ARPASIM, Bologna, Italy and CIMA , Savona, Italy'

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 gt 0
errfract lt 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
Case study 24 May 2006
7
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
(No Transcript)
9
Scores tp (10 h)
om1.76 mm
10
(No Transcript)
11
Case study 03-04 August 2006
03-08-06 13.15 spc reflectivity
03-08-06 13.15 gat reflectivity
03-08-06 13.15 gat quality
03-08-06 13.15 spc quality
12
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
13
(No Transcript)
14
Scores tp (18 h)
om11.9 mm
15
(No Transcript)
16
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

17
Thank you for the attention
18
Appendix
Addition of Q comp.
Data correction
Radar data resampling
Data comparison
Quality components
Radar precipitation verification
19
Addition of crucial quality components produces
relevant changes in Max_q method
om1.76 mm
20
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
21
Radar data resampling
az_min
az
az_max
250 m
22
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 interval30 min.
• only raingauges with the complete dataset
  (nmeasuresnhours2) are considered
• radar cumulated rain in 1 hour is calculated as
  weighted average of min 3, max 5 measures

23
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 BBMAX50 Qc
f(BB)f(qerr)f(Dtrs)f(Drrs) f(qerr) 1-
qerr1/1.5 pointing 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
24
Quality components (2/3)
Qd e -br
DISTANCE
clima
from Koistinen and Puhakka, 1981 adj-factor clima
r/g1-errfraz è ? e -br
FOCALIZATION/DIVERGENCE ERROR
Qd 1 (DVol/Vol)1/1.5 DVol volume variation
respect to standard propagation
25
Quality components (3/3)
ATTENUATION
Qd 1 (ATTENUATION RATE)1/1.5
Burrows and Attwood, 1949 l5cm, T18C
26
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
27
raingauges obs gt thr
radar obs gt thr
forecast