10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY
ERAD 2018 Abstract ID 329 1 [email protected]
1 Introduction
Radar echoes of non-meteorological targets must be necessarily identified and removed as far as hydrology
and precipitation estimates are concerned: relevant biases in the estimation of rainfall fields can be introduced
with a negative impact on the performance of using such data for meteorological and hydrological nowcasting,
applications.
Ground clutter and weather signals have different statistical properties, as the stability of reflectivity echoes
relative to very close volumetric cells is much higher in case of clutter than of weather signals. More complex is
the identification of sea clutter because it changes very rapidly in the horizontal direction, and presents a radial
attenuation as the distance from the radar increases. In addition, sea clutter values decrease as a function of
height, so vertical gradient signatures can be used in the clutter identification process.
Common used approaches, for non-doppler and non-polarimetric radar systems, identify clutter in the
reflectivity data by creating a static map of areas where clutter is most prevalent. Another method is to make a
more dynamic map that identifies clutter from image to image. Identification of clutter in reflectivity maps and
the relative correction schemes has been extensively described in literature (Aoyagi, 1983), (Andrieu, 1997),
(Creutin, 1997), (Grecu, 2000), (Steiner, 2002), (Siggia, 2004), (Berenguer, 2005), (Hubbert, 2009a), (Hubbert,
2009b).
In this work, two different algorithms for the removal of ground and sea clutter have been tested and
discussed for two case studies, under different meteorological conditions. Preliminary results show a strong
dependence of the analysed algorithms on the radar site: they must be necessarily customized to each radar
location, because of the ground and sea clutter signatures strongly dependent on local orography and territory.
2 Data and methods
2.1 Radar systems
This study employs data gathered by the X-band radar systems located along the coastal areas of Tuscany
region. The systems constitute aregional network: the first weather radar has been installed in Cima di Monte, a
mountain height about 470 m in the western part of Elba Island. The second one has been placed in the Livorno’s
port, on the top of a silo at an altitude of about 72 m asl. Finally, the third system has been installed in Castiglione
della Pescaia about 25km north of Grosseto, over the building of the local municipality at a height of about 15 m.
Table 1 shows the main characteristics of the non-Doppler and non-polarimetric systems (all of the same
type: WR10X produced by ELDES srl, https://www.eldesradar.com/prodotto/wr-10x/). They have a gate
resolution of 450 m and a maximum selectable range of 108km. A scan every 15 min and 10 elevations from 0.5°
to 5.0° in steps of 0.5° were selected for the operational configurations of the X-band network.
Table 1. WR10X radar technical specifications.
Parameter Value
Operating frequency 9.410 ± 0.03 GHz
Peak power 10 kW
Pulse width 0.6 μs
Pulse Repetition Frequency 800 Hz
Receiver dynamics >90 dB, 8 bits codify
Sensitivity 10 dBZ @ 25 km
Comparison of clutter detection schemes for non-Doppler,
non-polarimetric X band radars
Samantha Melani1,2, Andrea Antonini2, Francesca Caparrini1, Andrea Telleschi3, Andrea Lombardi3, Alessandro Mazza1,2
and Alberto Ortolani1,2
1CNR-IBIMET, Via G. Caproni, 8 50145 Firenze, ITALY 2LaMMA Consortium, Via Madonna Del Piano, 10 50019 Sesto Fiorentino (FI), ITALY
3ELDES srl, via di Porto, 2/B 50018 Scandicci (FI), ITALY
(Dated: 22 June 2018)
10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY
ERAD 2018 Abstract ID 329 2
Noise figure <4 dB
Minimum Detectable Signal <−100 dB
Antenna type Circular Pencil beam diameter 70 cm
Antenna 3 dB lobe <3.2° in elevation and in azimuth
Antenna gain 35÷40 dB
Antenna speed 20°/s
2.2 Sea and ground clutter removal algorithms
In the next sections, two different algorithms for the removal of sea and ground clutter will be presented,
one developed and implemented by ELDES (hereinafter named ALGO1), the other by LaMMA (Laboratory of
Monitoring and Environmental Modelling for the sustainable development) Consortium (hereinafter named
ALGO2).
2.2.1 ELDES’s algorithm (ALGO1)
Data produced by a weather radar are commonly affected by ground clutter at lower elevations. Non-
coherent radar, without Doppler capability, typically uses statistical clutter filters in order to detect and cancel
the clutter echoes.
The ground clutter filter of WR-10X radar is applied at the lowest elevation sweep, and it is based on the
following steps (Figure 1):
Statistical gates classification
(weather, clutter, noise, mixed)
Strong correction
Weak correction
Sweep classification(clutter, weather)
Cluttersweep
Weathersweep
Clutteredsweep
Unclutteredsweep
Figure 1: General scheme of the ELDES ground clutter algorithm.
For each gate, the classification is performed by analyzing the azimuth samples, available in the gate, of the
power level at the output of the receiver channel (Figure 2).
NO-DATA samples>50%
Gate nanalysis
NO-DATAgate
Stat. Estimator≤ Low Thr
Stat. Estimator> Hi Thr
CLUTTERgate
WEATHERgate
MIXEDgate
NO
NO
NO
YES
YES
YES
Figure 2: Scheme of the gate classification.
10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY
ERAD 2018 Abstract ID 329 3
Two different configurable Statistical Estimators can be used in order to detect the texture's differences
between clutter and weather: standard deviation and lag-1 correlation.
ESTIMATOR 1: Standard deviation
The standard deviation is obtained by sampling on 14 bit the power level at the output of the receiver
channel:
𝜎𝑔𝑎𝑡𝑒 =∑ (𝑃𝑛−𝑃𝑔𝑎𝑡𝑒)𝐿𝑛=1
𝐿, ( 2.1 )
where:
L = number of azimuth samples available in the gate;
𝑃𝑛= nth sample of the log received power;
𝑃𝑔𝑎𝑡𝑒̅̅ ̅̅ ̅̅ ̅ = average of azimuth samples available in the gate.
Generally, the clutter gates are characterized by low values of standard deviation but when the clutter is due
to woodlands in presence of wind or snowy close mountain areas such values increase up to assume those
corresponding to areas affected by weak or distant weather fronts.
ESTIMATOR 2: Lag-1 correlation
The spatial correlation at lag-1 is obtained by sampling on 14 bit the power level at the output of the receiver
channel:
𝑅𝑔𝑎𝑡𝑒(1) =∑ 𝑃𝑛∗𝑃𝑛+1𝐿−1𝑛=1
∑ 𝑃𝑛∗𝑃𝑛𝐿−1𝑛=1
, ( 2.2 )
where:
L = number of azimuth samples available in the gate;
𝑃𝑛= nth sample of the linear received power.
Such statistical index allows removing the clutter even when it is due to: woodland in presence of wind,
snowy mountains or urban area subjected to multipath effects.
The classification of the sweep is performed by counting the number of clutter marked as "CLUTTER gate":
if higher than a configurable threshold, it is classified as a "clutter sweep" (predominant clutter), otherwise it is
considered a "weather sweep" (predominant weather). Two different correction filters are applied depending on
the type of sweep.
The different management between clutter and weather sweeps has been implemented in order to avoid
that, in absence of weather events, the radar map shows too many residues of the filtering in correspondence of
ground clutter areas.
In presence of a clutter sweep (predominance of clutter), a strong correction is used. For a weather sweep, a
week correction is applied, for a further classification of mixed gates.
Strong correction (for "clutter sweep"): the sweep is compared with a static clutter map: each non-
WEATHER gate is set to NO-DATA.
Weak correction (for "weather sweep"): the sweep is compared with a static clutter map: each
WEATHER gate is kept, and a correction is applied to the MIXED gates. The gate is classified based
on the classification (WEATHER / CLUTTER) of the surrounding gates (square window
neighborhood of configurable dimension).
The sea clutter removal algorithm is based on the difference of reflectivity along the vertical profile, which is
a very good indicator of the presence of sea clutter. Generally, in rainy situations, the vertical reflectivity profile
is smooth and regular while in presence of echoes returned from the sea, the vertical reflectivity profile appears
uneven with a decrease/disappearance of the reflectivity values with the increasing of the antenna elevation
angle because the sea clutter is a phenomenon that appears only at lowest elevations. The gates of these sweeps
affected by sea clutter are compared with the gates of a reference sweep, acquired at the elevation considered the
limit angle beyond it the PPI images do not show sea clutter. The estimation of the vertical gradient is performed
considering the earth's curvature.
10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY
ERAD 2018 Abstract ID 329 4
Considering that also the echo returned from weather targets can show abrupt changes in vertical reflectivity
profile, when a gate is classified as sea clutter for the classification of the subsequent gates, the algorithm uses
two different thresholds T3 and T4, by using the same logic. The T1 and T2 thresholds are reactivated as soon as
the algorithm classifies a gate as not affected by sea clutter (Alberoni, 2001), (Capozzi, 2014).
Current sweepZi
Reference sweepZr
Weather
ΔZ=Zr-Zi>T1or
ΔZ>0 and Zr<T2
NO-DATA
NO YES
Figure 3: General scheme of the sea clutter algorithm.
2.2.1 LaMMA’s algorithm (ALGO2)
The LaMMA’s clutter algorithm consists of two schemes, one statistical for ground clutter and the other
based on texture features for sea clutter. The main characteristics are here reported, referring to (Antonini, 2017)
for more details.
The algorithm for ground clutter identification is based on the stability of radar signature when the radar
beam intersects and is backscattered by mountain and reliefs (Andrieu, 1997). The implemented algorithm is
based on (Sugier, 2002) and operates a statistical scheme based on the standard deviation of neighbor cells along
azimuth. The basic idea is that the reflectivity cells affected by ground clutter that are contiguous in azimuth are
very strongly correlated for the majority of cases because of the high spatio-temporal closeness of the
observations; this does not occur in the case of two precipitation backscattering cells which are highly variable in
space and time.
In summary, as shown in Figure 4, for each cell of the final product (1° in azimuth x 450 m of range
resolution), the standard deviation of the 40 maximum available observations is used to discriminate the ground
clutter from precipitation. The former is characterized by low standard deviations and not much fluctuating
values while the latter has higher standard deviations and values that are more variable. The algorithm uses two
thresholds (σmin, σmax) based on the specific characteristics of the area covered by radars and computed during
clear air conditions (total absence of precipitation and cloudiness), once collected a sufficient number of scans
(Figure 4a).
If the standard deviation is below the minimum threshold, the corresponding cell is classified as ground
clutter, if it is above the maximum threshold it is classified as precipitation. Each ground cell with an
intermediate standard deviation value is further processed by a module based on the KNMI operational clutter
removal scheme (Wessels, 1992), (Holleman, 2004).
Figure 4b shows the flowchart of the decision tree algorithm and the threshold values for each test.
10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY
ERAD 2018 Abstract ID 329 5
a) b)
Figure 4: Flow chart of the clutter removal scheme: (a) Set-up in clear air conditions; (b) Operational sea and ground clutter
identification algorithm.
For sea clutter removal, the (Steiner, 2002) algorithm was used (Figure 4b). It takes advantage of the 3D
reflectivity structure and was built upon three key parameters: the vertical extent of radar echoes (ECHOTOP),
the vertical gradient of reflectivity (VERTGR), and the spatial variability of the reflectivity fields (SPINCH). The
clutter scheme is a decision-tree algorithm in which the selected features for clutter-identification are analysed
according to a sequential logical chain by means of thresholds: each pixel of the polar base-scan is examined to
determine if it should be kept, removed, or potentially replaced.
Basically, the key parameters exploit:
ECHOtop test removes most of the sea clutter echoes that are separated from precipitation, because
of a lack of vertical extent of those echoes.
VERTGR test characterises the shallow extent of clutter, as even when rainfall affects meaningful
clutter areas, the negative values of the vertical gradient of reflectivity tend to be high.
SPINCH test grasps the spatial variability of clutter echoes, especially when embedded in
precipitation, as reflectivity fields present higher spatial variability and more significant fluctuations
with respect to rainfall echoes.
3 Results for some case studies
Two case studies for different meteorological conditions and clutter disturbances are shown, for Elba and
Livorno radars. The evaluation of both the algorithms performance are made in rainy conditions, for
precipitation embedded or not embedded in clutter echoes. Data from Castiglione della Pescaia are not available
for these case studies.
3.1 06 may 2017
A depression zone is located on the upper Tyrrhenian Sea, causing in the late morning the generation of
some convective weather systems over the sea that propagate easterly towards the northern and central areas of
the region. In the afternoon, new precipitative events generate over sea between Corsica and Elba Island, causing
thunderstorms especially in the Pisa and Lucca areas moving to the inner parts in the evening. Both radar
systems detect the presence of the storms very well, especially in marine areas where ground raingauges
measurements are not available.
10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY
ERAD 2018 Abstract ID 329 6
An extensive area of clutter is present over sea around both radar sites (Figure 5a,b and Figure 6a,b),
partially overlapped with precipitation for the Elba case study (see Figure 6a). Clutter disturbances around Elba
and Livorno sites are generally more pronounced for the lower elevations, even if sea clutter around Livorno is
often persistent also up to the highest elevations (i.e., 5.0°).
Both the clutter removal algorithms are really efficient in removing ground clutter on Corsica, Elba Island
and in the central part of Tuscany, where the reliefs produce very reflective echoes that could be misinterpreted
as precipitation if not correctly removed (Figure 5c,d).
Around Livorno site, some sea clutter features remain as the clutter echoes persist up to 5.0°; they are better
resolved by the ALGO2 as it uses the combination of different features, not only the 3D reflectivity structure (as
in ALGO1) that, in this case, is almost constant for all the elevations. On the other hand, ALGO2 presents the
tendency to remove meteorological reflectivity echoes in a greater extent respect to ALGO1. a) b)
c) d)
Figure 5. PPI radar scans for an elevation of 4.0° for 06 May 2017, 15:15 UTC for Livorno radar. a) Non-decluttered image, b) VMI
product, c) Decluttered image using ALGO1, d) decluttered image using ALGO2.
10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY
ERAD 2018 Abstract ID 329 7
In Figure 6 (a,b), disturbances due to ground clutter are evident along the mountains reliefs of Corsica and
the northern and central Apennines in Tuscany. Such features are more pronounced for the lower elevations
even if sea clutter around Elba site is persistent also up to the highest elevations (i.e., 2.5°).
The application of both the algorithms leads to a considerable reduction of sea and ground clutter while
retaining the all the precipitative features embedded in clutter returns. The precipitation overlapped to sea
clutter northern to the Elba Island is well retained by both the algorithms, even if ALGO2 seems to remove
meteorological radar returns in a greater extent, especially in the northern part of the region where it filters out
the echoes too much. Also, some ground disturbances remain on Elba island and along the coasts due to the
application of ALGO2.
a) b)
c) d)
Figure 6: PPI radar scans for an elevation of 1.5° for 06 May 2017, 18:30 UTC for Elba radar: a) Non-decluttered image, b) VMI
product, c) Decluttered image using ALGO1, d) decluttered image using ALGO2.
10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY
ERAD 2018 Abstract ID 329 8
3.2 23 may 2016
This case study is characterized by a relevant presence of ground clutter echoes over Elba site, along the
Tuscany coasts, over Corsica and in the inner areas of the southern part of the region (Figure 7a,b). Widespread
sea clutter disturbances are also present around Elba site that persist up to the highest elevations (up to 5.0°, not
shown). Some convective precipitative events also affected the study domain, starting along the coast early in the
morning and reaching the south-estearn part later in the afternoon.
a) b)
c) d)
Figure 7: PPI radar scans for an elevation of 1.5° for 23 May 2016, 09:00 UTC for Elba radar: a) Non-decluttered image, b) VMI
product, c) Decluttered image using ALGO1, d) decluttered image using ALGO2.
The decluttered images (Figure 7c,d) show the efficiency of both the algorithms in removing totally sea clutter
around Elba island, while retaining the main meteorological reflectivity echoes although ALGO2 reduces them in
a more consistent way. Some ground clutter noises remain over Elba Island and in the areas in front of it due to
the application of ALGO2 while ALGO1 algorithm proves to give good results also at far distances from radar
site.
10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY
ERAD 2018 Abstract ID 329 9
4 Conclusions
The performance of two different algorithms for sea and ground clutter removal during rainy conditions
have been analyzed, using data from two X-band, non-Doppler, non-polarimetric radars deployed along the
Tuscany coasts.
Preliminary results show how both the algorithms have to be necessarily customized for each radar location,
because ground and sea clutter signatures are highly dependent on local orography and territory. Future works
will be devoted to enlarge the casuistry of the weather events in order to tune in a more customized way the
thresholds used in the algorithms.
Both the algorithms efficiently remove ground clutter due to the massive presence of mountains in Tuscany,
Coarse and Elba, which produces strong radar echoes that, if not correctly identified and removed, could
produce high biases in rainfall fields estimations. Sea clutter is well removed for Elba radar, even if some clutter
disturbances remain visible around Livorno site as clutter affects the higher elevations (up to 5°). In addition,
precipitation features that are embedded in clutter echoes are well retained in their form and intensity.
Qualitative comparisons between the two different algorithms show the tendency of the ALGO2 to retain the
meteorological reflectivity echoes in a lesser extent, while leaving some ground clutter noises for the lower
elevations. When sea clutter extends to the higher elevations, ALGO2 seems to be a little bit more efficient
because of the use of additional features not present in ALGO1.
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