7.4: A Polar-Coordinate Real-TimeThree-Dimensional Rapidly Updating MergerTechnique for Phased Array Radar Scanning

Strategies

Valliappa Lakshmanan1,2, Kurt Hondl2∗

Abstract

The National Weather Radar Testbed (NWRT) phased array radar will notbe operated in fixed volume coverage patterns. Instead, the phased arrayradar will attempt to simultaneously maximize the utility of several possibleuses, such as 3D storm analysis, area surveillance and aircraft tracking. Inorder to do so, the phased array radar will employ adaptive scanning and in-tersperse meteorological scans with aircraft tracking. To a downstream visu-alization program or automated severe-weather detection algorithm operatingon phased array radar data, the incoming stream will be randomly organizedin space and time. It is up to the application to create a coherent view of theatmosphere from the phased array radar beams.

In this paper, we describe a method of creating such a coherent view. In po-lar coordinates, this involves creating a rapidly updated ”virtual volume scan”.The virtual volume scan is created by treating each of the phased array radarrange gates as ”intelligent agents” that place themselves in the resulting polargrid, know how to collaborate with other agents to create optimal estimatesof the radar values at each range gate of the virtual volume and know whenthey have either been superseded or are too old. The resulting virtual volume,created in real-time, is used by the downstream applications. This enables thedownstream applications to work with a regularly spaced grid that is createdat periodic intervals.

1. Motivation

The U.S. Navy loaned a SPY-1 phased ar-ray radar to the National Severe StormsLaboratory (NSSL) in 2000 and providedthe initial funding to help build the National

Weather Radar Testbed (NWRT). NSSLadapted the Navy radar for severe weathertracking so that the NWRT became opera-tional in 2003 and data have been collectedwith the radar since May 2004 (Forsyth et al.2005).

∗Corresponding author: lakshman@ou.edu 1The Cooperative Institute of Mesoscale MeteorologicalStudies (CIMMS), University of Oklahoma, 2The National Severe Storms Laboratory, Norman, OK. 3Schoolof Meteorology, U. Oklahoma

1

Conventional Doppler radar such asthose used operationally in the UnitedStates by the National Weather Serviceand the Federal Aviation Authority rely onmechanical scanning. It is necessary tomechanically move the radar to point to-wards the area being scanned. Mechani-cally scanned radars are used operationallyin terms of volume coverage patterns, de-fined in terms of the elevation angle, az-imuths scanned and the pulse repetition fre-quency used. The WSR-88D VCP 21(Crumand Alberty 1993; Smith 1995) (shown inFigure 1) is repeated every six minutes. Thevolume coverage pattern thus defines thespatial and temporal resolution with whicha 3D conical volume around the radar isscanned.

The phased array radar, on the otherhand, does not rely on mechanical scan-ning. Radar beams are directed indepen-dently at different locations in the 3D vol-ume being scanned, so that different partsof the area can be scanned at different spa-tial and temporal resolutions. This enablesthe phased array radar to scan storms athigher resolutions than clear air, repeatedlysample areas of interest at different pulserepetition frequencies. The electronic scan-ning is also much faster and the beams mul-tiplexed, so that an entire volume can bescanned in 50 seconds as compared to the360 seconds it would take for VCP 21 Mc-Nellis et al. (2005). It is also possible for theradar to scan different areas at different res-olutions, depending on the active weather atthat location, so as to further increase thetemporal sampling of thunderstorms. Thephased array radar can also perform sev-eral tasks at once. In particular, it can bothtrack aircraft and scan the atmosphere forweather.

Thus, phased Array radar are a promis-ing technology because they possess theability to send radar beams in several di-rections at once, potentially sensing a 3D

volume of the atmosphere in just a few sec-onds (compared with 5-6 minutes and sig-nificant vertical blind-spots for a conven-tional weather radar). This advantage ofphased array radar to simultaneously sensedifferent areas of the atmosphere is, how-ever, a significant challenge to downstreamapplications. Downstream applications areused to dealing with radar beams arrivingone after the other, with each beam scan-ning a slice of the atmosphere adjacent tothe previous beam. In a phased array radar,the synchronized radar beams have beenreplaced by an asynchronous stream of ran-domly situated radar beams. This makesdesigning analysis and visualization appli-cations especially hard.

It is up to the application to create acoherent view of the atmosphere from thephased array radar beams. In Section 2, wedescribe a method of creating such a coher-ent view. Because the NWRT PAR is not yetconfigured to perform adaptive scanning, arandomized input stream was created. Thesimulation to create the randomized inputstream is described in Section 3. Examplesof products created from the virtual volumeusing the method of this paper are shown inSection 4.

2. Method

It is up to downstream applications that usephased array radar data to create a coher-ent view of the atmosphere from the adap-tive and repetitive scans of the radar. Sucha coherent view can be formed by creatinga “virtual volume” of the atmosphere. Therange gates of this virtual volume are as-signed values by combining values from allthe rays that affect that location.

Each of the range gates in the phasedarray radar beams will be an intelligentagent imbued with the ability to know whereit impacts the final result, in what direction

2

it should move with time, when it ceases tobe useful, and when it should update itselfwith new values. The challenge here is toimbue the intelligent agent with all of this in-formation appropriately. In traditional radardata, where we used the intelligent agentmethodology to create multi-radar 4D gridsand products (Lakshmanan et al. 2006), thetask of programming the intelligent agentswas made much simpler by the fact thatthe radars behave in anticipatable ways (theVCPs again). Thus, each agent knows theagents that it will interact with, because theagents that will be created by the next scanof the radar can be anticipated. Becausea phased array radar is much less antici-patable, the intelligent agents have to havemuch more context. The agents in this casemonitor their immediate neighborhood andcollaborate with whichever agents they findin that neighborhood.

The intelligent agents collaborate tobuild a constantly updating 3D earth-relative grid in polar coordinates. The out-put grid is of constant resolution in cylindri-cal coordinates (we chose a 0.5 degree res-olution in azimuth and elevation and 0.5kmresolution in range for this study), and thevalue of each point in this 3D grid will beset through active collaboration by the vari-ous agents. This 3D grid is written out in aself-describing NetCDF format such that itcan be incorporated into existing radar anal-ysis algorithms such as those in the Warn-ing Decision Support System(WDSS; Lak-shmanan et al. (2007)).

The collaborative value produced by theintelligent agents can follow one of severalstrategies – we could use simply the latestvalue, the nearest neighbor or a weightedaverage of the values. One appropriatechoice is to choose as weights, the power-density (Doviak and Zrnic 1993) of thebeam at the grid-point such that the highestpower is at the center of the beam. In theresults demonstrated here, a power-density

weighting is used.

3. Simulated Input

The research being presented is being per-formed in anticipation of requiring visualiza-tion and data analysis algorithms when theNWRT phased array radar is used in anadaptive scanning mode. At present, theradar is used only to create traditional sec-tor scans. Therefore, the input to the polarmerger algorithm was simulated from realdata.

Data collected by the WSR-88D radarat Twin Lakes, Oklahoma City (KTLX) onMay 3, 1999 was used for this purpose.Complete volumes of this data were takenand split arbitrarily into sectors. The sec-tors were then randomly rearranged (so thatsectors at different elevation angles couldfollow each other). The time on the sectorswas modified so that it appeared that sec-tors at different elevation angles followedeach other.

The first few frames of the input se-quence are shown in Figure 2.

4. Results

The output grid was created at 0.5o × 0.5kmresolution at elevations starting at 0 de-grees and going up to 10 degrees in incre-ments of 0.5 degrees. The complete 3Dgrid was created every 60 seconds. A givengrid point will be affected by multiple beams.The final value at a grid point is a weightedsum of these observations, with the weightof any observation depending on the dis-tance of the grid point from the center ofthe beam that the observation comes from.The weighting function used was the powerdensity function given by Doviak and Zrnic(1993).

3

The first three frames of the 1-degree el-evation scan (produced every 60 seconds)is shown in the top row of Figure 3. A com-posite image created from all the elevationsis shown in the middle row. The 1-degree el-evation scan was chosen because it is trulyvirtual – the WSR-88D VCP used at theKTLX radar on May 3, 1999 provided 0.5and 1.5 degree elevation scans, but none at1 degree. The 0.5 and 1.45 degree scans(before randomization) from approximatelythe same time is shown in the bottom row.Note that the input scans are at 1o × 1kmresolution.

Cross-sections through the virtual vol-ume are shown in Figure 4.

Acknowledgments

Funding for this research was providedunder NOAA-OU Cooperative AgreementNA17RJ1227. The statements, findings,conclusions, and recommendations arethose of the authors and do not necessar-ily reflect the views of the National SevereStorms Laboratory (NSSL) or the U.S. De-partment of Commerce.

Thanks to Dave Priegnitz for commentson PAR scanning strategies.

References

Crum, T. and R. Alberty, 1993: The WSR-88D and the WSR-88D operational sup-port facility. Bulletin of the American Me-teorological Society , 74, 1669–1687.

Doviak, R. and D. Zrnic, 1993: DopplerRadar and Weather Observations. Aca-demic Press, Inc., 2nd edition.

Forsyth, D., J. F. Kimpel, D. S. Zrnic,R. Ferek, J. F. Heimmer, T. J. NcNellis,J. E. Crain, A. M. Shapiro, R. J. Vogt,and W. Benner, 2005: Progress reporton the national weather radar testbed(phased array). 21st Int’l Conf. on In-ter. Inf. Proc. Sys. (IIPS) for Meteor.,Ocean., and Hydr., Amer. Meteor. Soc.,San Diego, CA, 19.5.

Lakshmanan, V., T. Smith, K. Hondl, G. J.Stumpf, and A. Witt, 2006: A real-time,three dimensional, rapidly updating, het-erogeneous radar merger technique forreflectivity, velocity and derived products.Weather and Forecasting, 21, 802–823.

Lakshmanan, V., T. Smith, G. J. Stumpf, andK. Hondl, 2007: The warning decisionsupport system – integrated information.Weather and Forecasting, 22, 596–612.

McNellis, T., S. Katz, M. Campbell, andJ. Melody, 2005: Recent advances inphased array radar for meteorological ap-plications. 21st Int’l Conf. on Inter. Inf.Proc. Sys. (IIPS) for Meteor., Ocean., andHydr., Amer. Meteor. Soc., San Diego,CA, 19.6.

Smith, T., 1995: Visualization of WSR-88Ddata in 3d using application visualizationsoftware. 14th Conf. on Weather Fore-casting, Amer. Met. Society, Dallas, TX.

4

Figure 1: Volume coverage pattern (VCP) 21 of the WSR-88D. The VCP defines a regularscanning strategy and is commonly applied to mechanically scanning radars in opera-tional settings.

5

23:18:33 23:18:38 23:18:43

23:18:48 23:18:53 23:18:58

23:19:03 23:19:08 23:19:13

Figure 2: First few sectors of the simulated input sequence provided to the algorithm. Thedata are from KTLX on May 3, 1999, but have been randomly split into sector scans. Thetimes of the sector scans are simulated. Since the range rings go out to lower distancesat higher elevation scans, the extent of the range rings gives an idea of the elevation angleof the sector scan depicted.

6

23:18:43 23:19:48 23:20:53

23:18:43 23:19:48 23:20:53

0.5-deg scan at 23:16:33 1-deg scan at 23:17:12 Composite at 23:21:11

Figure 3: Top row: first few frames of reflectivity from the virtual volume at 1 degree.Middle row: first few frames of composite reflectivity from the volume volume from 0 to10 degrees at 0.5-deg, 0.5-km resolution. Bottom row: The 0.5-degree, 1.45 degree andcomposite reflectivity images from KTLX before randomization.

7

Figure 4: Cross-section and CAPPI through virtual volume.

8