AMERICAN METEOROLOGICAL SOCIETY 36th CONFERENCE ON RADAR METEOROLOGY

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PATTERN: ADVANTAGES OF HIGH-RESOLUTION WEATHERRADAR NETWORKS

Katharina Lengfeld1∗, Marco Clemens1, Hans Munster2 and Felix Ament11 Meteorological Institute, University of Hamburg, Germany2 Max-Planck-Institute for Meteorology, Hamburg, Germany

1 INTRODUCTION

Precipitation plays an important role in driv-ing the hydrological and the energy cycle of thelower atmosphere. For hydrometeorological appli-cations, e.g. flood forecasting and risk manage-ment, high-resolution precipitation observationsare needed to gain detailed insight in rainfall-runoff processes. Common radar systems as usedin nationwide networks generally operate at S-or C-band wavelength with spatial resolution inthe order of kilometers and temporal resolution ofabout 5 minutes. These systems cannot meet allpresent and future requirements. The requestedspatial and temporal resolution for hydrometeoro-logical applications is 0.1 km and 1 min, respec-tively (Einfalt, 2003). Therefore, radar systemscapable of producing reliable and accurate precip-itation estimates at high temporal and spatial res-olution are required.

High-resolution weather radar (HRWR) systemshave been developed in recent years that operatein the X-band frequency range and are capableof scanning precipitation with spatial resolutionof 60 m and temporal resolution of 30 s. Recentstudies (Lengfeld et al. (2012) and Trabal et al.(2013)) support observations at X-band frequen-cies as an alternative or an addition to S- and C-band, e.g in regions, where S- and C-band radarsare blocked by mountains. Besides higher resolu-tion, these systems are cost-effective compared toS- or C-band radars because of smaller antennasize (Bringi et al., 1990). Disadvantages of singleX-Band radars are relatively short range and largeinfluence of attenuation by liquid water (e.g. Atlasand Banks (1951); Gunn and East (1954); Wexlerand Atlas (1963); Dutton (1967)).

The project Precipitation and ATTenuation Es-timates from a high resolution weather Radar Net-work (PATTERN) funded by the German Re-search Foundation (DFG) intends to demonstratethat a network of HRWRs can overcome this ap-parent drawback and to identify advantages anddisadvantages of the network as well as single X-band radars. In section 2, a description of thespecifications of the modified HRWR systems as

∗Corresponing author address: Katharina Leng-feld, University of Hamburg, Meteorological In-stitute, Hamburg, Germany, +49 40 42838 5158,e-mail: katharina.lengfeld@zmaw.de

well as the design of the network will be given.Section 3 deals with algorithms used to deriveprecipitation from reflectivity measurements fromsingle radars and algorithms exploiting the bene-fits of having a network, e.g. for clutter removaland replacing disturbed pixels by measurementsfrom other radars instead of interpolating. In sec-tion 4, a comparison to measurements of weatherradar operating in C-band used by the GermanWeather Service (DWD) focuses on the ability ofhigh-resolution observations to give informationabout small scale structures of rain events. Sec-tion 5 gives a short summary and an outlook onfuture applications of high-resolution precipitationobservations using X-band radar networks.

2 RADAR NETWORK

The University of Hamburg and the Max-Planck-Institute for Meteorology set up a network consist-ing of four X-band radars (Hungirger Wolf Tower(HWT), Quarnstedt (QNS), Bekmunde (BKM)and Moordorf (MOD)) in the North of Hamburg,Germany (Fig. 1).

Figure 1: Position of the four network radars Hun-griger Wolf Tower (HWT), Quarnstedt (QNS), Bekmunde(BKM) and Moordorf (MOD) marked as red, the Hamburgradar marked in blue and their 20 km range. Referencestations OST, MST and WST are marked in green.

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The network is operational since January 2012.Each radar has a maximum range of 20 km. Thecovered region of the network is approximately 60km x 80 km. Two neighbouring radars are at least11 km apart, but not more than 16 km. There-fore, a large area within the network is covered byat least two radars at the border and up to fourradars in the center. Micro Rain Radars (MRR)at each radar site and three reference stations con-sisting of MRR and rain gauge complement thenetwork. These stations are used to calibrate andevaluate the X-band radars. An additional X-bandradar is set-up at the roof of the MeteorologicalInstitute of the University of Hamburg since May2013. The whole PATTERN catchment is coveredby a C-band radar operated by DWD.

The low-cost HRWRs used in PATTERN aremodified ship navigation radars that bridge thegap between rain gauges and high-end long rangesystems as C- or S-band radars. The radar frontend including scanning unit is based on a stan-dard navigation radar (Fig. 2a). The originalfan beam antenna is replaced by high gain pen-cil beam antenna; in order to reduce side lobes anoffset parabolic dish is used. Antenna and scan-ning drive is protected by a low-loss radome withair conditioning. Signal processing, data manage-ment and radar control is PC-based. Technicaldetails about the radar systems and the scanningscheme are listed in Table 1.

Table 1: HRWR specifications

PerformanceParameters Specifications

Range Resolution 60 mAzimuth Resolution 1◦

Time Resolution 30 sMaximum Range 20 kmCalibration Accuracy ± 1dBTransmit Power 25 kWFrequency 9410 MHzPulse Width 0.08 µsPulse Repitition Rate 2100 HzBeam Width 3◦

Figure 2: Modified ship navigation radar with parabolicdish (a), typical radar tower in Quarnstedt (b) and radarat Hungriger Wolf (c).

The radars obtain reflectivity measurementswith temporal resolution of 30 s, spatial resolu-tion of 60 m and azimuthal resolution of 1◦. Thescanning scheme is azimuthal only. Fixed eleva-tion angles can be adjusted for optimum operationaccording to site conditions. A typical radar sys-tem consists of radar, radome, a steel tower and acontainer. The radars MOD, BKM and QNS areset up on a 10 m high steel tower mounted on asteel construction on the roof of a container. Asan example radar QNS is shown in Fig. 2b. Thewhole system is 16 m high and fits into the con-tainer. Therefore, it can be moved easily comparedto other radar systems. HWT is placed on an al-ready existing steel tower at the airport HungrigerWolf (Fig. 2c).

3 DATA PROCESSING

The X-band radars used in the PATTERN net-work operate with pulse repetition frequency of2100 Hz. The antenna rotates with 24 rpm. Rawreflectivity signal does not only contain meteo-rological echoes from precipitation but also non-meteorological echoes (clutter) and backgroundnoise. Therefore, a number of correction algo-rithms need to be applied to the raw data in orderto isolate the actual precipitation signal. Distur-bances mainly caused by other radars and radiolinks are already elimated by comparing adjacentpulses before the reflectivities are recorded as 30second averages. In Fig. 3a the 30 s average rawreflectivities are shown for August 3rd, 2012, 13:35UTC for radar MOD. In the Western part of theradar image signal from precipitation is evident, inthe center of the image is small scale clutter andbackground noise superimposes the radar image.

The low-cost X-band radars cannot observeDoppler shift neither do they perform polarimetricmeasurements. Therefore, state of the art meth-ods to eliminate clutter cannot be used. Clutterdetection is only based on temporal and spatialvariability of reflectivity. Clutter is characterizedby high reflectivity values and can be divided intwo different types: Static and dynamic clutter.Static clutter is caused e.g. by trees, houses andwindmills and is present in almost every data setat the same radar pixels. To eliminate this type ofclutter a map is generated over ten days by count-ing the time steps at which reflectivity is higherthan a certain threshold. Range gates with 95%high reflectivity values are marked as static clut-ter.

Dynamic clutter is caused by birds, insectsor other radars operating in the same frequencyrange. For identifying dynamic clutter several al-gorithms are applied. The first algorithm is basedon the spatial structure of the reflectivity signal,

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(a) (b)

(c)

(d) (e)

Figure 3: Processing reflectivity data from radar MOD for August 3rd, 2012, 13:35 UT: (a) Raw reflectivity field, (b)clutter index field, (c) noise level, (d) comparison of radar MOD and MRR OST for September 1st to November 30th,2012, with number of events (N), mean bias (∆), maximum bias, root mean square error (RMSE) and correlation (corr),and (e) corrected and calibrated reflectivity field

so called texture of reflectivity (TdBZ), that iscalculated from averages of quadratic reflectivitydifferences of neighbouring range gates (Hubbertet al., 2009). A second feature of spatial heteoro-

geneity is the frequency of sign changes (SPIN) inreflectivities of neighbouring range gates (Hubbertet al., 2009). Pixels are marked as dynamic clut-ter if both, TdBZ and SPIN, exceed an earlier de-

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termined statistical threshold. Other signals, e.g.from external emitters affect whole azimuth anglesor certain distances and occur in form of spikes orrings in the radar image. Spikes are characterizedand identified by a sign change in difference be-tween neighbouring azimuth angles, rings by a signchange in differences between neighbouring radarbeams.

The PATTERN network has two features thatcan be used for further clutter detection: Hightemporal resolution and large overlapping areascovered by more than two radars. The high tem-poral resolution of 30 seconds of the PATTERNnetwork allows for the following clutter detectionmethod: By comparing the current radar image tothe two previous time steps, range gates with highreflectivities that are only present in the currentimage but not in the previous ones are most likelyclutter and are eliminated. The other advantageof the PATTERN network is the large area cov-ered by more than two radars. Therefore, multipleinformation on reflectivity of other radars can beused to identify clutter. A map of all clutter pixelsfor radar MOD is shown in Fig. 3b.

The radar signal is superimposed by a noise fieldthat consists of two different kinds of noise: Re-ceiver noise and stochastic or speckle noise. Re-ceiver noise is dependent on receiver temperatureand distance to the radar. The HRWRs usedin PATTERN cannot measured the receiver noiselevel. Therefore, it is determined by using a firstguess to distinguish between reflectivities causedby precipitation and by noise. From the rain-free parts a noise level for the current time stepis estimated and averaged with the noise level ofthe last 10 time steps. The noise function depen-dent on the distance to the radar and the esti-mated noise level, as well as the azimuthal meanof the radar signal and the radar signal at 120◦

are shown in Fig. 3c. After subtracting the noiselevel from the reflectivity field, precipitation signaland speckle noise still remains. To identify specklenoise, reflectivity within a three times three win-dow around a range gate is considered. If five ormore range gates have positive reflectivity valuesthe range gate is considered as precipitation, oth-erwise it is elimated as speckle noise.

In order to calibrate reflectivity measurementsof the X-band radars, observations from MRRs areused. Within the PATTERN region three refer-ence stations consisting of MRR and rain gaugeare operated in the overlapping area. Measure-ments from rain gauges are used to calibrate theMRRs. The X-band radars are calibrated by com-paring reflectivity measurements of correspondingrange gates to data obtained from the MRR clos-est to the radar. The corresponding MRR levelis calculated based on the elevation angle of the

X-band radar. A comparison between radar MODand the reference station OST is depicted in Fig.3e for September 1st until November 30th, 2012.Overall the X-band radar MOD fit the MRR OSTmeasurements quite well with a mean bias of 2.8dBZ and a correlation of 0.92.

As X-band frequency range is highly influencedby attenuation, an algorithm especially developedfor small single-polarized X-band radars is usedaccording to Nichol and Austin (2003). This al-gorithm allows for attenuation correction in realtime because of its low computation time and itshigher numeric stability compared to older meth-ods (Hitschfeld and Bordan (1954) and Hildebrand(1978)). The corrected and calibrated reflectivityfield is shown in Fig. 3e.

The algorithms described above are applied toall four radars. The corrected and calibrated re-flectivity fields for radars BKM, HWT, MOD andBKM are shown in Fig. 4a. Many gaps are ev-ident in the reflectivity fields of all four radarscaused by clutter. For single radars these gaps areusually filled by interpolation. Therefore, informa-tion about small scale structure of the rain eventgets lost. Having an overlapping network allowsfor filling most of the gaps with reflectivity mea-surements from other radars. The coverage withinthe network in Fig. 4b indicates that more than99% of the network area is covered by undisturbedreflectivities observation from at least one radar.Thus, gaps can be filled without interpolation andthe small scale structure of the rain event is keptas demonstrated in Fig. 4c. For the few datagapsleft, interpolation is used.

In order to obtain precipitation estimates fromthe reflectivity fields, a common Z-R-relation isused. Composites of all four network radars arecalculated on a rotated Cartesian grid with theequator shifted into the PATTERN region to allowfor equidistant grid cells. Reflectivities or precipi-tation rates of all range gates inherent in a certaingrid are averaged. The composite of the precipi-tation field for August 3rd, 2012, 13:35 UTC on a250 m grid is shown in Fig. 4d.

4 COMPARISON TO C-BAND RADAR

In the last section it has been shown that sev-eral algorithms are needed to obtain undisturbedcalibrated reflectivity fields from raw data for thePATTERN network. In order to give an estima-tion on the quality of the X-band radar products,reflectivity data is compared to the products of theradar Hamburg operated by DWD. Radar Ham-burg measures reflectivity in the C-band frequencyrange with spatial resolution of 1 km, azimuthalresolution of 1◦ and temporal resolution of 5 min.

A comparison of the composite of reflectivity

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(a) (b)

(c) (d)

Figure 4: (a) Corrected and calibrated reflectivity field of radars BKM, HWT, MOD and QNS for August 3rd, 2012,13:35 UTC, (b) network coverage, (c) filled reflectivity fields and (d) precipitation composite of all four radars

(a) (b)

Figure 5: ((a) Composite of reflectivity fields of all for network radars for August 3rd, 2012, 13:35 UTC and (d) reflectivityfield from radar Hamburg

fields of the four PATTERN radars (Fig. 5a) to theproduct of radar Hamburg (Fig. 5b) for August3rd, 2012, 13:35 UTC indicates high spatial accor-dance of both systems. Both, geographical posi-tion of the precipitation area as well as it’s max-ima, are displayed well by the PATTERN radarsand radar Hamburg. The higher resolution of thePATTERN product compared to the DWD radar

is particularly striking. This allows for enhancedand more detailed spatial allocation of precipita-tion. Nevertheless, reflectivity values are higher atradar Hamburg than in the PATTERN network.

The good agreement between PATTERN net-work and radar Hamburg in terms of reflectivity isalso evident in a long term comparison of both sys-tems in Fig. 6. All precipitation events that occur

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between July 17th and September 30th are takeninto account and reflectivity values are divided in2 dBZ steps. Here, as well as in the comparisonabove, the PATTERN network slightly underesti-mates the reflectivities measured by radar Ham-burg, but overall the PATTERN network providesreliable reflectivity data and promising results interms of higher resolution.

Figure 6: Comparison of reflectivity values from radarHamburg (abscissa) and radar MOD (ordinate) at four dif-ferent range gates from July 17th to September 30st, 2012.Absolute frequency is shown in different colors from lowlevels in blue to high levels in red.

5 SUMMARY & OUTLOOK

It has been shown, that the network of four low-cost high-resolution X-band radars used within thePATTERN project provide reasonable and reliableprecipitation estimates in high spatial and tempo-ral resolution. Several algorithms have been in-troduced to eliminate clutter and noise from rawreflectivity fields using the advantage of high tem-poral resolution and information of multiple radarsin overlapping areas within the network. A com-parison to measurements from a C-band radar op-erated by the DWD indicates that the PATTERNnetwork slightly underestimates reflectivity butdisplays the spatial structure of rain events verywell in higher resolution than nationwide radarnetworks can do.

The next step will be implementation of atten-uation correction algorithms that use the advan-tage of a network (e.g. Chandrasekar and Lim(2008); Srivastava and Tian (1996) and Testudand Amayenc (1989)). Better estimation of atten-uation can lead to better precipitation estimatesbecause the relation between attenuation and pre-cipitation is more stable than the relation betweenreflectivity and precipitation.

Furthermore, the fixed relation between radarreflectivity and precipitation will be replaced by

a dynamic relation that will be determined oper-ationally using measurements of the seven MRRsinstalled in the PATTERN catchment. This allowsfor an adaptation of the Z-R-relation to currentweather conditions, e.g. shower, light or strati-form rain.

The high resolution products of the PATTERNnetwork will also be used as input for rainfall-runoff simulations. Currently, hydrometeorologi-cal models use products from C- or S-band radarsas input with resolution of several minutes in timeand kilometers in space. The impact of higher spa-tial and temporal resolution on rainfall-runoff sim-ulation will be investigated.

AcknowledgmentThe authors thank the German Weather Service

(DWD) for making products of their C-band radarnetwork available for research purposes within theproject PATTERN.

Special thanks goes to Mareike Burba forpreparing the comparison of PATTERN networkand DWD radar products and providing the re-sults.

The project Precipitation and Attenuation Es-timates from a High-Resolution Weather RadarNetwork is funded by the Deutsche Forschungs-gesellschaft (DFG).

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