Geoscience Reference
In-Depth Information
3.2 General description of
QI
scheme
An idea of quality index (
QI
) scheme is often employed to evaluate radar data quality. In
this scheme the following quantities must be determined (Szturc et. al., 2011):
1.
Quality factors,
X
i
(where
i
= 1, …
n
) - quantities that have impact on weather radar-
based data quality. Their set should include the most important factors that can be
measured or assessed.
2.
Quality functions,
f
i
- formulas for transformation of each individual quality factor
X
i
into relevant quality index
QI
i
. The formulas can be linear, sigmoidal, etc.
3.
Quality indices,
QI
i
- quantities that express the quality of data in terms of a specific
quality factors
X
i
:
0
bad data
QI
1
good data
() 0, ) th r s
(1)
i
fX
i
i
4.
Weights,
W
i
- weights of the
QI
i
s. The optimal way of the weight determination seems to
be an analysis of experimental relationships between proper quality factors
X
i
and radar
data errors calculated from comparison with benchmark data (on historical data set).
5.
Final quality index,
QI
- quantity that expresses quality of data in total, calculated using
one of the formulae:
minimum value:
QI
min
QI
,
(2a)
i
additive scheme (weighted average):
n
,
(2b)
QI
QI
W
i
i
i
1
multiplicative scheme (multiplication):
n
n
.
QI
QI
W
or
QI
QI
(2c)
i
i
i
i
1
i
1
The latter seems to be the most appropriate and its form is open (e.g. changes in set of
quality indicators do not require the scheme parameterization).
4. Quality control algorithms for radar reflectivity volumes
Starting point in dealing with weather radar reflectivity data should be quality control of
3-D raw radar data. There are not many papers focused on quality characterization of such
data. Fornasiero et al. (2005) presented a scheme employed in ARPA Bologna (Italy) for
quality evaluation of radar data both raw and processed. The scheme developed in Institute
of Meteorology and Water Management in Poland (IMGW) in the frame of BALTRAD
project (Michelson et al., 2010) was described by Ośródka et al. (2010, 2012). Commonly
employed groups of quality control algorithms are listed in Table 1.
