Real time mitigation of ground clutter

John C. Hubbert∗, Mike Dixon and Scott Ellis

National Center for Atmospheric Research, Boulder CO

1. Introduction

The identification and mitigation of anomalous prop-agation (AP) and normal propagation (NP) clutter isan ongoing problem in radar meteorology. Scatter fromground clutter targets routinely contaminates radar dataand masks weather returns causing poor data quality.The problem is typically mitigated by applying a clutterfilter to all radar data but this also biases weather dataat zero velocity. Another solution has been to identifyradar data that is contaminated by ground clutter andthen censor that data. This strategy can work wellbut also can leave large areas of radar images blank.Modern radar processors make possible the real timeidentification and elimination of AP clutter. A fuzzylogic algorithm is used to distinguish between clutterechoes and precipitation echoes and subsequently aclutter filter is applied to those radar resolution volumeswhere clutter is present. In this way zero velocityweather echoes are preserved while clutter echoes aremitigated. Since the radar moments are recalculatedfrom clutter filtered echoes, the underlying weather echosignatures are revealed thereby significantly increasingthe visibility of weather echo. This paper describesthe fuzzy logic algorithm, CMD (Clutter MitigationDecision), for clutter echo identification. A new featurefield, CPA (Clutter Phase Alignment), is introducedand described. The CMD algorithm is illustrated withexperimental data from the Denver NEXRAD, KFTG.

2. Clutter Mitigation Decision Algorithm

The Clutter Mitigation Decision (CMD) algorithmuses a fuzzy logic approach to combine the informationfrom “feature fields” into a single decision making field.The feature fields CMD uses are: (a) texture of reflectiv-ity, (b) SPIN (Steiner and Smith 2002) of reflectivity and(c) CPA. The feature fields are transformed to interestvalues between 0 and 1 using membership functions. Theinterest values are then combined using decision rules andweights. The final result is normalized to the range 0 to1 and then a threshold is selected for the clutter classifi-cation boundary. The feature fields are calculated froma number of consecutive gates along a radar radial. Theseries of consecutive gates used is referred to as the “ker-nel”.

The texture of the reflectivity (TDBZ) is computed asthe mean of the squared reflectivity difference betweenadjacent gates,

TDBZ =

[M∑i

(dBZi − dBZi−1)2]

/M (1)

where dBZ is the reflectivity and M is the number gatesin the kernel. Using data from only one radial eliminatesthe need to buffer adjacent beam information into mem-ory and significantly reduces the algorithm complexityover the use of 2-D computations. The TDBZ featurefield is computed at each gate along the radial with thecomputation centered on the gate of interest.

The SPIN feature field is a measure of how often thereflectivity gradient changes sign along a direction in

∗Correspondence to: hubbert@ucar.edu

space (in this case the radar radial). For example, ifXi−1, Xi, and Xi+1 represents 3 consecutive dBZ valuesalong a radar radial, in order for a SPIN change to oc-cur, two conditions must be met: sign{Xi − Xi−1} =−sign{Xi+1 − Xi} and |Xi − Xi−1| + |Xi+1 − Xi|/2 >spin thres where spin thres is a reflectivity threshold typ-ically set to 5 dBZ. Thus, if both conditions are met, theSPIN variable increments by 1. Finally, SPIN is con-verted to a percentage by dividing the SPIN number bythe number of gates in the kernel.

CPA is a measure of temporal phase fluctuations ofechoes over typical data collection times for a single radarresolution volume. CPA is defined as the magnitude ofthe vector sum of the individual time series members, xi,divided by the sum of the magnitudes of the xi:

CPA =

∣∣∣∣∣N∑

i=1

xi

∣∣∣∣∣ /[

N∑i=1

|xi|

]. (2)

Thus, CPA ranges from 0 to 1 with 1 indicating a veryhigh probability of clutter. Intuitively, CPA is a goodindicator of clutter since by definition it is a metric ofthe primary characteristic of a stationary ground cluttertarget, i.e., low variability of backscatter phase. Notethat if the phase of the xi is a constant, CPA will beone regardless of the behavior of the magnitude of thexi. If the target is not completely stationary over themeasurement period, the mean velocity may differ from0 ms−1 and/or the width of the spectrum of the radarreturn signal may increase, both of which will decreaseCPA to below 1. For the very large majority of radarclutter returns examined here, CPA is greater than 0.90whereas CPA is less than 0.5 ms−1 zero for noise andweather echo with mean velocity magnitude greaterthan about 5% of the Nyquist velocity (ignoring thepossibility of velocities that “wrap” back to 0 ms−1 thatcould yield very high CPA). CPA can be relatively lowin clutter, though infrequently, and thus some pointsmay be mis-classified as precipitation but such pointsare nearly always “caught” by SPIN and TDBZ. AlsoCPA can be high for zero velocity narrow spectrumwidth weather. This is also rare as most weather usuallyhas a mean velocity of at least few tenths of a meter persecond which is typically enough to decrease CPA tonon-clutter classification levels. However, the few timeswhere CMD does miss-classify echoes can cause imagespeckle. In order to alleviate this, a 5 point median filteris used on CPA in range. It was found that doing thiseffectively mitigates image speckle while no appreciabledeleterious effects were observed.

3. Fuzzy logic algorithm

The single polarization feature fields, TDBZ, SPINand CPA, are converted to to so-called interest fields us-ing the fuzzy logic membership functions. The member-ship functions used in CMD are shown in Fig. 1. Theinterest fields vary from 0 to 1 with 1 indicating thestrongest clutter likelihood for the given variable. TheTDBZ and SPIN interest field are then combined witha fuzzy “or” rule: the maximum interest value of TDBZand SPIN is selected. The two remaining interest fields,MAX(TDBZ,SPIN) and CPA are multiplied by a-prioriweights of 1.0 and 1.01 respectively and normalized by

the sum of the weights. The weighted sum of interestvalues yields a probability of clutter between 0 and 1.Finally, gates with values greater than 0.5 probabilityare classified as clutter.

The chosen weight of 1.01 for CPA can be justified asfollows. For certain rare cases MAX(TDBZ,SPIN) is zerowhile the CPA interest is one. In this case the normal-ized weighted sum (or probability) would be 0.5 if bothinterest fields had weights of one, and the point wouldbe classified as not clutter. By giving CPA a weight of1.01, such cases are correctly classified as clutter. Casestudies have shown this to improve the performance ofthe CMD algorithm by removing isolated “speckles” ofmisclassification.

The general steps of the CMD algorithm are as follows:

1. Check if SNR > 3 dB, otherwise no filtering appliedat this gate.

2. Compute feature fields: TDBZ, SPIN and CPA.TDBZ is computed over 9 consecutive radar gateswhile SPIN is computed over 11 gates.

3. Apply membership functions to convert feature fieldsto interest values.

4. Combine TDBZ and SPIN interest fields using fuzzyor rule (maximum interest).

5. Compute normalized weighted sum of interest values(CMD field).

6. Threshold CMD at 0.5 to produce CMD clutter flag.

7. Apply clutter filter where CMD flag is set.

8. Recalculate the radar moments for the clutter-filtered gates

4. Experimental data

During the summer and fall of 2006 a radar time seriesrecorder was installed at the NWS (National WeatherService) Denver NEXRAD, KFTG, and these time serieswere used to develop and test CMD. Importantly for ourdata analysis, the the CMD processed moments can becompared directly to the KFTG moment data that arearchived at the NCDC (National Climatic Data Center).For the KFTG data set analyzed here, the comparableNCDC archived data were processed with the GMAPspectral clutter filter (Siggia and Passarelli 2004) appliedto all the data as specified by the NWS radar operators.As will be seen, large regions of zero velocity weatherdata that were effectively eliminated in the NEXRAD op-erational data by the GMAP clutter filter are preservedthrough use of the CMD algorithm.

To mitigate the range-velocity ambiguity problem, theNEXRAD radars scan the lower elevation angles twice:once using a long PRT (about 3.1 ms) so that the unam-biguous range is 460 km but the unambiguous velocity isabout 8 ms−1 and once using a much shorter PRT (about1 ms) to obtain velocity estimates with an unambiguousvelocity of about ±25ms−1 but the unambiguous rangeis about 150 km. Velocity estimates obtained from theshort PRT scan with overlaid echoes regions are censoredusing information from the long PRT scan. The follow-ing NEXRAD data consists of both long and short PRTscans. We first illustrate CPA using a KFTG clear airscan and then with a wide spread up-slope rain mixedwith snow KFTG data case. The second case has zerovelocity precipitation embedded in the Rocky Mountainsand thus is a good test of CMD performance.

First we demonstrate the the ability of CPA to identifyclutter via a KFTG clear air reflectivity PPI scan, gath-ered on 17 October at 0.5◦ elevation, shown in Fig. 2. Thefeature field CPA, that corresponds to Fig. 2, is shownin Fig. 3 . A large fraction of the clutter points haveCPA values greater than 0.9. Note the ability of CPA toidentify some of the ground clutter features that have lowCNR and are not obvious in the clear air reflectivity ofFig. 2. A few features are worth noting. Along the 90◦ ra-dial (zero degrees is North) there exists a line of elevatedCPA values. This line is routinely seen in KFTG dataand is due to multiple scatter from the Centennial Air-port control tower. Along the 20◦ radial at about 90 kmthere is a small area of CPA>0.9 which is not noticeablein Fig. 2. Similarly, there are some elevated radial linesof CPA at about 165◦. Also discernible in this region isa faint fine line, likely due to a convergence boundary,from about 190◦, 60km to 170◦, 90km. This fine line isnot identified as clutter by CPA.

In the next case, one half degree elevation angle PPIdata were gathered by KFTG on 26 October 2006 in awide-spread snow storm along the Eastern Foothills ofthe Rocky Mountains in Colorado. The time series datawere gathered and the CMD algorithm was run duringpost processing. (Note, however, that CMD is designedto run in real time and does so on S-Pol, NCAR’s dualpolarimetric S-band radar.) Figs. 4 and 5 show unfil-tered reflectivity and velocity, respectively, from the longPRT scan. The x- and y-axes span 250 km and the rangerings are in 25 km increments. The Rocky Mountains areeasily seen in the west portion of the PPIs. Peak reflec-tivities are about 40 dBZ in the storm (wet snow) whilethe reflectivity due to the mountain clutter is in excessof 65 dBZ. The velocity plot shows a clear 0 m s−1 isodopthrough the center of the plot (in gray). The reflectiv-ity plot shows areas marked by black lines that outlinethe location of the zero velocity isodop. The velocityfield shows areas, indicated by ovals, where the velocityhas folded back to 0 ms−1 (again in gray). This is deter-mined from the short PRT velocity (Nyquist velocity isabout 25 ms−1) which is shown in Fig. 6. The power inthese areas will be severely attenuated if a clutter filteris applied. It is these 0 velocity weather areas that theCMD should not identify as clutter. (Refer to Figure 2for the clear air reflectivity which shows the location ofground clutter, i.e., it is a clutter map for the regiondisplayed in Figs. 4 and 5.) Figure 7 shows the NCDCrecorded long PRT reflectivity which was processed us-ing a clutter filter everywhere which is typical in legacyNEXRAD processing. This is the data that was viewedand used by NWS forecasters, automated algorithms andoutside users. Note the reflectivity that has been elim-inated not only along the zero velocity isodop but alsoin the areas where non-zero velocity echoes have beenaliased to zero velocity as indicated in Fig. 5. This dataset demonstrates the problem of applying a clutter filtereverywhere, even if it is an advanced, adaptive spectralfilter.

The feature fields of TDBZ, SPIN and CPA are calcu-lated from KFTG time series and are shown in Figs. 8, 9and 10, respectively. The feature fields can be comparedto the clear air reflectivity of Fig. 2 that shows the lo-cation of ground clutter. The membership function forTDBZ gives an interest of one when TDBZ> 40 dBZ2

and from Fig. 8, values of TDBZ> 40dBZ2 identify theclear air clutter regions well. Similarly when the SPINfeature field is greater than 25, its interest value is 1 andthese values of SPIN correspond well to the clutter re-gion of Fig. 2. The corresponding feature field CPA isshown in Fig. 10 and should be compared to the clearair CPA field in Fig. 3. Some of the previously identified

clutter areas now have very low CPA values since theclutter echoes are dominated by overlaying precipitationecho. However, most of the very strong ground cluttertargets remain, such as the Rocky Mountains. Note theelevated CPA values along the zero velocity isodop whichwas indicated in Fig. 6. The CPA is higher in this regionsince 1) there is zero velocity narrow spectrum width pre-cipitation and 2) some of this area does contain groundclutter echo as is seen from Figs. 2 and 3. Many of theCPA values are less than 0.8 which is more indicativeof precipitation. There are also some pixels with CPA> 0.9 that are in fact precipitation. Hence the need forthe other feature fields of TDBZ and SPIN to properlyclassify such areas.

After the feature fields are constructed, CMD is thenused to create the clutter map shown in Fig. 11 with yel-low marking the regions to be filtered. This can be com-pared to the clear air reflectivity shown in Fig. 2. Thefrequency domain GMAP clutter filter is applied to thedata at those gates specified by Fig. 11. The resulting re-flectivity PPI is shown in Fig. 12 and should be comparedto Figs. 4 and 7. As can be seen, CMD has effectivelyeliminated most of the ground clutter that is evident inFig. 4. Next compare Fig. 12 to Fig. 7 where a clutterfilter was applied everywhere. Note the severe attenua-tion due to the clutter along the zero velocity isodop andin the two regions of velocity folded zero velocity isodop.By using CMD, the majority of the weather echo is pre-served. The CMD filtered reflectivity demonstrates thesignificant improvement in data quality when comparedto the NCDC archive data of Fig. 7.

To evaluate the performance of the CMD, the fractionof range gates, which CMD identifies for filtering, iscalculated as a function of CSR (clutter-to-signal ratio).The the lower the CSR the less likely the radar gatewill be identified as clutter. Gates where the SNR isless than 10 dB are excluded. The clutter power for allgates is estimated by the amount of power removed bythe filter (all gates are filtered). The remaining power isconsidered to be weather power. Gates with near zerovelocity (i.e., |vel| ≤ 2ms−1) are excluded since zero andnear zero velocity weather power could be classified asclutter power and this would bias the CSR estimate.The results are shown in Fig. 13. The solid line lineindicates the fraction of gates that are identified byCMD for filtering where as the dashed line indicatesthe fraction of gates that are not identified. These arecomplementary curves: one minus the solid line valuegives the dashed line value. As can be seen the crossoverpoint is located at about -8 dB CSR, that is, about50% of the gates with with CSR= -8 dB are identifiedby CMD for filtering. To detect clutter that is 8 dBbelow the weather signal is quite good since it wouldseem that the weather signal would dominate the cluttersignal. To explain this level of clutter identificationperformance, consider the mechanics of CMD and thespatial texture of clutter power. CMD operates over akernel of 7 to 11 gates. Over this interval, the clutterreflectivity is usually highly variable with possiblegate-to-gate reflectivity differences of 10 to 15 dBZ ormore. Gates with lower clutter power can have low CSR(CSR< 0 dB), if there is weather at the appropriatereflectivity level. The surrounding gates, however, canhave much higher clutter power (i.e., CSR> 0 dB). SinceCMD uses feature fields based on spatial texture forclutter identification, these gates with lower CSR can beidentified as clutter.

5. Conclusions

Both NP and AP ground clutter echoes routinelycontaminate weather radar data obscuring the desired

Figure 1: CMD membership functions and their break points.The feature field value is mapped to an interest field value.

weather echoes especially at low elevation angles. Previ-ously either clutter filters have been applied everywhereor clutter echoes have been identified in post processingwith the identified radar gates censored. This paperhas presented a practical, operational, real time fuzzylogic algorithm that first identifies clutter-contaminateddata and subsequently applies a clutter filter only tothose identified gates. The algorithm, CMD, effectivelyidentifies ground clutter and thus can provide a real timeclutter map on a radar beam-to-beam basis. The algo-rithm was demonstrated with single polarization datafrom the NEXRAD KFTG and with dual polarizationdata from NCAR’s S-Pol. For the single polarizationcase, the CMD-processed data was compared to theNCDC archived KFTG data which had been filteredeverywhere. The improvement in CMD processed dataquality was obvious especially along the zero velocityisodop where the NCDC data showed severe signalattenuation.

AcknowledgmentThis research was supported by the ROC (Radar Op-

erations Center) of Norman OK. The National Centerfor Atmospheric Research is sponsored by the NationalScience Foundation. Any opinions, findings and conclu-sions or recommendations expressed in this publicationare those of the author(s) and do not necessarily reflectthe views of the National Science Foundation.

References

Siggia, A. and R. Passarelli, 2004: Gaussian model adaptiveprocessing (GMAP) for improved ground clutter cancel-lation and moment calculation. Pro. of Third Euro.Conf. on Radar in Meteo. and Hydro (ERAD 2004).67–73, Visby, Gotland, Sweden.

Steiner, M. and J.A. Smith, 2002: Use of three-dimensionalreflectivity structure for automated detection and re-moval of non-precipitating echoes in radar data. JTECH,9(5):673-686.

Figure 2: A clear air PPI surveillance reflectivity scan (indBZ) gathered by the NEXRAD KFTG, Denver, showingground clutter. Data was collected on 13 October 2006 at0.5◦ elevation angle. The large reflectivities seen on the leftare due to the Rocky Mountains.

Figure 3: CPA feature field corresponding to the KFTG clearair reflectivity scan seen in Fig. 2. CPA is unit-less and rangesfrom 0 to 1

Figure 4: A long PRT, unfiltered reflectivity PPI data gath-ered with KFTG on 26 October 2006 in a wide-spread snowstorm along the Eastern Foothills of the Rocky Mountains inColorado. The elevation angle is 0.5◦. Color scale is in dBZ.

Figure 5: Unfiltered long PRT velocity corresponding toFig. 4. The folding velocity is about 8 ms−1. Color scaleis in meters per second.

Figure 6: Unfiltered velocity from a short PRT scan takenjust 3 minutes before the data corresponding to Fig. 4 and5. The folding velocity is about 25 ms−1. Color scale is inmeters per second

Figure 7: NCDC archived reflectivity data corresponding toFig. 4. A clutter filter was applied to all data.

Figure 8: The feature field TDBZ for data of Fig. 4. Areaswith values greater than 40 dBZ2 have a very high likelihoodof clutter.

Figure 9: The feature field of SPIN for data of Fig. 4. Areaswith SPIN values above 25 are very likely clutter.

Figure 10: The feature field CPA for data of Fig. 4. Areaswith CPA > 0.9 are very likely clutter. Compare these areasto the Rocky Mountain clutter areas in Fig. 4.

Figure 11: The CMD flag field corresponding to Fig. 4. Theclutter filter is applied at those gates in the yellow regions.

Figure 12: Same reflectivity data as Fig. 4 except a clutterfilter has been applied to those gates as indicated by the CMDflag field of Fig. 11. The zero velocity isodop is outlined witha black line.

Figure 13: A measure of CMD performance for the singlepolarization case. The solid line give the fraction of rangegates that are identified as contaminated by clutter echo asa function of the clutter-to-signal ratio. The dashed line issimply 1 minus the solid line value.