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! Q! DOW RADAR OBSERVATIONS OF WIND FARMS b y Ma l l i e To t h , Er i n Jo n e s , Du s t i n Pi ttm a n , a n d Da v i d So l o m o n

Mobile radar observations provide insight into the types of interference that can be expected in WSR-88D and local television radar operations as wind farms expand to locations closer to operational radars.

he growth of the alternative energy industry in the United States in recent years has led to an increasing T number of wind farms nationwide. In 2008, the Depart- ment of Energy announced plans for the country to meet 20% of its energy needs through wind power by 2030 (U.S. Department of Energy 2008); to meet this quota, an esti- mated 50,000 km2 of land and ocean will have to be utilized. Although the bulk of the new turbines are expected to be placed offshore, continental-based wind farms will occupy an area estimated to be about the size of Rhode Island. The expansion of wind farms has already proven to be problem- atic for the meteorological community, because turbines can interfere with Doppler observations. The nationwide Weather Surveillance Radar-1988 Doppler (WSR-88D) network provides coverage to nearly the entire continental United States at the level of ~10,000 ft (~3000 m) (Crum and Alberty 1993), which means that almost all land- based wind farms will be within some range of a WSR-88D, though many WSR-88Ds may still overshoot wind farms. Wind turbines already built within close enough range of these radars, however, have resulted in 

DOW7 at the Montezuma Wind Farm outside of Dodge City, KS during the 2010 season of the VORTEX2 project. Photo by Erin Jones. false returns in all three moments of the Doppler specific data thresholds (Stumpf et al. 1998; Mitchell spectrum: reflectivity, velocity, and spectrum width et al. 1998). The algorithms search for azimuthal shear (Crum et al. 2008). The farms are not filtered out as in adjacent beams and apply minimum strength and ground clutter, which is assumed to be stationary, aspect ratio thresholds to the data to eliminate weak because working turbines have velocity signatures or outlying segments. Thresholds are applied in both associated with them (Vogt et al. 2007). The return the reflectivity and radial velocity fields. Although from turbine blades has resulted in both the appear- only the mesocyclone detection algorithm requires ance of extremely high (>70 dBZ) radar reflectivity a time association, both employ a depth criterion for factor (hereafter referred to as reflectivity) values detection: 3 km for mesocyclone detection and 1.5 km and the detection of false mesocyclones by WSR-88D for detection. The mesocyclone algorithm algorithms (e.g. Burgess et al. 2007; Vogt et al. 2007). has been triggered by WTC observed by WSR-88Ds, Additionally, because the wind turbine clutter (WTC) and there is currently no automated method available is not filtered out automatically, derived products to remove this interference (Vogt et al. 2007). like precipitation estimates are also contaminated Attempts to mitigate the effects wind farms have (Crum 2010). on Doppler radars have mostly been successful Two WSR-88D locations, in particular, have been in removing the signatures created by stationary used in numerous studies of the impact of the turbine turbines, which are filtered out like ground clutter. clutter on Doppler radar operations (e.g., Burgess Contamination from moving blades, however, is dif- et al. 2007; Isom et al. 2009; Vogt et al. 2007; Hood ficult to remove without also removing the character- et al. 2010). The Dodge City, Kansas (KDDC), and istics of the weather occurring in the immediate sur- Great Falls, Montana (KTFX), WSR-88Ds have expe- rounding environment (Isom et al. 2009). Automated rienced interference from wind farms located within removal of WTC is important for the proper detection range of their sites. Data collection at the KTFX site, of features as well as for the proper application of de- which is 6 km away from a single wind farm, has been rived products, like precipitation estimation (Crum impacted by multipath scattering and multitrip false 2010). Although research on WTC mitigation is still echoes, and Doppler radar measurements at KDDC, in its early stages for WSR-88D operations, wind which has two wind farms within its operational farms are likely to impact radar observations made by range, have been contaminated by the false detection other platforms, such as the Collaborative Adaptive of tornadoes from WSR-88D algorithms, among other Sensing of the Atmosphere (CASA) network of small, things (Isom et al. 2009; Vogt et al. 2007). Additional relatively low-cost radars (see Brotzge et al. 2006; problems elsewhere in the country may stem from McLaughlin et al. 2009). This dense network of radars the placement of wind turbines on elevated ground, examines phenomena in the lowest 3 km of the atmo- which would enhance the amount of wind received sphere, the primary region experiencing interference by the turbine but consequently create interference from wind farms. Furthermore, nonmeteorological with the radar at ranges that might otherwise be too radars such as those used for air traffic control have distant. also encountered mitigation issues with the advent of WSR-88D mesocyclone detection algorithms wind farms (Perry and Biss 2007). and tornado detection algorithms identify potential This study uses a mobile Doppler radar to examine vortices by evaluating the velocity field and applying wind turbine clutter on a small scale within close proximity to wind turbines (less than 1 km). These radar observations are compared to the radar obser- AFFILIATIONS: To t h , Jo n e s , Pi ttm a n , a n d vations of a local television station and the closest So l o m o n —Department of Earth and Atmospheric Sciences, WSR-88D. Data from a clear-air day and a day with Purdue University, West Lafayette, Indiana CORRESPONDING AUTHOR: Mallie Toth, Department of Earth intense rainfall and strong winds show a range of and Atmospheric Sciences, Purdue University, 550 Stadium Mall environmental and operating conditions in which Dr., West Lafayette, IN 47907 wind turbines can create interference. E-mail: [email protected] BACKGROUND. The abstract for this article can be found in this issue, following the The Doppler on Wheels (DOW) table of contents. is a mobile 3-cm-wavelength Doppler radar with a DOI:10.1175 / 2011BAMS3068.1 ~1° beamwidth. It was developed for the collection

In final form 11 March 2011 of high-resolution Doppler radar data at close range ©2011 American Meteorological Society to meteorological phenomena such as tornadoes and their associated parent storms, hurricanes, and

988 | august 2011 location. The authors were part of two teams that deployed the DOW on different days to varying loca- tions in close proximity to the Benton County Wind Farm (Figs. 1, 2), an 87-turbine installation located just northwest of the Purdue University campus in Indiana (Benton County Wind Farm 2008). This research focuses not only on the observa- tions of wind farms made by the DOW but also on observations made by other regional radars. The closest WSR-88D to the Benton County Wind Farm is in Indianapolis (KIND) at about 105-km range, and, because of its placement, the radar has not been compromised by any of the existing Indiana wind farms. KIND, like all radars in the nationwide

Fi g . 1. DOW7 at the Benton County Wind Farm in WSR-88D network, is a 10-cm-wavelength radar with Indiana during DROPS (photo credit: Erin Jones). a ~1° beamwidth. The local Lafayette television sta- tion radar (WLFI) operates at 5.4 GHz and has a 5.4° other phenomena for which finescale resolution ob- beamwidth (Federal Communications Commission servations are desired (Wurman et al. 1997). DOWs 2011), obviously much larger than that of the DOW have participated in numerous field programs in or the WSR-88Ds. This difference plays a significant the United States and abroad since the debut of role in the amount of wind farm interference observed DOW1 during the Verification of the Origins of by the radars. Rotation in Tornadoes Experiment (VORTEX) in The amount of interference also depends on the 1995 (Wurman 2001). Several educational deploy- height of the radar beams relative to the turbines. ments have since been undertaken by the DOWs, The height of beams above the ground increases with some before the DOWs were formally supported by radar range due to the nonzero elevation angles of the National Science Foundation (NSF; Richardson the radar scans and the curvature of Earth. Under et al. 2008). The DOW radars are now NSF Lower typical operating conditions (i.e., a standard atmo- Atmospheric Observing Facilities and can be re- sphere in the absence of significant surrounding quested from the NSF through an expedited process terrain changes), for example, a target 105 km away for educational purposes (see www.eol.ucar.edu/ from a WSR-88D, the approximate distance from deployment/educational-deployments). A DOW Indianapolis to the Benton County Wind Farm would was deployed under this education- al program to Purdue University for the DOW Radar Observations at Purdue Study (DROPS) in the fall of 2009. DROPS was a field program led primarily by students and was part of the Department of Earth and Atmospheric Science’s Radar Meteorology course. During the field program, graduate students and senior undergraduates broke into teams, prepared daily weather briefings, and made decisions on when and where to deploy the DOW. The students were taught the basics of radar operation in the course and, during the deploy- ment, were responsible for using

knowledge they gained in class to Fi g . 2. Map detailing the deployment location of DOW7 with respect determine appropriate scanning to the turbines (represented by the green circles) (map source: strategies for the given weather and Indiana Geological Survey 2011).

AMERICAN METEOROLOGICAL SOCIETY august 2011 | 989 First deployment. The first DOW deployment of interest was on 4 November 2009, a day with scat- tered clouds and temperatures around 13°C at the time of the de- ployment (1743–1806 UTC) (Table 1). The nearby Wolcott wind profiler recorded southerly–southwesterly winds of ~5–10 m s−1 from the surface to 1 km during this pe- riod (Fig. 5). Accordingly, many of the wind turbines were facing south or southwestward. Clear- air plan position indicator (PPI) Fi g . 3. Radar images from WLFI and KIND. The approximate loca- tion of the WLFI radar is denoted by the pink square. Although and range height indicator (RHI) KIND overshoots the wind turbines [(right) shown by black dots] scans were conducted at close range and samples precipitation returns, WLFI has enhanced reflectivity (~0.5 km to the nearest turbine) signatures from the two local wind farms (circled in yellow) (source: with a pulse repetition frequency WLFI 2011). (PRF) of 2500 Hz (Nyquist velocity of ±19 m s−1; Table 2). be sampled at approximately 1500 m AGL by the The data collected on day 1 resulted in unusual center of a beam at a 0.5° elevation angle, well above patterns of reflectivity and velocity (Fig. 6). In addi- the height of the wind turbines (see Rinehart 2004). A tion to a high reflectivity return (~50 dBZ) from the target closer to the radar, 15 km, for instance, would wind turbines, streaks in reflectivity, velocity, and be sampled at a 0.5° elevation angle at approximately spectrum width extending along the radial behind 150 m AGL. With the maximum heights of wind tur- some turbines were noticed. Such patterns appeared bines being, on average, between 100 and 150 m above in scans only up to ~4.5° in elevation; regions of high ground level, interference with a radar operating with a 1° beamwidth will most likely occur when turbines are within 15 km of a radar location (Benton County Wind Farm 2008). Furthermore, with increasing distance the beam spreads and samples a larger vol- ume of space; although here we have provided height estimates for the center of the beam at certain dis- tances from the radar, it is the entire sample volume that impacts the power returned to the radar. The Benton County wind turbines reach a maximum height of 118 m with one blade pointing upward. As mentioned previously, the KIND radar easily overshoots the wind farm from more than 100 km away, but the WLFI radar with its 5.4° beamwidth is located approximately 40 km away from the wind farm site, thereby enabling the wind farm to create interference (see examples in Fig. 3). The DOW, at a much closer range to the wind farm, samples the Fi g . 4. Diagram (not to scale) detailing how the dis- wind turbines quite easily. These relationships are tance to the wind farms and the beamwidth of the more clearly detailed in Fig. 4. WSR-88D, WLFI, and DOW7 radars impact how much wind turbine interference is observed. The center of the KIND beam is around 1.5 km AGL upon reaching FIELD EXPERIMENT AND DATA. DOW7 was the Benton County Wind Farm; DOW7 samples the deployed by two different student groups in close individual turbines at various levels depending on their proximity to the Benton County Wind Farm during location relative to the DOW. Depending on the scan- DROPS: once on a clear-air day and once during ning strategy of the WLFI radar, the wind turbines will heavy precipitation and high winds. be sampled at various levels.

990 | august 2011 Ta b l e 1. Roof observations on the four-story civil engineering building at Purdue University during both deployments. 4 Nov 2009 17 Nov 2009 Time of observation (UTC) 1750 –1810 1500–1600 Max temperature 12.6°C 6.1°C Min wind speed (kts) 16 17 Prevailing wind direction 210° 60°

reflectivity then appeared sporadically at 5° and impacts velocity measurements and derived products. above. Spectrum width was also high in regions of KIND does not experience these interference effects clear air but was much lower in the streaks behind because of its great distance from the Benton County the turbines. In the Doppler velocity fields, azimuthal Wind Farm. Although the WLFI radar is affected by velocity differences (ΔV) across adjacent beams of the Benton County Wind Farm, it has not experienced as high as 30 m s−1 gate to gate were present in the similar multipath scattering issues in clear-air or streaks behind the turbines. Because such segments precipitation scans, possibly because of its large beam- of azimuthal shear occurred along extensive radi- width. The volume being sampled is so large compared als, however, they did not have the signature of an to the turbines that any anomalous returns from the isolated vortex (e.g., see Stumpf et al. 1998; Mitchell turbines are averaged into a single return of high re- et al. 1998). Nor did the Doppler velocity returns from flectivity without any defining spikes, as is typically the field of wind turbines exhibit random behavior, seen by the WLFI radar on clear-air days (Fig. 3). likely because the operational wind turbines were largely oriented at the same angle with respect to the mean wind. It would therefore be reasonable to infer that the Doppler velocity returns from a field of similarly oriented rotating turbines would follow a predictable pattern, as was observed; radial couplets of outbound and inbound velocities changed in intensity and orienta- tion as the angle of the radar beam changed with respect to the orien- tation of the turbines. This occurs because, excepting cases in which the turbines are directly facing the radar, the rotating blades would dis- play motion both toward and away from the receiver. WSR-88Ds have experienced similar interference on a larger scale. Multipath scattering has resulted in spikes of reflectivity beyond the location of the turbines; however, because of scaling, range, and beam- width with respect to the target, these extensions along a radial are not as apparent in WSR-88D data as they

are in the provided DOW examples. Fi g . 5. Vertical wind profiles from the nearby Wolcott, Indiana, wind Wind turbine interference in re- profiler for both deployment days (source: National Oceanic and flectivity measurements thereafter Atmospheric Administration 2010).

AMERICAN METEOROLOGICAL SOCIETY august 2011 | 991 Ta b l e 2. Scanning strategies for 4 Nov 2009 at 40.562°N, 87.224°W. Scan type Elevation range Azimuth range Azimuth/elevation rate PPI 0.5°–15.5° 45°–135° 5° s−1 RHI 0.5°–15.5° 60°–90° 5° s−1 RHI 0.5°–15.5° 90°–120° 5° s−1 PPI 0.5°–15.5° 10°–180° 5° s−1

Second deployment. The second deployment of the site was not as close to the wind turbines as the clear- DOW at the wind farm was on 17 November 2009, the air deployment site, evidence of the wind farm was day with the most extensive and intense precipitation expected in scans above 1.5° in elevation. It is likely in the entire DROPS field study. This deployment that precipitation scatterers overwhelmed the return was slightly south of the 4 November location (see from the turbines in these conditions. Tables 2, 3). The temperature remained around 6°C throughout the deployment (~1500–1600 UTC; see DISCUSSION. The patterns in the reflectiv- Table 1). Winds recorded at the Wolcott wind profiler ity and velocity fields observed on the clear-air (outside the precipitating area during most of the de- day (4 November) may be attributed to multipath ployment) were easterly at ~5–20 m s−1 up to 1 km in scattering, a result consistent with the spurious re- altitude during the period (Fig. 5). PPI and RHI scans flectivity observed by multiple WSR-88D facilities were conducted with a 2500-Hz PRF (Nyquist velocity (KDDC, KTFX, and KTYX). Because of the proximity of ±19 m s−1; Table 3). The data recorded during the of the DOW to the wind turbines, it is reasonable to precipitating deployment contain no streaks along a expect high power returns. There is a notable dif- radial; the wind turbines, however, could still be dis- ference between the patterns observed in all three tinguished. High reflectivity returns (~50 dBZ) and moments on the clear-air day and the precipitating strong azimuthal velocity differences (ΔV ~ 30 m s−1) day. One potential explanation for these differences is were present in scans up to 1.5° elevation, above changes in the wind turbine blades’ “angle of attack,” which no further evidence of the wind farm was or the angle at which the individual blades are tilted apparent (Fig. 7). Although the second deployment in the vertical with respect to the mean wind flow to generate optimal lift. It should be noted that, for the purpose of this discussion, the angle of attack is not an idea of meteorological origin but is instead a concept of fluid dynam- ics that may be used by wind energy corporations to maximize the energy generated from wind farms given specific wind conditions (Lanzafame and Messina 2009). For wind tur- bine blades, the optimal range of the angle of attack that provides the highest turbine speed is 1°–15° relative to the wind direction (Tonch 2003). The standard angle of attack is 4° for the most efficient wind- to-energy conversion. It is possible that during the 4 November scans the turbine blades’ angle of attack, determined by the south-southwest wind direction, caused the turbine Fi g . 6. (left) DOW7 0.5° elevation velocity and (right) reflectivity scans on the clear-air day, 4 Nov 2009. Range rings are at 1-km blades to be oriented in such a way spacing. Note the presence of the wind turbines at the highest power that their vertical tilt, with respect returned values. to the radar beam, increased the

992 | august 2011 Ta b l e 3. Scanning strategies for 17 Nov 2009 at 40.534°N, 87.217°W. Scan type Elevation range Azimuth range Azimuth/elevation rate PPI 0.5°–10.5° (0.5° steps) 360° 30° s−1 RHI 0.5°–10.5° (0.5° steps) 90°–270° (10° steps) 5° s−1 RHI 0.5°–30.5° 90°–270° (10° steps) 5° s−1

amount of power returned and therefore caused high above the base radar scan, which may result in the spikes in the reflectivity and velocity fields. This type base couplet being flagged but may not enact the of blade orientation-dependent interference was also detection algorithm. A WSR-88D, however, located noted by Isom et al. (2009). within its optimal range (~15 km) from the wind The precipitating day (17 November) observa- farm may detect the turbines and, under certain tions differed from the clear-air day observations conditions, activate the detection algorithms. The in that multipath scattering was not present. In WLFI radar detects the wind farm in both clear-air addition to the position of the DOW with respect and precipitating conditions; this is partly due to to the different angle of attack (which perhaps led the wide beam of the radar and its distance to the to a decrease in the blades’ radar cross section), the site. It is reasonable to assume that the WLFI radar presence of heavy rainfall understandably resulted samples a sizeable vertical and horizontal portion primarily in observations of precipitation targets of the wind farm as its sample volume increases because the radar cross section of precipitation with range. The averaged sample volume, which targets would have dwarfed that of the turbine includes reflectivity and velocity values from the blades. The DOW data confirm that wind farms turbines, appears as if it has sampled a storm with can cause velocity couplets and anomalously high high reflectivity values in the region of the wind reflectivity values in precipitation. Although the farm, regardless of whether precipitation is in the high reflectivity values are not unexpected when the area. WTC is likely already posing problems to DOW or any weather radar samples a large, solid other local television stations across the country target, the velocity couplets are reason for concern. with similar Doppler radars, although the full ex- Deployment data show the couplets do not extend tent of the problem is still unknown.

C O N C L U S I O N S A N D FUTURE WORK. The deployment of the DOW near the Benton County Wind Farm in November 2009 re- sulted in the observation of features that have been found in previous studies on the impact of wind farms on WSR-88D operations, including multipath scattering and velocity couplets that appear as isolated tor- nadic vortices. The clear-air day ex- hibited mostly multipath scattering, whereas the main observations on the precipitating day were of isolated velocity couplets. In the DOW data, the features are exaggerated, because of the close proximity of the DOW relative to the wind farms. The data also suggested that the radar return from the turbines, especially in the Fi g . 7. (left) DOW7 0.5° elevation velocity and (right) reflectivity scans on the precipitating day, 17 Nov 2009. The location of the case of velocity returns, may be af- ~30 m s−1 ΔV is circled; this turbine is approximately 5.5 km from the fected by the wind direction and radar. Range rings are at 1-km spacings. the turbine blades’ angle of attack,

AMERICAN METEOROLOGICAL SOCIETY august 2011 | 993 which impacts the orientation of the turbine blades with DOW7 during DROPS. Thanks especially to Jeff with respect to the radar beams. Velocity couplets on Trapp for bringing DOW7 to Purdue and providing guid- the precipitating day (17 November) were only present ance for the preparation and content of this manuscript. in the lowest elevation scans, which may result in the We would like to express our appreciation to Don Burgess identification of a low-level couplet by the WSR-88D for providing his expertise on our results. We’d also like detection algorithms. to acknowledge our other team members, Dan Sheehan As large-scale wind farms are still relatively new and Brenton Kramer, as well as Ernie Agee for his interest in the United States, their impacts on the nation’s in this project. network of Doppler radars and television station radars have yet to be fully understood. Strategies to mitigate the effects that wind farms have on WSR- REFERENCES 88D products and detection algorithms are being Benton County Wind Farm, 2008: Self-guided driving discussed and developed, but the full range of prob- tour of Indiana’s first wind farm. Pamphlet, 1 pp. lems associated with the expansion of wind farms [Available online at www.earlparkindiana.com/ will continue to unfold. Current attempts to remove windtour/TourPage1.jpg.] the signatures of wind turbines from WSR-88D data Brotzge, J. A., K. Droegemeier, and D. J. McLaughlin, in particular operations have been fairly successful 2006: Collaborative Adaptive Sensing of the Atmo- (Isom et al. 2009), but these mitigation strategies will sphere (CASA): New radar system for improving need to be expanded to better filter out the WTC analysis and forecasting of surface weather condi- under various circumstances operationally. The tions. Transp. Res. Rec., 1948, 145–151. authors expect the data collected during the DROPS Burgess, D. W., T. D. Crum, and R. J. Vogt, 2007: Impacts field program will be the first of many smaller-scale of wind turbine farms on WSR-88D radars. Pre- observations of wind farms made intentionally or prints, 33rd Int. Conf. on Radar Meteorology, Cairns, by circumstance in the next few years; indeed, op- QLD, Australia, Amer. Meteor. Soc., 13B.6. [Avail- erations during the second phase of the VORTEX2 able online at http://ams.confex.com/ams/33Radar/ project were hindered by the presence of wind farms techprogram/paper_123311.htm.] at target storm locations, and some of the project’s Crum, T., 2010: Wind farms and the WSR-88D: An mobile Doppler radar data may contain wind farm update. Nexrad Now, 19, 17–22. artifacts. It is the authors’ hope that such observa- —, and R. L. Alberty, 1993: The WSR-88D and the tions begin to highlight the need for a better under- WSR-88D operational support facility. Bull. Amer. standing of wind farm impacts on the varying array Meteor. Soc., 74, 1669–1687. of Doppler radars and radar networks being used —, E. Ciardi, and J. Sandifer, 2008: Wind farms: operationally and in research throughout areas of Coming soon to a WSR-88D near you. Nexrad Now, the globe where wind energy is an important power 18, 1–7. source. The authors also hope that this work serves Federal Communications Commission, cited 2011: to illustrate the importance of enabling students at Wireless Telecommunications Bureau universal various educational levels in the atmospheric sci- licensing system. [Available online at http://wireless. ences to have the opportunity to participate in field fcc.gov/uls.] work and to engage in the investigation of current Hood, K., S. Torres, and R. Palmer, 2010: Automatic problems in their area of study. Such programs can detection of wind turbine clutter for weather radars. not only prove to be an excellent learning experience J. Atmos. Oceanic Technol., 27, 1868–1880. but may also create a foundation upon which further Indiana Geological Survey, cited 2011: IndianaMap. research can be based. [Available online at http://inmap.indiana.edu.] Isom, B. M., and Coauthors, 2009: Detailed observations ACKNOWLEDGMENTS. The DOWs are supported by of wind turbine clutter with scanning weather radars. National Science Foundation Grant NSF-ATM-0734001. J. Atmos. Oceanic Technol., 26, 894–910. DROPS was made possible by the National Science Lanzafame, R., and M. Messina, 2009: Optimal wind Foundation Lower Atmospheric Observing Facilities for turbine design to maximize energy production. Proc. National Center for Atmospheric Research Earth Observa- Inst. Mech. Eng., 223A, 93–101. tory Laboratory Educational Deployments. We would like McLaughlin, D., and Coauthors, 2009: Short-wavelength to express appreciation to the Center for Severe Weather technology and the potential for distributed net- Research, including Josh Wurman for his input on the works of small radar systems. Bull. Amer. Meteor. manuscript and Justin Walker for his time spent assisting Soc., 90, 1797–1817.

994 | august 2011 Mitchell, E. D., S. V. Vasiloff, G. J. Stumpf, A. Witt, M. D. Tonch, F., cited 2003: The blade designer program—A Eilts, J. T. Johnson, and K. W. Thomas, 1998: The free basic help tutorial in blade design. [Available National Severe Storms Laboratory tornado detection online at www.windstuffnow.com/main/blade_ algorithm. Wea. Forecasting, 13, 352–366. design_help.htm.] National Oceanic and Atmospheric Administration, U.S. Department of Energy, 2008: 20% wind energy by cited 2010: NPN profiler. [Available online at www. 2030: Increasing wind energy’s contribution to U.S. profiler.noaa.gov/npn/profiler.jsp.] electrical supply. U.S. Department of Energy Rep., Perry, J., and A. Biss, 2007: Wind farm clutter mitigation 248 pp. [Available online at www.20percentwind. in air surveillance radar. IEEE Aerosp. Electron. Syst. org/20percent_wind_energy_report_revOct08.pdf.] Mag., 22, 35–40. Vogt, R. J., and Coauthors, 2007: Weather radars and Richardson, Y., P. Markowski, J. Verlinde, and J. Wur- wind farms—Working together for mutual benefit. man, 2008: Integrating classroom learning and Extended Abstracts, WIND-POWER 2007 Conf. and research: The Pennsylvania Area Mobile Radar Exhibition, Los Angeles, CA, AWEA, 1–7. Experiment (PAMREX). Bull. Amer. Meteor. Soc., WLFI, cited 2011: Radar center. [Available online at 89, 1097–1101. www.wlfi.com/subindex/weather/radar_center.] Rinehart, R. E., 2004: Radar for Meteorologists. Wurman, J., 2001: The DOW mobile multiple-Doppler Rinehart, 482 pp. network. Preprints, 30th Int. Conf. on Radar Meteorol- Stumpf, G. J., A. Witt, E. D. Mitchell, P. L. Spencer, ogy, Munich, Germany, Amer. Meteor. Soc., 95–97. J. T. Johnson, M. D. Eilts, K. W. Thomas, and —, J. M. Straka, E. N. Rasmussen, M. Randall, and A. D. W. Burgess, 1998: The National Severe Storms Zahrai, 1997: Design and deployment of a portable, Laboratory mesocyclone detection algorithm for the pencil-beam, pulsed, 3-cm Doppler radar. J. Atmos. WSR-88D. Wea. Forecasting, 13, 304–326. Oceanic. Technol., 14, 1502–1512.

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