The new DWD polarimetric weather radar network: a new radar data processing framework and new...

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

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

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


Radar data flow

central unit onsite unit


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.


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?


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


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


Radar sites: ISN - MHP - MEM

91 km65 km

mem = Memmingenmhp = Hohenpeißenbergisn = Isen


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


Convective / stratiform examples: 21.4.2014

LNM: disdrometer rainrate (mm/h)

comparison of Z fromthe 3 radars with Z from disdrometer


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


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


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


Event based analysis

corresponding event based averaged Z:

21.4.-1.7.2014 2.7.-28.7.2014


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.


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?


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.


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


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.


Thank you!

The new DWD polarimetric weather radar network: a new radar data processing framework and new...

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