Geoscience Reference
In-Depth Information
Removal of “low” spurious echoes.
“Low” spurious echoes are all low-reflectivity echoes detected
at low altitudes only. No meteorological echo can exist here. All the “low” echoes are removed.
The algorithm can be treated as a simple method to deal with biometeor echoes (Peura, 2002).
Meteosat filtering.
As a preliminary method for non-meteorological echo removal the filtering
by Meteosat data on cloudiness can be used. A Cloud Type product, which is provided by
EUMETSAT, distinguishes twenty classes of cloud type with the classes from 1 to 4 assigned
to areas not covered by any cloud. All echoes within not clouded areas are treated as
spurious ones and removed. Such simple technique can turn out to be quite efficient in the
cases of anomalous propagation echoes (anaprop) over bigger areas without clouds
(Michelson, 2006).
Speck removal.
Generally, the specks are isolated radar gates with echo surrounded by non-
precipitation gates. Number of echo gates
in a grid around the given gate (e.g. of 3 x 3 gates)
is calculated (Michelson et al., 2000). If a certain threshold is not achieved then the gate is
classified as a speck, i.e. measurement noise, and the echo is removed. Algorithm of the
reverse specks (i.e. isolated radar gates with no echo surrounded by precipitation gates)
removal is analogous to the one used for specks.
Using artificial intelligence techniques
. Artificial intelligence algorithms, such as neural
network (NN), are based on analysis of reflectivity structure (Lakshmanan et al., 2007). The
difference is that similarity of the given object pattern to non-meteorological one, on which
the model was learned, is a criterion of spurious echo detection. For this reason NN-based
algorithms are difficult to parameterize and control their running.
Using dual-polarization observations
. The basis is the fact that different types of targets are
characterized by different size, shape, fall mode and dielectric constant distribution. In
general, different combinations of polarimetric parameters can be used to categorize the
given echo into one of different types (classes). The fuzzy logic scheme is mostly employed
for the combination. Such methods consider the overlap of the boundaries between
meteorological and non-meteorological objects. For each polarimetric radar observable and
for each class a membership function is identified basing on careful analysis of data. Finally,
an object is assigned to the class with the highest value of membership function.
The most often horizontal reflectivity (
Z
H
), differential reflectivity (
Z
DR
), differential phase shift
(Ф
DP
), correlation coefficient (
ρ
HV
), and analyses of spatial pattern (by means of standard
deviation) of the parameters are employed in fuzzy logic schemes. Radars operating in
different frequencies (S-, C-, and X-band) may provide different values of polarimetric
parameters as they are frequency-dependent. For that reason, different algorithms are
developed for identification of non-meteorological echoes using different radar frequencies,
see e.g. algorithms proposed by Schuur et al. (2003) for S-band radars and by Gourley et al.
(2007b) for C-band. A significant disadvantage of such techniques is that they are
parameterized on local data and conditions so they are not transportable to other locations.
Quality index
. Quality index for the gates in which non-meteorological echoes are detected is
decreased to a constant value using formula similar to Equation (3).
An example of algorithms running for spike- and speck-type echoes removal is depicted in
Figure 2b (for Legionowo radar).
