applications of polarimetric radar data from ashes … of polarimetric radar data from ashes and...

Applications of polarimetric radar data

From ashes and dust to rain and wind

Radar workshop 2015

19+20 November 2015 – Monash University, Melbourne

R. Hannesen

ORGANISATION

Echo classification

Bush-fire observations

Dust storm observations

Volcanic ash detection

Insects observation

Rainfall and QPE

Integrated wind shear retrieval

MULTIPLE MOMENTS; DIFFERENT ECHO TYPES

Z ZDR LDR

ρHV KDP ΦDP

ECHO CLASSIFICATION – MEMBERSHIP FUNCTIONS

2D; sharp boundaries 1D; fuzzy logic

ECLASS – HYDROMETEOR CLASSIFICATION

ORGANISATION

Echo classification

Insects observation

Rainfall and QPE

AQUA-MODIS Image

+ METEOR 500 CDP Reflectivity

AQUA-MODIS Image

BUSHFIRE EXAMPLE

Santa Fe,

Rio Paraná,

Argentina

30 June 2008,

17:50 UTC

100 km100 km100 km

100 km

BUSHFIRE EXAMPLE

ZDR ΦDPρHV

Santa Fe,

Rio Paraná,

Argentina

30 June 2008,

16:00 – 19:30 UTC

PROBABILITY DENSITY FUNCTION PDF

Z - stddev

Z - average

ZDR - stddev

ZDR - average

Reflectivity Differential Reflectivity

PROBABILITY DENSITY FUNCTION PDF

ρHV - stddev

ρHV - average

Differential Phase Shift - stddev

Radial Velocity - stddev

Polarimetric Correlation Coefficient Other

ORGANISATION

Echo classification

Insects observation

Rainfall and QPE

KUWAIT: DUST STORM

Z ZDR V

ρHVKDP ΦDP

KUWAIT: TYPICAL CLEAR AIR DATA

Z ZDR V

ρHVKDP ΦDP

ECHO TYPE: PDF

Z - stddev

ZDR - average

W - average

ΦDP - stddev

ECHO CLASSIFICATION

Dust probability based on:

• Reflectivity (average)

• Reflectivity (stddev)

• Radial velocity (stddev)

• Spectral width (average)

• Spectral width (stddev)

• Differential reflectivity (average)

• Differential reflectivity (stddev)

• Polarimetric correlation (average)

• Polarimetric correlation (stddev)

• Differential phase shift (stddev)

• Height

Sea Clutter probability based on:

• Radial velocity (average)

• Ocean flag

• Elevation angle

Final Classification:

Dense dust

Other echo

3D SEGMENTATION AND FURTHER CHECKS

0.3 deg PPI 1.2 deg PPI

Contour of

3D area

3D area is Dust storm only if:

Volume above minimum threshold

Vertical reflectivity gradient sufficiently negative

ORGANISATION

Echo classification

Insects observation

Rainfall and QPE

EYJAFLALLALÖKULL 2010; BY C-BAND RADAR

C-band radar at Keflavik Airport

operated by Icelandic

Meteorological Office

-20 0 20 40 60 dBZ

Reflectivity and Differential Reflectivity

VOLCANIC ASH AND WEATHER ECHOES

GRIMSVOTN ERUPTION STARTING 2011-05-22

altitude 45m

Volcano

altitude 1600m

Dual-Pol scan parameters:

• 14 slices 0.5 to 40.0 deg elevation

• 120 km range, 200 m x 1.0 deg

resolution

• Long pulse, PRF 550 Hz

• 18 deg/sec; sampling 23 pulses;

range sampling 3

50DX Radar operated

by Icelandic

Meteorological Office

X-BAND DATA OF GRIMSVOTN ERUPTION

Grimsvotn eruption on 22-May 2011: Ash data (mostly)

MODIS image 13:05 UTC

22-May 2011, 07:12 UTC (4.8 deg PPIs): Ash data (mostly)

Reflectivity

Differential Reflectivity

Specific Diff. Phase

Correlation Coefficient

MODIS image 14:00 UTC

Grimsvotn eruption on 23-May 2011: Ash and Precipitation

23-May 2011, 13:24 UTC (2.9 deg PPIs): Ash and Precipitation

Reflectivity

Differential Reflectivity

Specific Diff. Phase

Correlation Coefficient

Reflectivity and Differential ReflectivitydB

Simulation

Observation (PDF)

2011-05-22

05:00 - 07:45 UTC

-20 0 20 40 60 dBZ

Probability

(scaled to peak)

VOLCANIC ASH CLASSIFICATION

Grimsvotn summary:

Volcanic eruption is detected, classified and quantified

Polarimetric data fit partially to simulation results

Polarimetric data of volcanic ash similar to precip data

Automatic detection (precip-ash-separation) still difficult

Quantification somewhat questionable

Distant volcanic ash too weak for radar observation, but hazardous to air traffic

ORGANISATION

Echo classification

Insects observation

Rainfall and QPE

RADAR+LIDAR LLWAS SYSTEMS - FRA, MUC

X-Band Polarimetric Radar

3D scanning

IR Doppler Lidar

3D scanning

Radar+Lidar scans: 3D-scan @ 5 mins interval + 3deg PPI @ 1 min interval

PRECIPITATION: PERFECT MATCHING

Radar Lidar

3 deg PPI radial velocity, Frankfurt , 2013-07-07 23:00 UTC

INSECTS: SOMETIMES MATCHING …

Radar Lidar

8 deg PPI radial velocity, Frankfurt, 2014-03-20 12:23 UTC

… AND SOMETIMES NOT

8 deg PPI radial velocity, Munich, 2013-07-25 12:20 UTC

RADAR DATA PROCESSING

Original Data ECLASS Keep Meteo only

Radial Velocity Radial Velocity

• Keep Insects only .

For ATC

For this study

INSECTS: RADAR-LIDAR SEASONAL DIFFERENCE

Bias RMSE

May 0.52 1.19

Jun 1.34 2.1

Jul 1.47 1.97

Aug 1.56 1.88

Sep 0.79 1.47

Oct 1.4 1.57

Mar 1.33 1.8

VVP vector difference (second-lowest 500 m average), Munich, May 2013 – March 2014

For comparison:

Rain cases only

RADAR DUAL-POL DATA AND INSECT MIGRATION

Matching

Frankfurt,

2014-03-20,

12:23 UTC

Rather

homogeneous

Reflectivity ZDR

FDPrHV

RADAR DUAL-POL DATA AND INSECT MIGRATION

Not matching

Munich,

2013-07-25,

12:23 UTC

Bi-modal

azimuthal

patterns

Reflectivity ZDR

FDPrHV

CONCEPTUAL INSECT MODEL

Insect from front or rear from side higher Z, ZDR from top

Munich, 2013-07-25 12:23 UTC

ZDR, 8 deg PPI

VVP vector Lidar

INSECTS: SUMMARY

Radar and Lidar measure same VVP winds in case of precipitation

both are excellent wind profiling tools then

In case of insects, radar derived VVP winds can differ by several m/s

radiosonde and AMDAR data confirm Lidar VVPs then

Insects: differences depend on season and on time of day

Bi-modal azimuthal ZDR patterns in case of significant VVP vector differences

Magnitude and direction of ZDR patterns correlate quite well with VVP vector

difference

dual-polarization radar to identify migrating insect cases

ORGANISATION

Echo classification

Insects observation

Rainfall and QPE

POLARIMETRIC PARAMETERS AND RAIN

Few small drops

Many small drops

Few large drops

Rain rate

moderate

Differential Reflectivity can improve Z-R relations

but ZDR needs to be measured with very high accuracy then

Specific differential phase shift KDP is un-biased from attenuation; and KDP-R

relations depend less from DSD than Z-R relations do

sophisticated ΦDP processing necessary; works not good in weak rain

DPSRI – DUAL-POLARIZATION RAINFALL INTENSITY

S-band: NEXRAD algorithm

C-band and X-band:

C-band

X-band

C-band rainfall intensity example

DPSRI Z-R relation only

S-band 6hr accumulation (Singapore, 25th July 2010)

Dual-Pol Z-R relation only

DUAL-POL RAINFALL ESTIMATION

adjusted to gauges

ORGANISATION

Echo classification

Insects observation

Rainfall and QPE

ROSHEAR – RUNWAY-ORIENTED SHEAR

The smaller the

angle difference |α-

β|, the better

ICAO DOC 9817:

Gain = increasing

headwind or

decreasing tailwind

Loss = decreasing

headwind or

increasing tailwind

Wind shear = loss

or gain above 15 Kt

Severe wind shear

= gain above 30 Kt

Microburst = loss

above 30 Kt

ROSHEAR – RUNWAY-ORIENTED SHEAR

X-band Radar

Frankfurt,

6th Aug 2013,

14:06 UTCradial velocity

RADAR-LIDAR COMBINATION EXAMPLES

After ECLASS: MET echoes onlyLidar

Frankfurt, Thunderstorm 6th Aug 2013

3 deg PPI radial valocity plus runway-oriented shear

RadarLidarCombined

RADAR-LIDAR COMBINATION EXAMPLES

Munich:

Showers on

8th Apr 2014

Vertical wind

profiles

3:00 hrs

Selex ES GmbH

Raiffeisenstrasse 10

41470 Neuss, Germany

Tel: +49 (0) 2137 782-0 www.selex-es.de

Dr. Ronald Hannesen

r.hannesen@selex-es-gmbh.com

Thank you !

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