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Near real-time radar reflectivity calibration and monitoring using ground clutter Valentin Louf (Monash, BOM) A. Protat (BOM), C. Jakob (Monash) Radar Workshop 2016, Melbourne Ground clutter? How about no! Ground clutter is responsible for:

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Near real-time radar reflectivity calibration and monitoring using ground clutter

Valentin Louf (Monash, BOM)

  • A. Protat (BOM), C. Jakob (Monash)

Radar Workshop 2016, Melbourne

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2 Ground clutter? How about no! Ground clutter is responsible for:

  • Spurious signal.
  • Bias reflectivity.
  • Skew Doppler velocity

estimates. What we (traditionally) want:

  • Identify and eliminate

ground clutters.

Radar Workshop 2016, Melbourne

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Can a constructive use of ground clutter be made?

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4 Can a constructive use of ground clutter be made?

  • Study of beam patterns

(Rinehart and Frush, 1983).

  • Estimation of the attenuation

caused by rainfall (Delrieu et al., 1997).

  • Estimation of the refractive

index of air (Fabry, 2003).

  • Estimation of the topographic

structure (Mesnard et al. 2003)

  • Monitoring of radar

calibration, proposed by Rinehart in 1978.

Figure from Louf et al (2015)

Radar Workshop 2016, Melbourne

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5 Monitoring of radar calibration.

1960: Atlas and Mossop 1978: Rinehart 2008: Silberstein et al. 2009: Marks et al. 2015: Wolff et al.

Not a new technique… but not a common one.

Radar Workshop 2016, Melbourne

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6 How can we use ground clutters?

  • The meteorological radar equation:

π‘Žπ‘› π‘’πΆπ‘Ž = 10 log10 𝑄

𝑠 + 20 log10 𝑆 βˆ’ 10 log10 𝐷

  • With:

– π‘Žπ‘›: reflectivity, – 𝑄

𝑠: echo power,

– 𝑆: the range. – 𝐷: the weather radar constant.

  • 10 log10 𝐷 represents the radar sensitivity (in dB).

Radar Workshop 2016, Melbourne

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7 How can we use ground clutters?

  • Clutter is due almost entirely to human-made structure:

– Building microphysics do not change drastically with time:

  • Ξ”10 log10 𝑄

𝑠 = 0

– It is unlikely that the range itself changes significantly over time:

  • Ξ”20 log10 𝑆 = 0
  • Therefor:
  • Ξ”π‘Žπ‘› = Ξ”10 log10 𝐷

The variations of ground echoes reflectivity are directly linked to the variations in radar calibration.

Radar Workshop 2016, Melbourne

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8 How can we use ground clutters?

  • Construct a clutter map.

– Select all PPIs for a day without precipitation (within the 5, or 10km of the radar). – Keep the echoes with a reflectivity above 50 dBZ and a frequency

  • f occurrence superior to 50%.
  • Apply the mask.

– Collects the clutter area reflectivity for a given moment/day/month. – Computes the probability density function (PDF) calculate the 95th percentile of reflectivity.

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9 Computation of the Relative Calibration Adjustment (RCA) offset

  • The RCA is defined as being:

𝑆𝐷𝐡 𝑒𝐢 = 𝐷𝐸𝐺95π‘’β„Ž(π‘Žπ‘π‘π‘‘π‘“π‘šπ‘—π‘œπ‘“) βˆ’ 𝐷𝐸𝐺95π‘’β„Ž(π‘Žπ‘‘π‘šπ‘£π‘’π‘’π‘“π‘ )

  • The RCA value is the adjustment (in dB) needed to obtain

agreement to the baseline.

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10 Why using the cumulative distribution function?

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The results for CPOL.

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12 The RCA on CPOL: average seasons

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13 The good

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14 The bad

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15 The ugly

A software update that was not suppose to change anything.

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16 Back to normal

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17 Long-term evolution of calibration

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18 Β« Since it is unlikely that the terrain itself changes significantly over such periods of time [a day], and if other explanations such as variations in radar calibration or electromagnetic properties of the ground can be ruled out,

  • ne must conclude that it is the medium in which the radar

wave travels that experiences these changes, in this case,

  • air. Β»

Fabry (2003) in Β« Meteorological Value of Ground Target Measurements by Radar Β»

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19 Diurnal cycle of the RCA

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What future for this technique?

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21 Implementation on Indian radar’s network

Amar Jyothi’s work.

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22 Test on operational radars (Melbourne).

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23 Conclusions

  • Conclusion on the radar technique:

– Calibration – Monitoring – Easy checking on large dataset.

  • Conclusion on the atmospheric research:

– Clutters are not noise, they contain valuable informations!

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The new CPOL dataset

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25

  • Level 1:

– Raw data calibrated and filtered data. (ZH, ZC, ZDR, KDP, PHIDP, RHOHV, WIDTH)

  • Polar. (Every 10 mins)
  • Cartesian. (Daily mean)
  • Level 2:

– 2D: Conv/strat, Z(2.5 km), rainfall, 0dBZ, cloud top height 17 dBZ, 20 dB, 40 dB

  • 3D: 3D winds, hydrometeor

classification.

  • Level 3:
  • Climatology.
  • Area mean rainfall, Peak

rainfall 99.5th percentile), Area fraction of convective and stratiform, Area fraction vs height, Stats on wind and convective mass flux…

The new CPOL dataset (delivery in January)