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![Page 1: 1 ARPA-SIM, Bologna, Italy and CIMA, Savona, Italy. Improving the radar data mosaicking procedure by means of a quality descriptor Fornasiero, A., Alberoni,](https://reader035.fdocuments.us/reader035/viewer/2022070414/5697c00e1a28abf838cc9d8d/html5/thumbnails/1.jpg)
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.
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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.
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Issues
1. definition and testing of radar data composition methods taking into account data quality
2. verification of quality definition consistency with data reliability
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Appendix
Radar data resampling
Data comparison
Qualitycomponents
Radar precipitation verification
Data correction
Addition of Q comp.
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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
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az
az_min
az_max
250 m
Radar data resampling
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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
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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
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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)
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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
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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
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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”