the new dwd polarimetric weather radar network: a new radar data processing framework and new...
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The new DWD polarimetric weather radar network:a new radar data processing framework andnew products
Michael Frech1, Nils Rathmann2, Jörg Steinert2
Patrick Tracksdorf2 and Manuel Werner2
DWD, German Meteorological Service
1Meteorological Observatory Hohenpeißenberg2Research & Development, Central Office, Offenbach
Radar Hohenpeißenberg
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Overview
1. Introduction
2. The new polarimetric radars
3. The new radar data processing
scheme POLARA
4. Data quality - product quality,
verification aspects.
6. Summary
Michael Frech Slide 2
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DWD weather radar network
research system:
MHP – Hohenpeißenberg
(research, quality control,
algorithm development &
verification,
hardware testing)
Folie 3
MHP
17 operational systems1 research system
EEC DWSR5001/SDP/CE SIDPOLC-band500 kW peak power, magnetron systemsPulse widths: 0.4 and 0.8 µsbeam width 1°operational range resolution: 250m -1kmScan strategy: 5 min update rate
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Michael Frech Slide 4
Radar data flow
central unit onsite unit
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Michael Frech Slide 5
POLARA - polarimetric radar alogorithms
components of software suite POLARA:
1. radar data and system monitoring - onsite (Michael Frech)
2. data quality agorithms - central (Manuel Werner)
3. Hydrometeor classification (HMC) - central (Jörg Steinert)
4. Quantitative precipitation estimates (QPE) – central (Patrick Tracksdorf)
1 & 2 are essential for the performance of 3 & 4, especially with polarimetric systems
The system is currently in a pre-operational evaluation phase.New products (QPE & HMC) are currently validated.
for further aspects of POLARA we refer to the ERAD 2014 contributions: http://www.pa.op.dlr.de/erad2014/The European Radar Conference ERAD will take place 1.-5. September, 2014, Garmisch-Partenkirchen, Germany.
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Michael Frech Slide 6
Consistency – Calibration – Stability
Consistency among the radar systems is important.
Homogeneity of data quality of the radar network is an important goal of quality control
stability of hardware is important -> stability of calibration.
Aspects of this is demonstrated doing a radar – radar comparison against disdrometer measurements (ground truth).
This inevitably highlights issues related to the interpretation of radar data against insitu measurements.
What is the truth?
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Michael Frech Slide 7
Monitoring absolute calibration: concept
1st range bin in the far field
650 m
10 m
25 m
Radar
disdrometer
Zh,v,radar
ZPWS
Disdrometer - radar comparison: reflectivity factorsZ
PWS versus Z
h,v,radar from birdbath scan
requirements & assumptions: precip. > 10 dBZno change in DSD with height (verified with MRR data)no attenuation.liquid phase (use Doppler data; -8 < v < -4 m/s)no bright band (T(650 m) > 4 °C).ρ
HV > 0.98
birdbath scan is available every 5 min.it is part of the operational scan strategy
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Michael Frech Slide 8
Absolute calibration: PWS - Radar -Radar
Compare the consistency of 3 radarsCompare the 3 radars against a disdrometer at MHP (Hohenpeißenberg)
2 operational systems: MEM (Memmingen) and ISN (Isen near Munich)
use the precipitation scan (quality controlled data by POLARA)
1 research system: MHP, use the 90° birdbath scan (primarily used forcalibration of differential moments)
Questions:
potential calibration issues and how they relate to QPE (i.e. the product of interest)
issues related to time - space variability
Quantitative precipitation estimate (QPE): at MHP, use standard DWD Z/R relation ship (use birdbath data!) ISN and MEM: use polarimetric QPE estimators
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Michael Frech Slide 9
Radar sites: ISN - MHP - MEM
91 km65 km
mem = Memmingenmhp = Hohenpeißenbergisn = Isen
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Michael Frech Slide 10
Isen: Precipitation-Scan, geometry
91.2 km
´Version 2.22, 11.09.2013 tm,mf
Radar (1000m AGL)
r
Isen
MHP
1650m
1490m
3111m250m
„verification range bin“
1° PWS
Verification: aspects to recall
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Michael Frech Slide 11
Convective / stratiform examples: 21.4.2014
LNM: disdrometer rainrate (mm/h)
comparison of Z fromthe 3 radars with Z from disdrometer
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Michael Frech Slide 12
one – to – one comparison
disdrometer versus radar: MHP (Hohenpeissenberg)
April 2014 – July 2014only for precipitation events > 15 minutes
reflectivity factor rain rate
disdrometer disdrometer
radar
radar
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Michael Frech Slide 13
Hohenpeissenberg disdrometer versus Isen (ISN) radar
one – to – one comparison: radar Isen
April 2014 – July 2014
reflectivity factor rain rate
disdrometer disdrometer
radar
radar
large scatter mainly due to sampling volume differences
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Michael Frech Slide 14
Event based statistics
In order to diminish the large variability which relates to the inherent time - space variability of the measurements, we
- > consider event based analysis: based on on-site disdrometer:
●at least 15 minutes of precipitation●end of event defined if there is no precipation for 5 minutes
For each event:
compute precipitation amount and mean reflectivity factor Z from disdrometer and radar data
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Michael Frech Slide 15
Event based analysis
corresponding event based averaged Z:
21.4.-1.7.2014 2.7.-28.7.2014
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Michael Frech Slide 16
Event based analysis
21.4.-1.7.2014 2.7.-28.7.2014
ISN and MEM: a bias can be seen, MHP good agreement.scatter becomes smaller with increasing precipitation amounts.
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Michael Frech Slide 17
Event based statistics
Site Bias (dB) Bias (dB)
MHP +1.7 +0.5
MEM -1.2 -0.65
ISN -4.3 -3.6positive: underestimationnegative: overestimation..of the radar relative to the disdrometer
Site NB NB
MHP -0.19 -0.05
MEM +0.35 +0.23
ISN +0.68 +0.53
QPE (sum)
21.4.-1.7.2014 1.7.-28.7.2014 1.7.-28.7.201421.4.-1.7.2014
NB = normalized bias,
= <R>/<LNM> -1
Z
Adjustment of MHP calibration: Z bias reduced by 0.5 – 1 dB, QPE bias reduction 19% to 5%
ISN & MEM: overestimate of QPE by 20% (MEM) and 60% (ISN)
Reason?
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Michael Frech Slide 18
Memmingen mis-calibration?
Is there a mis-calibration ?
luckily, we have a disdrometer at the MEM site:
Site bias (dB)
MEM +0.3
→ underestimate of +0.3 dB relative to the disdrometer
calibration is within the target accuracy.
no issue with absolute calibration of MEM.
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Michael Frech Slide 19
Memmingen mis-calibration?
Is there a radar receiver (rx) mis-calibration ?
check of solar power seen by the radar (part of operational radar monitoring)
Site rx – bias (dB)
MHP -0.2
MEM -0.8
ISN -0.3everything relative to the solar power at C-Band:
negative numbers = radar is overestimating solar power
Overall: hardware cannot explain the observed biases
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Michael Frech Slide 20
Conclusions
Biases seen in the radar comparison with MHP surface observationsare linked to the inherent time / space sampling differences of the measurement systems.
Initially a potential miscalibration of the radar system in MEM was suspected:
However:- solar monitoring suggests only a bias smaller 1 dB- local disdrometer – radar comparison in MEM: indicates the same
It remains a challenge to relate local measurents (typically considered as the truth)with radar data.
Absolute calibration can be monitored with the birdbath scan in combination with disdrometer measurements
Uncertainties are reduced by a thorough monitoring of the radar data and system.This monitoring is essential for an objective interpretation radar products
for QPE: event based statistics reduce the uncertainties substantially.
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Michael Frech Slide 21
Thank you!