Improving the radar data mosaicking procedure by means of a quality descriptor
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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

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


Arpa sim bologna italy and cima savona italy

the story began two years ago...


Quality descriptor erad 2004

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.


Issues

Issues

  • definition and testing of radar data composition methods taking into account data quality

  • verification of quality definition consistency with data reliability


The compared methods

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


Case study 24 may 2006

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


Arpa sim bologna italy and cima savona italy

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?


Scores tp 10 h

Scores – tp (10 h)

om=1.76 mm


Case study 03 04 august 2006

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


Arpa sim bologna italy and cima savona italy

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


Scores tp 18 h

Scores – tp (18 h)

om=11.9 mm


Concluding

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


Arpa sim bologna italy and cima savona italy

Thank you for the attention


Appendix

Appendix

Addition of

Q comp.

Data

correction

Radar data

resampling

Data

comparison

Quality

components

Radar precipitation

verification


Data correction

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


Radar data resampling

Radar data resampling

az_min

az

az_max

250 m


Data comparison

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


Quality components 1 3

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


Arpa sim bologna italy and cima savona italy

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


Arpa sim bologna italy and cima savona italy

Quality components (3/3)

ATTENUATION

Qd = 1 – (ATTENUATION RATE)1/1.5

Burrows and Attwood, 1949

l=5cm, T=18°C


Radar precipitation verification 1 2

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


Arpa sim bologna italy and cima savona italy

raingauges obs > thr

radar obs > thr

“forecast”


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