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ARPA-SIM, Bologna, Italy and CIMA , Savona, Italy.

Improving the radar data mosaicking procedure by means of a quality descriptor. Fornasiero, A., Alberoni, P.P., Amorati, R., Marsigli, C. ARPA-SIM, Bologna, Italy and CIMA , Savona, Italy. 1. the story began two years ago. Quality Descriptor (ERAD, 2004).  [0, 1].

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ARPA-SIM, Bologna, Italy and CIMA , Savona, Italy.

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  1. Improving the radar data mosaicking procedure by means of a quality descriptor Fornasiero, A., Alberoni, P.P., Amorati, R., Marsigli, C. ARPA-SIM, Bologna, Italy and CIMA , Savona, Italy. 1

  2. the story began two years ago...

  3. Quality Descriptor (ERAD, 2004)  [0, 1] Qd= quality before correction Qc = quality of the correction errfract > 0 errfract < 0 Fornasiero A. et al, 2005 : Effects of propagation conditions on radar beam-ground interaction: impact on data quality, ADGEO Fornasiero A., 2006 : On the uncertainty and quality of radar data, PhD thesis.

  4. Issues • definition and testing of radar data composition methods taking into account data quality • verification of quality definition consistency with data reliability

  5. The compared methods Short pulse areas QUALITY-BASED APPROACHES • MAX_Q: maximum quality • AVE_Q: quality-weighted average CLASSIC APPROACHES: • MAX_Z: maximum reflectivity • MIN_DIST: minimum distance • AVE_DIST: r-2 weighted average San Pietro Capofiume Gattatico

  6. 24-05-06 14.30 gat reflectivity 24-05-06 14.30 spc reflectivity 24-05-06 14.30 gat quality 24-05-06 14.30 spc quality Case study – 24 May 2006

  7. 24-05-06 14.30 gat-spc reflectivity 24-05-06 14.30 gat-spc reflectivity 24-05-06 14.30 gat-spc reflectivity MAX_Z MAX_Q AVE_Q 24-05-06 14.30 gat-spc weight 24-05-06 14.30 gat-spc weight 24-05-06 14.30 gat-spc weight is the distance effect dominant?

  8. Scores – tp (10 h) om=1.76 mm

  9. Case study – 03-04 August 2006 03-08-06 13.15 gat reflectivity 03-08-06 13.15 spc reflectivity 03-08-06 13.15 gat quality 03-08-06 13.15 spc quality

  10. MAX_Z MAX_Q AVE_Q 03-08-06 13.15 gat-spc weight 03-08-06 13.15 gat-spc weight 03-08-06 13.15 gat-spc weight attenuation effect is of crucial importance 03-08-06 13.15 gat-spc reflectivity 03-08-06 13.15 gat-spc reflectivity 03-08-06 13.15 gat-spc reflectivity

  11. Scores – tp (18 h) om=11.9 mm

  12. Concluding.. • Quality information improves precipitation estimate in radar compositsin convective cases, respect to traditional composition methods • The wider is the spectrum of error sources enclosed within the quality descriptor, the more accurate is the composed precipitation field, even if some errors are not corrected • AVE_Q is preferable with respect to other method especially when there is a lack of informations in Q • The distance-based methods seem to be preferable respect to MAX_Z • It is necessary to test the method in stratiform cases, after inserting VPR-related quality component into the Q function

  13. Thank you for the attention

  14. Appendix Addition of Q comp. Data correction Radar data resampling Data comparison Quality components Radar precipitation verification

  15. Data Correction • Doppler filter • Choice of the minimum elevation that is not affected by clutter and with a beam blocking rate lower than 50% • Topographical beam blocking correction, based on a geometric optics approach • Anomalous propagation clutter suppression Fornasiero, A. , Bech, J., and Alberoni, P. P. Enhanced radar precipitation estimates using a combined clutter and beam blockage correction technique. pp 697-710. SRef-ID: 1684-9981/nhess/2006-6-697

  16. Radar data resampling az_min az az_max 250 m

  17. 1 KM 1 KM 1 2 3 2 5 6 4 5 6 4 7 8 9 Data comparison • radar data are resampled in a 1kmx1km grid • the observation is compared with the nearest radar measure • the precipitation is accumulated from the beginning to the end of the event • raingauges sampling interval=30 min. • only raingauges with the complete dataset (nmeasures=nhours*2) are considered • radar cumulated rain in 1 hour is calculated as weighted average of min 3, max 5 measures

  18. Quality components (1/3) CLUTTER Qd = 0 if clutter is present from VCT Qc = 0.5  Q* = 0.5 Qd =0.8 if the test is not applied BEAM BLOCKING Qd = f(BB)= 1-(BB/BBmax)1/1.5withBBMAX=50% Qc = f(BB)*f(qerr)*f(Dtrs)*f(Drrs) f(qerr)= 1- qerr1/1.5pointing error f(Dtrs)= e-Dtrs/DTtime distance from radios.DT= 4 h f(Drrs)= e-Drrs/DR space distance from radios.DR= 50 KM derived from Bech et al., 2003

  19. Quality components (2/3) Qd= e -br DISTANCE clima from Koistinen and Puhakka, 1981 adj-factor clima = r/g=1-errfraz è  e -br FOCALIZATION/DIVERGENCE ERROR Qd = 1 – (DVol/Vol)1/1.5 DVol= volume variation respect to standard propagation

  20. Quality components (3/3) ATTENUATION Qd = 1 – (ATTENUATION RATE)1/1.5 Burrows and Attwood, 1949 l=5cm, T=18°C

  21. Radar precipitation verification (1/2) ... is conducted as verification of categorical forecasts of discrete predictands Categorical: only one set of possible events occurs Discrete predictand: takes only one of a finite set of possible values

  22. raingauges obs > thr radar obs > thr “forecast”

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