The new DWD polarimetric weather radar network: a new radar data processing framework and new products Michael Frech 1, Nils Rathmann 2, Jörg Steinert 2 Patrick Tracksdorf 2 and Manuel Werner 2 DWD, German Meteorological Service 1 Meteorological Observatory Hohenpeißenberg 2 Research & 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 systems 1 research system EEC DWSR5001/SDP/CE SIDPOL C-band 500 kW peak power, magnetron systems Pulse widths: 0.4 and 0.8 µs beam width 1° operational range resolution: 250m -1km Scan strategy: 5 min update rate

Michael Frech Slide 4 Radar data flow central unit onsite unit

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: The European Radar Conference ERAD will take place September, 2014, Garmisch-Partenkirchen, Germany.

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?

Michael Frech Slide 7 Monitoring absolute calibration: concept 1st range bin in the far field 650 m 10 m 25 m Radar disdrometer Z h,v,radar Z PWS Disdrometer - radar comparison: reflectivity factors Z PWS versus Z h,v,radar from birdbath scan requirements & assumptions: precip. > 10 dBZ no 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

Michael Frech Slide 8 Absolute calibration: PWS - Radar -Radar Compare the consistency of 3 radars Compare 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 for calibration 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

Michael Frech Slide 9 Radar sites: ISN - MHP - MEM 91 km 65 km mem = Memmingen mhp = Hohenpeißenberg isn = Isen

Michael Frech Slide 10 Isen: Precipitation-Scan, geometry 91.2 km ´Version 2.22, tm,mf Radar (1000m AGL) r Isen MHP 1650 m 1490 m 3111m 250 m „verification range bin“ 1° PWS Verification: aspects to recall

Michael Frech Slide 11 Convective / stratiform examples: LNM: disdrometer rainrate (mm/h) comparison of Z from the 3 radars with Z from disdrometer

Michael Frech Slide 12 one – to – one comparison disdrometer versus radar: MHP (Hohenpeissenberg) April 2014 – July 2014only for precipitation events > 15 minutes reflectivity factorrain rate disdrometer radar

Michael Frech Slide 13 Hohenpeissenberg disdrometer versus Isen (ISN) radar one – to – one comparison: radar Isen April 2014 – July 2014 reflectivity factorrain rate disdrometer radar large scatter mainly due to sampling volume differences

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

Michael Frech Slide 15 Event based analysis corresponding event based averaged Z:

Michael Frech Slide 16 Event based analysis ISN and MEM: a bias can be seen, MHP good agreement. scatter becomes smaller with increasing precipitation amounts.

Michael Frech Slide 17 Event based statistics SiteBias (dB) MHP MEM ISN positive: underestimation negative: overestimation..of the radar relative to the disdrometer SiteNB MHP MEM ISN QPE (sum) NB = normalized bias, = / -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?

Michael Frech Slide 18 Memmingen mis-calibration? Is there a mis-calibration ? luckily, we have a disdrometer at the MEM site: Sitebias (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.

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) Siterx – bias (dB) MHP-0.2 MEM-0.8 ISN-0.3 everything relative to the solar power at C-Band: negative numbers = radar is overestimating solar power Overall: hardware cannot explain the observed biases

Michael Frech Slide 20 Conclusions Biases seen in the radar comparison with MHP surface observations are 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.

Michael Frech Slide 21 Thank you!