?


Observations vs simulations zhh and kdp as a function of temperature1

Observations vs. Simulations : Zhh and Kdp as a function of temperature

15 dBZ bias in snow/graupel/ice

=> model overestimation ?

0.5°/km bias for all temperatures

=> model overestimation ?

=>underestimation of radar retrieved Kdp?


Observations vs simulations kdp as a function of zhh in rain and snow

Observations vs. Simulations : Kdp as a function of Zhh in rain and snow

=> Overestimation bythemodel due to an inaccurate DSD ?

=> Underestimation of radar Kdp for the maximum values ?


Future work

Future work

  • Carry on investigating the differences between radar and model

  • Use of DSD from disdrometers/MRR?

  • Radar/model comparisons at C and X-bands for this case and other cases

  • Test the sensitivity of the simulated polarimetric variables to the simulator parameters (dielectric function, oscillation, hydrometeors shapes) and try to adjust them in order to minimize the differences between observation and simulations

  • Compare the hydrometeor contents from the model with the hydrometeor types derived from the fuzzy-logic radar classification and try to use the model to help improve the radar classification

  • Final aim: assess how and in which conditions the polarimetric variables could be used for data assimilation in NWP models


Development of a polarimetric radar simulator

Any questions ?


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