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Identification of Snow and Rain at the Surface using Polarimetric Radar
Martin Hagen and Alice Dalphinet*
DLR Institut für Physik der AtmosphäreDLR Institut für Physik der AtmosphäreOberpfaffenhofen, Germany
* MétéoFrance, Villeneuve d'Ascq, France
Hydrometeor Classification
Why again (and again)?
• Radars use different wavelengths• Radar operators have different preferences• Radars can provide different sets of radar products• Radars provide radar products with different quality• Radars provide radar products with different quality• Hydrometeor classifiers can be combined with additional
products (like temperature profiles, … )• …
� Different membership functions and relative weights areused for fuzzy-logic hydrometeor classifiers
www.DLR.de • Chart 2 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
Hydrometeor Classification
DLR’s POLDIRAD
uses most times and seldomalternate H V hybrid (STAR)switching mode mode
� rhoHV(1) is lower than rhoHV(0)
� LDR is available
www.DLR.de • Chart 3 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
Identification of Rain and Snow at the Surface
Aviation and road authorities want to know:
• when does it start to snow?(when does rain turn in snow?)
• how long does it snow?
• how much snow will fall?
How can we contribute withpolarimetric weather radar?
What additional tools ormeasurements would berequired?
www.DLR.de • Chart 4 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
Identification of Rain and Snow at the Surface
Challenges
• What happens between the radar beam and the surface?
1.0°
0.5°
1.5°
950
1500
2050
?Poldirad at 603 m ASLMUC at 50m range445 m ASL
• Light rain and snow are hard to distinguish
• How to identify freezing rain or drizzle?
www.DLR.de • Chart 5 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
0 15 30 45 60 75400?
From Classification to Precipitation at Ground
Bright band identification
2nd Hydrometeor classification
1st Hydrometeor classification
www.DLR.de • Chart 6 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
2nd Hydrometeor classification
Weather situation classification
Precipitation at ground
Hydrometeor Classification Process
1st Step
fuzzy logic
2nd Step
inputZH LDR ρHV
ZDR VH
output weather not weather
input output
ZH Light raininput output
ZH Clutter2nd Step
fuzzylogic
ZH
ZDR
LDRρHV
BB altitudeIs-it-stratiform?
Light rainModerate rainHeavy rainDry snowWet snowHailWet hailGraupelCrystals
ZH
ZDR
ClutterInsects, birds
Fuzzy-logic Classification
• POLDIRAD can use LDR and ρHV for the identification of melting hydrometeors
• The current classification is optimized for winter conditions.
• 1-D and 2-D membership functions are used:
78
2D Zh - Zdr Parameter Space for Rain
light rain 01.0
mem
bers
hip
func
tion light rain
ρHV Membership for Rain and Snow
• Definition of membership functions and weights are empirical
www.DLR.de • Chart 8 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
-101234567
10 0 10 20 30 40 50 60Z
dr (
dB)
Zh (dBZ)
light rain 0light rain 1moderate rain 0moderate rain 1heavy rain 0heavy rain 1
0.80 0.84 0.88 0.92 0.96 1.000.0
0.2
0.4
0.6
0.8
mem
bers
hip
func
tion
ρHV
light rainmoderate rainheavy raindry snowwet snowcrystals
Bright Band Identification
Modelized rain / dry snow limit (MRSL)
Estimation of MRSL for volume scan sectors
drysnow
freezinglevel
MRSL
psnow
wet
Classes:wet snowothers
www.DLR.de • Chart 9 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
rain/snowlimit 0 1
memb. fnct.
MRSL
prain
wetsnow
rain... ...
... 2.1
2.0
1.91.9
1.92.5 2.6
2.52.6
MRSLheights
Verification of Estimated Bright Band Height
• Micro Rain Radar MRR (located at Lichtenau 27 km SW) provides good indication of bright band height (change of fallspeed)
Fall speed
Reflectivity
www.DLR.de • Chart 10 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
Hei
ght (
m)
3000
2500
2000
1500
8 10 12 14 16 18Time (UTC)
Hydrometeor Classification
• Final classification
• Reflectivity
www.DLR.de • Chart 11 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
Weather Situation Classifier
Is it stratiform or convective?
• Indicator for convective: ZH > 40 dBZ; ZH > 55 dBZ; season
• Use of hydrometeor classification
convective
favouringhydrometeors
hail, graupel
adversehydrometeors
snow Hei
ght
snow
rain
www.DLR.de • Chart 12 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
convective
stratiformrainstratiformsnowstratiformfront
hail, graupel
snowrain
snow
snow
snow
hail, graupel
hail, graupelrain
hail, graupel
Hei
ght
rain
snow
snow
snow rain
Hei
ght
Hei
ght
Precipitation at Surface
• For stratiform precipitation events:• ZH < 20 dBZ• Season• Is the melting layer visible; is it at ground?
situationcon-vect.
stratiform
• Without further observations an extrapolation of radar hydrometeor classification towards the ground is limited to weather situations with standard linear temperature profile
www.DLR.de • Chart 13 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
situationvect. ML front no ML
hydro-met.
rain, hail, graupel
rain, snow
snow
Precipitation at Surface
• Example of a cold front approaching from the north
DWD radar composite Poldirad reflectivity12:30 UTC
Observationsat radar site
www.DLR.de • Chart 14> Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
13:30 UTC
-8°C
12°C
924hPa
932hPa
∆t = -5°C
∆p = 2.5 2°C
Precipitation at Surface
• Example of a cold front approaching from the north
snow atground
Verification using MRR
www.DLR.de • Chart 15 > Martin Hagen > ERAD2012 > Toulouse > 25 - 29 June 2012
rain atground
Verification using MRRprecipitation fall velocities
snowrain
13 14 UTC
2500
AGL
0 m
Summary
Characteristics
• Globally robust method
• Correct detection of the non-meteorological echoes
• Acceptable restitution of the
Limits
• Robust detection of snowfalls
• Detection of a front delineating an area of rain and of snow at the ground• Acceptable restitution of the
melting layer
• Quite good detection of the snowfalls
the ground
• Limited precision in the hydrometeors classes
• Validity of the membership functions
Conclusions – Perspectives
• Importance and performance of radar measurements for observation and nowcasting on mesoscale
• The results of the detection of the bright band and snowfall are encouraging
• Up to now only standard linear temperature profiles are coveredcovered
Perspectives / Future work• Improvement of the membership functions and weights
• Include surface temperature and temperature profiles from aircraft observations (AMDAR) or NWP temperature fields