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techniques is usually chosen in such a way as to ensure C( ∆x mk,0) ≈ 0.2 - 0.7; e.g., Awe(1964), Vincent (1984), Hocking et al. (1989). The range of temporal separations for fittingEqs. (15) and (16) to experimental data is typically chosen in such a way as to cover CF fromapproximately 0.05, and higher. Tens, or even hundreds of data points including rather largevalues of τ are employed into the fitting. On the contrary, the SF requires as smallseparations as possible to approximate the derivatives. The smaller are τ and ∆xrmk, therr rbetter one can estimate the derivatives ∂S( xak,, t)∂tand ∂S( xak,, t)∂ xak,, and the moreaccurate are the SF-based measurements; see PPa for details. For this reason, only a fewseparations, typically not more that τ = ± δ t,± 2 δ t,and ± 3δt are used in STARS. Therefore,CF and SF-based techniques use the different parts of the functions: those at large and smallseparations, respectively.Utilizing different physical features of the diffraction pattern and using different ranges oftemporal separations, CF and SF-based techniques are differently affected by noise.Theoretically CF are unaffected by white noise (except for the auto CF at zero lag) while arestrongly affected by any noise with a finite temporal scale, especially with a large one such asground clutter. As any differential values, both auto and cross SF are strongly affected by anynoise with a small temporal scale at all lags, in particular by a white noise. On the other hand,SF is not sensitive to noise with a large temporal scale such as ground clutter, or hard targets;this STARS feature was found theoretically in PPa (sec. 4), and then demonstratedexperimentally in Praskovskaya et al. (2003) and Praskovsky et al. (2003c). The feature canbe easily understood from the definition of SF, Eq. (2). A statistical difference between twosignals at separation τ cannot “sense” a process with a temporal scale T cor>> τ becausesuch a process is merely filtered by the increment.As shown in the previous section, the coefficients d , d , d , and 0 1 2dautoin the cross andauto SF (4) and (5) can be obtained from Eqs. (14) - (20) for the cross and auto CF at τ → 0 .However, Eqs. (11) and (28) for the coefficient d 2( ∆x rmk) and its CF-based estimated% ( )2∆x r2mkdiffer by the term with umk. This term describes intensity of the turbulentvelocity along the baseline ∆xrmkand plays a very important role in the STARS method. Itleads to the operational Eq. (13) for estimating intensity of the horizontal turbulent velocitiesand the momentum flux uv . Similar terms at p > 2 would lead to equations for estimating4 4 2 2u , v , uv , and so on. One can see from Eqs. (11) - (13) that measuringcharacteristics of the horizontal turbulent velocities with STARS is possible becauser r2d ( ∆x ) ≠ d ( x ). The term umkappears in the derivation of Eq. (11) from Eq. (7) as2 mk auto a,k22imk ,()imk ,22U t = U + uimk ,. From a physical point of view, the term Uimk,() t reflectsrather obvious fact: the rates of spatial and temporal changes in the diffraction pattern along∆x rmkare proportional to the square of the instantaneous velocity component along thebaseline. This can be obtained from simple dimensional considerations, and it is provenrigorously in PPa for any scalar random field. At the same time, the HAD coefficientsc ( )2∆x r mkand cauto( x r a,k) in the cross and auto CF are identical; Eq. (19). It is noteworthy thatEq. (19) is the major operational equation in the FCA technique; Briggs (1984, p. 176). Onecan see that c 2( ∆x r2mk) does not contain umkwhich is quite natural. Cross CF C( ∆x rmk, τ )r rrreveals a statistical similarity between the signals Ex (ak ,+ ∆ xmk, t+τ ) and E( xak,, t); it tracksthe diffraction pattern in its motion from x r r rak ,to x ak ,+ ∆xmk. It is quite obvious that468
fluctuations umk( t ) along the baseline ∆xrmkr rsignals but only make Ex (ak ,+∆ xmk, t+τ )cannot affect the statistical similarity between themore ``blurring'', that is, only the mean speedUmkcan be detected while tracking motion of the pattern as a whole. To get a formal2explanation of the above statement, one should note that the term with Umk+ umkin Eq.(11) for d 2( ∆x r mk) is simulated by the term c 2 ( )1∆x rmkin Eq. (28). The latter contains onlyrUmk, Eq. (18). One can obtain from simple dimensional considerations that c 1( ∆x mk) ∝Umk+ umkalthough the last term is zero by definition. This is the place where thehorizontal turbulent velocity formally disappears from the coefficient c 2( ∆x r mk) in Eq. (15)for cross CF.Therefore, the conceptual difference between CF and SF-based approaches to analyzingreceived signals for SA radars, namely tracking the diffraction pattern and evaluating therates of changes in the pattern, respectively, lead to significant practical differences. Inparticular, one can potentially estimate the different order moments of all turbulent velocity2components separately with the SF-based SA techniques while only w can be estimatedwith the CF-based techniques. Other characteristics such as the turbulent kinetic energy, eddydissipation rate, and so on can be estimated with CF-based techniques only by assuming theisotropy, the dynamic equilibrium, or a specific functional form of the turbulence spectrum,and/or with other restrictive assumptions; e.g., Briggs (1980), Hocking (1983a, 1989),Hocking et al. (1989), DLH.One can conclude that CF and SF-based SA techniques do not compete but rathercomplement each other. The UCAR-STARS method could become a useful alternative to thetraditional, CF and spectra-based data analysis techniques for SA radars.Acknowledgements. The NCAR is sponsored by the National Science Foundation (NSF). The firstauthor (AP) was sponsored by the NCAR/RAP Director's fund, and the second author (EP) wassponsored by the NSF Grant ATM-0122877.REFERENCES2Awe, O., Effects of errors in correlation on the analysis of the fading of radio wave,J. Atmos. and Terr. Phys., 26, 1257-1271, 1964.Briggs, B. H., Radar observations of atmospheric winds and turbulence: a comparison oftechniques, J. Atmos. and Terr. Phys., 42, 823-833, 1980.Briggs, B. H., The analysis of spaced sensor records by correlation techniques, MAPHandbook, 13, 166-186, 1984.Briggs, B. H., G. J. Phillips, and D. H. Shinn, The analysis of observations on spacedreceivers of the fading radio signals, Proc. Phys. Soc. London, 63, 106-121, 1950.Briggs, B. H., and R. A. Vincent, Spaced-antenna analysis in the frequency domain,Radio Sci., 27, 117-129, 1992.Doviak, R. J., R. J. Lataitis, and C. L. Holloway, Cross correlations and cross spectra forspaced antenna wind profilers, 1, Theoretical analysis, Radio Sci., 31, 157-180, 1996.Fukao, S., and R. D. Palmer, Spatial and frequency domain interferometry using the MUradar: A tutorial and recent developments, J. Geomag. Geoelectr., 43, 645-666, 1991.Hocking, W. K., On the extraction of atmospheric turbulence parameters from radarbackscatter Doppler spectra--I. Theory, J. Atmos. Terr. Phys., 45, 89-102, 1983a.Hocking, W. K., The spaced antenna drift method, MAP Handbook, 9, 171-186, 1983b.Hocking, W. K., Target parameter estimation, MAP Handbook, 30, 228-268, 1989.Hocking, W. K., P. May, and J. Röttger, Interpretation, reliability, and accuracies of469
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MST10Tenth InternationalWORKSHOPOn
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MST 10 Group PictureMay 15 th , 200
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TENTH INTERNATIONAL WORKSHOP ON TEC
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Table of ContentsPREFACE ..........
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I.2.18 FURTHER OBSERVATIONS OF PMSE
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I.3.27 NEW MST RADAR METHODS FOR ME
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I.4.19 STUDY OF A MESOSCALE LAND-TO
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I.5.502 AN ATTEMPT TO CALIBRATE THE
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PrefaceMST10The Tenth International
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and the local organizing committee,
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The operational aspects and recent
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To improve the understanding of dyn
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Improving MST radar resolution by u
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latitudes, arguing that the former
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Report on Session I.3 “Winds, Wav
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Turbulence.The session then moved i
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Report on Session I.4 “Meteorolog
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Multiple Antenna Profiling Radar (M
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Report on Session II “Novel Persp
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synchronized by GPS and connected v
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The highly positive response of the
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10th International Workshop on Tech
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10th International Workshop on Tech
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Session I.1: Radar scattering proce
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⎡7800 ∂q⎤(3)⎢ 15500q⎥M =
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Figure 2 compares profiles of ω B
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Figure 1: Data from the MST radar a
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Ri =shear2ωB22⎛ ∂u⎞ ⎛ ∂v
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Meridional (deg)1050−5−10Echo P
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Subarray Configuration, Capon Metho
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Fig. 2 PMSE plot (SOUSY Svalbard Ra
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VHF radar interferometry had shown
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turbulence. From Figures 1 and 2, i
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SummaryFrom the aspect sensitivity
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a specific range. In this work, syn
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P C(dB)(a) Echo Power60504030200.70
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most occidental area of South Ameri
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Quegan, 1992). It should be useful
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measurements, is shown in figure 1(
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The present observations thus empha
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RECENT OBSERVATIONS OF E REGION FIE
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measured spectra in order to separa
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drifts at E and F regions heights b
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• Are characterized by type II ec
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The main parameters that could be o
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THE ROLE OF UNSTABLE SPORADIC-E LAY
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(σ H / σ P )E y (where σ H and
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STUDY OF A LOW E-REGION QUASI-PERIO
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100 m/syrFigure 4: Geometry of the
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Where the notations carry same mean
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Where dx/dt and dy/dt are the veloc
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non-negativity of the image is prio
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spectrum corresponding to the backs
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The Artigas and Machu-Picchu Statio
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Arguments against the second altern
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Extension of the Kolmogorov spectru
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volumeESRvolumeSSRSSR SSRESR ESRSSR
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individual particles in a statistic
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Anomalous spectraTalkner [4] studie
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To observe the E region FAI echoes,
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matter daytime or nighttime and are
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two sub-arrays, each sub-array made
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4. SummaryHere we describe a radio
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Figure 1. E-region DPS4 spectra, ri
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effects. It is therefore plausible
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1996 and high in 2002). The electri
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electric fields map along the geoma
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SNR condition is always difficult a
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Figure-4 Power spectrum plots of a
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As we show below, Jicamarca offers
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particularly during daytime counter
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where ˆδ nm = (δ l − δ m )
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PMSE is more easily interpreted as
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ResultsTemperature climatology• s
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Simultaneous PMSE and NLC observati
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ResultsSeasonal variationMSE are no
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Figure 2: MSE parameters as functio
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Session I.3: Winds, waves and turbu
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poor. It was shown that poor perfor
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The use of diffraction pattern simi
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F 13 (ξ x ′) = (1 − cos 2ψ N)
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Figure 3: (a) Baseline length depen
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3. ResultsFigure 1 shows the amplit
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variability.Figure 4 shows the aver
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Figure 1. Period-time amplitude wav
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the 12-h and 24-h periodicities in
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winds and variances at altitudes 70
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similar correlation with larger mag
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of the refraction index are not equ
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Fig. 8. Zonal wind (top) and temper
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STUDIES ON ATMOSPHERIC GRAVITY WAVE
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1100-1200 hours. From this plot 6.3
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STUDIES ON WINDS AND MOMENTUM FLUXE
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3.4 Monthly variation of momentum f
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DEEP PENETRATIVE CONVECTION AND GEN
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ly changes direction with time with
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189
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191
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193
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These two last relations are the on
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3.2.1 From v ′2 to ɛ kThe questi
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5.2 Frequency distributionsSeveral
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ReferencesAlisse J.-R. and C. Sidi.
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Röttger J. and C.H. Liu. Partial r
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Specular reflection is negligible f
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−0.5C n2 Distribution (PROUST & S
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the beamwidth, α is the zenith ang
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this latitude in late April). Surfa
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Fig. 1. Median (upper) winds and (l
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Fig.1. Lines of constant radial vel
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which have not yet been considered
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is calculated from the radiosonde t
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Figure 3: Time-altitude cross-secti
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Absorption of cosmic noise is cause
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Incoherent scatter spectracompared
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variations of spectral widths, refr
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Figure 1. Diurnal variation of Turb
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further experimental test of the ST
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waves can be observed more clearly
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winter and a minimum in summer near
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The main recently assumed mechanism
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Figure 2. Statistics of numbers of
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LIDAR OBSERVATIONS OF MIDDLE ATMOSP
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latitudinal dependence of the seaso
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APPLICATION OF THE DUAL-BEAMWIDTH M
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Figure 2: Mean zonal and meridional
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JLKLLK/ILIL6II/G/II/G/LEODFLK/LO/DD
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The monthly diurnal mean of horizon
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Session I.4: Meteorological Phenome
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Integration of the VHF wind profile
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Figure 1 : Map of the Lago Maggiore
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precipitating clouds, the cyclonic
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Eyewall(a)(b)(c)(d)Fig. 3: Radius-h
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as low as 0.55. The Piura 50-MHz ra
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place features in the profile with
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• Period 1 (June 1-10): SCCs exis
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Sumatera occurs by stratiform cloud
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very interesting to note the double
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20(a) Convective Region(b) Transiti
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Typhoon LekimaFig. 2 Typhoon Lekima
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Fig. 5 Passage of typhoon Hayan on
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the 50 cm 2 sensor head, enabling t
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22 June 2000 23:22:23 - 23:24:20 at
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the time-height section of a convec
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Thurai, M., T.Iguchi,T. Kozu, J.D.E
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There are two main mechanisms which
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Horel, J.D. and Cornejo-Garrido, A.
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estimating two independent and redu
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RASS THETA (K) RASS v-component (m/
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Surface winds usually range from 10
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weak (maximum of 5 m s -1 ) and the
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The vertical component from the VTB
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Figure 8 shows the profile of virtu
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2 3D ( , ) ( ) ( ) ˆ ( ) ˆp∆ xm
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presented in Praskovsky et al. (200
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In Fig. 2 a sketch of a mountain le
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In Fig. 8 the lamina is aligned on
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Figure 1: composite day (13 days) o
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virtual sensible heat fluxes ( Wm-2
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3. Characteristics of precipitation
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Fig. 3: Horizontal distribution of
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3. Results and discussionUHF profil
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Figure 4. Monthly variation of slop
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system with two receiving antennae,
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cycle of precipitation. Figure 3(a)
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Histogram of Zonal Velocityh=5.13 K
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5. Summary of results and concludin
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the two nearby major cities, Chenna
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Boundary layer height, km32.521.5(a
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Figure 1:Data from the MST radar at
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spectral processing. In this partic
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3. Results and discussionIn the pre
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4. SummaryAn attempt has been made
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The purpose of this study is to exa
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Figure1. Three-day average of momen
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3. The improved method to estimate
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But in case of period until 1800 UT
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Session I.5: Operational Aspects an
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The profilers of WINDAS weredesigne
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NOAA, 1994 : Wind profiler assessme
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FIRST RESULTS OF THE BOUNDARY LAYER
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Figure 3. Pulse Design Diagram to s
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MOVEABLE UHF/S-BAND PROFILER/DISDRO
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one-minute Z values determined from
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TOWARD A MULTISENSOR GROUND BASED R
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3. Cloud Evolution Case StudyOn the
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DEVELOPMENT OF A DIGITAL RECEIVER F
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Experiment 1 Experiment 2IPP 999Km
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ELECTRONIC DIGITAL BEAMFORMING IMPL
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The main element of the unit (figur
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.ON-LINE ADAPTIVE DC-GROUND-CLUTTER
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esulting in a widening of the bandw
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ON THE RADIATION EFFICIENCY OF COCO
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Table 2: Experiment #1 results, Tra
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A NEW NARROW BEAM MF RADAR AT 3 MHZ
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Off-zenith beams towards N, S, E, W
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95ALWIN VHF radar: signal powerdB80
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ANTENNA BEAM VERIFICATION USING COS
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Figure 2: A 45 MHz reference sky te
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AN ATTEMPT TO CALIBRATE THE UHF STR
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is at 4.7 km, but in the first 4-ga
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QUALITY CONTROL FOR DOPPLER WIND PR
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The resulting moments are subjected
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SOUSY RADAR AT JICAMARCA: SYSTEM DE
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NEControl andcomputer roomCompCoute
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THE EQUATORIAL ATMOSPHERE RADAR:SYS
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Table 1: Specifications of the EAR
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VHF ATMOSPHERIC AND METEOR RADAR IN
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ased upon a geotechnical engineerin
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VORTICAL MOTIONS OBSERVED WITH THE
Page 423 and 424: interpolation from the original gri
Page 425 and 426: A NEW MINIRADAR TO INVESTIGATE URBA
Page 427 and 428: e eliminated to analyze correctly t
Page 429 and 430: Session PWG 1: System Calibrations
Page 431 and 432: Session PWG 2: Data Analysis, Valid
Page 433 and 434: • the time series vectors x(k) of
Page 435 and 436: Fig. 4: reflectivity of the hydrome
Page 437 and 438: Session PWG 3: Accuracies and Requi
Page 439 and 440: Session PWG 4: International Collab
Page 441 and 442: Session II.E: NOVEL PERSPECTIVES AN
Page 443 and 444: 2 Proposed AlgorithmReceived signal
Page 445 and 446: N#4E-1-16E1A1#1#2A-1-1C1#3C-1-19Fig
Page 447 and 448: of conventional DCMP, with which th
Page 449 and 450: applying the directional constraint
Page 451 and 452: while for a beam of finite width, t
Page 453 and 454: In order to make the process more q
Page 455 and 456: WHAT IS TURBULENCE SEEN BY VHF RADA
Page 457 and 458: Enhanced resolution applyingpulse s
Page 459 and 460: Beckmann, Spizichino, 1964Fig. 8 Po
Page 461 and 462: Fig. 11 Plots of temporal variation
Page 463 and 464: Fig. 14 Distributions of phase cent
Page 465 and 466: Hocking, W.K., Radar studies of sma
Page 467 and 468: THE STRUCTURE FUNCTION-BASED APPROA
Page 469 and 470: { Ui( t), Vi( t), Wi( t)} = { Ui ,
Page 471 and 472: 161). Assumption 2H: the instantane
Page 473: Only the second order SF are consid
Page 477 and 478: VHF PARASITIC RADAR INTERFEROMETRYF
Page 479 and 480: • All the costs of transmitter pr
Page 481 and 482: Figure 2: Example of range-azimuth
Page 483 and 484: Target Altitude, km1009080706050403
Page 485 and 486: ReferencesGriffiths, H. D., A. J. G
Page 487 and 488: Participants ListAvery, James P.Uni
Page 489 and 490: Hysell, David L.EAS/Cornell Univers
Page 491 and 492: Silva, Robert R.ATRADAustraliarsilv
Page 493 and 494: AAdachi, A. .......................
Page 495: TTabary, P. .......................
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