bird migration flight altitudes studied by a network of ... et all 2010 bird migration... · bird...

15
doi: 10.1098/rsif.2010.0116 published online 2 June 2010 J. R. Soc. Interface Adriaan M. Dokter, Felix Liechti, Herbert Stark, Laurent Delobbe, Pierre Tabary and Iwan Holleman operational weather radars Bird migration flight altitudes studied by a network of References ref-list-1 http://rsif.royalsocietypublishing.org/content/early/2010/05/29/rsif.2010.0116.full.html# This article cites 45 articles, 4 of which can be accessed free P<P Published online 2 June 2010 in advance of the print journal. This article is free to access Rapid response http://rsif.royalsocietypublishing.org/letters/submit/royinterface;rsif.2010.0116v1 Respond to this article Subject collections (341 articles) environmental science (10 articles) biometeorology Articles on similar topics can be found in the following collections Email alerting service here right-hand corner of the article or click Receive free email alerts when new articles cite this article - sign up in the box at the top publication. Citations to Advance online articles must include the digital object identifier (DOIs) and date of initial online articles are citable and establish publication priority; they are indexed by PubMed from initial publication. the paper journal (edited, typeset versions may be posted when available prior to final publication). Advance Advance online articles have been peer reviewed and accepted for publication but have not yet appeared in http://rsif.royalsocietypublishing.org/subscriptions go to: J. R. Soc. Interface To subscribe to This journal is © 2010 The Royal Society on June 2, 2010 rsif.royalsocietypublishing.org Downloaded from

Upload: vanmien

Post on 16-Apr-2019

215 views

Category:

Documents


0 download

TRANSCRIPT

doi: 10.1098/rsif.2010.0116 published online 2 June 2010J. R. Soc. Interface

Adriaan M. Dokter, Felix Liechti, Herbert Stark, Laurent Delobbe, Pierre Tabary and Iwan Holleman operational weather radarsBird migration flight altitudes studied by a network of

Referencesref-list-1http://rsif.royalsocietypublishing.org/content/early/2010/05/29/rsif.2010.0116.full.html#

This article cites 45 articles, 4 of which can be accessed free

P

J. R. Soc. Interface

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

*Author for c

doi:10.1098/rsif.2010.0116Published online

Received 1 MAccepted 10 M

Bird migration flight altitudesstudied by a network of operational

weather radarsAdriaan M. Dokter1,*, Felix Liechti2, Herbert Stark2,Laurent Delobbe3, Pierre Tabary4 and Iwan Holleman1

1Royal Netherlands Meteorological Institute, De Bilt, The Netherlands2Swiss Ornithological Institute, Sempach, Switzerland

3Royal Meteorological Institute of Belgium, Brussels, Belgium4Meteo France, Toulouse, France

A fully automated method for the detection and quantification of bird migration wasdeveloped for operational C-band weather radar, measuring bird density, speed and directionas a function of altitude. These weather radar bird observations have been validated withdata from a high-accuracy dedicated bird radar, which was stationed in the measurementvolume of weather radar sites in The Netherlands, Belgium and France for a full migrationseason during autumn 2007 and spring 2008. We show that weather radar can extract nearreal-time bird density altitude profiles that closely correspond to the density profilesmeasured by dedicated bird radar. Doppler weather radar can thus be used as a reliablesensor for quantifying bird densities aloft in an operational setting, whichwhen extendedto multiple radarsenables the mapping and continuous monitoring of bird migration fly-ways. By applying the automated method to a network of weather radars, we observedhow mesoscale variability in weather conditions structured the timing and altitude profileof bird migration within single nights. Bird density altitude profiles were observed that con-sisted of multiple layers, which could be explained from the distinct wind conditions atdifferent take-off sites. Consistently lower bird densities are recorded in The Netherlandscompared with sites in France and eastern Belgium, which reveals some of the spatialextent of the dominant Scandinavian flyway over continental Europe.

Keywords: bird migration; radar ornithology; weather radar;altitude profile; verification

1. INTRODUCTION

Spatio-temporal information on bird migration is ofinvaluable use to scientists and society alike, but sofar no sensor networks have been established that pro-vide continuous and automated quantification of birdmovements over large areas. Comprehensive monitoringof bird migration at continental scales can provide fun-damental insight into migration patterns, the impact onmigratory flight of synoptic scale factors like weatherand orography and the selection of stop-over areas bymigratory birds (Gauthreaux & Belser 2003, 2005;Diehl & Larkin 2005). Strong potential exists for theapplied use of continuous bird migration observations,for example in aviation for purposes of improvingflight safety. In particular, military low-level flyinghas a high risk of en route bird strikes and spatialbird migration information is essential for generatingreliable flight warnings to pilots. Other warning systemscould be envisioned to reduce the collision risk ofbirds with wind farms, off-shore platforms and other

orrespondence ([email protected]).

arch 2010ay 2010 1

man-made structures, by allowing people to predictand adapt to specific mass migration events.

Radar has had an immense impact on ornithology andthe study of bird migration, because of its unique abilityto monitor bird movements up to high altitudes anddistances during both night and day (Eastwood 1967;Alerstam 1990; Bruderer 1997a,b; Gauthreaux & Belser2003). Weather conditions (Bruderer 1997b; Erni et al.2002), topographical features like coastlines (Alerstam1978; Buurma 1995) and orography (Bruderer 1996) allinfluence migratory flight. The exact interplay of thesesynoptic scale factors and the migrational movementsof birds is hard to study in general, mainly because ofa lack of observational data.

Operational weather radar networks exist in, forexample, Europe and the United States for meteoro-logical applications. These networks have a large arealcoverage as illustrated in figure 1, showing part of theEuropean network OPERA (Operational Programmefor the Exchange of weather RAdar information;Holleman et al. 2008a). Although designed for precipi-tation monitoring, weather radars can also observebiological scatterers. Boundary layer clear-air weather

This journal is q 2010 The Royal Society

mailto:[email protected]://rsif.royalsocietypublishing.org/

355 0 5 10

45

50

0 100 200

km

7

15

23

31

39

47

55

(dBZ)

EUMETNET/OPERA radar network

Wideumont

Trappes

Den Helder

De Bilt

Figure 1. Map of operational weather radars for part of western Europe. Radar sites are indicated by bullets, the weather radarsused in this study are labelled and coloured red. The OPERA reflectivity composite is overlaid for 19 April 2008 19.30 UTC.

2 Bird migration flight altitudes A. M. Dokter et al.

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

radar echoes are caused predominantly by arthropods(mostly insects) and flying birds (Wilson et al. 1994;Martin & Shapiro 2007; Contreras & Frasier 2008). Ifweather radar could detect and quantify aerial bird den-sities automatically, the existing weather radar networkinfrastructure could be used as a bird migration sensornetwork with an unprecedented coverage.

Although weather radars have been used in ornitholo-gical research for several decades (Gauthreaux & Belser1998), to our knowledge only two cases of continentalbird migration were studied by a weather radar network(Diehl et al. 2003; Gauthreaux et al. 2003), mainlybecause of the labour-intensive analytical work involved.To enable comprehensive monitoring of bird migrationby weather radar networks, it is essential to (i) developan automated and portable method for both the detec-tion and the quantification of aerial bird densities and(ii) validate the reliability of this method with indepen-dent reference data. In this research article, we addressboth these requirements and report on the developmentand validation of a bird migration quantification algor-ithm, extracting altitude profiles of bird density, speedand direction. By applying the algorithm to a networkof weather radars, we observed the progression ofmigratory flights along the migratory direction over amesoscale range of several hundred kilometres.

2. BIRD RADAR FIELD CAMPAIGNS

To obtain reference data for validating weather radarbird observations, we organized extensive field

J. R. Soc. Interface

campaigns with a high precision dedicated bird radar.An ex-military pencil-beam X-band (l 3 cm) radarof the type Superfledermaus (Bruderer et al. 1995)was stationed in the measurement volume of C-bandweather radars in The Netherlands (19 August16September 2007), Belgium (18 September22 October2007) and France (10 March9 May 2008; figure 1).The campaigns produced a large reference dataset con-sisting of bird densities and flight directions at 200 maltitude intervals and 1 h time intervals for a four-month period in total. The bird radar system hasbeen well calibrated and makes use of state-of-the-artecho identification and quantification procedures(Schmaljohann et al. 2008; Zaugg et al. 2008). Foreach detected echo wing beat patterns are automati-cally analysed, by which non-bird echoes like insectscan be properly excluded. Bats cannot be automaticallydistinguished from birds, but are relatively less abun-dant in the study area (Limpens et al. 1997), and,therefore, unlikely to have influenced measurements.The bird radar thus provides a high-quality referencefor validating weather radar bird observations over afull migratory season. Bird radar measurementprocedures are detailed in appendix A.

3. BIRD DETECTION ANDQUANTIFICATION BY WEATHERRADAR

Bird density quantification by weather radar relies onaccurate methods of target identification. Bird-scattered

http://rsif.royalsocietypublishing.org/

Bird migration flight altitudes A. M. Dokter et al. 3

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

signals need to be automatically distinguished from allother types of echoes, which include precipitation,ground echoes related to anomalous propagation,clear-air returns by insects and to some degree Braggscattering from refractive index turbulence. Bragg scat-tering can be present especially at the top of theafternoon convective boundary layer (Wilson et al.1994; Contreras & Frasier 2008), but is highly wave-length-dependent, appearing weaker on C-band byover 10 dBZe when compared with S-band (Wilsonet al. 1994; Martin & Shapiro 2007).

Individual bird echoes can in principle be resolved byhigh-resolution weather radar (Nebuloni et al. 2008),but usually the spatial resolution of operational basedata is too coarse. Also, wing beat patterns cannot berecorded in operational scanning modes. Therefore,alternative methods of target identification need to bedeveloped. At C-band bird-scattered signals are typi-cally found at low reflectivity factors of 210 to10 dBZe (Koistinen 2000), but insects (Wilson et al.1994) and hydrometeors (i.e. water and ice particles)(Doviak & Zrnic 1993) can give rise to signals of similarstrength. Additional information is necessary toseparate out bird-scattered signals.

Central to our method of identifying bird-scatteredechoes is an analysis of the radial velocity of the scat-terers, which is measured routinely by Doppler weatherradars. With the exception of strongly convective sys-tems, the wind field carrying hydrometeors is spatiallysmooth and slowly varying, resulting in a low spatialvariability of the measured radial velocities (Holleman2005). This also holds for Bragg-scattered echoes andechoes caused by insects, whose active flight tends tobe slow and of which migration is predominantly wind-borne (Drake & Farrow 1988; Chapman et al. 2003).Bird migration gives rise to a much higher spatial varia-bility in radial velocity (Koistinen 2000; Zhang et al.2005; van Gasteren et al. 2008; Holleman et al. 2008b),arising from individual variations in speed and directionof flying birds (see 4.1).

For most Doppler radars, an additional measure ofradial velocity variance is available, namely spectrumwidth, which derives from the echo pulse statisticsunderlying a single resolution volume (instead of resol-ution-volume to resolution-volume variances). It hasbeen suggested that spectrum width can be useful intarget identification of various biological scatterers(Larkin & Diehl 2002), but in this study spectralwidth was highly variable over different cases andmeteorological conditions to make straightforward useof it in an automated algorithm. Nonetheless, there ispotential for using spectrum width in bird detectionalgorithms, especially when using more sophisticatedDoppler spectrum analysis that extends beyond theassumption of a Gaussian Doppler spectrum by mostsignal processors, which is often invalid for birds(Koistinen 2000).

The developed bird detection algorithm produces avertical profile of bird density, speed and directionevery 515 min at 200 m altitude resolution, basedon data at the 525 km range. At these closedistances, the radar beam is sufficiently narrow toprobe distinct altitudes, and range-dependent biases

J. R. Soc. Interface

are largely avoided, which otherwise need to be com-pensated for (Buler & Diehl 2009). Each extractedaltitude profile is based on a spatial average overthe 525 km range measurement window. Forbroad-fronted movement like most passerinemigration (Bruderer 1997b), such an average will berepresentative, as migration will be spatially homo-geneous. The algorithm is not designed to monitorvery local migration features within the field ofview of the radar. Detailed definitions are given inappendix A.

3.1. Bird detection

The algorithm is based on the existing wind-profilingalgorithms for Doppler weather radars, using the volumevelocity profiling (VVP) technique (Waldteufel &Corbin 1979; Holleman 2005). This technique was success-fully applied in the context of bird migration profiling in arelated study by van Gasteren et al. (2008). The VVPanalysis delivers an altitude profile of the average speedand direction of the scatterers by fitting the data to a con-stant velocity model (see equation (A 1)). Additionally, ateach height, a radial velocity standard deviation sr iscalculated (see equation (A 2)). Recent studies havesuggested that sr is a good discriminator between high-quality wind measurements and bird-contaminated windprofiles (Koistinen 2000; van Gasteren et al. 2008;Holleman et al. 2008b). van Gasteren et al. (2008)showed that air layers with a high radial velocitystandard deviation (sr . 2 m s

21) occurred at altitudes,where simultaneously bird migration was detected by anindependent bird radar, which demonstrated that sr isalso a good indicator for the presence of birds. Reliablebird density quantification could not be demonstratedin the study of van Gasteren et al. (2008) owing tocontamination from residual precipitation and a largedistance between the weather radar and bird radarsites (80 km). In 4.1, we validate sr as a discriminatorfor bird presence, where we will also discuss possibleevents unrelated to bird migration that can cause highvalues of sr.

3.2. Removal of non-bird echoes

In addition to the sr criterion, we developed a targetidentification scheme to filter out non-bird echoesfrom the radar volume data. All reflectivity factorsabove 20 dBZe are masked as non-bird echoes, sincesuch high values occurred rarely during bird migrationin our study period and exceed reflectivity factorsexpected for broad-fronted passerine migration(20 dBZe corresponds to approx. 3500 passerine-sizedbirds per cubic kilometre at C-band (see equation(3.1) and figure 5 legend). For single-polarizationDoppler weather radar, we developed a cell-findingalgorithm that selects within each elevation scancontiguous areas above a certain reflectivity threshold(see appendix A).

When selecting contiguous reflectivity areas, spur-ious precipitation cells may be detected in areas ofintense bird migration as well. Examples of selectedcells are given in figure 2, showing plan position

http://rsif.royalsocietypublishing.org/

reflectivity factor (dBZ) radial velocity (m s1)

s cel

l (m

s1 )

20

(a) (b)

(i)

(ii)

13 7 0 7 13 20 15 10 5 0 5 10 15

00

5

10

15

5 10 15 20 25

reflectivity factor, Zcell (dBZ)

Figure 2. (a) Plan position indicators (PPIs) for reflectivity factor and radial velocity for a bird migration event ((i) 5 October2007, 00.02 UTC) and an event with weak convective showers ((ii) 26 September 2007, 16.32 UTC). The PPIs show the 1.28elevation scan up to 25 km range. Borders of identified reflectivity cells are indicated in red. (b) Radial velocity standard devi-ation scell as a function of cell-averaged reflectivity factor Zcell for reflectivity cells detected during intense bird migration events(green bullets, scell 9+2 m s21) and during events with convective showers (blue bullets, scell 0.5+ 0.2 m s21) for theWideumont weather radar. Selected intense migration events were 4, 5, 6, 7 and 13 October 2007 from 17.00 to 09.00 UTCnext day. Selected convective shower events were 24 September 2007, 9.00 to 18.00 UTC; 26 September 2007, 07.00 to 18.00UTC; 17 October 2007, 06.00 to 17.00 UTC; 18 October 2007, 09.00 to 17.00 UTC. Only cells consisting of over 800 resolutionvolumes are shown to limit the number of scatter points. For the largest reflectivity cell in each PPI, a connecting solid line isdrawn to its corresponding scatter point. Cells inside the yellow shaded segments (scell , 5 m s

21 or Zcell . 15 dBZe) are classifiedas non-bird reflectivity cells and removed from the scan.

4 Bird migration flight altitudes A. M. Dokter et al.

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

indicators for a case of bird migration (figure 2a (i)) andprecipitation (figure 2b (ii)). To identify selected cells inbird migration areas and discard them from the precipi-tation map, for each selected cell an average nearestneighbour variance scell is computed for the radial vel-ocities (see equation (A 3)). Analogous to sr discussedpreviously, the variance scell is lower for wind-bornescatterers (both hydrometeors and insects) than forbirds performing active flight. This difference is illus-trated in figure 2b, where scell is plotted as a functionof the average cell reflectivity factor for a number ofcells detected during intense bird migration events(green bullets) and cells detected during events withconvective showers (blue bullets). A combined criterionusing reflectivity and radial velocity information isused to assign cells to the precipitation map: cellswith either a low variance or a high reflectivity factor(scell , 5 m s

21 or Zcell . 15 dBZe) are removed fromthe data as non-bird scattering. From the remainingareas, an average reflectivity is calculated for each200 m altitude interval.

We find that bird migration gives rise to a relativelyhigh fraction of radial velocity dealiasing outliers(up to 15%) compared with precipitation (1%) anddaytime clear air echoes (Holleman & Beekhuis 2003)using the dual-PRF (pulse repetition frequency)

J. R. Soc. Interface

technique. This is likely related to the fact that birdscattering arises from a few large complex targets forwhich the backscatter phase varies in time withslight geometry changes of the bird body. In theVVP analysis, radial velocity outliers are removed byan iterative fitting procedure. In the calculation ofscell, the identification of outliers is less straigh-tforward and has not been implemented. As a result,values of scell for bird migration are typically higherthan for sr, and therefore also the optimal thresholdin scell is higher (5 m s

21) than for sr (2 m s21).

One polarimetric weather radar was available in ourstudy (i.e. in Trappes, France). By taking advantage ofthe additional information, especially the removal ofprecipitation from the data is improved. We excludecontiguous areas with a high correlation coefficient,rHV . 0.9 (indicative of hydrometeors (Bringi &Chandrasekar 2001)) or high differential reflectivity,ZDR . 3.0 dB (indicative of insect; Mueller & Larkin1985; Achtemeier 1991; Zrnic & Ryzhkov 1998;Bachmann & Zrnic 2007). For polarimetric radar, theVVP analysis of radial velocity data remains necessaryto determine the presence or absence of birds, becausewe found that the ZDR criterion is often insufficient tofilter out insect echoes, especially during cases withstrong convective mixing.

http://rsif.royalsocietypublishing.org/

Bird migration flight altitudes A. M. Dokter et al. 5

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

3.3. Quantification of bird density

Bird density information can be obtained from reflectiv-ity measurements, analogous to quantitativeprecipitation estimation. For S-band weather radars,empirical relationships have been found between reflec-tivity and the volumetric density of migrating birds(Gauthreaux & Belser 1999; Diehl et al. 2003), butonly for a few preselected cases of bird migration inthe absence of non-bird scatterers like precipitation.At a radar wavelength l, the reflectivity h, equivalentreflectivity factor Ze and bird density rbird are relatedaccording to (Doviak & Zrnic 1993; Black & Donaldson1999; Gauthreaux & Belser 1999)

h 103p5

l4jKmj2Ze rbirdsbird; 3:1

with h in cm2 km23, Ze in mm6 m23, Km (m2 2 1)/

(m2 2) with m the complex refractive index of thescatterers, l (in cm) the radar wavelength, rbird inbirds per km23 and sbird the bird radar cross sectionat C-band in square centimetres. The bar over the rbird-sbird product denotes that this quantity is strictly anaverage over the different bird types or species i accord-ing to

Pi rbird,isbird,i and over the scanned viewing

angles. With jKmj2 0.93 for water (Doviak & Zrnic1993), we find using equation (3.1) that h is pro-portional to Ze by a factor of 28.0 at S-band (l 10 cm) (see also Black & Donaldson (1999)), 361 atC-band (l 5.3 cm) and 3.51 103 at X-band (l 3.0 cm). Bird radar cross sections and therefore reflec-tivities h are of the same order of magnitude at theseradar bands. However, because of the l24 proportional-ity in equation (3.1), birds cause much higherreflectivity factors Ze in S-band compared with C-band (and much lower reflectivity factors in X-band).

Weather radar reflectivity can be converted toapproximate bird densities by dividing h in equation(3.1) by an average bird radar cross section sbird,which will be determined by the validation using birdradar measurements.

4. RESULTS AND DISCUSSION

4.1. Validation weather radar

In figure 3, we compare the bird density height profiledetermined by bird radar and weather radar for theperiod of 27 October 2007. A considerable nightly var-iance in flight altitudes and densities is recorded by thebird radar (figure 3a) during this period. While on 4October, migration does not exceed 1.5 km, on 6 and7 October, the bird radar records migration extendingabove 3 km height. These altitude profiles are closelyreproduced by the weather radar algorithm(figure 3b). Also, a remarkable correspondence isobserved between both sensors for the recorded absol-ute numbers of birds, as illustrated by the closelyoverlaying height-integrated bird densities (figure 3c).

For the quantitative verification, measurements wereused from all three campaigns in De Bilt, Wideumontand Trappes, comprising 118 days of continuous data.We limit the quantitative verification of the weather

J. R. Soc. Interface

radar algorithm to night-time hours only. During day-time bird flocks are common (Bruderer 1997b), whichshow no clear wing beat pattern and may thereforenot be recognized by the automatic target identificationof the bird radar. For this reason, the dawn ascent ofdaytime migrating birds is observed much more clearlyby the weather radar than by the bird radar (e.g. on57 October; figure 3). During night-time, the weatherradar recorded above noise reflectivity factors in 61 percent of the surveyed timeheight layers (i.e. a specificheight layer measured at a specific time) for airspaceup to 4 km above ground level (AGL), while the birdradar recorded a non-zero bird density in only 38 percent of the surveyed timeheight layers. The datasetthus contains representative cases both with and with-out migrating birds.

4.1.1. Bird detection. Figure 4 shows for a large numberof timeheight layers the radial velocity standard devi-ation, sr (see equation (A 2)) as a function of rawreflectivity (figure 4a, non-bird echoes included) andas a function of bird reflectivity (figure 4b, non-birdechoes excluded). Bird densities as determined by thebird radar are indicated by colours. The large majorityof non-zero bird densities are observed for timeheightlayers with sr . 2 m s

21, which confirms that a radialvelocity standard deviation sr 2 m s21 successfullydiscards non-bird echoes. In figure 4a, a large numberof points cluster around sr 1 m s21 at high raw reflec-tivity values, which can be attributed to precipitationevents. A considerable number of timeheight layerswith sr . 2 m s

21 have obtained a high raw reflectivityfrom precipitation contaminations. The effect of remov-ing these non-bird echoes on the reflectivity is shown infigure 4b. Precipitation-contaminated scatter pointsshift horizontally to lower reflectivity values, and highreflectivity gets always associated to high bird densities.Some residual precipitation contaminations remain inthe region sr , 2 m s

21, while timeheight layers forwhich all resolution volumes get assigned to theprecipitation map are fully removed.

We define true bird presence when the bird densityrbird determined by bird radar exceeds 1 bird perkm23 and define a criterion for bird presence in weatherradar data by sr . 2 m s

21. By collocating the birdradar and weather radar measurements, we can thusgroup all timeheight layers into categories for thenumber of correct detections H, missed detections M,false detections F and correct non-bird detections Z.For the combined set of campaigns, we find that theweather radar system has a high probability of detec-tion (POD) to be H/(H M) (POD 97%). Theweather radar algorithm detects birds even at verylow densities and bird migration events are unlikely tobe missed. The false alarm ratio (FAR), F/(H F), ishowever rather high (FAR 42%), but the majorityof these false alarms occur in a regime of very lowbird reflectivity. Precipitation contaminations withreflectivity factors of 230 to 210 dBZe are frequent,but, as will be discussed below, such reflectivity factorscorrespond to very low bird densities below 1 bird perkm23 only. The rate of false detection steeply decreases

http://rsif.royalsocietypublishing.org/

02 Oct 2007 03 Oct 2007 04 Oct 2007 05 Oct 2007 06 Oct 2007

time

0

50

100

150

bird

den

sity

(bi

rds

km2

)

1

2

3

4

heig

ht A

GL

(km

)

0

1

2

3

4(a)

(b)

(c)

0

1

2

3

4

1

2

3

4

1

2

3

4

heig

ht A

GL

(km

)he

ight

AG

L (

km)

1

2

3

5

10

15

25

50

100

200

400

dens

ity (

bird

s km

3)

16

12

8

4

0

4

8

12

refl

ectiv

ity f

acto

r (d

BZ

)

Figure 3. Comparison of the bird density altitude profiles determined by (a) bird radar and (b) weather radar. (c) Height-integrated bird densities are displayed for both weather radar (red) and bird radar (blue). Weather radar reflectivities were con-verted to bird density by assuming a constant weather radar cross section at C-band of sbird 11 cm2 (see legends for figures 5and 6). The period between sunset and sunrise is shaded in grey. Wind barbs in (c) show the wind profile from the HIRLAM numeri-cal weather prediction model. Each half barb represents 10 km h21 and each full barb 20 km h21.

6 Bird migration flight altitudes A. M. Dokter et al.

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

with increasing bird reflectivity. Calculating the PODand FAR statistics for the subset of timeheightlayers with a reflectivity factor greater than 25 dBZe,we find POD 100% and FAR 3% only. Thisregime of bird reflectivity factors is of most interest,as it corresponds to events with moderate to high birddensities greater than 10 birds per km23.

Large radial velocity standard deviations, sr, canalso occur when the actual velocity field is not uniform(e.g. during strong wind shear; Caya & Zawadzki1992; Boccippio 1995) or when the terminal fall velocityis not constant (e.g. in a mixture of snow and rain;Browning & Wexler 1968). Such nonlinear wind fieldsmay produce a high radial velocity standard deviationin the VVP analysis, which would falsely indicate anevent of bird migration. Fortunately, nonlinear windfields are usually associated with strong convection orfrontal passage, which is often accompanied by precipi-tation. Precipitating areas will be removed by theprecipitation masking algorithm, based on a high reflec-tivity factor (greater than 15 dBZe) or a low value ofscell. Since scell is a locally calculated quantity overthe nearest neighbours of each resolution volume, its

J. R. Soc. Interface

value increases only owing to velocity variations overscales of up to a few kilometres and not by large-scalenon-uniformities. Our bird radar measurements verifythat most daytime weather radar echoes in clear airwere caused by other scatterers than birds, whichwere correctly removed by the weather radar algorithmbased on a low value of sr. This confirms the suggestionby Caya & Zawadzki (1992) that in clear air strongdepartures of the wind field from linearity are rare, atleast during our verification period.

The high spatial variability in radial velocity of birdscompared with wind-borne scatterers like insects andhydrometeors can be explained from the relativelysparse distribution of birds in airspace. When usingclose range data only (less than 25 km) up to bird den-sities of a few hundred birds per km23, a radarresolution volume contains only one up to a few individ-ual birds (assuming an even spatial distribution). Theradar resolution is therefore sufficient to resolve partof the speed and directional variation between individ-ual birds. In contrast, insect aerial densities tend to bemuch higher than bird aerial densities (Chapman et al.2003). Each radar sample volume is thus more

http://rsif.royalsocietypublishing.org/

0

1

2

3

4

5

6

r (

m s

1)

1 10 102 103 104

raw reflectivity (cm2 km3)

30

(a) (b)

20 10 0 10 20 30

raw reflectivity factor (dBZe)

r = 2 m s1

1 10 102 103 104

20 10 0 10 20 30

1

235

101525

50

100

200

400

bird

den

sity

, bi

rd (

km3

)

bird reflectivity, (cm2 km3)105

bird reflectivity factor (dBZe)

1050

Figure 4. Radial velocity standard deviation sr as a function of raw reflectivity (a, reflectivity including non-bird echoes) and as afunction of bird reflectivity (b, reflectivity cleaned from non-bird echoes). Each scatter point refers to a timeheight layer, i.e. aspecific height layer measured at a specific time. To limit the number of scatter points, only data for the Wideumont campaignare shown (22 September21 October 2007, 14 742 detected non-empty timeheight layers). In colour, the bird density is indi-cated as measured simultaneously by the bird radar. The large majority of non-zero bird densities are observed for timeheightlayers with sr . 2 m s

21.

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4% timeheight layers

100

1000

ref

lect

ivity

, (c

m2

km3

)

10

5

0

5

flec

tivity

fac

tor

(dB

Z)

Bird migration flight altitudes A. M. Dokter et al. 7

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

homogeneously filled and individual speed and direc-tional variations of insects are averaged out. To somedegree this smoothing effect is also observed for birds.As seen in figure 4, the radial velocity standard devi-ation sr slightly decreases when the bird densityincreases. A second cause of the high radial velocity var-iance observed for birds is their relatively fast activeflight (1025 m s21; Alerstam et al. 2007), whichallows for more variation in speed and directionwithin individuals than for insects, of which theairspeed is low (Reynolds et al. 2010).

10bird

20

15 re

1 10 100bird radar,

bird (km3)

=17

cm2

= 5

cm2

Figure 5. Correlation weather radar reflectivity and bird radardensity in Wideumont, Belgium, corresponding to nightly(18.0006.00 UTC) bird density estimates for 14 742 timeheight layers recorded at 15 min time interval and 0.2 km heightinterval for the continuous period of 22 September21 October2007. We find sbird 11+6 cm2 and a correlation coefficientR2 0.73 (as calculated from a set of statistically independentbootstrap samples). Solid black line; best fit: h 11rbird

4.1.2. Quantification of bird density. In agreement withequation (3.1), we find a strong linear correlationbetween reflectivity h (measured by weather radar)and bird density rbird (measured by bird radar), as illus-trated in figure 5. An effective cross section for weatherradar at C-band can be calculated as sbird h/rbird(see equation (3.1)). We find a median sbird of 11+ 6cm2, with seasonal trends shown in figure 6. Eachdata-point refers to a nightly average of h/rbird, omit-ting timeheight layers with a low bird reflectivity(less than 25 dBZe). From mid-September to mid-October (figure 6a), the average daily cross section issignificantly increasing (F22 13.7, p , 0.002) from7 to 15 cm2 by 0.3+ 0.1 cm2 d21. A reverse temporaltrend is observed in spring when the cross sectionsignificantly decreases (F41 4.3, p , 0.05) from16 to 10 cm2 by 0.1+ 0.04 cm2 d21 in the periodmid-March to early May (figure 6b).

Wing beat data from the bird radar indicate thatnocturnal bird migration during all field campaignswas strongly dominated by passerine migration. Seaso-nal changes in sbird must thus be explained fromchanges in the average body size of the migrating

J. R. Soc. Interface

passerines. We can exclude insect scattering as a contri-bution to the observed cross-section changes, as thenumber of identified insect echoes by the bird radardecreased in autumn and increased over the course ofspring, which are in fact the reverse trends that wouldexplain the observed cross-section variations (back-ground scattering by insects contributes to themeasured reflectivity h by weather radar, but not torbird determined by bird radar, leading to an effectivelylarger sbird). All large passerines (greater than 50 g;

http://rsif.royalsocietypublishing.org/

01 Oct 15 Oct0

10

20

30(a) (b)

15 Mar 01 Apr 15 Apr 01 May

bir

d (c

m2

km3

)

Figure 6. Seasonal trend in bird radar cross-section sbird at C-band. During autumn, the cross section increases in time (a, Wide-umont campaign), while in spring, the cross section decreases (b, Trappes campaign). These trends reflect the increasing/decreasing proportion of larger bird species (mainly Turdus thrushes) during the migratory season.

8 Bird migration flight altitudes A. M. Dokter et al.

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

Dunning 1993) in western Europe are Turdus thrushes,with the exception of some species that are mostly day-time migrants like the European starling Sturnusvulgaris (Alerstam 1990). Both from visual obser-vations, ringing data and radar observations(Alerstam 1976, 1990; Lensink et al. 2002), it is wellestablished that the migration of Turdus species inwestern Europe increases steeply during October,while their return migration peaks relatively early inMarch. The increasing/decreasing abundance of largepasserines in autumn/spring thus explains the seasonalchanges in weather radar cross section.

For reasons of simplicity, we chose to use a constantbird radar cross section-sbird of 11 cm

2 when convertingweather radar reflectivity into bird density byequation (3.1).

During some nights in early September, larger effec-tive sbird was observed up to 30 cm

2 (data not shown).These cross sections are too large for the predominantlysmall passerines that migrate during this time of theseason (Alerstam 1976). We may speculate that theseevents are related to migratory noctuid moths engagingin southward return migrations (Chapman et al. 2003,2010); however, dedicated measurements on the insectcomposition in the atmosphere would be necessary toconfirm this. Because of the low migration intensitiesin early autumn, even a small insect background canhave a relatively strong impact on the effective weatherradar cross section. By mid-September, insect contami-nation no longer had clear impact on sbird. Duringnocturnal spring migration, insect contamination isminor up to early June. Weather radar-extracted noc-turnal bird densities drop below 5 birds per km23 bymid-May at the end of the migration season. Inspring, insect contaminations thus result in a bias ofat most 5 birds per km23; however, the contaminationis likely less since by mid-May birds (e.g. Swifts) arestill aloft and insect densities are much lower inMarch and April.

Although the aerial mass density of insects can belarger than for birds (Chapman et al. 2003), scatteringby birds is often enhanced relatively because of a largerradar cross section. For insects less than 1 cm, scatter-ing enters the Rayleigh regime, where the radar crosssection steeply decreases with the size of the object,while birds invariably give rise to a strong resonant

J. R. Soc. Interface

(Mie) scattering. It will strongly depend on the typicalinsect size and densities whether insect contaminationcontributes significantly to the total measured reflectivity(Vaughn 1985).

Differences in the representativity of the bird radarreference data and the weather radar data will contri-bute to the spread in the correlation between birdradar and weather radar in figure 5. An important differ-ence between the two datasets is the size of the surveyedvolume. For each timeheight layer, the weather radarscans a 4 102 km3 volume, while for the same timeheight layer the fixed pencil beam of the bird radarsurveys only a 0.010.1 km3 volume, depending on thealtitude (see appendix A for the exact scanningstrategies). Only in the case of ideal intense broadfront migration (Bruderer 1997b) will the correlationbetween the two radars be unaffected by the discrepancyin surveyed volume.

4.2. Migration timing and altitude use

The effect of the environmental wind on flight altitudesbecomes evident by comparing the bird density profilewith the wind profiles calculated by the HIRLAM numericalweather prediction model (Unden et al. 2002) (figure 3).In line with previous studies (Bruderer & Liechti 1995;Liechti 2006), we observe that birds adjust their flightaltitude to make optimal use of tail winds along thepredominant migratory direction (in this case towardssouthwest). For example, the HIRLAM wind profileshows that on the night of 34 October wind conditionsat low altitude were more favourable than at highaltitude, where migrants would have encounteredstrong south-westerly head and side winds. On thisoccasion, migration did not extend above 1 km. Onthe other hand, on the night of 56 October, tailwinds were present above 1 km and a large fraction ofmigration took place at high altitude.

Although decisions of birds to start migrating or notare primarily based on local weather conditions (Erniet al. 2002), we find that migration patterns observedat single sites can strongly depend on weather con-ditions elsewhere. This applies particularly tonorthern temperate climate, where frequent passage ofhigh and low pressure systems causes a large spatialvariability in weather. At individual sites, timing and

http://rsif.royalsocietypublishing.org/

Bird migration flight altitudes A. M. Dokter et al. 9

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

altitude profile of bird migration was observed to beinfluenced by weather conditions at locations furtherup the migratory flyway. We will restrict our analysisto a spring migration event (1920 April 2008),which illustrates the non-local character of birdmigration. The general mechanism of flight altitudeselection and the adaptive response of flying birds tochanging meteorological conditions are beyond thescope of this manuscript.

We used four weather radars marked by red bulletson the map of figure 1. The OPERA reflectivity compo-site is overlaid to give an impression of the synopticweather situation. Active fronts related to a depressionabove the Bay of Biscay caused precipitation andeasterly winds above southern France. A weak occlusionfront at the DutchBelgian border slowly movednorthward and diminished in activity in the course ofthe night.

Figure 7a shows the retrieved bird density profiles forthe four sites ordered from north (top) to south(bottom). Arrows indicating flight directions are over-laid with the bird densities, while barbs in the pinkboxed insets show the HIRLAM wind profile at 00.00UTC. Large differences are visible between the sites,both in total density, timing and altitudinal profile ofthe migration.

Migration in Trappes is characterized by strongdeparture and ascent to high altitudes above 2 km,where birds experience favourable tail winds along thepredominant migratory direction (according to ourbird radar-tracking measurements during nocturnalmigration towards 418 clockwise from north) as revealedby the wind profile. Low-altitude migration is avoidedbecause of unfavourable easterly low-level winds.The migration intensity quickly drops after 00.00 UTCand the arrival of birds from southern latitudes is lim-ited. The drop in bird density is related to the weatherconditions south of Trappes, which, due to rain and east-erly winds, were unsuitable for migration.

Migration in Wideumont (280 km north east ofTrappes) starts out with strong departure at lower alti-tudes. Below 2 km, altitude tail winds are morefavourable than above 2 km, where initially strong wes-terly winds prevail (wind data not shown). After 22.00UTC, a remarkable double-layered bird density heightprofile is observed, with one band centred around800 m AGL and a second band centred around1900 m AGL. Since we started observing bird migrationby weather radar in autumn 2007, such double-layeredaltitude profiles have been observed regularly duringspring migration. The top altitude flight band appearsafter 4 h of flight time, which corresponds with ameasured bird ground speed of 70 km h21 to birdshaving travelled over a distance of about 280 km.These birds must therefore have departed in the vicinityof Trappes in northern France, where birds chose aflight altitude of 2500 m. Note that Wideumont islocated 585 m mean sea level (MSL), therefore the1900 m altitude band closely matches the cruising alti-tude after departure near Trappes. The double-layereddensity profile is not explained by the local wind profilein Wideumont. Rather, it is consistent with themigrants maintaining a constant flight altitude (above

J. R. Soc. Interface

sea level) once they had completed the ascent phaseof their migratory flight.

In De Bilt north of Wideumont, no birds depart inthe early night. This is explained by the weak occlusionfront slightly south of de Bilt, which blocks mostmigration. With this front weakening in activity,migration conditions become more favourable over thecourse of the night, and after 00.00 UTC, we do observethe passage and arrival of migrating birds. Nomigration is observed on the most northerly locatedweather radar in Den Helder.

Consistently lower bird densities are recorded in DeBilt and even more so in Den Helder compared withthe sites in Trappes and Wideumont. We calculatedmigration traffic rates (Schmaljohann et al. 2008)defined as the number of migrating birds per hour cross-ing a 1 km broad vertical plane perpendicular to themigration direction at flight altitudes between 0.2 and6 km. For nocturnal migration (between 18.00 and05.00 UTC), seasonally averaged traffic rates are listedin table 1 during an autumn period (22 September21 October 2007) and a spring period (15 March15 May 2008). Ordering the radars according to theirorthogonally projected distance from a line along themigration direction (towards 2218 in autumn and 418in spring as determined by the bird radar), we observea northwest to southeast increase in the number ofmigrating birds. This increase reveals some of thespatial extent of the dominant Scandinavian migrationflyway. We may expect many Scandinavian breedingbirds to fly across Denmark and the southern tip ofSweden. Following their endogenous migration direc-tion, birds will concentrate east of The Netherlandstowards the centre of the flyway in Germany, easternBelgium and France. Once data from the full OPERAweather radar network become available, it will be poss-ible to map for the first time the extent and dimensionsof entire migratory flyways over Europe.

5. CONCLUSIONS AND OUTLOOK

We have developed an automated method for birddetection and bird density quantification by weatherradar, extracting bird density, speed and direction asa function of altitude. The algorithm can run in nearreal-time with a time and altitude resolution of 5 minand 200 m, respectively, and was applied to four differ-ent Doppler weather radars in The Netherlands,Belgium and France. Generated altitude profiles arebased on data up to 25 km range and thus provide alocal characterization of the migration profile. Theextracted weather radar bird densities have been vali-dated with data from a high-accuracy dedicated birdradar, which was stationed in the measurementvolume of different weather radars during the peakmigration season of autumn 2007 and spring 2008. Wefind that Doppler weather radar is highly successful indetecting migrating birds and quantifying bird densitiesas a function of altitude. The probability of detection is(POD) is very high (99%), the false alarm ratio (FAR)is low (3%, provided that bird densities less than 10birds km23 are discarded) and weather radar reflectivity

http://rsif.royalsocietypublishing.org/

19-Apr-08 18:00 20-Apr-08 00:00 20-Apr-08 06:000

50

100

150

19-Apr-08 18:00 20-Apr-08 00:00 20-Apr-08 06:000

50

100

150

0

1

2

3

4

0

1

2

3

4Den Helder

19 Apr 2008 18.00 20 Apr 2008 00.00 20 Apr 2008 06.000

50

100

150

0

1

2

3

4

0

1

2

3

4De Bilt

0

1

2

3

4

0

1

2

3

4Wideumont

18.00 20.00 22.00 00.00 02.00 04.00 06.00 08.00 10.000

1

2

3

4

0

1

2

3

4Trappes

1 2 3 5 10 15 25 50 100 200 400density (birds km3)

16

(a)

(b)

12 8 4 0 4 8

reflectivity factor (dBZ)

Den Helder

De Bilt

Wideumont

Trappes

heig

ht A

GL

(km

)

bird

den

sity

(b

irds

km

2)

time (UTC)

Figure 7. Bird densities as a function of time and altitude at different weather radar sites (figure 1) for the night of 19 April 2008;(b) shows the height-integrated bird densities over 0.24 km AGL. The period between sunset and sunrise is shaded in grey andcivil twilight is shaded in light grey. The pink boxed inset on the right-hand side of each panel shows the HIRLAM wind profileat 00.00 UTC. Bird flight speed and directions are indicated as barbs overlaid on the altitude profile. Each half barb represents10 km h21 and each full barb 20 km h21. Around 00.00 UTC, a double-layered bird density profile is observed in Wideumont.We attribute the top band to birds that departed in the vicinity of Trappes, while the lower band results from birds departingmore locally.

10 Bird migration flight altitudes A. M. Dokter et al.

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

can be quantitatively correlated to the bird densitiesdetermined by the bird radar.

By applying our bird quantification method to a net-work of weather radars, we observed how mesoscaleweather structured the intensity and timing of birdmigration. In particular, the altitude profiles at

J. R. Soc. Interface

individual sites were shown to be affected by theweather conditions at other locations. While the localwind field is an important variable for understandinglocal bird densities aloft, weather conditions in thevicinity of take-off sites can also strongly determinethe observed flight altitude profiles.

http://rsif.royalsocietypublishing.org/

Table 1. Average nocturnal migration traffic rates (in birdskm21 h21) during an autumn period (22 September21October 2007) and a spring period (15 March15 May 2008)for the four radar sites shown in figure 1. Autumn data forTrappes were unavailable. Average bird ground speeds usedin the calculation were obtained from the VVP radialvelocity analysis. Both in autumn and spring, a northwest tosoutheast increase in the number of migrating birds isobserved.

migration traffic rate (birds km21 h21)

season Den Helder De Bilt Trappes Wideumont

autumn 2007 243 504 857spring 2008 85 170 515 562

Bird migration flight altitudes A. M. Dokter et al. 11

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

The current study shows that automated birdmigration monitoring by operational weather radar net-works is feasible. The developed methods for birddetection and quantification can be easily extended tofull operational weather radar networks. With thedevelopment of the OPERA data centre for radar datawithin the coming 2 years (Holleman et al. 2008a),the establishment of a continent-wide bird migrationsensor network in Europe is within reach. Besidesbetter scientific understanding of continental birdmigration, such a network can enable important appli-cations both in aviation flight safety and environmentalimpact assessments for land use planning and develop-ment. Large-scale continuous monitoring by radarnetworks may improve our understanding of avian-borne disease spread, by revealing in more detail thetemporal and spatial dynamics of migratory flyways,though we know of no examples of the use of radar inavian epidemiology so far (Peterson & Williams 2008).

Currently, polarimetric weather radar is rapidlybecoming the new operational standard. Dual-polariz-ation techniques will likely contribute to futureimprovements of the current bird migration quantifi-cation algorithm. We expect that automated birdmigration quantification can also be implemented forweather radars operating at different radar wavelengthslike S-band and X-band; however additional researchand validation is required, because of the very differentrelative cross section of hydrometeors, insects and birdsat these wavelengths. Weather radars are capable of pro-viding spatial information on bird migration by usingdata from their full surveillance area (Gauthreaux &Belser 2003; Diehl & Larkin 2005; Buler & Diehl 2009).A challenging task will be the development of automatedmethods to identify and quantify these spatial migrationpatterns at regional scales. Dedicated methods to identify(flocks of) large birds will be of interest to aviation, asthese birds pose the highest collision risk.

For early-warning systems of bird migration, oper-ational bird density forecasts are essential, whichcould be made in combination with spatially explicitbird migration models (Erni et al. 2005; Vrugt et al.2007). Such models may account for the inherentlynon-local character of bird migration and deal withthe sparseness of radar observations, but will depend

J. R. Soc. Interface

on data assimilation of areal bird density information.In meteorology, the integration of models and obser-vations has become common practice and hasgreatly improved weather prediction. We expect thata similar synergy between bird migration observationsby weather radar and migration models willgreatly improve the description and predictability ofbird movement.

We would like to thank Robert Diehl and four anonymousreviewers for their constructive comments on themanuscript. We would like to thank our field campaigncollaborators: Claudio Koller, Thomas Luiten, ValereMartin, Pirmin Nietlisbach, Marco Thoma and AndreasWagner. Ton Vermaas of the Royal Dutch Airforce atSoesterberg Airfield, Serge Sorbi of the Belgian RoyalAirforce at Saint-Hubert Air Base and Mr Bizeul ofLyonnaise des Eaux provided extensive support stationingthe bird radar. We thank Hans Beekhuis, Geert DeSadelaer, Christophe Ferauge and Jean-Claude Heinrich fortechnical assistance. We also thank Hans van Gasteren andJudy Shamoun-Baranes for useful discussions. This workwas conducted within the framework of the European SpaceAgencys (ESA) Flysafe project.

APPENDIX A. MATERIALS ANDMETHODS

A.1. Weather radar methods

The weather radars used in this study were locatedin Den Helder, The Netherlands (52.954 N/4.791 E,51 m MSL), De Bilt, The Netherlands (52.103 N/5.179 E, 44 m MSL), in Wideumont, Belgium(49.915 N/5.505 E, 592 m MSL), and in Trappes,France (48.775 N/2.009 E, 191 m MSL). Typical C-band weather radar technical parameters are listed inHolleman et al. (2008b). Range/azimuth resolutionequalled 1 km/18, 1 km/18, 250 m/18 and 240 m/0.58for the four radars, respectively. All available Dopplerscans were used in the analysis, i.e. depending on theradar from 8 to 13 scans distributed between 0.4 and258 elevation.

For 200 m height intervals up to 6 km AGL, weapproximate the local velocity field by a spatially con-stant model. The components of the local velocityfield in the x-, y- and z- directions are approximatedby constants u0, v0 and w0. The radial velocity Vr isthen given by

Vru;f sinf cos u u0 cosf cos u v0 sin uw0; A 1

with u, f the elevation and azimuthal angle, u0 and v0the Cartesian ground speed components and w0 the ver-tical speed (Browning & Wexler 1968; Waldteufel &Corbin 1979). After a first least-squares model fit toequation (A 1), points with a deviation from themodel of over 10 m s21 are removed as outliers resultingfrom dealiasing errors (Holleman 2005). Final velocityparameters are determined by a second model fit.From the fit residuals for each resolution volume i ofthe radial velocity data to equation (A 1), we determine

http://rsif.royalsocietypublishing.org/

12 Bird migration flight altitudes A. M. Dokter et al.

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

the VVP retrieved radial velocity standard deviation srin a least-squares sense:

s2r 1

N MXN

i

Vr;i Vrfi; ui2; A 2

where Vr,i are the observed radial velocities, N is thenumber of data points and M is the number of esti-mated parameters in the radial velocity model (M 3in equation (A 1)). Height-intervals with data gapsalong the azimuth larger than 458 are rejected(Holleman 2005), which requires migration within themeasurement window to be broad-fronted up to a cer-tain degree. Data points N are all resolution volumeswith an above-noise reflectivity (including thoseassigned to the precipitation map), excluding gatesidentified as clutter (see below).

For single-polarization Doppler radar data, contigu-ous cells of reflectivity are detected by a cell-searchingalgorithm (Gonzales & Woods 1992), which definescells as groupings of resolution volumes within anelevation scan for which each resolution volume has areflectivity factor greater than 0 dBZe and at least fivedirectly neighbouring resolution volumes that alsomeet this requirement. We find that a minimumthreshold of 0 dBZe cuts out nearly all precipitation ifan additional fringe of 3 km width is added aroundthe selected cells to remove the low-reflectivity bordersof precipitation areas. Cells within elevation scans areanalysed by a cell-averaged nearest neighbour radialvelocity standard deviation scell, according to

s2cell kkV 2r lneigh kVr l2neighlcell; A 3

where the brackets k. . .lneigh denote an average over aresolution volume and its eight direct neighbours andk. . .lcell denotes an average over all resolution volumesassigned to a cell.

For dual-polarized Doppler radar, we make com-bined use of dual-polarization and Doppler quantities.Areas consisting of non-bird scatterers are removedbased on a high cross-polar correlation coefficientrHV . 0.9 or a high differential reflectivity, ZDR . 3.0.Height layers with low radial velocity standarddeviations sr , 2 m s

21 are rejected like in single polar-ization Doppler radar, which discards insect echoes forwhich dual-polarization moments often overlap withthose of birds.

To exclude spurious echoes owing to ground clutter,static clutter maps were generated for each weatherradar by performing a reflectivity average over severalclear-air days outside the bird migration season foreach resolution volume. All resolution volumes with areflectivity average above 210 dBZe during these daysare rejected permanently. A dynamic clutter map wasimplemented that excludes resolution volumes with aDoppler velocity in the interval [21,1] m s21, which fil-ters out most echoes related to anomalous beampropagation (Doviak & Zrnic 1993).

A.2. Bird radar methods

The bird radar has been stationed within the measure-ment volume weather radars in De Bilt, Wideumont

J. R. Soc. Interface

and in Trappes. Hourly migration traffic rates weredetermined at the airfield of Soesterberg (52.13 N/5.28 E, 10 m MSL), the airfield of St Hubert(50.03 N/5.44 E, 577 m MSL) and at Flins sur Seine(48.58 N/1.52 E, 59 m MSL) at 6, 12 and 24 kmdistance from the respective weather radar sites.

Fixed beam measurements (Schmaljohann et al.2008) were carried out at three elevation angles (5.68,22.58, 798), which allowed a good coverage of all flightaltitudes up to 6 km. The beam was directed towardsWNW (2938), thus perpendicular to the mainmigratory direction. During night-time, the threeelevations were monitored every half hour between17.00 and 05.00 UTC, and for the rest of the dayevery hour. Detection range was restricted to 7.5 kmand recording time for a single elevation was 4 min.Echoes showing wing beat patterns corresponding tobirds were automatically selected by a vector supportmachine (Zaugg et al. 2008), which was trainedbased on a large sample of visually classified targetsby an expert.

Between fixed beam measurements, the radar wasoperated in tracking mode and tracks of single targetswere recorded for at least 20 s to determine flightpaths. During the night, this was performed by an auto-matic search algorithm randomly selecting targets fromall relevant heights. During daytime, tracking was per-formed manually with an operator selecting targets onthe radar screen and an observer watching through atelescope mounted parallel to the radar antenna.Tracked targets were identified by the observer in thebest case to species level.

Bird densities are calculated from the number ofrecorded echoes, the average bird ground speed vectorand the calibrated bird- and aspect-specific surveyedvolume (Schmaljohann et al. 2008).

REFERENCES

Achtemeier, G. L. 1991 The use of insects as tracers for clear-air boundary-layer studies by Doppler radar. J. Atmos.Oceanic Technol. 8, 746765. (doi:10.1175/1520-0426(1991)008,0746:TUOIAT.2.0.CO;2)

Alerstam, T. 1976 Nocturnal migration of thrushes (Turdusspp.) in southern Sweden. Oikos 27, 457475.

Alerstam, T. 1978 Reoriented bird migration in coastal areas:dispersal to suitable resting grounds? Oikos 30, 405408.

Alerstam, T. 1990 Bird migration. Cambridge, UK: Cam-bridge University Press.

Alerstam, T., Rosen, M., Backman, J., Ericson, P. G. P. &Hellgren, O. 2007 Flight speeds among bird species: allo-metric and phylogenetic effects. PLoS Biol. 5, e197.(doi:10.1371/journal.pbio.0050197)

Bachmann, S. & Zrnic, D. 2007 Spectral density of polari-metric variables separating biological scatterers in theVAD display. J. Atmos. Oceanic Technol. 24, 11861198. (doi:10.1175/JTECH2043.1)

Black, J. E. & Donaldson, N. R. 1999 Comments on Displayof bird movements on the WSR-88D: patterns and quanti-fication. Weather Forecast. 14, 10391040. (doi:10.1175/1520-0434(1999)014,1039:CODOBM.2.0.CO;2)

Boccippio, D. J. 1995 A diagnostic analysis of the VVP single-Doppler retrieval technique. J. Atmos. Oceanic Technol.12, 230248. (doi:10.1175/1520-0426(1995)012,0230:ADAOTV.2.0.CO;2)

http://dx.doi.org/doi:10.1175/1520-0426(1991)008%3C0746:TUOIAT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1991)008%3C0746:TUOIAT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1991)008%3C0746:TUOIAT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1991)008%3C0746:TUOIAT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1991)008%3C0746:TUOIAT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1991)008%3C0746:TUOIAT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1991)008%3C0746:TUOIAT%3E2.0.CO;2http://dx.doi.org/doi:10.1371/journal.pbio.0050197http://dx.doi.org/doi:10.1175/JTECH2043.1http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1039:CODOBM%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1039:CODOBM%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1039:CODOBM%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1039:CODOBM%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1039:CODOBM%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1039:CODOBM%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1039:CODOBM%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1995)012%3C0230:ADAOTV%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1995)012%3C0230:ADAOTV%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1995)012%3C0230:ADAOTV%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1995)012%3C0230:ADAOTV%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1995)012%3C0230:ADAOTV%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1995)012%3C0230:ADAOTV%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1995)012%3C0230:ADAOTV%3E2.0.CO;2http://rsif.royalsocietypublishing.org/

Bird migration flight altitudes A. M. Dokter et al. 13

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

Bringi, V. N. & Chandrasekar, V. 2001 Polarimetric Dopplerweather radar. Cambridge, UK: Cambridge University Press.

Browning, K. A. & Wexler, R. 1968 The determination ofkinematic properties of a wind field using Doppler radar.J. Appl. Meteor. 7, 105113.

Bruderer, B. 1996 Vogelzugforschung im bereich der Alpen19801995. Ornithol. Beob. 93, 119130.

Bruderer, B. 1997a The study of bird migration by radar.Part I: the technical basis. Naturwissenschaften 84, 18.(doi:10.1007/s001140050338)

Bruderer, B. 1997b The study of bird migration by radar.Part II: major achievements. Naturwissenschaften 84,4554. (doi:10.1007/s001140050348)

Bruderer, B. & Liechti, F. 1995 Variation in density andheight distribution of nocturnal migration in the south ofIsrael. Isr. J. Zool. 76, 11061118.

Bruderer, B., Steuri, T. & Baumgartner, M. 1995 Short-rangehigh-precision surveillance of nocturnal migration andtracking of single targets. Isr. J. Zool. 41, 207220.

Buler, J. J. & Diehl, R. H. 2009 Quantifying bird densityduring migratory stopover using weather surveillanceradar. IEEE Trans. Geosci. Remote Sens. 47,27412751. (doi:10.1109/TGRS.2009.2014463)

Buurma, L. S. 1995 Long-range surveillance radars asindicators of bird numbers aloft. Isr. J. Zool. 41, 221236.

Caya, D. & Zawadzki, I. 1992 VAD analysis of nonlinear windfields. J. Atmos. Oceanic Technol. 9, 575587. (doi:10.1175/1520-0426(1992)009,0575:VAONWF.2.0.CO;2)

Chapman, J. W., Reynolds, D. R. & Smith, A. D. 2003 Verti-cal-looking radar: A new tool for monitoring high-altitudeinsect migration. Bioscience 53, 503511. (doi:10.1641/0006-3568(2003)053[0503:VRANTF]2.0.CO;2)

Chapman, J. W., Nesbit, R. L., Burgin, L. E., Reynolds, D.R., Smith, A. D., Middleton, D. R. & Hill, J. K. 2010Flight orientation behaviours promote optimal migrationtrajectories in high-flying insects. Science 327, 682685.(doi:10.1126/science.1182990)

Contreras, R. F. & Frasier, S. J. 2008 High-resolution obser-vations of insects in the atmospheric boundary layer.J. Atmos. Oceanic Technol. 25, 21762187. (doi:10.1175/2008JTECHA1059.1)

Diehl, R. H. & Larkin, R. P. 2005 Introduction to theWSR-88D (NEXRAD) for ornithological research. InBird conservation implementation and integration in theAmericas: Proc. 3rd Int. Partners in Flight Conf. 2002,Asilomar, CA, 2024 March 2002, pp. 876888. U.S.Department of Agriculture Forest Service Pacific SouthwestResearch Station.

Diehl, R. H., Larkin, R. P. & Black, J. E. 2003 Radarobservations of bird migration over the great lakes. Auk120, 278290.

Doviak, R. J. & Zrnic, D. S. 1993 Doppler radar and weatherobservations, 2nd edn. New York, NY: Academic Press.

Drake, V. A. & Farrow, R. A. 1988 The influence of atmos-pheric structure and motions on insect migration. Annu.Rev. Entomol. 33, 183210.

Dunning, J. B. 1993 CRC handbook of avian body masses.Boca Raton, FL: CRC Press, Inc.

Eastwood, E. 1967 Radar ornithology. London, UK: Methuen.Erni, B., Liechti, F., Underhill, L. G. & Bruderer, B. 2002

Wind and rain govern the intensity of nocturnal birdmigration in central Europea log-linear regression analy-sis. Ardea 90, 155166.

Erni, B., Liechti, F. & Bruderer, B. 2005 The role of wind inpasserine autumn migration between Europe and Africa.Behav. Ecol. 16, 732740. (doi:10.1093/beheco/ari046)

van Gasteren, H., Holleman, I., Bouten, W., van Loon, E. &Shamoun-Baranes, J. 2008 Extracting bird migration

J. R. Soc. Interface

information from C-band weather radars. Ibis 150,674686. (doi:10.1111/j.1474-919x.2008.00832.x)

Gauthreaux, S. A. & Belser, C. G. 1998 Displays of bird move-ments on the WSR-88D: patterns and quantification.Weather Forecast. 13, 453464. (doi:10.1175/1520-0434(1998)013,0453:DOBMOT.2.0.CO;2)

Gauthreaux, S. A. & Belser, C. G. 1999 Reply to Black andDonaldsons comment on Display of bird movements onWSR-88D: patterns and quantification. WeatherForecast. 14, 10411042. (doi:10.1175/1520-0434(1999)014,1041:R.2.0.CO;2)

Gauthreaux, S. A. & Belser, C. G. 2003 Radar ornithologyand biological conservation. Auk 120, 266277.

Gauthreaux, S. A. & Belser, C. G. 2005 Radar ornithologyand the conservation of migratory birds. Technicalreport, USDA Forest Service.

Gauthreaux, S. A., Belser, C. G. & Blaricom, D. V.2003 Using a network of WSR-88D weather surveillanceradars to define patterns of bird migration at largespatial scales. In Avian Migration, pp. 335346. Germany:Springer.

Gonzales, R. C. & Woods, R. E. 1992 Digital imageprocessing. Reading, MA: Addison-Wesley.

Holleman, I. 2005 Quality control and verification of weatherradar wind profiles. J. Atmos. Oceanic Technol. 22,15411550. (doi:10.1175/JTECH1781.1)

Holleman, I. & Beekhuis, H. 2003 Analysis and correction ofdual-PRF velocity data. J. Atmos. Oceanic Technol. 20,443453. (doi:10.1175/1520-0426(2003)20,443:AACODP.2.0.CO;2)

Holleman, I., Delobbe, L. & Zgonc, A. 2008a Update on theEuropean weather radar network (OPERA). In Proc.5th Eur. Conf. on radar in meteorology and hydrology,Helsinki, Finland, 30 June4 July 2008. Finnish Meteor-ological Institute.

Holleman, I., van Gasteren, H. & Bouten, W. 2008b Qualityassessment of weather radar wind profiles during birdmigration. J. Atmos. Oceanic Technol. 25, 21882198.(doi:10.1175/2008JTECHA1067.1)

Koistinen, J. 2000 Bird migration patterns on weather radars.Phys. Chem Earth B 25, 11851194. (doi:10.1016/S1464-1909(00)00176-3)

Larkin, R. P. & Diehl, R. H. 2002 Spectrum width of flyinganimals on Doppler radar. In Proc. 15th Conf. on Biome-teorology/Aerobiology and 16th Congress ofBiometeorology, Kansas City, MO. Boston: AmericanMeteorological Society.

Lensink, R., van Gasteren, H., Hustings, F., Buurma, L., vanDuin, G., Linnartz, L., Vogelzang, F. & Witkamp, C.2002 Vogeltrek over Nederland 19761993. Haarlem:Schuyt & Co.

Liechti, F. 2006 Birds: blowin by the wind? J. Ornithol. 147,202211.

Limpens, H., Mostert, K. & Bongers, W. 1997 Atlas van deNederlandse vleermuizen. Utrecht, The Netherlands:KNNV Uitgeverij.

Martin, W. J. & Shapiro, A. 2007 Discrimination of bird andinsect radar echoes in clear air using high-resolution radars.J. Atmos. Oceanic Technol. 24, 12151230. (doi:10.1175/JTECH2038.1)

Mueller, E. A. & Larkin, R. P. 1985 Insects observed usingdual-polarization radar. J. Atmos. Oceanic Technol. 2,4954. (doi:10.1175/1520-0426(1985)002,0049:IOUDPR.2.0.CO;2)

Nebuloni, R., Capsoni, C. & Vigorita, V. 2008 Quantifyingbird migration by a high-resolution weather radar. IEEETrans. Geosci. Remote Sens. 46, 18671875. (doi:10.1109/TGRS.2008.916467)

http://dx.doi.org/doi:10.1007/s001140050338http://dx.doi.org/doi:10.1007/s001140050348http://dx.doi.org/doi:10.1109/TGRS.2009.2014463http://dx.doi.org/doi:10.1175/1520-0426(1992)009%3C0575:VAONWF%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1992)009%3C0575:VAONWF%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1992)009%3C0575:VAONWF%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1992)009%3C0575:VAONWF%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1992)009%3C0575:VAONWF%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1992)009%3C0575:VAONWF%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1992)009%3C0575:VAONWF%3E2.0.CO;2http://dx.doi.org/doi:10.1641/0006-3568(2003)053[0503:VRANTF]2.0.CO;2http://dx.doi.org/doi:10.1641/0006-3568(2003)053[0503:VRANTF]2.0.CO;2http://dx.doi.org/doi:10.1126/science.1182990http://dx.doi.org/doi:10.1175/2008JTECHA1059.1http://dx.doi.org/doi:10.1175/2008JTECHA1059.1http://dx.doi.org/doi:10.1093/beheco/ari046http://dx.doi.org/doi:10.1111/j.1474-919x.2008.00832.xhttp://dx.doi.org/doi:10.1175/1520-0434(1998)013%3C0453:DOBMOT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1998)013%3C0453:DOBMOT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1998)013%3C0453:DOBMOT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1998)013%3C0453:DOBMOT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1998)013%3C0453:DOBMOT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1998)013%3C0453:DOBMOT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1998)013%3C0453:DOBMOT%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1041:R%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1041:R%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1041:R%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1041:R%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1041:R%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1041:R%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0434(1999)014%3C1041:R%3E2.0.CO;2http://dx.doi.org/doi:10.1175/JTECH1781.1http://dx.doi.org/doi:10.1175/1520-0426(2003)20%3C443:AACODP%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(2003)20%3C443:AACODP%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(2003)20%3C443:AACODP%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(2003)20%3C443:AACODP%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(2003)20%3C443:AACODP%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(2003)20%3C443:AACODP%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(2003)20%3C443:AACODP%3E2.0.CO;2http://dx.doi.org/doi:10.1175/2008JTECHA1067.1http://dx.doi.org/doi:10.1016/S1464-1909(00)00176-3http://dx.doi.org/doi:10.1016/S1464-1909(00)00176-3http://dx.doi.org/doi:10.1175/JTECH2038.1http://dx.doi.org/doi:10.1175/JTECH2038.1http://dx.doi.org/doi:10.1175/1520-0426(1985)002%3C0049:IOUDPR%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1985)002%3C0049:IOUDPR%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1985)002%3C0049:IOUDPR%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1985)002%3C0049:IOUDPR%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1985)002%3C0049:IOUDPR%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1985)002%3C0049:IOUDPR%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1985)002%3C0049:IOUDPR%3E2.0.CO;2http://dx.doi.org/doi:10.1109/TGRS.2008.916467http://dx.doi.org/doi:10.1109/TGRS.2008.916467http://rsif.royalsocietypublishing.org/

14 Bird migration flight altitudes A. M. Dokter et al.

on June 2, 2010rsif.royalsocietypublishing.orgDownloaded from

Peterson, A. T. & Williams, R. A. J. 2008 Risk mapping ofhighly pathogenic avian influenza distribution andspread. Ecol. Soc. 13, 15.

Reynolds, A. M., Reynolds, D. R., Smith, A. D. & Chapman,J. W. 2010 A single wind-mediated mechanism explainshigh-altitude non-goal oriented headings and layering ofnocturnally migrating insects. Proc. R. Soc. B 277,765772. (doi:10.1098/rspb.2009.1221)

Schmaljohann, H., Liechti, F., Bachler, E., Steuri, T. &Bruderer, B. 2008 Quantification of bird migration byradara detection probability problem. Ibis 150,342355. (doi:10.1111/j.1474-919X.2007.00797.x)

Unden, P. et al. 2002 HIRLAM-5 scientific documentation.Technical report. Swedish Meteorological and HydrologicalInstitute (SMHI).

Vaughn, C. R. 1985 Birds and insects as radar targets. Proc.IEEE 73, 205227.

Vrugt, J. A., van Belle, J. & Bouten, W. 2007 Pareto frontanalysis of flight time and energy use in long-distancebird migration. J. Avian Biol. 38, 432442. (doi:10.1111/j.0908-8857.2007.03909.x)

J. R. Soc. Interface

Waldteufel, P. & Corbin, H. 1979 On the analysisof single-Doppler radar data. J. Appl. Meteor. 18,532542. (doi:10.1175/1520-0450(1979)018,0532:OTAOSD.2.0.CO;2)

Wilson, J. W., Weckwerth, T. M., Vivekanandan, J.,Wakimoto, R. M. & Russell, R. W. 1994 Boundary layerclear-air radar echoes: origin of echoes and accuracyof derived winds. J. Atmos. Oceanic Technol. 11,11841206. (doi:10.1175/1520-0426(1994)011,1184:BLCARE.2.0.CO;2)

Zaugg, S., Saporta, G., van Loon, E., Schmaljohann, H. &Liechti, F. 2008 Automatic identification of bird targetswith radar via patterns produced by wing flapping. J. R.Soc. Interface 5, 10411053. (doi:10.1098/rsif.2007.1349)

Zhang, P., Liu, S. & Xu, Q. 2005 Identifying Doppler velocitycontamination caused by migrating birds, part I: featureextraction and quantification. J. Atmos. Oceanic Technol.22, 11051113. (doi:10.1175/JTECH1757.1)

Zrnic, D. S. & Ryzhkov, A. V. 1998 Observations of insects andbirds with a polarimetric radar. IEEE Trans. Geosci.Remote Sens. 36, 661668. (doi:10.1109/36.662746)

http://dx.doi.org/doi:10.1098/rspb.2009.1221http://dx.doi.org/doi:10.1111/j.1474-919X.2007.00797.xhttp://dx.doi.org/doi:10.1111/j.0908-8857.2007.03909.xhttp://dx.doi.org/doi:10.1111/j.0908-8857.2007.03909.xhttp://dx.doi.org/doi:10.1175/1520-0450(1979)018%3C0532:OTAOSD%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0450(1979)018%3C0532:OTAOSD%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0450(1979)018%3C0532:OTAOSD%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0450(1979)018%3C0532:OTAOSD%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0450(1979)018%3C0532:OTAOSD%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0450(1979)018%3C0532:OTAOSD%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0450(1979)018%3C0532:OTAOSD%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1994)011%3C1184:BLCARE%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1994)011%3C1184:BLCARE%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1994)011%3C1184:BLCARE%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1994)011%3C1184:BLCARE%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1994)011%3C1184:BLCARE%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1994)011%3C1184:BLCARE%3E2.0.CO;2http://dx.doi.org/doi:10.1175/1520-0426(1994)011%3C1184:BLCARE%3E2.0.CO;2http://dx.doi.org/doi:10.1098/rsif.2007.1349http://dx.doi.org/doi:10.1175/JTECH1757.1http://dx.doi.org/doi:10.1109/36.662746http://rsif.royalsocietypublishing.org/

Bird migration flight altitudes studied by a network of operational weather radarsIntroductionBird radar field campaignsBird detection and quantification by weather radarBird detectionRemoval of non-bird echoesQuantification of bird density

Results and discussionValidation weather radarBird detectionQuantification of bird density

Migration timing and altitude use

Conclusions and outlookWe would like to thank Robert Diehl and four anonymous reviewers for their constructive comments on the manuscript. We would like to thank our field campaign collaborators: Claudio Koller, Thomas Luiten, Valre Martin, Pirmin Nietlisbach, Marco Thoma and Andreas Wagner. Ton Vermaas of the Royal Dutch Airforce at Soesterberg Airfield, Serge Sorbi of the Belgian Royal Airforce at Saint-Hubert Air Base and Mr Bizeul of Lyonnaise des Eaux provided extensive support stationing the bird radar. We thank Hans Beekhuis, Geert De Sadelaer, Christophe Ferauge and Jean-Claude Heinrich for technical assistance. We also thank Hans van Gasteren and Judy Shamoun-Baranes for useful discussions. This work was conducted within the framework of the EuropAppendix A. materials and methodsWeather radar methodsBird radar methods

References