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Operational use of dual-polarisation: lessons

learned at Météo France after 8 years of

experience at all wavelengths (S / C / X)

P. TabaryMétéo France

Head of Weather Radar Centre

pierre.tabary@meteo.fr

TECO201218 October 2012

Brussels

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• The French metropolitan radar network

• Demonstrated benefits of polarimetry at X / C / S bands

• Challenges / Open issues

Outline of the presentation

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In 1991 : 11 radars

In 2012

26 radars

All Doppler (Triple-PRT)

18 C band (13 DPOL)

6 S band (2 DPOL)

2 X band (2 DPOL)

DPDPDP

DP

DP

DP

DPDP

DPDP

DP

DP

DP : DPOL RadarPurple = SGreen = CBrown = X

DP

The French metropolitan radar network in 2012

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2004 : First polarimetric radar installed in Trappes

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Polarimetry Roadmap 2004 - 2014 2004: First C-band dual-pol radar installed in Trappes

2004 – 2008: Demonstration of the benefits for: Non Precipitation Echo ID Attenuation Correction Self-consistency calibration Rainfall rate retrieval Hydrometeor Classification

2012: 1ST version of DPOL processing chain operational Non Precipitation Echo ID Basic DP-based Attenuation Correction

2014 (plan): 2ND version of DPOL processing chain operational Hydrometeor ID (Rain, Hail, Wet Snow, Dry Snow, …) Improved Rain Rate Estimation : Hybrid “Z-KDP” estimator

• Data Quality

• Calibration

• Monitoring

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Histograms of dual-polarisation variables (HV and texture of ZDR) in precipitation, ground-clutter and clear-air.

HV Texture of ZDR

no precipitationGourley, JJ, P. Tabary, J. Parent-du-Chatelet, 2007: A fuzzy logic algorithm for the separation of precipitating from non-precipitating echoes using polarimetric radar, J. Atmos. Oceanic Technol. Vol. 24, No. 8, 1439–1451.

Automatic Non Precipitation Echo ID

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Echo Type

Yellow = Precipitation

Green = clear air

Blue = ground-clutter

Reflectivity (dBZ)

Gourley, JJ, P. Tabary, J. Parent-du-Chatelet, 2007: A fuzzy logic algorithm for the separation of precipitating from non-precipitating echoes using polarimetric radar, J. Atmos. Oceanic Technol. Vol. 24, No. 8, 1439–1451.

200 km

Automatic Non Precipitation Echo ID

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Quantitative Precipitation Estimation

Evaluation at hourly time step against rain gauges in rain

Comparison restricted to within 60 km of the radar

Evaluation at the 3 wavelengths : X / C / S

Comparison of 3 different rain rate estimators

QPE algorithm is adapted from Tabary (2007) and includes : VPR and beam blocking correction, advection correction, ….

No real-time gauge adjustment is applied “radar only” QPE

Tabary P. 2007. The New French Operational Radar Rainfall Product. Part I: Methodology. Wea. Forecasting. 22: 393-408.

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Results at S-band - Summer 2010 - 1 radar - 4 EventsEvaluation at hourly time step against rain gauges

RR NB corr≥5.0 -0.27 0.82

RR NB corr≥5.0 -0.18 0.84

RR NB corr≥5.0 -0.09 0.88

“Z-KDP”

- If KDP < 1°/km Use of Z-R (Marshall-Palmer) with attenuation correction

- If KDP > 1°/km Use of R(KDP)

Z-R (Marshall-Palmer) with attenuation

correction

PIA (dB) = 0.04 * DP (°)

Z-R (Marshall-Palmer) without attenuation

correction

RR = Hourly Rain Gauge Accumulation (in mm)NB = Normalized Bias (Radar vs. Gauge)Corr = Correlation coefficient

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Results at C-band - Summer 2010 - 4 radars - 26 EventsEvaluation at hourly time step against rain gauges

DBP2

No

RG

Adj

RR NB corr≥5.0 -0.47 0.54

RR NB corr≥5.0 -0.34 0.70

RR NB corr≥5.0 -0.19 0.79

“Z-KDP”

- If KDP < 1°/km Use of Z-R (Marshall-Palmer) with attenuation correction

- If KDP > 1°/km Use of R(KDP)

Z-R (Marshall-Palmer) with attenuation

correction

PIA (dB) = 0.08 * DP (°)

Z-R (Marshall-Palmer) without attenuation

correction

RR = Hourly Rain Gauge Accumulation (in mm)NB = Normalized Bias (Radar vs. Gauge)Corr = Correlation coefficient

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Results at X-band – 2011 - 1 radar - 4 EventsEvaluation at hourly time step against rain gauges

RR NB corr≥5.0 -0.74 0.52

RR NB corr≥5.0 -0.51 0.63

RR NB corr≥5.0 -0.28 0.70

“Z-KDP”

- If KDP < 0,5°/km Use of Z-R (Marshall-Palmer) with attenuation correction

- If KDP > 0,5°/km Use of R(KDP)

Z-R (Marshall-Palmer) with attenuation

correction

PIA (dB) = 0.28 * DP (°)

Z-R (Marshall-Palmer) without attenuation

correction

RR = Hourly Rain Gauge Accumulation (in mm)NB = Normalized Bias (Radar vs. Gauge)Corr = Correlation coefficient

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Data Quality: Polarimetric monitoring indicators

If well calibrated / processed (DP & ZDR), polarimetric variables improve the quality of all conventional radar products;

If not well calibrated / processed, polarimetric variables may lower the quality of all conventional radar products;Examples : 1) Large biases on ZDR may strongly impact rain rate estimation (0.2 dB ~ 15%)2) Remaining ground-clutter may corrupt entire range profiles because of errors in DP offset computation

Need to have very robust calibration, monitoing & correction procedures

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12 & 13-10-2010Maintenance on the radar

28-03 & 01-03-2011Maintenance on the radar

Long-term monitoring of polarimetric indicatorsBlaisy (C-band) – August 2010 April 2011

DP offset

9 months

ZDR for ZH=20-22 dBZ

ZDR at 90°

HV

Typical scatter ~ 0.3 dB(Required: 0.2 dB)

Slight positive bias (+0.2 dB)

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12 & 13-10-2010Maintenance on the radar

28-03 & 01-03-2011Maintenance on the radar

Long-term monitoring of polarimetric indicatorsBlaisy

DP offset

9 months

ZDR for ZH=20-22 dBZ

ZDR at 90°

HV

Typical scatter ~ 0.3 dB(Required: 0.2 dB)

Slight positive bias (+0.2 dB)

Stability of ZDR is close to – but still slightly below - requirements (0.3

dB vs. 0.2 dB required)

Temperature & electronic calibration procedures are thought to be

responsible for the observed scatter

Work under progress …

The quantitative use of ZDR remains a challenge …

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Conclusions

Polarimetry has become the new standard in operational radar networks

Polarimetry improves the quality of all radar products (e.g. rain rate estimation) especially at high frequency (X)

New products can be proposed with polarimetry (e.g. hydrometeor classification)

Phase-based parameters (DP and KDP) are very valuable for attenuation correction and rain rate estimation

The quantitative use of ZDR is still a challenge (calibration / stability issues vs. 0.2 dB precision required)

The benefits for Quantitative Precipitation Estimation have been demonstrated in rain. Solid precipitation estimation is still an open area of research

Rain gauges are still needed !

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Questions