JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, A12302, doi:10.1029/2007JA012882, 2008
Inferring D region parameters using improved incoherent scatter radar techniques at Arecibo Shikha Raizada,1 Michael P. Sulzer,1 Craig A. Tepley,1 Sixto A. Gonzalez,1 and Michael J. Nicolls2 Received 15 October 2007; revised 22 July 2008; accepted 14 August 2008; published 5 December 2008.
[1] With the increased sensitivity and bandwidth, Arecibo 430 MHz incoherent scatter
radar was run on 23 July 2006 using two radar modes with a focus on measuring D region parameters. One mode measured the ion line spectra over D region altitudes; the other mode was used as a new way to calibrate the electron densities in the D region and also to correct the ion line spectra. The ion line primarily provided a power profile connecting the D region to altitudes where the plasma line is useful. The plasma line provided the absolute electron density, used in the lower altitudes for calibration, and at F region altitudes for determining the baseline level in the D region spectra due to the folding-in of F region scattered power. It is found that the electron line contribution is about 1–3 times the ion line between 60–75 km altitude ranges. Electron concentration was found to be in the range of a few hundred to 5000 cm3 in the 65–90 km altitude range. The new technique agrees well with the previous measurements. The D region spectral widths were used to calculate the ratio of negative ion to electron concentration (l). Between 0900 and 1100 LT, a 4 km thick layer of negative ions was observed with l 1.6 ± 0.3 around 73–77 km. In the altitude range of 81–91 km, we inferred temperatures 195–215 K using ion-neutral collision frequencies determined from the spectral widths. Citation: Raizada, S., M. P. Sulzer, C. A. Tepley, S. A. Gonzalez, and M. J. Nicolls (2008), Inferring D region parameters using improved incoherent scatter radar techniques at Arecibo, J. Geophys. Res., 113, A12302, doi:10.1029/2007JA012882.
1. Introduction [2] The complex behavior of the ionospheric D region originates mainly owing to (1) relatively high pressure, which causes major and minor species to be important in the photochemical reactions, and (2) ion formation resulting from a variety of ionization sources. The D region of the ionosphere is characterized by the presence of negative ions located below 70– 80 km that result from collisions between electrons and neutrals, also known as attachment, for example, O2 þ O2 þ e ! O 2 þ O2 þ 0:5 eV
is the most significant reaction. The other processes are O2 þ N2 þ e ! O 2 þ N2 þ 0:5 eV O3 þ e ! O þ O2 þ 0:4 eV
1 Space and Atmospheric Sciences, NAIC/Arecibo Observatory, Arecibo, Puerto Rico. 2 Center for Geospace Studies, SRI International, Menlo Park, California, USA.
Copyright 2008 by the American Geophysical Union. 0148-0227/08/2007JA012882
These negative ions further enter into additional reactions resulting in more complex ions and can be lost by photo detachment, associative detachment, two-body ion-ion recombination or collisional detachment [Schunk and Nagy, 2000]. The knowledge of the ratio of negative ion to electron concentration (referred to as l) is an important parameter to understand D region chemistry as it determines the effective recombination coefficient. The information about the positive and negative ions in the D region is based on a limited number of rocket flights and modeling studies [Kull et al., 1997, and references therein]. However, in situ measurements do not provide data with good temporal resolution and hence the effects of waves cannot be resolved using mass spectrometer-based observations. [3] The Arecibo 430 MHz incoherent scatter radar (ISR) offers an excellent opportunity to investigate the D region with good temporal and spatial resolutions. It has been shown that Thomson scatter gives information about many D region parameters, like electron density, negative ion to electron ratio, temperature, and mean ion-mass [Tepley et al., 1981; Chau and Kudeki, 2006, and references therein]. For collisionless plasmas and a radar wavelength exceeding the Debye length, the scattered spectrum is characterized by an ion component whose mean spectral width is determined by thermal motions. Similarly, another spectral feature referred to as the electron line, which is much broader than the ion line, exists for electrons only [Dougherty and Farley, 1963; Mathews, 1978]. The ion component of the D region Thompson scatter spectrum can be expressed in
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terms of the normalized ion-neutral collision frequency, Yin: yin ¼
n in 1=2 2 kVi
ð1Þ
where n in is the ion-neutral collision frequency, k the scattering wave number, and Vi the mean ion thermal speed. The effect of Yin on the Thomson scatter spectrum is such that large Yin values result in a narrowing of the ion line component of the power spectrum in the absence of the negative ions. Theoretical work using a fluid or continuum approach [Tanenbaum, 1968] showed that the spectral width of the power spectrum increases in the presence of negative ions [Mathews, 1978]. Similar results could be obtained from various other approaches, one that employs a relaxation-type collision model [Dougherty and Farley, 1963] and the other uses a random walk plus a ballistic path type treatment of collisions [Hagfors and Brockelman, 1971]. [4] The spectrum of the incoherent scatter signal depends on many parameters like the electron density Ne, electron and neutral temperatures, Te and Tn, mean mass of the positive ion mi, ratio of negative ion to electron density l, ion-neutral collision frequency, and radar wavelength, lR. When the electrons are in thermal equilibrium with the neutrals, the analytical function for the spectrum [Dougherty and Farley, 1963] can be expressed as sðw þ dwÞ ¼
Ne re2 yin dqi 2p 1 q2i y2in
ð2Þ
where s is the scattering cross section, Yin is the normalized ion-neutral collision frequency, re is the classical electron radius (5.29 1011 m), w is the frequency variable and w0 is the radar frequency, and qi is the normalized frequency expressed as below: qi ¼
1=2 wlR mi 4p 2KB Ti
ð3Þ
mi is the mass of the positive ion, KB is the Boltzmann constant, and Ti is the ion temperature. Equation (2) can be rearranged in terms of the frequency (f) in Hz as follows: sðwo þ wÞ ¼
A
W2 2
W 2 2
þf2
df þ A2
ð4Þ
This is a Lorentzian function with width W (Hz) and with a peak of magnitude A and base level A2, where 32pKB Ti l2R mi n in
ð5Þ
Ne re2 l2R mi n in 32p2 KB Ti
ð6Þ
W¼
A¼
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min in/Ti. However, the above expressions are only valid for the case of ‘‘large’’ Ne (exceeding 104 cm3). For a more realistic situation as in the case of a Debye limited region, i.e., where a = 4plD/lR is not negligible (where lD is Debye length, lR is the radar wavelength), the total scattered power PD and the WD for the cases when Te/Ti = 1 can be expressed as below: PD ¼
Ne ð1 þ 2lÞ ð2ð1 þ lÞ þ a2 Þð1 þ a2 Þ
WD ¼ CT
ðð1 þ lÞÞ þ a2 =2Þ n in mi ð1 þ a2 Þ
ð7Þ
ð8Þ
where T is the neutral temperature, l is the ratio of negative ion to electron density, and C is a constant expressed as 32pKB/l2R [Mathews, 1978; Mathews et al., 1982]. Because of low D region electron densities, the spectral measurements of this region are difficult and only a very few measurements have been made in the past [Tepley and Mathews, 1978; Ganguly et al., 1979; Tepley et al., 1981; Rietveld and Collis, 1993; Chau and Kudeki, 2006; Janches et al., 2006]. With the advent of new data taking software and better signal processing techniques, man made artificial interference can be removed more efficiently giving us a better opportunity to investigate the D region. One of the studies related to meteor dust in the D region was first suggested by Kelley et al. [1998]. Recently, a new algorithm was developed to infer properties of charged meteor smoke particles in the D region using the Arecibo ISR [Strelnikova et al., 2007], which is based on the theoretical work by Cho et al. [1998] Apart from this, Thomson scatter techniques can also be used to infer mesospheric temperatures and densities in the 80– 100 km altitude region [Tepley and Mathews, 1978]. This method relies on the fact that the spectral shape depends on Yin, as given by expression (1). Since it is dependent on temperature and n in, previous measurements by Tepley and Mathews [1978] determined Yin from the experimental spectra assuming a model temperature at some reference height. They assumed that the atmosphere is isothermal along with the fact that n in, varies with altitude according to the Barometric law. In this work, we have used equations (5) and (8) to determine n in from the spectral widths and calculated temperatures using the barometric law. To determine the experimental n in profile, we have considered the principal ions to be mainly NO+ and O+2 giving a mean mass of 31 amu. In addition, heavy cluster ions occur below 90 km but this analysis considers major ions to be NO+ and O+2 on the basis of Narcisi et al. [1972], who found O+2 and NO+ to be the dominant ions above 84 km. The errors in estimating temperature from variations in ion mass are about 1/30 (31 ± 1 amu) [Tepley and Mathews, 1978]. [5] The paper is organized in the following way. Section 2 describes the techniques used for the observations along with signal processing. The data analysis is explained in section 3, which is followed by the description of the methodology used for inferring negative ion in section 4. Section 5 explains the technique used for deriving meso-
Thus, the width and the peak amplitude of the incoherent scatter spectrum depend on the observable physical quantity 2 of 13
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spheric temperature using ISR and finally section 6 summarizes our conclusions.
2. Observations 2.1. D Region Spectra/Coded Long Pulse [6] We ran a 12 h ISR experiment during the day of 23 July 2006 to measure D region spectra that was used to determine the vertical distribution of negative ions in the D region. A 52 baud D region code was used for this study with a 2 ms IPP and a baud length of 1 ms (range resolution of 150 m). The data were collected for the beam pointed at zenith. About 20 s of the data were collected in the D region mode for the spectra at lower altitudes, while 10 s was used for running the CLP mode. [7] Determination of negative ions requires an accurate knowledge of how artificial interferences influence the measured spectral width. Weak signals from the D region are contaminated by scatter from sources like ships, airplanes, etc., that make it difficult to study. Improvements in the quality of the information required for this study necessitates our being able to discriminate against such artificial (i.e., man made) interference. Recording every pulse allowed us to perform statistical studies and hence identify those processes influencing the D region Thomson scatter spectral width in a more reliable way. In order to remove such interference we performed FFTs on the raw data using 512 points and the resulting spectra for each height were stored. Then, a median filter was applied to every 2 min of these spectra (frequency bin in time), eliminating the interference without affecting the statistics. This procedure removed low duty cycle interference like sporadic events caused by airplanes intercepting the radar beam, that appear as a spike in frequency bin. In addition, the standard data processing technique at Arecibo is primarily designed to remove sources of ‘‘dc’’ such as voltage offsets in the A/D converters, which would be effective against clutter or any signal with a very narrow bandwidth at zero frequency. This method (that depends on the availability of computing power) uses a very long set of data, and removes the mean with no significant data loss. In the absence of sufficient computing power and memory, a two-pulse clutter subtraction technique [Rietveld and Collis, 1993, and references therein] achieves similar results for narrow clutter, but also has some disadvantages. [8] Some examples of incoherent scatter (IS) spectra obtained using 512 points at different altitude ranges in the D region are shown in the Figure 1. The thick solid line is the Lorentzian fit to the IS spectral data. It is clearly evident that the spectral width of the ion line becomes narrower with decreasing altitude as a consequence of the thermal motions of the ions in the D region that are affected by their collisions with the neutrals. However, the effect of negative ions is not evident in Figure 1, but it is clearly seen in Figure 2. Figure 2 shows the altitudinal variation of the logarithmic full width at half maximum (FWHM) of the Lorentzian spectra that have been fitted to the data for two different times. Above about 70 km, the FWHM decreases with falling altitude, and a linear fit to the FWHM values occurring in the 70– 75 km height range and extrapolated to lower altitudes is shown as a dashed red line. This line is just a guide so that one can see the deviation from the expected trend in the spectral width
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variation. In other words, Figure 2 clearly demonstrates a slight increase in FWHM (10– 20 Hz) at altitudes below 70 km, which indicates the existence of negative ions as predicted by the theory [Mathews, 1978]. This increase in spectral width is greater than the fundamental spectral resolution that is close to 1 Hz and after convolution with the rectangular window it reduces to about 2 Hz. Theory also predicts similar changes in the spectral width in the lower D region due to increased ion temperature or decreased ion neutral collision frequency, but these possibilities can be ruled out considering the realistic geophysical variation of these two parameters. Neutral turbulence is also known to broaden the spectrum, the contribution originating from the root mean square velocity of instantaneous measurements [Hocking, 1996; Latteck et al., 2005]. However, the spectra are usually averaged for several minutes that represent gravity waves and oscillations, which give rise to turbulence on breaking. A 10 Hz broadening would result from velocity variance of 12.2 m2 s2. However, the measured vertical velocity variances from Arecibo are less than 2 m2 s2 during morning hours (0930– 1130 LT) in July for the altitude range of 66– 85 km [Janches et al., 2006; Fritts and Janches, 2008]. Occasionally, during later noon intervals between 1430 and 1630 LT, the velocity variances can peak to 5 m2 s2 around 75 km and above. This will contribute to a spectral width broadening of 6.3 Hz, which is about 30 times smaller than the non-turbulent contribution at those altitudes. Also, another factor that could broaden the spectral width is the occurrence of coherent irregularities caused by neutral turbulence [Chau and Kudeki, 2006; Woodman and Guille´n, 1974]. It is important to also consider Doppler broadening of the spectrum due to line of sight neutral winds that can introduce errors in the estimation of the negative ions. Considering the beam width to be 1/6th of a degree, and the measured horizontal winds at 70 km 10 m/s [Hines et al., 1993], the spectral broadening would not exceed 0.09 Hz. 2.2. Plasma Line Data [9] In order to calculate the electron density, plasma line measurements were performed using the new EchoTek digital receiver system at the observatory. This system provides the high data rate and large storage capability necessary for the plasma line measurements, while the older system still handles the ion line spectral information. Figure 3 shows the temporal and height variation of F region electron density for 23 July 2006. Around 1030 LT, the F region peaks at 3 1011 m3 occurring between a 200– 250 km height range. After 1230 LT, the F region moves upward and maximizes at 5.4 1011 m3 around 300 km. Since the IPP was 2 ms during this experiment, F region aliasing to the lower D region occurs from 300 km and above. The plasma line data were used to determine the electron densities by combining power profiles obtained from CLP and D region data. It has also been used to infer the F region aliased power into the D region for estimating the total power, which requires subtracting the F region folded power from the baseline of D region spectra.
3. Analysis [10] The first few lags of the auto correlation function determined from CLP data were used to obtain power
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Figure 1. Power spectra obtained using 512 points of the raw data integrated for 20 min for six different altitude regions. Thick black line is the Lorentzian fit to the data (see text for more details). profiles in the E region. The power profiles are then calibrated with respect to the electron concentration profiles inferred using plasma line data between altitudes of 100 and 150 km. This is done by chi square minimization between the two data sets in the above altitude regime. Figure 4 displays the results after normalizing the CLP power pro-
files relative to electron density profiles obtained from plasma line (shown as dashed red lines). The calibrated electron density profiles for different times are displayed as solid black lines in Figure 4. The usefulness of this methodology is seen from the calibration being performed at lower altitudes without worrying about Te/Ti corrections
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Figure 2. Altitudinal variation of full width at half maximum (FWHM) obtained from the Lorentzian fit to the data. Dashed line is the extrapolation of FWHM to lower altitudes suggestive of lower values as compared to those observed below 70 km that are higher most likely owing to presence of negative ions.
Figure 3. Range-time plot of electron densities in the F region. F region peaks after 1400 LT around 300 km. 5 of 13
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Figure 4. Calibrated electron density profiles are shown as black lines, while dashed lines represent the F region electron densities inferred using plasma line data. Calibration of the power profiles is performed in 110– 150 km altitudes.
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Figure 5. Left panel shows height variation of baseline determined from the ISR spectra (solid black line), while the dotted line displays F region power aliased to lower regions, which has been used to infer the electron line contribution to the total power (square points) shown in the right panel (details in the text). as compared with the F region peak using ionosonde data and scaling from an ion line profile. [11] Since in the lower D region, electron line contributes significantly to the total power, it cannot be neglected if one needs to infer electron concentration in this region [Mathews, 1986]. Considering that the baseline of the D region spectra is equal to the sum of the electron line contribution and the aliased F region power: Baseline ¼ PE þ K x FP þ NS
ð9Þ
where PE is the electron line power, FP is the F region power aliased to D region altitudes, NS is the noise level, and K is a calibration constant. The calibration constant is determined every 1 km since D region electron density does not change drastically in this short interval resulting in almost no variation of electron line power in this duration. The base level or dc level of spectra is obtained by fitting a folded Lorentzian to the data and the system noise is subtracted
from it, which is estimated from the 52– 55 km altitude region where electron density is negligible. As seen from Figure 3, the F region is only well developed after about 1230 LT, but too small to cause any appreciable aliasing to the lower D region before noon. Therefore, from equation (9), it is evident that electron line power is equal to baseline minus system noise. As mentioned earlier, determination of the power profile in the D region requires knowledge of both ion line and electron line, so we infer the latter using equation (9) and its variation with altitude is displayed in Figure 5. The solid line in the left panel of Figure 5 shows the altitudinal variation of the baseline, while the dotted line displays aliased F region power. The right panel of Figure 5 exhibits the altitude variation of electron line power that becomes significant below 78 km and ranges between 0.1 and 0.5 times the baseline value. [12] In order to obtain electron density profiles extending to D region altitudes, we need to determine power profiles that connect both the D and E regions. As explained in the
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Figure 6. Variation of electron concentration obtained using CLP and D region spectra calibrated relative to the plasma line (solid line). Dotted line shows Ne obtained using Barker code, while dashed line is the IRI model. above paragraph, the E region power profiles are determined from high-resolution CLP data, while in the D region they are inferred using the measured ionline spectra. Power profiles in the lower D region are determined by integrating the ion line and electron line contributions for each spectrum between altitudes of 60 and 92 km and are normalized to electron density values in the 90 –92 km altitude range. Figure 6 shows electron density profiles (solid line) determined using the above methodology. Also shown are the comparisons with the previous data from Arecibo that were obtained in 27 June 2006 using Barker coded power profiles normalized relative to ionosonde data. The dashed line displays Ne from IRI model. There is a good agreement between the new technique and IRI model at the lower D region altitudes. It is important to note that the far field for Arecibo is approximately 130 km (D2/lR, where D is antenna diameter and lR is radar wavelength) and D region measurements reported here are in the near field. Breakall and Mathews [1982] investigated antenna near-field effects and found that the gain correction factor in the 60– 100 km
altitude range is close to 0.85– 0.9 for ISR measurements at Arecibo. This implies that the D region electron densities reported here are underestimated by less than 15%.
4. Methodology for Inferring Negative Ions [13] The effect of negative ions is to broaden the ISR spectrum, the spectral width given by equation (4), which is modified to the expression (8) in the Debye limited region. A closer look at this reveals that it can be approximated by a Lorentzian function as given below [Mathews, 1986; Rietveld and Collis, 1993]. Fð f Þ ¼
A0 1 þ ðð f DÞ=W Þ2
þB
ð10Þ
where A0 is the amplitude of the spectrum, D is the Doppler shift for the center frequency, W is the spectral width, and B is the base level or DC level of the spectrum. Rietveld and
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Figure 7. Examples of height variation of negative ion to electron density ratio (l) demonstrating errors at different altitudes. During morning hours, layer between 73 and 77 km exhibits l exceeding 1. Collis [1993] demonstrated that the spectral widths obtained by fitting a ‘‘true’’ Lorentzian function as expressed in equation (10) are underestimated owing to the aliasing of the spectrum, which alters the spectral width. They mention clearly that when the intrinsic width is large, there may be aliasing of the wings of the Lorentzian spectrum, and so analyzing truncated auto-correlation function (ACF) is a better approach. They found it more convenient to implement the fitting technique in the time (ACF) domain. We found it more convenient to work entirely in the equivalent frequency domain, where the effect of too low a sampling rate (that is, too long a time between radar pulses) results in an aliased spectrum. It is a well known fact that aliasing results from the folding-in of the power that lies outside the bandwidth decided by the IPP, which in this experiment is ±250 Hz. By shifting the true Lorentzian as expressed by equation (10) by an amount equal to the bandwidth and superimposing it on itself gives the ‘‘observed or folded’’ Lorentzian (details in Appendix A). Thus, the resulting expression for the folded Lorentzian is a function of true widths. This ‘‘folded’’ Lorentzian was then used to least square fit the data; hence estimate the ‘‘true’’ spectral widths. Even though at lower altitudes below 85 km spectra are narrow and aliasing would not affect the spectral width at these altitudes, we used a ‘‘folded Lorentzian’’ function as a part of our standard analysis. The EISCAT D region observations reported by Collis and Ro¨ttger [1990] are similar to Arecibo data. This approach is different from the one employed in the previous work by Ganguly et al. [1979], where a comparison of experimental Thompson
scatter power spectral data with libraries of theoretical spectra was done. However, a time efficient procedure is to use the least square fitting of the folded Lorentzian function and use the modified expression of FWHM expressed by equation (8) to infer negative ions. Figure 7 exhibits the altitude variation of negative ion to electron density ratio (l) at two different times along with the error bars that decrease with increasing altitude. Figure 8 displays contours of l showing a weak layer with l 1.6 ± 0.3 during the morning hours. As mentioned in the introduction, the most significant source of negative ions is the attachment reaction involving two oxygen molecules and electron with reaction rate 4 1030exp(193/T), where T is the neutral temperature [Turunen et al., 1996]. Any increase in temperature can accelerate the rate of the reaction governing the attachment of electrons with O2 resulting in an increase in negative ions. Gravity waves (GW) are known to modulate temperature structures and the interaction between GW and tides are believed to form mesospheric inversion layers (MIL). At low latitudes and midlatitudes the mesospheric inversion layer can be as wide as 10 km with enhanced temperatures up to 15 – 50 K perturbation amplitude [Meriwether and Gardner, 2000]. Thus, a temperature increase as a result of GW activity can result in a negative ion layer observed during morning hours. It is evident that below 70 km an appreciable number of negative ions with l exceeding a value of 1 occur, implying that the majority of negative particles are in the form of negative ions owing to neutrals attaching to electrons. This value is close to the earlier study by Ganguly et al. [1979],
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Figure 8. (a) Range-time variation of l showing that negative ions become important below 70 km. (b) Same as Figure 8a except that the lower altitudes are saturated to reveal the variation of l in the layer located near 75 km. who found the level to be at 68 km that agreed well with the theoretical work by Thomas et al. [1973]. However, the l = 1 level shifts to higher altitudes at 72 km during the late noon or early evening hours. [14] Kull et al. [1997] developed a chemical state model for positive and negative ions by considering about 400 ion and photo-chemical reactions with a two dimensional neutral model for minor atmospheric constituents in the altitude range of 50– 84 km, for latitudes between 50° and +50°. Their study revealed that the transition altitude between negative ions and electrons is located around 75 km. In Figure 8b we have saturated the color scale of the loweraltitude region to reveal the variation of negative ions within the layer observed around 75 km during early morning hours. Our observations show that the transition region where l < 1 is located above 72 km except within the layer around 75 km.
from the experimental spectral width that will vary according to the relation: n in ¼ n in ðz0 Þeðzz0 Þ=H
where z0 represents reference height, H is the scale height given by KBT/Mg; M = 28.84 amu, is the mean molecular mass of the neutral atmosphere, and g is the acceleration due to gravity. The experimental profiles of n in were determined from equations (5) using the spectral widths obtained from the folded Lorentzian fit. A linear regression is performed between the experimentally determined n in[Exp] values and altitude for every 1.4 km with neutral temperature calculated from the slope of the following equation: lnðn in ½ Exp Þ ¼
5. Mesospheric Temperatures Inferred From Incoherent Scatter Spectrum [15] The general expression for the backscatter is given by equation (2) and depends on temperature, n in, and ionmass. Various in situ experiments have established ion-mass as a well known quantity [Grebowsky and Aikin, 2002, and references therein], which leaves temperature and n in as unknowns. As discussed in the introduction, the spectral width is a function of n in and is expressed by the relations (5) and (8). Assuming the mesosphere to be locally isothermal with some specific temperature, we can determine n in
ð11Þ
ð z z0 Þ þ lnðn in ½z0 Þ H
ð12Þ
where n in[Exp] is the ion-neutral collision determined from the data. Figure 9 displays the altitudinal variation of mesospheric temperature at two different times, which are mostly higher than the MSISE-90 model. Higher temperatures observed below 87 km reported from Jicamarca were due to neutral turbulence [Chau and Kudeki, 2006]. Density perturbations due to a wave can modify ion-neutral collision frequency that can result in higher temperatures. Previous work has shown the existence of anomalies where temperature can be enhanced by 10– 25 K, but can be as
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Figure 9. Examples of temperature variation inferred from ion-neutral collision frequency determined from ISR spectral widths. Also shown is MSIS model for comparison. high as 100 K above nominal profiles [Tepley et al., 1981; Dao et al., 1995; Meriwether et al., 1994; Stella et al., 2001]. Such enhancements in temperature are usually located between 70 and 90 km with a width of nearly 10 km and can be due to mesospheric inversion layers (MILs). The origin of MILs is still an open question. Various factors like dynamical heating due to gravity wave breaking, chemical heating events, and large amplitude waves can play a significant role in modifying the thermal structure of the mesosphere. It is believed that the temperature enhancement can be caused through the interaction of gravity waves with tides and MILs [Meriwether and Gardner, 2000; States and Gardner, 1998].
6. Conclusions [16] We have analyzed the ion line spectra measured from the D region ionosphere using the 430 MHz ISR at Arecibo to infer negative ions and mesospheric temperatures. A new approach was developed to determine D region electron densities by calibrating the power profiles obtained from CLP data relative to the F region densities in the 100 – 150 km altitude range. Below 75 km, the electron line component required to determine the power profiles was
estimated using plasma line data that provided information about aliased F region power to the lower D region. We found electron densities to range between 101 and 104 cm3 in the 60– 100 km altitude region. A comparison of Ne with the previous data obtained from Arecibo on 27 June 2006 using Barker code shows a good agreement up to 90 km. Between 90 and 100 km, Ne values are lower than the earlier data as well as IRI model, which is possibly due to high variability of the D region over Arecibo. The spectral widths of the IS spectra were used to determine the negative ion to electron density ratio (l) below 80 km. We found that above 70 km, l 0 during morning and noon hours except in a layer that occurs in the altitude range of 73– 77 km and disappears after 1100 LT. The transition level between the negative ions and electrons defined by l = 1 occurs between 68 and 70 km during morning and shifts to 72 km during late noon hours. The variation in l is less than 5% for changes of ±20% in temperature and electron concentration. [17] Using the dependence of ion-neutral collision frequency on the scale height of the neutral atmosphere, we estimated mesospheric temperatures in the 81 – 91 km altitude region. The temperatures were usually larger than the MSIS model, which can be a result of wave activity. Such measurements in conjunction with daytime resonance/
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Figure A1. An example of aliased or folded spectrum and a true Lorentzian function. Rayleigh lidars can be used to obtain temperatures independently that will allow a direct determination of ion-neutral collision frequency without using model temperatures. Also, combining Rayleigh and resonance temperatures inferred using lidars along with ISR data will facilitate a better understanding of the coupling between different altitude regions and shed new light on gravity wave breaking.
folded spectrum shown by a dotted line, while the solid line represents its true spectrum that would be the case if there were no folding of power from frequencies outside the bandwidth. The folded Lorentzian (dashed curve) can be expressed in terms of the true Lorentzian as follows:
Appendix A
where F(f) is the true Lorentzian expressed by equation (10) and BW is the bandwidth of the observations. Thus, using the above equation to fit the observed spectra gives the ‘‘true’’ widths.
[18] The spectrum of the measured scattered signal is modified from the expected Lorentzian when signal power from outside the spectral window is folded in. The width of this window is determined by the interpulse frequency. Thus, in order to infer ‘‘correct or true widths,’’ one needs to generate a modified Lorentzian that can be expressed in terms of the true Lorentzian parameters like Width, Amplitude and DC level. Using this function for fitting then gives us the ‘‘correct width.’’ It is evident from Figure A1 that the true Lorentzian, as expressed by equation (10), if used to fit the observed spectrum (dashed curve), will overestimate the widths. It is important to note that this is important only at higher altitudes (above 85 km) where widths are broad and the lower D region (where negative ions are important) is not affected. Figure A1 displays an example of an aliased or
F0 ðf Þ ¼ Fðf Þ þ Fðf BWÞ þ Fðf þ BWÞ
[19] Acknowledgments. This work was performed at the Arecibo Observatory, which is operated by Cornell University through a cooperative agreement with the National Science Foundation. We appreciate technical help provided by P. Perillat. Suggestions by all the reviewers are highly appreciated. [20] Zuyin Pu thanks Jorge Chau and two other reviewers for their assistance in evaluating this paper.
References Breakall, J. K., and J. D. Mathews (1982), A theoretical and experimental investigation of antenna near-field effects as applied to incoherent backscatter measurements at Arecibo, J. Atmos. Terr. Phys., 44(5), 449 – 454, doi:10.1016/0021-9169(82)90051-4.
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S. A. Gonzalez, S. Raizada, M. P. Sulzer, and C. A. Tepley, Space and Atmospheric Sciences, NAIC/Arecibo Observatory, HC-03 Box 53995, Arecibo, Puerto Rico 00612. (
[email protected]) M. J. Nicolls, Center for Geospace Studies, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA.
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