1

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.

Quality Descriptor (ERAD, 2004)

Q index Unit

Anomalous P.

[]: 0, 1, 2

distance [m]

Beam Block.

[dB]

vol [dB]

PIA [dB]

i

iQQ *

)1)(1(1* cd QQQ

fractd errQ 1

fract

d

errQ

1

1

Qd = quality before correction

Qc = quality of the correction

11 true

fract R

Rerr

5.11

105.1

1

10

dBZ

truetrue Z

Z

R

R

[0, 1]

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

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

2. verification of quality definition consistency with data reliability

The compared methods

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 averageGattatico

San Pietro Capofiume

Short pulse areas

Case study – 24 May 2006

24-05-06 14.30 gat quality

24-05-06 14.30 spc reflectivity24-05-06 14.30 gat reflectivity

24-05-06 14.30 spc quality

MAX_Z MAX_Q AVE_Q24-05-06 14.30 gat-spc weight 24-05-06 14.30 gat-spc weight 24-05-06 14.30 gat-spc weight

24-05-06 14.30 gat-spc reflectivity 24-05-06 14.30 gat-spc reflectivity 24-05-06 14.30 gat-spc reflectivity

Scores – tp (10 h)threshold (mm) 1 2 4 6

observations 94 72 52 30

00.20.40.60.8

11.21.41.61.8

2

0 2 4 6

AVE Q MAX Q MAX Z MIN_DIST AVE_DIST om=1.76 mm

0.20.30.40.50.60.70.80.9

1

0 1 2 3 4 5 6

threshold (mm)

hss

0.8

1

1.2

1.4

1.6

1.8

2

0 1 2 3 4 5 6threshold (mm)

bs

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6

threshold (mm)

pod

0

0.1

0.2

0.3

0.4

0.5

0.6

0 1 2 3 4 5 6threshold (mm)

fa

0

2

4

6

8

10

0 1 2 3 4 5 6threshold (mm)

rmse

N

-1

0

1

2

3

4

5

6

0 1 2 3 4 5 6threshold (mm)

bia

sN

best method1 mm 2 mm 4 mm 6 mm

hss maxZ aveQ aveQ maxQhr maxZ aveQ aveQ maxQts maxZ aveQ aveQ maxZbs aveQ mindist mindist aveQ

pod maxZ maxZ maxZ maxZfa aveQ aveQ aveQ maxQ

rmseN aveQ aveQ avedist aveQbiasN aveQ/avedist avedist aveQ aveQ

worst method1 mm 2 mm 4 mm 6 mm

hss mindist mindist avedist avedisthr mindist mindist avedist maxZts mindist mindist avedist aveQbs maxZ maxZ maxZ maxZ

pod mindist mindist avedist avedistfa maxZ maxZ maxZ maxZ

rmseN maxZ maxZ maxZ maxZbiasN maxZ maxZ maxZ maxZ

Case study – 03-04 August 2006

03-08-06 13.15 gat quality

03-08-06 13.15 spc reflectivity03-08-06 13.15 gat reflectivity

03-08-06 13.15 spc quality

MAX_Z MAX_Q AVE_Q

03-08-06 13.15 gat-spc reflectivity 03-08-06 13.15 gat-spc reflectivity 03-08-06 13.15 gat-spc reflectivity

03-08-06 13.15 gat-spc weight 03-08-06 13.15 gat-spc weight 03-08-06 13.15 gat-spc weight

Scores – tp (18 h)threshold (mm) 1 2 4 6 10 15

observations 100 94 84 77 69 48

00.20.40.60.8

11.21.41.61.8

2

0 2 4 6

AVE Q MAX Q MAX Z MIN_DIST AVE_DIST om=11.9 mm

0.20.30.40.50.60.70.80.9

1

0 3 6 9 12 15

threshold (mm)

hss

0

0.1

0.2

0.3

0.4

0.5

0.6

0 3 6 9 12 15threshold (mm)

fa

0

2

4

6

0 3 6 9 12 15threshold (mm)

rmseN

-1

0

1

2

3

4

0 3 6 9 12 15threshold (mm)

bia

sN

0.8

0.85

0.9

0.95

1

0 3 6 9 12 15

threshold (mm)

pod

1

1.2

1.4

1.6

1.8

0 3 6 9 12 15threshold (mm)

bs

best method0.3 mm 1 mm 2 mm 4 mm 6 mm 10 mm 15 mm

hss aveQ maxQ maxQ aveQ aveQ aveQ aveQhr aveQ maxQ maxQ aveQ aveQ aveQ aveQts aveQ maxQ maxQ aveQ aveQ aveQ aveQbs aveQ aveQ aveQ aveQ avedist aveQ aveQ

pod maxZ maxZ maxZ maxZ maxZ maxZ maxZfa aveQ aveQ maxQ aveQ aveQ aveQ aveQ

rmseN aveQ aveQ aveQ aveQ aveQ aveQ aveQbiasN aveQ aveQ aveQ aveQ aveQ aveQ aveQ

worst method0.3 mm 1 mm 2 mm 4 mm 6 mm 10 mm 15 mm

hss max_Z max_Z max_Z max_Z max_Z max_Z max_Zhr max_Z max_Z max_Z max_Z max_Z max_Z max_Zts max_Z max_Z max_Z max_Z max_Z aveQ max_Zbs max_Z max_Z max_Z max_Z max_Z max_Z max_Z

pod min_dist min_dist min_dist min_dist min_dist ave_Q min_distfa maxZ maxZ maxZ maxZ maxZ maxZ maxZ

rmseN maxZ maxZ maxZ maxZ maxZ maxZ maxZbiasN maxZ maxZ maxZ maxZ maxZ maxZ maxZ

Concluding..

• Quality information improves precipitation estimate in radar composits in 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

Appendix

Radar data resampling

Data comparison

Qualitycomponents

Radar precipitation verification

Data correction

Addition of Q comp.

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

az

az_min

az_max

250 m

Radar data resampling

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

1 KM

1 KM 2

4

5

6

1

5

32

4 6

7 8 9

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)1/1.5 with BBMAX=50%

Qc = f(BB)*f(err)*f(trs)*f(rrs)

f(err)= 1- err1/1.5 pointing error

f(trs)= e-trs/Ttime distance from radios. T= 4 h

f(rrs)= e-rrs/R space distance from radios. R= 50 KM

derived from Bech et al., 2003

DISTANCE Qd= e -r

from Koistinen and Puhakka, 1981adj-factor clima = r/g=1-errfraz

è e -r

clima

FOCALIZATION/DIVERGENCE ERRORQd = 1 – (Vol/Vol)1/1.5

Vol = volume variation respect to standard propagation

Quality components (2/3)

ATTENUATION

Qd = 1 – (ATTENUATION RATE)1/1.5

5.11

10101

PIA

Qd

Quality components (3/3)

kmdBRATTway

/*0018.005.1

1

Burrows and Attwood, 1949

=5cm, T=18°C

Radar precipitation verification (1/2)

Categorical: only one set of possible events occurs

Discrete predictand: takes only one of a finite set of possible values

... is conducted as verification of categorical forecasts of discrete predictands

yes no

yes a b

no c d

raingauges obs > thr

radar

obs

>

thr

score formula what does it represent? bpv wpv

BS=bias score (a+b)/(a+c) how often event is forecasted respect to observed

1

HR = hit rate (a+d)/n fraction of correct forecasts 1 0

TS=Threat score or critical succes index

a/(a+b+c) number of correct ‘yes’ forecasts/ total forecasts or

observed (HR after removing correct ‘no’ forec.)

1 0

POD=probability of detection

a/(a+c) prob. event forecasted, given that it occurred

1 0

FA=false alarms ratio

b/(b+c) proportion of forec. that failed 0 1

HSS= Heidke skill score

.... (HR-HR random)/(1-HR random) 1 -

rmseN (1/n(fi-oi)2)/om rmse nomalized respect to mean observed field

0

biasN (1/n(fi-oi))/om bias nomalized respect to mean observed field

0

“forecast”