P5.4 WSR-88D REFLECTIVITY QUALITY CONTROL USING HORIZONTAL

P5.4 WSR-88D REFLECTIVITY QUALITY CONTROL USING HORIZONTAL AND VERTICALREFLECTIVITY STRUCTURE

Jian Zhang1, Shunxin Wang1, and Beth Clarke1

1Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK

1. INTRODUCTION

WSR-88Ds (Weather Surveillance Radar – 1988Doppler) can detect both meteorological and non-meteorological scatters. The non-meteorologicalscatters include ground and sea clutter due to normal oranomalous radar beam propagation, biological targetssuch as birds and insects, electronic interferences, etc.Power returns from these scatters can contaminateradar observations and cause users to misinterpret thedata. Moreover, contaminated radar data will havenegative impacts on downstream applications andweather products such as precipitation estimations.

Some clutter suppression procedures have beenimplemented in radars’ signal processing before baselevel data are disseminated to users. Howeversignificant non-meteorological echoes often remain afterthe clutter suppression. To remove the non-meteorological echoes, many different approaches havebeen suggested. A complete review of the approachescan be found in Steiner and Smith (2002). Generally,spatial (horizontal and vertical) variations of reflectivity,velocity and spectrum fields are analyzed and differenttypes of echoes are identified based on various featuresin the variations. For instance, intensity and texture ofreflectivity fields along with other features in velocity andspectrum width fields are used as inputs to fuzzy logic(Kessinger et al. 2003) and neural network (Lakshman2003) procedures. Steiner and Smith (2002) found thatmost useful parameters are the vertical extent of radarechoes, the horizontal variability of reflectivity field, andthe vertical gradient of reflectivity. However, since theparameters in Steiner and Smith (2002) were computedwith respect to radar tilts, their technique could removeshallow precipitation at far ranges yet retain some clearair returns and clutter at close ranges.

In this paper we present a new technique that usesvertical parameters similar to those in Steiner and Smith(2002) but the parameters are computed with respect toheight instead of to radar tilts. By using the physicalheight, the impact of beam spreading on the algorithmcan be significantly reduced. The horizontal variabilityparameter used in the new technique is the texture ofreflectivity (Kessinger et al. 2003).

____________________________________________*Corresponding Author address: Jian Zhang, 1313Halley Circle, Norman, OK 73069; e-mail:[email protected].

In the next section, Sect. 2, the methodology of thereflectivity QC algorithm is presented. Example resultsof the QC algorithm for various cases are shown insection 3. A summary is given in section 4.

2. METHODOLOGY

2.1 Characteristics Of Different Echo Types

Radar reflectivity observations contain twoimportant classes: precipitation and non-precipitationechoes. There are three major types of non-precipitation echoes in reflectivity fields. The first typeof non-precipitation echoes is from insects, birds, dust,chaff and other particulates in the atmosphere that arelarge enough to return some power to the radar. Thesecond type is ground clutter echoes caused by radarbeams hitting buildings and mountains, or sea clutterechoes caused by radar beams hitting oceanic waves.The third type of non-precipitation echoes is ground orsea clutter under special atmospheric refractive indexconditions where radar beams propagate in a path thatbends downward toward the earth’s surface. This is socalled “abnormal propagation (AP)” situations in radarmeteorology. The normal propagation path of radarbeams is usually bending upward with respect to theearth’s surface.

The non-precipitation echoes of first type (exceptfor chaff) are usually below 3 km above ground level.Furthermore, these echoes associated with biologicaltargets and dust are usually weak in intensity (less than35dBZ) and contain small horizontal scale noises inreflectivity fields. Ground and sea clutter echoes, eitherfrom normal or anomalous propagations, are usuallycorrelated with terrain/ocean and are usually in thelower tilts of radar observations.

Precipitation echoes can be classified into twogroups: convective and stratiform. Convectiveprecipitation systems are typically associated with highrain rates and strong horizontal reflectivity gradients,and they usually have smaller horizontal scales than dostratiform precipitation systems. Convective precipitationechoes have large vertical scales that usually extendabove 5 km above ground level. The large verticaldepth of convective precipitation echoes makes itrelatively easy to distinguish them from the non-precipitation echoes.

Stratiform precipitation echoes are characterizedby light to moderate rain rates, weak horizontalreflectivity gradients, and large aerial coverage (usuallygreater than 500 km2). It is not uncommon thatstratiform precipitation cloud tops are below 4.5 km oreven 3 km above ground level (AGL). The shallownessof these stratiform precipitation echoes sometimesmakes it difficult to distinguish them from the non-precipitation echoes. However, stratiform precipitationis relatively uniform in space and the associatedreflectivity fields are usually very smooth horizontally.Since majority of the non-precipitation echoes containsmall-scale noises in reflectivity fields, a criteria relatedto the horizontal smoothness of reflectivity field wouldhelp identify stratiform precipitation against non-precipitation echoes. It is noteworthy, though, thatsometimes AP echoes, such as those in a flat terrainregion under severe super refractive atmosphericconditions, can have very smooth horizontal reflectivityfields. In this situation, the AP echoes are not readilyseparated from stratiform precipitation echoes andadditional criteria need to be used for accurateidentification of the AP echoes. One such criterion isthe aerial extent of the echoes. As mentioned before,the stratiform precipitation usually extends 100s to1000s km2. AP echoes, except for rare occasions, areusually localized and have little continuity in azimuthdirection. Therefore a neighborhood continuity checkcan help identify and remove some of the AP echoes.

2.2 Procedures Of The Reflectivity QC Algorithm

Figure 1 shows an overview flowchart of the newreflectivity QC algorithm. Detailed descriptions of eachstep are given in the following.

2.2.1 Noise filter

The input data to the reflectivity QC algorithm is avolume of 3D reflectivity field on a spherical grid (range,azimuth and elevation). A simple filter is applied to theraw reflectivity field to remove isolated reflectivity pointsor lines. For any given reflectivity bin, X, the number (N)of non-missing reflectivity observations is counted in abox of 5 × 5 bins that is centered at the given bin. Thepercentage (PX) of non-missing values in the box is thencalculated by:

PX = N/Ntotal (1)Here N total (= 25) represents the total number ofbins/pixels in the box. If P X is less than a giventhreshold (default value = 75%), then the pixel X isconsidered an isolated point and is removed. The filteris shown to be very effective in removing speckleechoes (Fig.2).

Fig. 1 Flowchart of the reflectivity QC algorithm

Fig. 2 An example 0.5° tilt reflectivity field before (a)and after (b) the noise filter.

2.2.2 Horizontal reflectivity texture

The horizontal smoothness of reflectivity fields ismeasured by a parameter called texture of reflectivity(TDBZ, Kessinger et. al., 2003). It is the mean squaredifference between adjacent reflectivity values along aradial. The formula for computing TDBZ at a given binis:

€

TDBZ =Zi, j − Zi, j−1( )

2i=1nrays∑j=1

ngates∑

N(2)

Here i and j are indices of reflectivity bins in azimuth andrange directions, respectively. Z represents reflectivityvalues in dBZ; nrays and ngates are number of bins in abox centered at the given bin; N = nrays × ngatesrepresents total number of bins in the box. The defaultvalues for nrays and ngates are 7.

Figure 3 shows example reflectivity and theassociated TDBZ fields for a squall line case. Thereflectivity field is from the 0.5° tilt. Large TDBZ valuesare associated with the convective cells as well as withthe AP clutter echoes behind the squall line. Therefore,by texture parameter itself one cannot distinguishbetween precipitation and non-precipitation echoesaccurately.

Fig. 3 An example TDBZ field (b) and the associatedbase reflectivity field (a) on 0.5° tilt.

2.2.3 The upper reference tilt

As discussed in section 2.1, most of convectiveprecipitation and many stratiform precipitations havecloud tops exceeding 3 km above the ground level.Non-precipitation echoes, except for chaff echoes,however, are usually contained in the lowest 3 km of the

atmosphere. Thus the vertical continuity of reflectivity atand above 3 km above ground level becomes a veryuseful criterion to distinguish precipitation from non-precipitation echoes. For some shallow stratiformprecipitation echoes, additional criteria such asreflectivity texture field will be used to prevent falseremoval of precipitation echoes.

In the present QC algorithm, the vertical scale ofechoes is represented by a parameter called verticaldifference of reflectivities (VDZ). The VDZ is thedifference between a reflectivity at a given gate on thetilt that is under QC and a reflectivity at the same gateon an upper tilt that intersects any height between 3 and4.5 km above the radar level. Note that different tiltsintersect a constant height level at different ranges. Forinstance, at the range of 30 km the 3 km above radarlevel (ARL) height intersects the 6th tilt (6° elevationangle) for VCP 21 (Fig. 4). At the ranges of 90 and 160km, the 3 km ARL height intersects the 2nd (1.45°elevation angle) and 1st (0.5° elevation angle) tilts,respectively (Fig. 4). For certain gates, there are nocorresponding upper tilts exactly intersects with 3 kmARL height. Under these circumstances, slightly higherheight values (up to 4.5 km ARL) are used to find anupper reference tilt so that the VDZ parameter can beobtained. Since the operational WSR-88Ds scan in pre-specified elevation angles, the upper reference tiltindices associated with the 3 – 4.5 km ARL height levelcan be pre-determined for each gate. This set of gateson different tilts make up a hybrid tilt which will bereferred as “upper reference tilt” (URT) hereafter. Notethat different VCPs generally have different upperreference tilts.

Fig. 4 Radar beam propagation paths (height versusrange) under standard atmospheric refractionconditions.

In the current QC algorithm, the VDZ is calculatedfor each gate by subtracting the reflectivity value at agate on the tilt under QC by the reflectivity value at thesame gate on the upper reference tilt. Before VDZ is

computed, reflectivity values on the upper reference tiltare pre-processed. The purpose of the pre- process isto identify apparent non-precipitation echoes on theupper reference tilt using a tilt-to-tilt vertical check andhorizontal reflectivity texture check.

2.2.4 Vertical difference of the reflectivity andnon-precipitation echo removal

The vertical difference of the reflectivity (VDZ) fieldis calculated as the following:

VDZ = (Z – ZURTa)/(HURT - H) (3)

Here Z is the reflectivity value under examination; ZURTa

is the pre-processed reflectivity value on the upperreference tilt. H and HURT are the heights associatedwith the two reflectivities Z and ZURT

a, respectively. Theunit of VDZ is dBZ/km and it is set to missing wheneither Z or ZURT

a are missing. If VDZ value at any givengate is lager than a threshold (default value = 20.0dBZ/km), then the reflectivity observation at the gate isconsidered a non-precipitation echo and the reflectivityvalue will be replaced by a missing flag.

Figure 5 shows an example VDZ field and theassociated base reflectivity field at the 0.5° elevationangle. The small VDZ values are associated with theprecipitation areas in the squall line, while the large VDZvalues are well correlated with the AP clutter behind thesquall line. Therefore the VDZ parameter is a very goodparameter for separating precipitation and non-precipitation echoes in this case.

The VDZ field can only be obtained for the gateswithin certain range from the radar. This is because thatthe upper reference tilt is bounded by 4.5 km ARLheight. In all WSR-88D VCPs, the bottom of the 2nd tiltis at ~1°. The 4.5 km ARL height intersects thiselevation angle at ~160 km of range (Fig. 4). ThereforeVDZ parameter can only be used for ranges shorterthan 160 km for reflectivity QC in the first tilt. For highertilts, the maximum range for a valid VDZ parameter iseven shorter. This constraint limits the usefulness of thevertical reflectivity consistency check. But the constraintis necessary for preventing the QC procedure to removeshallow stratiform precipitation echoes at the longranges. Additional information such as satelliteobservations can be used to remove clutter in theseregions.

The procedures described in sections 2.2.1 to 2.2.4are repeated for all the tilts except for the highest one(see section 2.2.5) in a volume scan. The processstarts from the lowest tilt and progresses upward. Sincereflectivities on an upper tilt can be used for QC ofreflectivities on lower tilts, any errors in the echoidentification on the upper tilt could impact qualities ofthe reflectivity QC on lower tilts. The bottom to top

approach can avoid this potential downwardpropagation of erroneous echo identifications.

Fig. 5 An example VDZ field (b) and the associatedbase reflectivity field (a) on 0.5° tilt.

2.2.5 Quality control of the top tilt

Since the calculation of VDZ parameter at a giventilt requires information from upper tilts, it cannot beused for reflectivity quality controls in the highest tilt of avolume scan. In this case, only reflectivity texture fieldis used in the QC procedure. If the texture value at anygiven gate is greater then the threshold (a default valueof 35 dBZ2), then the reflectivity observation at the gateis considered a non-precipitation echo and is removed.

3. CASE STUDY

The current reflectivity quality control algorithm hasbeen extensively tested using about 200 volume scansof base data from different radar sites and from differenttimes (seasonal and diurnal). The algorithm performsvery well in majority of cases (> 90%). Below areexamples of reflectivity fields before and after the QC forvarious cases.

3.1 Stratiform Rain With Birds Echoes

Radar: KTLX (Twin Lakes, OK)

Date: 05/04/99Time: 0704 UTC

Stratiform precipitation is seen to the East of theradar (Fig.7). Birds’ echoes surround the radar behindthe precipitation region. The precipitation echoes arerelatively deep (echo top > 5 km). The QC algorithm isvery successful in removing the birds’ echoes in thiscase and the VDZ field played a critical role.

Fig. 6 The first tilt reflectivity before (upper panel) andafter (lower panel) the QC.

3.2 Mixed Convective And Stratiform Rain AndClear Air/Biological Echoes

Radar: KTLX (Twin Lakes, OK)Date: 05/13/04Time: 1439 UTC

The challenge for the QC of this case is to removethe clear/biological echoes (noisy, light bluereflectivities) around the radar while retaining a smallcluster of convective echoes to the southwest of theradar (Fig. 7). The result showed that the QC algorithmperformed well in this regard.


3.3 AP

Radar: KFWS (Fort Worth, TX)Date: 04/20/95Time: 0453 UTC

Extensive AP echo surrounds the radar and is alsoseen to the North of the radar (Fig. 8). Nearest to theradar, significant echo is still present in the second tiltand some of the AP at further range is associated withsignificant velocity. Therefore, distinguishing AP fromthe precipitation to the east is a challenge. In addition, itis essential to retain the complete structure of highreflectivity cells embedded in the squall line. The VDZprovided some useful information regarding the natureof the echo and majority of the AP echoes near theradar are successfully removed. However, the VDZparameter is not useful for the longer ranges. Asignificant amount of AP echoes is left after the QC (theechoes to the North-Northeast of the radar and to thewest of the northern end of the squall line, Fig.10). Afew scattered high reflectivity clutter near the radar wereleft because they have vertical continuity up to 3 kmARL height and higher. Additional information, such asthat from satellite imagery data, will be used to removethese residual AP clutter.


3.4 Clear Air Return

Radar: KIWA (Phoenix, AZ)Date: 02/06/04Time: 1506 UTC

In clear air, a speckled reflectivity field is observedaround the radar, with values ranging from ~10 – 35dBZ (Fig. 9). The echo appears in early to late morningand is likely results from refractive index gradients in thepresence of a shallow nocturnal inversion. Turbulencewithin the boundary layer creates fluctuations intemperature and humidity leading to changes in therefractive index. This may cause the radar beam tointercept trees, building or even the ground. The noisefilter and the TDBZ parameter played important roles inremoving these non-precipitation echoes.

Fig. 9 The first tilt reflectivity before (left panel) andafter (right panel) the QC.

3.5 Outflow Boundary

Radar: KTLX (Twin Lakes, OK)Date: 05/08/03Time: 2201 UTC

In this case an outflow boundary results in a line ofecho to the west-southwest of the radar (Fig.10). Insectand/or particulates are concentrated by the outflow andreturn power to the radar. Due to the shallowness of theechoes, they were identified as non-precipitation echobased on the VDZ criteria and were removed from thereflectivity field after QC (Fig.10). For precipitationestimation algorithm this is a desired feature of QCalgorithms. But it is noteworthy that new storms areoften triggered along these outflow boundaries.Therefore removing these echoes may not be a desiredfeature for convective initiation applications.


3.6 Precipitation At Long Range

Radar: KFSX (Flagstaff, AZ)Date: 09/24/03Time: 1654 UTC

In this case, stratiform precipitation is seen at longrange (Fig.11). Very little reflectivity is seen in thesecond tilt and it is not in the same locale as the echo inthe first tilt. This is a challenge for QC, as verticalreflectivity checks could lead to the removal ofprecipitating echo. The QC algorithm successfullyretained the precipitation echoes indicating that TDBZand VDZ identified areas of precipitation well. The 4.5km ARL height constraint helped save the echoes atvery far ranges.

This case also shows an example of the radarbeam ducting and intercepting mountains (The WhiteMountains) to the southeast of the radar. This cluster ofreflectivity slightly higher than other observed echo isAP and ideally should be removed by QC. However,since the region is beyond the range constraint for theVDZ parameter, the AP echoes are not removed. Thisis a very challenging situation and satellite data cannothelp due to the fact that there are clouds andprecipitation in the same region. Terrain data combined

with time series of reflectivity may help identify thesetypes of AP echoes correctly.


4. SUMMARY

A reflectivity quality control algorithm has beendeveloped for identifying and removing non-precipitationechoes from the WSR-88D base reflectivity fields. Thealgorithm assumes that precipitation and non-precipitation echoes have different horizontal andvertical reflectivity structures. Two main parameters,vertical difference of reflectivity (VDZ) and horizontaltexture of reflectivity (TDBZ), and a set of physicallybased rules and criteria are developed for the QCalgorithm.

The reflectivity QC algorithm has been extensivelytested using about 200 volume scans of base level datafrom different radar sites and from different seasonaland diurnal times. Results have shown that thereflectivity QC algorithm is very successful in identifyingnon-precipitation echoes such as ground clutter (underboth normal and anomalous propagation conditions),clear air and insect echoes, and birds’ echoes. Thesenon-precipitation echoes consists more than 90% of thenon-precipitation echoes in the WSR-88D reflectivitydata. These echoes, if not removed, will contaminate

meteorological products derived from the radar data. Byapplying the QC algorithm, these noises are largelyremoved and the resultant reflectivity data can provide“cleaner” and more accurate weather products.

The current reflectivity QC algorithm uses onlythree-dimensional reflectivity data. The advantage isthat the algorithm is relatively simple and highly efficient.Thus it is very suited for operational applications.There are a couple of issues remain to be addressed.One of the issues is that AP clutter at long ranges issometimes hard to be separated from the shallowstratiform precipitation echoes, especially when the APclutter shows smooth texture in reflectivity field. A studyis currently undergoing in which satellite imagery data,terrain information, and time series of reflectivity areused to further distinguish non-meteorological echoesfrom meteorological echoes on the bases of the currentQC algorithm. Polarimetric radar research has shownpromising results on echo classifications, and futureefforts will include the integration of polarimetric radardata in the reflectivity QC process.

Acknowledgements

Major funding for this research was provided underthe Aviation Weather Research Program NAPDT(NEXRAD Algorithms Product Development Team)

MOU and partial funding was provided under NOAA-OUCooperative Agreement #NA17RJ1227 and through thecollaboration with the Central Weather Bureau ofTaiwan, Republic of China.

This research is in response to requirements andfunding by the Federal Aviation Administration (FAA).The views expressed are those of the authors and donot necessarily represent the official policy or position ofthe FAA.

References

Kessinger et. al., 2003: The radar echo classifier: afuzzy logic algorithm for WSR-88D, 3rd Conferenceon Artificial Intelligence Applications to theEnvironmental Science, AMS, 9-13 February 2003,Long Beach, CA.

Lakshmanan, V., 2003: Quality control of WSR-88Ddata. Preprints for 31st Radar Conference. Amer.Meteor. Soc., Seattle, WA. 522-525.

Steiner, M. and J. Smith, 2003: Use of three-dimensional reflectivity structure for automateddetection and removal of non-precipitation echoesin radar data. J. Atmos. Ocea. Tech., 19, 673-686.

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P5.4 WSR-88D REFLECTIVITY QUALITY CONTROL USING HORIZONTAL