Comparison of clutter detection schemes for non Doppler, non...

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)

https://www.eldesradar.com/prodotto/wr-10x/


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.



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.



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.



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.



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.



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




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


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.



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.

References

Alberoni, P. A. (2001). Use of the vertical reflectivity profile for identification of anomalous propagation. Meteorol. Appl., 8,

257–266. Andrieu, H. C.-D. (1997). Use of a weather radar for the hydrology of a mountainous area. Part I: Radar measurement

interpretation. J. Hydrol., 193, 1–25.

Antonini, A. M. (2017). On the Implementation of a Regional X-Band Weather Radar Network. Atmosphere, 8, 25.

Aoyagi, J. (1983). A study on the MTI weather radar system for rejecting ground clutter. Papers in Meteor. and Geophy, 33,

187–243.

Berenguer, M. S.-D. (2005). A fuzzy logic technique for identifying nonprecipitating echoes in radar scans. J. Atmos.

Oceanic Technol., 23, 1157–1180.

Capozzi, V. P. (2014). X-band weather radar monitoring of precipitation fields in Naples urban areas: data quality,

comparison and analysis. Proc. of ERAD 2014 - 8th European Conference on Radar in Meteorology and

Hydrology, Garmisch-Partenkirchen, Germany.

Creutin, J.-D. A. (1997). Use of a weather radar for the hydrology of a mountainous area. Part II: Radar measurement

validation. J. Hydrol., 193, 26–44.

Grecu, M. K. (2000). An Efficient Methodology for Detection of Anomalous Propagation Echoes in Radar Reflectivity Data

Using Neural Networks. J. Atmos. Oceanic Tech., 23, 121-129.

Holleman, I. B. (2004). Review of the KNMI Clutter Removal Scheme. In Technical Report, KNMI TR-284. Royal

Netherlands Meteorological Institute (KNMI): De Bilt, The Netherlands.

Hubbert, M. D. (2009a). Weather Radar Ground Clutter. Part I: Identification, Modeling, and Simulation. J. Atmos. Oceanic

Tech., 26, 1165-1180.

Hubbert, M. D. (2009b). Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering. J. Atmos. Oceanic

Tech., 26, 1181-1197.

Siggia, A. D. (2004). Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment

calculation. Proc. ERAD 2004, 2, 421–424.

Steiner, M. a. (2002). Use of three-dimensional reflectivity structure for automated detection and removal of non-

precipitating echoes in radar data. J. Atmos. Oceanic Technol., 19, 673–686.

Sugier, J. C. (2002). Detection and removal of clutter and anaprop in radar data using a statistical scheme based on echo

fluctuation. Proc. of the 2nd European Radar European Conference on Radar in Meteorology, ERAD 2002, (S. 17-

24). Delft, The Netherlands, 18–22 Nov.

Wessels, H. B. (1992). Automatic Suppression of Anomalous Propagation Clutter for Noncoherent Weather Radars. In

Scientific Report WR-92-06. Royal Netherlands Meteorological Institute (KNMI): De Bilt, The Netherlands.

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