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Federal Department of Home Affairs FDHAFederal Office of Meteorology and Climatology MeteoSwiss
Mesoscale Atmospheric SystemsRadar meteorology 2Urs GermannMeteoSwiss, Locarno-Monti
With material from H Wernli, I Zawadzki, and the radar colleagues at MeteoSwiss
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2Urs Germann, MeteoSwiss
A band of bandsFrequency band meteorological application
20-300 MHz 1-15 m VHF wind profiler, ocean surface motion
400-900 MHz 0.3-0.7 m UHF wind profiler
1 GHz 0.3 m L-band boundary layer wind profile
2-4 GHz 7-15 cm S-band long-range precipitation radars
4-8 GHz 4-7 cm C-band long-range precipitation radars8-16 GHz 2-4 cm X-band precipitation radars, marine radars16-20 GHz 1-2 cm Ku-band radars
35 GHz 8.5 mm Ka-band precipitation and cloud radars
90-100 GHz 3 mm W-band cloud radars, Mie minimum
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3Urs Germann, MeteoSwiss
Part 1: Radarnetworking
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4Urs Germann, MeteoSwiss
Optimal wavelength for radar in the AlpsArguments for C-band compared to S-band• Better weather-to-clutter ratio ( -4 in Rayleigh approximation)• Narrower beam and lower side lobes (for given antenna size)
• Better chances to detect weak echoes in snow over the Alps• Installation and operations affordable on mountain peaks• Receiver can be mounted on antenna (better sensitivity, no dual rotary joint)
Arguments for S-band• Attenuation negligible• Larger Nyquist interval
Swiss radar network5.5 cm (C-band)
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5Urs Germann, MeteoSwiss
4th Swiss radar generation (MeteoSwiss)R
adA
lp
Albis, 938mrenewed 2012
Plaine Morte, 2937m 2014
New
site
Lema, 1626mrenewed 2011
Dole, 1682mrenewed 2011
Weissfluh, 2850m2016
New
site
Fully automatic
C-band volume-scanningDual-polarisation Doppler radar
with receiver-over-elevation-design
2850
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6Urs Germann, MeteoSwiss
Europe has a densenetwork of operational weather radars.
Composite is used for• large-scale monitoring of
precipitation,• numerical weather
prediction,
and increasingly also for• applications in aviation,
hydrology, climatology, • and research.
http://eumetnet.eu/activities/observations-programme/current-activities/opera
European radar composite OPERA
OPERA programme manager: E Saltikoff, FMI
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7Urs Germann, MeteoSwiss
US radar network «NEXRAD»
• totally 159 S-band Doppler radars
• recentlyupgraded todual-polarizationtechnology
• good coverage in flat regions
• limited coveragein mountainousregions
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8Urs Germann, MeteoSwiss
Other networks• Radar networks in many other regions around the world:
Canada, Japan, Australia, …
• S-Korea: Exceptionally dense network thanks to thesuperposition of networks from several independentoperators. Challenge due to orography similar to Switzerland.
• CINRAD: New generation radar network in China with totally216 radars. 144 operational by end of 2012.
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9Urs Germann, MeteoSwiss
Part 2: Meteorologicalprocessing
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10Urs Germann, MeteoSwiss
PPI, RHI, HTI, CAPPI, HARPI, …PPI (Plan Position Indicator): Measurements froma 360° scan withconstant elevationangle, projected on the ground.
RHI (Range Height Indicator):Measurements froma scan with constantazimuth
HTI (Height Time Indicator):Measurements overtime period withantenna pointing in the vertical direction
easting
north
ing azimuth
distance on the ground
heig
ht
time
heig
ht
CAPPI (Constant AltitudePlan Position Indicator):
Horizontal cross section at a selected height level through a
volume of PPI scans. The x and y axes of the plot correspond to
easting and northing.
HARPI (Height AzimuthRange Position Indicator):
Vertical cross section at constantrange through a volume of PPI scans. The x and y axes of theplot correspond to azimuth and
height.
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11Urs Germann, MeteoSwiss
PPI scan
First measurements of new Plaine Morte radarduring installation, November 2014
Doppler velocity Reflectivity Zh
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12Urs Germann, MeteoSwiss
Quantitative precipitation estimation QPE
reflectivity
estimate ofprecipitation@ ground
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13Urs Germann, MeteoSwiss
D6, D3, D4
Radar reflectivity Z
log
Volumetric liquid water content W
Rainfall rate R
N(D) is the drop size distributionvt(D) is the terminal fall velocity of drops
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14Urs Germann, MeteoSwiss
Drop size distribution N(D)
How can we estimate R from Z if N(D) varies so much? Fortunately, we know a lot about N(D) and we can obtain reasonable estimatesof R from Z using approximations, so-called Z-R relations.
And there is dual-polarization, that helps a lot …
From microwave disdrometer
Figures: Zawadzki (McGill), GW Lee
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15Urs Germann, MeteoSwiss
Deq = 2.6 mm 3.4 mm 5.8 mm
Deq = 7.4 mm 8.0 mm
Raindrops falling with their terminal velocity are oblate.
Drops can be described as rotationalellipsoids with the axis a and b
Observations in a vertical wind tunnel
(small drops are spherical, large ones are oblate)
(Pruppacher & Klett)
Shape of falling drops
baa
b
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16Urs Germann, MeteoSwiss
Dual-polarization radarDual polarization radarstransmit and receive horizontally and vertically polarized electromagnetic waves.
Rain Graupel Hail
Most dual-pol radars transmit H and V simultaneously, and process H and V of the return signal in parallel in 2 separate receiver channels.
Some dual-pol radars first transmit H and receive H and V, thentransmit V and again receive H and V, but …
Dual-polarisation radarPOLDIRAD, DLR (since 1986)P Meischner, M Hagen, et al.
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17Urs Germann, MeteoSwiss
Why dual-polarization radars?
• Distinguish between meteorological (hydrometeors) and non-meteorological targets (ground clutter, birds, insects, aircrafts).
• Classification of hydrometeors (large drops, small drops, ice crystals, snowflakes, melting snow, graupel, hail).
• Better data quality (redundant receive channels, hardware monitoring, …)
• Correct for attenuation of radar signal in heavy rain.• Better estimation of rainfall rates, especially in presence of large
drops.
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18Urs Germann, MeteoSwiss
Dual-polarization measurables
ZH, ZV Reflectivity particle size + number (precipitation rate)
ZDR Differential reflectivityZH / ZV
particle shape(target id, rainfall rate)
KDP Specific differential propagation phase
particle shape(intense rain, target id, attenuation correction)
LDR Linear depolarisation ratioZVH / ZH
shape + orientation(target id, melting snow)
ρHV Correlation coefficient variety of shapes(target id)
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19Urs Germann, MeteoSwiss
Target identification from dual-polarization
Z (dBZ)
ZDR(dB)
KDP(°/km)
ρHV(0) LDR (dB)
Rain 10 – 55 0 – 5 0 – 10 ≈ 1.0 < -30
Ice crystals < 15 0 – 2 0 ≈ 0.99 < -30
Snow aggregates
< 25 0 – 2 0 ≈ 0.99 < -30
Graupel up to 40 ≈ 0 ≈ 0 > 0.95< 0.95 melting
< -30< -25 melting
Hail up to 70 ≈ 0 ≈ 0 0.9 – 0.95< 0.9 melting
> -25-25 – -15 melting
Insects < 5 5 – 10 ? 0.9 – 1.0 ? < -30 ?Birds < 5 3 – 6 ? 0.9 – 1.0 ? < -30 ?Ground clutter
any noisy noisy 1 stopped < 0.6 rotating
antenna
> -20
From theory and observations.
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20Urs Germann, MeteoSwiss
From theory and observations.
Insects < 5 5 – 10 ? 0.9 – 1.0 ? < -30 ?Birds < 5 3 – 6 ? 0.9 – 1.0 ? < -30 ?Ground clutter
any noisy noisy 1 stopped < 0.6 rotating
antenna
> -20
Z (dBZ)
ZDR(dB)
KDP(°/km)
ρHV(0) LDR (dB)
Rain 10 – 55 0 – 5 0 – 10 ≈ 1.0 < -30
Ice crystals < 15 0 – 2 0 ≈ 0.99 < -30
Snow aggregates
< 25 0 – 2 0 ≈ 0.99 < -30
Graupel up to 40 ≈ 0 ≈ 0 > 0.95< 0.95 melting
< -30< -25 melting
Hail up to 70 ≈ 0 ≈ 0 0.9 – 0.95< 0.9 melting
> -25-25 – -15 melting
Target identification from dual-polarization
In reality things are more complex:
• melting hail and graupel can havehigh ZDR and high KDP
• ice dendrites can have high ZDR• (…)
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21Urs Germann, MeteoSwiss
Dual-polarization hydrometeor retrieval
Hail cell in Rubigen (BE)as seen by dual-polarizationradar on Albis in operational mode (!) at 90 km distance.
Besic, Figueras, Grazioli, Gabella, Germann and Berne (2016)
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22Urs Germann, MeteoSwiss
Real-time radar-raingauge mergingRadar-only
Space-time co-kriging with external driftwith data transformation and sophisticated quality control
Sideris, Gabella, Erdin, Germann (QJRMS, 2013)
Radar – raingauge integration
For demonstration purposes we have selectedan example where the radar has severlyunderestimated rainfall at ground. In most cases, errors are smaller.
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23Urs Germann, MeteoSwiss
Part 3: Meteorological studies
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24Urs Germann, MeteoSwiss
Stratiform precipitation and bright band
• vertical wind < ~ 1 m/s• horizontal scale ~ 100 km• stable stratification• external lifting (e.g. through fronts or orography)• in polar regions and in midlatitudes
24
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25Urs Germann, MeteoSwiss
Stratiform precipitation
Figure from Zawadzki et al (McGill)
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26Urs Germann, MeteoSwiss
Bright band
Within melting layer: Particles have size of snowflake, but dielectric constant ofwater. On top, some stick together and form large aggregates.
► Layer of enhanced reflectivity («bright-band»).
Below melting layer: Increase in dielectric constant and decrease in number densitydue to larger fall speed roughly have same effect but withopposite sign. (In reality more complex: break-up and more.)
► Before and after melting reflectivity is similar.
Figure from Zawadzki et al (McGill)
bright-band
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27Urs Germann, MeteoSwiss
Some convective activity
Figure from Zawadzki et al (McGill)
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28Urs Germann, MeteoSwiss
Dynamics and microphysics
Figure from Zawadzki et al (McGill)
• light snow• heavy snow• riming• supercooled cloud• supercooled drizzle• secondary ice• bi-modal Doppler
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29Urs Germann, MeteoSwiss
Convective storms
HARPI display by Puigdomenech
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30Urs Germann, MeteoSwiss
With a little help from the public
P Noti et al (Uni Bern, Mobiliar Lab, MeteoSwiss)
We get about14’000 hail reports
from app usersevery year,
a unique datasource for radar
hail research.
Hail cell of 6 June 2015
MESHS: maximum hailstone size from radar
Overlay: hail sizereports from app users
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31Urs Germann, MeteoSwiss
Hail cell in forecast modelR
adar
CO
SMO
fore
cast
mod
el
WR
Ffo
reca
stm
odel
Trefalt et al (2017)
COSMOuses
radar foranalysis
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32Urs Germann, MeteoSwiss
Hail cell climatology
Radar hail mapping2002-now
Data base with totally> 30’000 hail cells
• Automatic detection• Climatology• Automatic alert
PhD projects LNisi, S TrefaltUni Bern, Mobiliar Lab, MeteoSwiss
Hail cell trajectories (JUL 2002-2013)
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33Urs Germann, MeteoSwiss
Tornado visiting PARADISO field experimentAdaptive high-resolution scanning by X-band radar(triggered by C-band thunderstorm tracking)
PARADISO field experiment(MeteoSwiss, armasuisse, EPFL, IACETH)
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34Urs Germann, MeteoSwiss
Diurnal cycle
Data JJA (June-August) of6 years (2005-2010)
Mandapaka, Germann, Panziera, QJRMS, 2012
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35Urs Germann, MeteoSwiss
Flooding in Maggia river 18 events in 2005-2012<
10 m
/s
9 most intense floods
10-1
5 m
/s>
15 m
/s
Rank 9-18 floods
Panziera, et al. (QJRMS, 2010, 2011, 2015)
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36Urs Germann, MeteoSwiss
End
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37Urs Germann, MeteoSwiss
void