i'; ).,....t) C\ . (> '~) ~ -41tce r h1su

Indian J.6tiTIiJ'pf Radio&.~ PhysicsVol.zi, Dec~ber1994,pp. 36i'"374<"''"'

... (4)

... (2)

... (3)

2

'f/ = ~ K4 ~n("K)

where,!t is the radar frequency.Water vapour pressure is the most important factor

contributing to signal returns in tht lower tropos­phere, because .of the high humidity. In the uppertroposphere, pressure is the primary contributingfactor. Echoes from the mesosphere are normallyobserved during daylight hours and arise fromthe neutral turbulence fluctuations producing re­fractive index fluctuations that are enhanced byfree electrons at these heights. The most difficult

... (1)

where, PI' Ae, r and /).r are transmitted power,effective area of antenna, range and range resolu­tion, respectively. The volume reflectivity, r}, is ex­pressed in terms of the 3-dimensional wave num-

,T!}( c:4 J?.2.,~.,~.l?k?!

Processing and early observations of Indian MST radar d~

J '. '>" in its ST mode of operation D- ' ..I . l { " ",o~,' (r/rdl'~~:~:~" ._", .. ~,r()W~~ 5e(..f..la~~;:~!t .....

~"- .cR•••••••\R"""~S M'!"""·'·-.fi'" -;:; --~ace Physics Laboratory, Vikram Sarabhai Space Centre, Trivandrum 695~022--Received,t8'Noverhber 199J; revised received 21'A1'JrlH994taccep.te(t2.2.Sg>.t~

---.........Jr~'ftwares developed in Space Physics Laboratory, VSSC, Trivandrum, for processing theMST:, dat, "e de<cribed. The roftw"e w"' evaluated uMg ,;mutated data typical of the datafrom the Indian MST radar. Using this software, the data acquired from the Indian MST radar in itsST mode of operation during 15-16 Feb. 1992 were processed and the results are presented. The sixsets of data, each of duration 45-70 min, obtained at 4 hourly intervals were used to study the stablelayers near the tropopause and at 7-8 kIn height. The early results on the tidal wind components in

the lower troposphere ( < 8 km) are also presente:) ~4Q Cf/1 Introduction ber Kof turbulent. fluctuations as4

The middle atmosphere (10-100 km) can becalled the 'sphere of ignorance', because very fewobservation techniques can access thiS region. DI­rect measurements of the atmosphere using bal-

loons, aircraft and rockets are usually not contin- h K- K- K- (,., d ." t d f . 'd t... were, = i - sIan S S an or mCI enuous m time. The ground-based observations us- d d d" ) 1:' th f b k'" .. an scattere ra 10 waves . rOr e case 0 ac-mg radIO and optical remote sensmg techmques . K- K- d' K . d' t f.. scatter, = - ,an IS expresse m erms 0are evolved to provIde measurements WIth rela- 1 ' gili f d' Atively good temporal and spatial resolutions 1. wave en 0 ra 10 waves, , as

TheMST (¥e.sosphere Stratos~he~ and ~ .' (2.7l) 4.7l~osphere l radars are ultra sensitive VHF (30- K =IK I =2 - =-00 MHz) and UHF (300-3000 MHz) radars to ;.,;.,

study the weak backscattering arising from refrac- Th d' . b k d ainl b th; ... e ra 10 wave IS ac scattere m y y etIve mdex fluctuatIOns m the neutral atmosphere '.....

d I . h Th d' I . d I .. A 12 Founer component of Irregulantles of atmos-an ower IOnosp ere. e ra la wm ve oClty IS .. , .d f th D I f h'f phenc refractive mdex, due mamly to turbulence.

measure rom e mean opp er requency Sit·· ' .. d f th tIn h b. The refractIve m ex /J 0 e a osp ere can eof returned signal of the MST radar. The turbul- ~r,.. b d d d f h 'd h expressed as a function of pressure P (mbat) and

ence mtenslty can e e uce rom t e WI t or I d' N ( -3)2 . e ectron enslty m aspower of the echo spectrum . We consider the e

radar echo power, P" from an isotropic scattering 3 73 x 10-1 e 7.76 x 10-5 P 40.3N

volume. The scattering cross-section per unit scat- It = 1 +' 2 . + T- l etering volume is called 'reflectivity' ('f/) and has a T t

dimension of m -1 (Le. m21m3). Because the echopower is proportional to the scattering volume,the radar equation for a uniformly distributed tar­get is given by3

362 INDIAN J RADIO & SPACE PHYS, DECEMBER 1994

Another technique used is the least square fittingof the observed spectrum with a Gaussian so asto minimize the squared sum of the residuals

3 Estimation of spectral parametersFor the signal returns of MST radars, we can

assume its power spectrum to be Gaussian, de­scribed as

where, Pro fd and 0 are the echo power, meanDoppler shift and the spectral width, respectivelyOne of the techniques to determine Pro fd and 0is the moments method. The zeroth, first and sec­ond moments of the spectral density correspondto Pro fd and 0, respectively. For an observedspectrum 5'(1;) obtained at M discrete frequencies/;

by changing the parameters P., fd and o. It wasfound that this fitting technique used for the MUradar observations gave better results in compar­ison with other methods, particularly, in the re­gion where the SNR was poors. The spectral par­ameters may also be estimated directly from thecomplex auto-correlation function of the timeseries of the radar returns 1• For good SNR of thedata all the methods give nearly the same values,

... (9)

... (6)

... (8)

... (5)

... (7)

AI

£2 = I [S'(j;) - S(j;,P,,fd' 0)]2i~l

5(f) = ; exp[ - (f- fd)'-]v 2n 0 202

;=1

AI

P, = I 5'(/;)

tudc of the statistical fluctuation in the spectracan be reduced by averaging successive powerspectra, which is called 'incoherent integration'When we execute N incoherent integrations, thestandard deviation of the power spectrum becomesequal to S(f)/NII2, where S(f) is the power spec­tral density at the Doppler frequency, f Thus, forexample, when we do not apply incoherent inte­gration, the statistical fluctuations of the powerspectral estimates are 100%.

1 AI

fd = P, i~/;5'(/;)and

AI

2 I,\, 2 ,

o = p,/:; (/;- fd) 5 (fJ

region to be observed by the MST radar tech­nique is near the stratopause because of the com­bined effect of exponentially decreasing atmos­pheric density and lack of sufficient free electrons 5 •

The signal returns in MST radar are very weakand often buried in noise. In this paper, we out­line the basic data processing techniques usedwith MST radar observations, and the softwaredeveloped and evaluated at the Space Physics La­boratory, VSSC, Trivandrum, for use with theradar data acquired from the National MST radarFadlity (NMRF), Tirupati. The evaluation of thesoftware using simulated data that resemble theactual data from a typical MST radar is presentedin brief. Some early observations from the MSTradar data are also presented in this paper.

2 Data processingI In the middle atmosphere the correlation timesof the VHF and UHF returns from turbulence

were found"'!> to be in the range of 0.1-10 s. Thisis very large compared to the inter-pulse period(IPP) of the radar which is typically less than amillisec. While processing MST radar data, twointegration techniques, namely, 'coherent' and 'in­coherent', are adopted in order to increase the sig­nal-to-noise ratio (SNR) and/or to reduce the sta­tistical fluctuations in the echo power spectraSince the coherence times of the radar returns are

much larger than IPP, many samples of the quad­rature detectors outputs of the radar signal areaccumulated in order to reduce the number of da­ta samples. This procedure is called 'coherent in­tegration', because the integration is done whenthe phase is the same. By executing N coherentintegrations, signal and noise powers are found toincrease by N2 and N, respectively and, so weimprove the SNR by N time~

In order to obtain the Doppler shift producedby the motion of the scattering volume, we needto compute the spectral information of the re­turned signal. The most popular means for com­puting power spectrum of the radar returns is thatby using the Fast Fourier Transform (FFT) algo­rithm. The power spectrum shows a statisticalfluctuation due to the random nature of the tur­

bulence. The receiver output is a time series ofdata of a random process with Gaussian distribu­tion. Because FFT is a linear transformation and

the power 'spectral density (PSD) is the squaredsum of both the real and imaginary parts of thespectral components, the PSD shows a statisticalfluctuation with a x2 distribution7• Thus tl1estandard deviation of the power spectrum is pro­portional to the spectral density itself. The ampli-

I i I, HI I " 1'1' n'I"I'!'II,' '1 'I

REDDI & NAIR: PROCESSING OF INDIAN MST RADAR DATA IN ITS ST MODE OPERATION 363

where, Pl is the signal power and PN is the noisepower in each frequency interval of the spectrumand M is the number of discrete frequencies atwhich the spectrum is computed. The noise pow­er PN is computed as the average noise power perfrequency bin using the spectra for the 50th-55thrange bins where the signals can be reasonably takento be very insignificant.

The Indian MST radar main beams can beoriented at 20° zenith angle along north, east,south and west directions and towards zenith in

two polarizations (X and Y) separately. Pairs ofnon-coplanar beams can be used to obtain thehorizontal wind velocity vector. The radial veloc­ity UR obtained from the oblique beam in theE-W plane is given by

UR = U sin 0 + Wcos 0 . " (11)

but for signals buried in noise, the auto-correla­tion function method was found to be inaccurate.For estimating !ct and a2, the fitting method isused in almost all the radar observations. It is be­cause the least square fitting method is expectedto show better results compared to other tech­niques, particularly, in the region of low SNR. Inthe present study, Eqs (6)-(8) have been used toe~timate Pp fd and a2. The clutter and/or DC pe­destals on the time series were removed by sub­tracting the average value of the time series fromthe individual values. This invariably resulted inzero amplitude for the zero frequency. Then theamplitude for zero frequency was assigned by theaverage value of the amplitudes of the frequenciesimmediately before and after the zero frequency.

The SNR of the spectrum is defined as thepower ratio between the signal and the noise as

... (14)

\\'here, W is the radial velocity measured from thezenith beam.

where, U, W and 0 are zonal and vertical windcomponents and the zenith angle of the beam, re­spectively. If we neglect the spatial difference inthe wind fields over the limited area of the radarobservations, then by using the zenith and one ofthe two oblique beams in the E-W plane, the zon­al wind component U can be calculated as

PI

SNR= MPN

U= UR - Wcos 0sin 0

'" (10)

... (12)

The radial velocities URE and URW obtainedfrom the eastward and westward oblique beamscan be written as

URE = U sin 0 + Wcos 0

URW = - U sin 0 + Wcos 0 ... (13)

Again assuming no spatial difference in the windfield, U and W can be calculated as

U = (URE - URW )/(2 sin 0)

W= (URE + URW )/(2 cos 0)

Similarly, from the north and south beams thehorizontal wind velocity component (V) in theN-S plane can be obtained. The values of W mea­sured from the vertical beam, and the pairs of an­tennas in the E-W and N-S planes separately canbe used as a check on the quality of the radar da­ta.

In the above equations, zenith angles are as­sumed to be the same for the east and westbeams and for north and south beams. If the rad­ar beams are not oriented exactly towards thenorth, east, west and south, or, the zenith anglesare not the same and if the exact orientations ofall the beams of the MST radar are preciselyknown, then, the radial velocity measured foreach beam can be expressed in terms of the scalarproduct of the wind velocity vector and the unitvector along the beam direction. The six equ­ations thus obtained, taking the six measured ra­dial velocities into account, can be solved by leastsquare methods to compute the three componentsof the velocity vector.

For the Indian MST radar, unless set other­wise manually, oblique beams are oriented exactlyin the magnetic E-W and N-S planes and the zen­ith angles of these beams in 20°. We also assumethat the zenith beams are oriented exactly towardszenith.

4 Development of software and its evaluationusing simulated dataData were acquired in the ST mode operation

of the Indian MST radar from 0826 hrs 1ST on15 Feb. to 0400 hrs 1ST on 16 Feb. 1992 at 4

hourly intervals. The six data runs during the24 h were of duration 45-70 min each~ The 'ex­periment specification file' (ESF) of the data runsis given in Table 1.

The choice of the 4 Ii s wide transmitter pulsewas to improve the SNR of the data, at the ex­pense of range resolution, compared to a possible1 /.l s wide uncoded transmitter pulse. The nwn-

364 INDIAN J RADIO & SPACE PHYS, DECEMBER 1994

Table I-Specifications of the experiments

Transmitter pulse width 4 f.J, s (uncoded)

Range resolution 600 m

Transmitter PRF 2 kHz

ber of FFT points was limited by the pre-decidedsetting of the system to a maximum of 512 points.As had been explained earlier, the SNR improve­ment by coherent integration is directly propor­tional to the number of integrations, whereas forincoherent integration the improvement is propor­tional to the square root of the number of integr­ations. Long and continuous time series can besplit up into successive data blocks for spectralcomputations, and the coherent integration ofseveral complex spectra of successive data blocksleads to better improvement of SNR and spectralstability. Since FFT is a linear transform, the co­herent integration can also be done in time do­main by averaging data from transmitter pulsesseparated by multiples of N, the number of sam­ples required for spectral computations. If thesuccessive data blocks are not continuous in time,integration in spectral time domain cannot bedone by taking the phase of the spectral compo­nents into account. When the data are not contin­

uous, incoherent spectral averaging is the onlymeans of SNR improvement at the expense oftime resolution and the SNR improvement is onlyproportional to the square root of number of in­tegrations. The spectra computed for long datablocks (512 samples 'and more) would result insplitting of spectral peaks and generate spuriousspectral datails not present in the data, particular­ly, if the SNR of the data is poor. In our software,it was decided to carry out coherent integration inthe spectral domain by averaging the complexspectra from successive intervals of data. Dividingthe 512 samples of the I and Q outputs into 4blocks each of 128 samples, and averaging the

and

n = 1, 2 ..... 60 Number of the range bin. Therange increase or decreasebetween successive rangebins was 0.6 km.

The Doppler frequency was assumed to varysinusoidally with altitude, from - 0.5 Hz to4.5 Hz with a vertical wavelength of 30 km.

For each range bin, 512 I and Q samples Wereobtained as a data block. Each sample was aver­aged for 128 successive t~ansmitter pulses. Thedata block of 512 samples were divided into 4sub-blocks of successive time intervals., The com­plex spectra of each sub-block was computed se­parately and then the four spectra were coherent­ly averaged to obtain the spectra for each rangebin of the data block. For the chosen radar PRFand for coherent averaging of 128 successive

.,,- "

... (18)

... (17)

... (15)

... (16)

t = 1,2 ..... m Transmitter pulse number

A,(n) = 0.01 eKJft-=-t.38551 n)

P( n,t) = 10 7r t[ CQS (2,+ 2.5 cos 2~ n) ]

I(t,n) = As(n) cos [p(n,t)]

Q(t,n) = As(n) sin [p(n,t)]

where,

four spectra in the complex domain it was foundthat it leads to cleaner spectra compared to theindividual spectra. This, however, reduced thespectral resolution, but it was decided that theresolution of the Doppler frequency can be im­proved while computing the spectral moments.

In order to validate the software, simulated datatypical to atmospheric radar data were generatedand the results from the computations were com­pared with the input parameters used for simulat­ing the data.

It is reported 1 that the rate of decrease of MSTradar echo varies from 1.4 dB/km to 2.4 dB/km.

For our simulation we assumed the signal powerto decrease at a rate of 2.0 dB/km. The 12-bitND convertor used in the MST radar shouldpermit the SNR improvement of 66 dB (one bit isused for sign of the sampled voltage) in the data pro­cessing. The Doppler frequency shift for the tropo­spheric signals for the expected horizontal windspeeds, would be well within ± 5Hz. The altitudeand time variations of the I and Q channel samples forthe simulation can be written as

60 (12-252 f.J, s at intervalsof 4 f.J, s)

128

512

I and Q channels

E, W, Zx, Zy, N, S insuccession

Minimum 12 and maximum20

OdB

in 58th range bin

No. of range gates/scan

No. of coherent (1& Q channels)integrations for each range gate

No. of data points for each FIT

Type of data

Beam scans/data set

No. of scans for eachbeam/data set

Receiver attenuation

Calibration pulse

ry--I i I '" I , I' '!" 'I II 1'1 ll""I'11 'I' I~

REDDI & NAIR: PROCESSING OF~NDIAN MST RADAR DATA IN ITS ST MODE OPERATION 365

transmitter pulses, the Nyquist frequency of thespectra and the spectral resolution for each rangebin work out to be 7.8125 Hz and 0.122070313respectively. In Fig. 1 the lower panel shows thespectra normalized to the maximum amplitudesfor each range bin, and the upper panel shows theinput time series for the I channel, which are alsonormalized to the maximum amplitude in eachrange bin. The Q channel would be similar to theI channel, except for a phase shift of 90° for theoscillations.

To simulate the noise typical of the MST rad­ars, pseudo random numbers with peak-to-peakamplitude varying between + 1 and -} wereadded to the 512 I and Q samples obtained aftercoherently averaging 128 consecutive values. Thenoise added to each bin remaining the same, andthe signal power decreasing at a rate of 2 dB/km(1.2 dB per range bin) results in the SNR for the60th range bin were found to be -72 dB with refe­rence to the SNR for the first range bin. The 512 I andQ samples thus corrupted with random noise were

35

35

3025

- 13

20~ -Q)

~15

::l .oJ...•.oJ- 10<

5

16 32Time

, I ,

80 96112128

.064 s) I

I1II

~-----.---

-

8•-4-6 -2 0 2Frequency (Hz )

Fig. i-Simulated MST radar data [Top panel shows the tIme series of I channel and bottompanel the spectra of the time series, both normalized to the respective maximum in each range

bin.]

366 INDIAN J RADIO & SPACE PHYS, DECEMBER 1994

divided into 4 sub-blocks each of 128 samples.The complex spectra of the four sub-blocks wereagain averaged and 512 such spectra, each for 60range bins, were obtained for testing the improve­ment in SNR by coherent averaging of the spect­ra. The series of pseudo random numbers addedto the different blocks of samples are ensured notto be the same by choosing the seed for generat­ing the random numbers sequences from the com­puter time clock. In Figs 1-3 the lower panelsshow the spectra obtained for the 60 range gatesafter coherent integration; and the upper panelsshow the time series of the I channel obtained

from the inverse Fourier transform of the spectrafor each range bin. In Fig. 2, the left two panelsshow the time series (top panel) and the typicalspectrum (botton panel) of a typical data run. Themiddle panel shows the results typical of 64 co­herent integrations and the right panel for 512 in­tegrations. The integration in this simulation is co­herent because the time series of the Doppler sig­nal is the same.

In Fig. 3 the lower panel shows the un normal­ized simulated spectra for the 60 range bins andthe upper panel shows the same spectra contami­nated with noise (as described before) after 512coherent integrations. The vertical scale for rangebins above 25 is enlarged 20 times in order toshow the spectral peaks prominently. It was seenthat the SNR of the spectra shows improvementsdirectly proportional to the number of coherentintegration. This is depicted in Fig. 4. The SNRwas calculated as follows. The integrated power inall the frequency bins was computed for eachrange bin. The average of the integrated powerfor the range bins 55-60 was taken as the averagenoise power PN in each range bin. The integratedpower of the spectra in each mage bin, Pi' is thesum of the signal power P, and PN in the bin. TheSNR for each range bin is given by

... (19)

We have taken that the signal in the range bins55-60 to be below detection level and the averageof the integrated power in these range bins is tak­en as PN•

The altitude variation of SNR (in dB) for co­herent number of integrations 1, 2, 4, 8, 16, 32,64, 128, 256 and 512 are respectively shown asA, B, C, D, E, F, G, H, I and J, in Fig. 4. Figure4' shows that, increasing of the number of integr­ations by a factor of two increased the SNR by

3 dB, which is expected for the case of coherentintegrations carried out ih the present analysis.

Figure 5 shows the difference, !if, between theactual Doppler frequency used in the simulationof the I and Q channels and the Doppler frequen­cy taken as the frequency bin for which the signalamplitude is a maximum. The left most, un­marked altitude variations of !if in Fig. 5 corre­sponds to no noise condition. The alphabets A, B,C, D, E, F, G, H, I and J correspond to the num­ber of coherent integrations 1, 2, 4, 8, 16, 32, 64,128, 256 and 512, respectively. The difference,being negative always and less than - 0.1221 Hz,is because of the vertical wavelength of 30 km as­sumed for the sinusoidal altitude variation ofDoppler frequency with an amplitude of 2.5 Hzand the resolution of the spectra computed. Evenwhen there is no noise in the data, the Dopplerfrequency computed from the spectrum differsfrom the simulated Doppler frequency in eachrange gate. The difference between the Dopplerfrequency computed (not the simulated frequency)for various levels of SNR and the frequency com­puted for no noise conditions is exactly zero, forgood signal-to-noise ratio. This is shown in Fig. 6.In both Figs 5 and 6, if the frequency differenceexceeds ± 1 Hz, then it was not shown. From Fig. 6we note that by averaging 512 spectra coherently,the altitude up to which the Doppler frequencycan be measured is increased by about 14 km.

A few remarks on coherent integrations usedfor improving the SNR of MST radar data are ne­cessary. The integration in time domain reducesthe amplitude of the Doppler frequency by a fac­tor F= sin (lTfct!i tHlTfct!i t), where !it is t!1e inte­gration time duration. For the present data spe­cifications, F works out to be 0~84, 0.77, 0.7 and0.64, respectively, for fd = 5, 6, 7 and 7.8125 Hz.This reduction in signal power up to a maximumof 4 dB, depending on the value of fd' is usuallyignored for various reasons .

The coherent integration in spectral domain ap­pears, at first sight, to redwce the amplitude of theDoppler frequency if we consider a very descretemonochromatic fct( - fN <fd <fN) not equal to anintegral multiple of 1/ T,' where, T is the sub-blockdata length. In such cases the amplitude of fd

from all sub-blocks is reduced and the phase of fdfor successive sub-blocks would not be the same.

For extremely specific fd' the phas~ difference be­tween successive four sub-blocks could be 90° or180°, and when we coherently integrate four suchspectra, the average amplitude can become zero.For the case of a monochromatic discrete!ct, the

1'1 " I 1 II' I " I' !'""I'!! 111 I~ 'I I tI, IL~n,1111' II III ~Ill;;III I UIIIIiI III ',I~;,[ II. ""

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368 INDIAN J RADIO & SPACE PHYS, DECEMBER 1994

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spectral width is defined by T and to a lesser de­gree on fd value. In the present simulations therewas no deliberate attempt to make fd at all heightsequal to an integral multiple of 1/ T, and we havenot encountered a single case of the computedspectral amplitudes becoming zero. There weresome small differences between the simulated and

computed !ct values, and as is expected, the differ­ence between them was always less than one fre­quency bin (1/ T). There were also some smalldifferences between the computed and simulatedamplitudes and the differences never exceeded2 dB. The expected improvements in SNR wasnoticed at all heights and for each value of the

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Fig. 3-Unnormalized spectra of the simulated MST radar data.[Bottom panel is for no noise (SNR = infinit)') and the upper panelis for noise-added data and after 512 integrations. The y-axis scale

enlarged 20 times for range bins above 25 (J-5 km).]

Fig. 5-Differences between the computed frequency and thesimulated frequency as function of altitude [The left extremeprofile is for the case of no noise; Profiles A to J are after inetegrating the noise-added data in binary steps of 1 to 512;

Frequehcy differences exceeding 1 Hz are not shown.]

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grations in binary steps from 2 to 512.]

Fig. 6-Same as Fig. 5, but for the frequency difference be­tween the computed spectra of noise-lj.dded. data and the.

spectra for which no noise was added.

I! I' '" I

REDDI & NAIR: PROCESSING OF INDIAN MST RADAR DATA IN ITS ST MODE OPERATION 369

number of integrations. It should be mentionedthat the SNR improvement by unlimited numberof coherent integrations,' as demonstrated, maynot be practicable always.

The above. problem of amplitude reduction andunequal phases from successive sub-blocks of da­ta is essentially due to the assumed monochro­matic !J. The Doppler spectra of MST radar arenever monochromatic and the widths of the spect­ra are always many times the spectral resolution.This is due to the finite radar transmitter pulsewidth, more particularly the pulse width broaden­ing by the antenna beamwidth2, and the wind gra­dients. A monochromatic fct can occur in actualdata only if the wind does not change within theheight and time intervals over which the spectrumis integrated. We do not envisage such singulari­ties to occur in the MST radar spectra.

5 Evaluation and analysis of MST radar data inits ST mode of operationSix sets of MST radar data were acquired on

15-16 Feb. 1992. The duration of the data 'for thedifferent sets varied froni 40 min to 70 min andthe number of scan cycles varied from a minimumof 12 to a maximum of 19. Table 2 shows thetimes and the number of scans in the data sets.

While scrutinizing the data in ~etail it was no­ticed that, occasionally, there were some wild va­lues several hundred times larger than full scalerange of ± 5 V of the ND converter. In about60% of the scans (each of 60 range bins), therewas at least one wild value. They were found tooccur randomly in any range bin. In about 10% ofthe scans, there were two or three wild values.More than 4 values in a scan were never ob­served. Since it is known that the full scale rangeof the ND converter is ± 5 V, any value exceed­ing this range should be considered spurious.Since there are only a few in each scan cycle, itwas decided to replace them by linear interpola-

tion (average of the samples on either side of thewild point in the same range bin).

The spectra of all the scans listed in Table 2were computed for all the 60 range gates. Thespectra for the 60 range bins in each data setwere averaged separately and the averaged spec­tra for a typical data set is shown in Fig. 7. InFig. 7 the spectrum for each range bin is normal­ized with the maximum spectral amplitude for therange bin. The spectrum for the two zenith beamsin Fig. 8 shows clearly two regions, one in loweraltitudes ot 1.2 to 10 km and another from 16 to18 km, where there is one discernible spectralpeak. The signal is discernible in each for theoblique beams only up to about 8 km; the ceilingheight up to which the signal is seen being slightlyhigher during the nighttime. It must be mentionedthat incoherent averaging was found to improvethe noise level in all the range bins but have notimproved the ceiling altitude from which signalcould be extracted. This is most probably due tothe low receiver output level because of which thesignal was below the least significant bit of theND converter and could not be extracted fromnoise by integrating the spectra.

6 Results

6.1 Altitude variation of the radar signal strength

The integrated signal power (zeroth spectralmoment) for each range bin was computed separ­ately for each scan of the data. The signal powerversus the number of the range bin for the six da­ta sets is shown separately for each beam orienta­tion in Fig. 8. For the zenith beams, the signaldecreased by about 35 dB in the altitudes range1.2-8.4 km. This indicates the signal decrease at arate of nearly 5 dB/km which is larger than thevalues 1.4-2.4 dB/km reported from the Jicamar­ca radar and the SOUSY radar. There is largescatter in the rate of signal decrease at differentranges. The reasons for these large changes in the

Table 2-Scans details of MST radar data acquired during 15-16 Feb. 1992

Time duration

Number of scansDate From

ToZenith-YZeruth-YEast-YWest-YNorth-XSouth-X

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08260939191919191918

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Feb. 15

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Feb. 15

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372 INDIAN J RADIO & SPACE PHYS, DECEMBER 1994

t

case of stratified stable layers, a distinct aspectsensitivity, persisting for long durations of theradar signals, is expected. In case of scatteringfrom turbulent layers an isotropic response andshort persistency are expected. Thus, it is obviousthat the signal enhancements in the 10-11, 24-25,and 27th range bins observed in the zenith beamsare produced by the process of Fresnel reflectionand not by turbulence scatter. The most interest­ing feature is that the layers producing Fresnelreflections in the zenith beams and the turbulence

scatter in the oblique beams persist for durationsof at least 24 h.

The signal enhancements observed in isolatedrange bins in the zenith beams are attributed tothin sheets of enhanced potential temperature gra­dients produced by Kelvin-Helmholtz (K-H) insta­bility. Further observations on the thin (less than600 m) layers producing enhanced radar signalfor durations of the order of a day should lead tobetter UJlderstanding of the K-H instability me­chanism.

The consistent feature observed in all the datasets for the two zenith beams is the signal in­crease by more than 15 dB in the 24th (16.2 km)and 25th (16.7 km) range bins. In the 26th rangebin the signal level is the same as the floor noise.In 27th range bin, again there is a signal increaseby about 7 dB. In the four oblique beams, there isno indication of any signal increase for range binsabove 15. Again, there are signal enhancements in[he range bins 10 and 11 for both the zenithbeams. In all the data sets for the oblique beams,there is no indication of a signal increase in the11th range bin. Instead, we find increased signalin 1)1ostof the data sets for oblique beams in the9th range bin. A layer of enhanced turbulence ata particular height should produce enhanced· sig­nal strength in a higher range bin for the obliquebeams compared to zenith beams.

It was reported6,9,10 that VHF radars show echopower enhancements near about the tropopauselevel and that there is a significant correlation be­tween the tropopause height detected by radio­sonde and VHF radarll. The lower stratosphereis stably stratified which can give rise to enhancedradar signal strength by partial reflection 12. In

received signal power are not readily known. For

the oblique beams the received signal power dec­reased to the minimum value (which is taken asthe base level noise power) at a slightly lowerslant distance compared to the corresponding dis­tance (altitude) for the zenith beams. The onesignificant feature noted in Fig. 8 is that the noiselevel at different times of the day, and for differ­ent beam directions, is nearly the same and within5 dB. There are, however, some large increases ofthe base level noise in the north and east beamsduring 0321-0401 hrs 1ST, and to a lesser extentduring 1133-1233 hrs 1ST on the zenith-X beam.These enhancements could be produced by thesystem sometimes, or else could be due to astrong enough radio source transiting the beam.,The fact that the enhancement on the zenith-X

beam during 1133-1233 hrs 1ST was not ob­served on the zenith-Y beam implies that the ob­served enhancement is of radar system origin;and, in fact, in the averaged spectra spurious en­hancement was observed at -7 Hz and - 4.5 Hzin all the range bins for the zenith-X beam: butnot in zenith-Y beam. Similar spectral peaks were

observed in the north and east ~eam during ~2 Meanwindsand!idal~nd~0321-0401 hrs 1ST. The ""5 dB varIation of the The data acquued In SIX data runs were over abase level noise during a day could be due to the period of 24 h at intervals of about 4 h.Using thediurnal variation of the received cosmic noise in mean Doppler spectra as shown in Fig. 7 for thethe 53 MHz band at Gadanki. six data runs, the mean and 24 and 12 hourly

components were computed as follows. For eachdata run, the weighted mean Doppler frequency h(first moment of the spectrum) was computed se­parately for each range bin. The six L'values dur­ing the day for each range bin were taken as atime series to compute the mean wind and thediurnal and semidiurnal wind amplitudes andphases. This was done separately for each of thetwo zenith beams and each of· the four obliquebeams. It was found that for each beam orienta­tion, the amplitudes and the phases (times of max­imum westerly and southerly amplitudes) of the24- and 12- h components for adjacent range binsvaried, significantly.' Since the vertical wavelengthof the tidal modes is larger than a few tens of km,Ne have computed the average vectors for fivemccessive range bins (3 km averages). The aver­age vectors were attributed to the mean height ofthe five successive bins. The standard deviationsof the mean vectors for the five consecutive

range bins were also computed. Since the signalfor range bins above ten (see Fig. 8) was muchbelow the noise level, the values for the two meanaltitudes for range bins less than 10 only are pre­sented in Table 3. The standard deviations of theamplitudes are shown in the brackets and zero

I I , • 'I' 'I " '" I I" """I'll"" !~,

REDDI & NAIR: PROCESSING OF INDIAN MST RADAR DATA IN ITS ST MODE OPERATION 373

Table 3-Mean wind, diurnal and semidiurnal wind amplitudes and phasesAntenna

Mean DiurnalSemi diurnalbeam altitudeMean windkIn

m/sAnu1litude*PhaseAmplitude*Phasem/s degm/sdeg

Zenith-Y3.3- .028 (.090).114(.146)99.9 (29.9).017 (.095)18.4 (17.4)

6.3- .085 (.044).065 (.103)172.2 (76.3).042 (.069)134.8 (60.8)

Zenith-X3.3- .053 (.058).095 (.162)123.1 (42.7).045 (.070)88.1 (28.3)

6.3- .142 (.080).051 (.113)207.1 (155.0).029 (.028)136.0 (50.7)

East-Y3.3-9.694(1.353)1.807 (1.635)54.8 (13.1).579 (.628)77.5 (21.5)

6.30.387 (.905).683 (1.280)178.2 (84.7)1.100 (.692)55.5 (6.4)

j,

West-Y3.30.004 (2.170)1.240 (2.248)188.7 (84.6).528 (.687)307.7 (57.2)6.3

-2.101 (.755).875 (.800)100.8 (21.6).249 (.448)3.5 (20.5)North-X

3.32.010(1.527).817 (1.443)112.8 (54.6).536 (.798)52.7 (10.5)6.3

.943 (.478).526 (1.475)274.7 (158.3).644 (.751)233.5 (95.6)South-X

3.3- 1.445 (.909).044 (1.603)48.3 (3.0).163(.748)121.9 (63.7)6.3

- .902 (.169).458(1.209)299.8(112.3).5~8 (.365)311.2 (34.1)

Note: The values within the brackets are the standard deviations.*Zero phase corresponds to 0900 hrs 1ST.

phase for 24- and 12-h components correspondsto 0900 hrs LT.

From Table 3, we find that the mean and am­plitudes of the 24- and 12-h components at allheights for the zenith beams (which represent ver­tical velocity) are much less than the correspond­ing values for oblique beams. We expect the meanwinds for east and west beams to be equal inmagnitude but opposite in sign. Further, for the24- and 12-h components, the correspondinrphases on the east and west beams should differby 180°, while the amplitudes should remain thesame. Similar phase reversal is expected for thenorth and south beams. In Table 3, it is not tooobvious that these indeed are observed always.The mean winds on the. two zenith beams agreereasonably well, while the agreement is rather po­or on the oblique beam. For the diurnal compo­nents the amplitudes agree well on east and westbeams, north and south beams, and for the twozenith beams. For the semidiumal componentthere is good agreement of the amplitudes on thetwo zenith beams, but the amplitudes on the eastand north beams are, in general, larger than thecorresponding amplitudes on the west and south9.eams. In general, the expected 180° phase differ­ence between the east and west, and north and southbeams is not observed. This can be attributed to

the gr~ater sensitivity of the phase to poor SNRand smaller amplitudes. For small amplitudes ofthe harmonic components computed from the

time series, the phase works out to be the ratio oftwo small numbers,. and the presence of noise in agood measure would lead to very erratic phasevalues. The standard deviations of the diurnal am­plitudes are, in general, larger than those of semi­diurnal amplitudes. We conclude that the ampli­tudes of the diurnal and semidiurnal component~in the lower troposphere are very small and itmay be difficult to obtain the phase of these os­cillations with reasonable confidence,. unless theSNR of the radar data is improved by a largemeasure. Further, for any reliable study of tidalwinds, data for 10 days should be combined, aswas recommended for tidal studies using datafrom meteor wind radars and partial reflectionradars 13.14 •

AcknowledgementsThe data used in the study were acquired at the

NMRF, Tirupati. The authors acknowledge Dr SH Damle, Prof. P B Rao and Dr A R Jain fortheir support, and Messrs Murali, Tapas andother engineering staff at the facility for enthusias­tically participating in running the system and ex­plaining the system.

References1 Woodman R F & Guillen A, J Atmos Sci (USA), 31

(1974)493.2 Hocking W K, Radio Sci( USA), 20 (1985) 1403.

374 INDIAN] RADIO & SPACE PHYS, DECEMBER 1994

3 JonesJ R P, QJ R Metorol 50('( UK), 88 (1962) 485.4 Tatarski V I, The Effect of Turbulent Atmosphere on

Wave Propagation, U.S. Dept of Commerce, USA, 1971.5 Balsley B B & Gage K S, Pure & Appl Geophys (USA),

118(1983)452.6 Gage K S & Green] L, RadioSci( USA), 13 (1978) 991.7 Bendat ] S & Piersol A G, Random Data: Analysis and

Measurement Procedures (Wiley Interscience, New York,USA),1971.

8 Yamamato M, Sato T, May P T, Tsuda T, Fukao S & Kato S,Radio Sci( USA), 23 (1988) 1013.

9 Rottger] & Liu C H, Geophys Res Leu (USA), 5 (1978)357.

10 Chechowsky P, Schmidt G & Ruster R, Radio Sci (USA),19(1984)441.

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12 Rottger J, Map Handbook, edited by S K Avery (Univer­sity of Illinois, USA). Vot 2, 1981. P. 341.

13 Bernard R, J Almos & Terr Phys (UK), 43 (1981 ) 663.14 ViaIF,JAllllos&TerrPhys(UK),51 (1989)3 .

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