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Gravity wave momentum fluxes in the MLT—Part I: Seasonal variation at Collm (51.3°N, 13.0°E)
Journal of Atmospheric and Solar-Terrestrial Physics 73 (2011) 904–910
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Gravity wave momentum fluxes in the MLT—Part I: Seasonal variation at Collm (51.31N, 13.01E) Manja Placke a,n, Gunter Stober a, Christoph Jacobi b a b
Leibniz Institute of Atmospheric Physics at the Rostock University, K¨ uhlungsborn, Germany Institute for Meteorology, University of Leipzig, Stephanstr. 3, 04103 Leipzig, Germany
a r t i c l e in f o
a b s t r a c t
Article history: Received 20 November 2009 Received in revised form 21 May 2010 Accepted 14 July 2010 Available online 1 August 2010
By using a method presented by Hocking (2005), wind variances and gravity wave momentum fluxes in the mesosphere and lower thermosphere have been derived from all-sky interferometric meteor radar wind measurements at Collm, Germany (51.31N, 13.01E), during 2004 through 2009. We present vertical profiles of long-term mean monthly mean variances and momentum fluxes. Gravity wave variances show a semi-annual oscillation around 90 km altitude, and generally increase with height. An exception are the summer months where the maximum is measured around 88 km and amplitudes are decreasing above that height. A comparison of momentum fluxes and mean background winds emphasizes the coupling between gravity waves and the background circulation. 3-monthly means of wind and wind variance for individual seasons show stronger interannual variability in winter than in summer, and an indication for mesosphere wind filtering of gravity waves being responsible for this variability. & 2010 Elsevier Ltd. All rights reserved.
Keywords: Gravity waves Middle atmosphere dynamics Meteor radar
1. Introduction Gravity waves (GW) are vertically and horizontally propagating waves that are associated with the buoyancy restoring force in a stably stratified atmosphere (e.g., Andrews et al., 1987). They are generally forced in the lower atmosphere, for instance through orography, at fronts, jets, through geostrophic adjustment or convective processes (e.g., Pavelin and Whiteway, 2002), and depending on the middle atmosphere circulation, may propagate upward to the mesosphere/lower thermosphere (MLT) region. Owing to the small horizontal phase speed of GW, which is of the order of the background mean flow, GW are very sensitive to wind filtering in the middle atmosphere. Therefore, their amplitudes depend on both the seasonal cycle as well as on planetary waves (e.g., Manson et al., 2003; Jacobi et al., 2006). Through transport and deposition of momentum and energy GW lead to the typical circulation patterns in the MLT such as the lower thermosphere wind reversal and the cold summer mesopause. Hence, the knowledge of GW momentum fluxes is crucial to understand the dynamics and energetics of the MLT as well as the coupling of atmospheric layers from the troposphere to the MLT. To obtain information about GW different kinds of instruments, such as radars (e.g., Manson et al., 2002; Gavrilov et al.,
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Corresponding author. Tel.: +49 38293 68204; fax: + 49 38293 6850. E-mail address: [email protected] (M. Placke).
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2002; Jacobi et al., 2006), lidars (e.g., Rauthe et al., 2008), rocket soundings (e.g., Rapp et al., 2004) or satellites like TIMED (SABER) (e.g., Krebsbach and Preusse, 2007), have been used. Lidar measurements are frequently limited to nighttime and demand cloudless conditions. Rocket soundings can provide detailed information about the vertical structure of the mesosphere, but they are expensive and the data has a strongly limited time resolution. Satellites can measure the potential energy of GW activity globally, but due to their usually coarse global resolution and their limited scanning angle they cannot capture the whole spectrum of GW (Alexander et al., 2002). Medium frequency radars (e.g., Manson et al., 2002) are able to measure wind variance continuously, while recently Hocking (2005) has proposed a method to derive also GW momentum fluxes from all-sky meteor radars. These radars detect the wind-drifted ionized ambipolar plasma trails from meteoroids which enter the Earth’s atmosphere. The Doppler shift of these plasma trails is suitable to determine the radial drift velocity, which is mainly caused by the neutral wind field in the altitude of observation and thus contains also information of GW (and planetary wave) activity. Since meteor radars operate continuously, automatic, and with relatively less expense, they are suitable to monitor GW at long time scales. There have been a number of studies using radar that presented the climatology of GW over some stations at midlatitudes. Most studies use the horizontal wind variances as a proxy for GW activity. Generally, GW amplitudes increase with
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altitude and maximize in winter; however, in some cases a summer maximum at lower MLT altitudes has been reported (Gavrilov et al., 2002; Jacobi et al., 2006; Beldon et al., 2009). Thus, in the lower thermosphere GW maximize during winter, while in the upper mesosphere large amplitudes are reported both for summer and winter. Since meteor radar based climatologies of MLT GW are still rare, we present GW variances and fluxes over Collm, Germany (51.31N, 13.01E), based on the method proposed by Hocking (2005), which has been applied to the wind measurements obtained from the Collm meteor radar operating since summer 2004. The paper is organized as follows. In Section 2, the radar system and data analysis is briefly described, including a description of a correction for background wind shear influence on the variance analysis. In Section 3, the seasonal variation of GW variances and momentum fluxes is presented, including a discussion of their interannual variability. Section 4 concludes the paper by summarizing the obtained results for GW activity and interaction with the background circulation.
2. Collm meteor radar measurements and data analysis An all-sky meteor radar has been operated at Collm (51.31N, 13.01E) continuously since summer 2004 to measure hourly winds and temperatures in the height region between 80 and 100 km (Jacobi et al., 2007; Stober et al., 2008). The radar operates at a frequency of 36.2 MHz and a peak power of 6 kW, a pulse length of about 13 ms and a pulse repetition frequency (PRF) of 2144 Hz with a 4-point coherent integration, resulting in an effective PRF of 536 Hz. The radar includes a 3-element Yagi transmitter antenna and a 5-antenna interferometer, consisting of 2-element Yagi receiving antennas with horizontal distance of 2 or 2.5 wavelengths, respectively, to detect meteor positions. Radial winds for single meteors are detected through the phase progression of the received signal at each receiver. The hourly or 2-hourly mean winds are analyzed through projecting the hourly or 2-hourly mean wind to each radial wind vector in the respective time interval and minimizing the squared differences. Basic features of the data analysis have been described by Hocking et al. (2001). In the present analysis, we disregard meteors that have zenith angles of less than 101 or more than 501 as well as those with a radial drift velocity greater than 200 m/s. The limit to 501 zenith angle is explainable by the antenna pattern of a Yagi antenna (Singer et al., 2004; Stober et al., 2010). Meteors with larger zenith angles are outside the 3-dB beam width so that the derived estimates of height and radial drift velocity have an increased uncertainty. In addition, the distance between the radar and the meteors becomes too large for an exact detection, or even exceeds the radar range. The limitation to 101 off zenith as minimum has been chosen because meteor detections close to the zenith are very rare and partly give imprecise results for the azimuth angle. The used limit for the radial drift velocity avoids the inclusion of rapidly decaying meteors. These meteors tend to produce aliased radial drift velocity estimates of sometimes above 200 m/s. The decay time of the meteor defines the resolution of the Doppler spectrum from which the radial drift velocity is determined. The employed effective PRF does not allow to resolve decay times (lasting from the peak amplitude till the complete fading of the echo into the cosmic noise background) below 10 ms, which would result in an aliasing velocity of approximately 200 m/s. Meteors with such rapid decay times are rare events, but sometimes included in the data and have to be removed. The background wind data evaluation has been carried out for 6 height gates, each of 3 km vertical extent and centered at 82, 85,
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88, 91, 94 and 97 km. As maximum meteor rates are measured at about 90 km altitude (e.g., Stober et al., 2008), the GW variance and momentum flux results with highest accuracy and least data gaps can be obtained near that height. The continuous operation of meteor radars makes it possible to derive wind variances (u0 2, v0 2, w0 2) and GW momentum fluxes (u0 v0 , u0 w0 , v0 w0 ) in the MLT at time scales of 2 h by applying the method proposed by Hocking (2005) to the meteor radar data sets. Here u0 , v0 and w0 are the fluctuating eastward, northward and vertical winds. Note that for the mean second moments overbars (as usually used for labeling mean values) are omitted in our notation. The method includes a least squares fit of the deviation between the measured radial wind velocity and the mean winds projected onto the radial winds. It is assumed that this fluctuation is essentially associated with GW (Hocking, 2005). For determining the demanded parameters with the meteor radar at Collm an averaging interval of 2 h was found to be optimum. In case of shorter time intervals there is too little data extent of meteor events to get reliable results. For longer averaging intervals more than 2 h the temporal resolution is reduced. Furthermore, possible variance owing to the 6- or 8-h tide may affect the results when using too long analysis intervals. For further analysis, data points with 2-hourly horizontal mean winds above 150 m/s and 2-hourly meteor counts less than 30 per height gate were disregarded. The threshold of 150 m/s for the horizontal wind is derived from statistics. There are only a few events where the wind velocity did exceed this value, which are mainly caused by radial drift velocities close to 200 m/s. Importantly, this limit is also in line with the vast data set of high resolution in-situ wind measurements presented by Larsen (2002, see Figs. 2–4 of the same reference). The limitation of the minimum meteor counts is necessary to guarantee calculations with a sufficient number of reasonably uniformly distributed meteors. In case of too few data points as often appearing in the upper and lowermost height gate no results can be presented. Within each height gate in the case of background (prevailing and tidal) wind shear artificial variance is measured owing to the vertical distribution of meteors within the 2-h time interval. This is due to the fact that the calculated mean wind and variance is attributed to the nominal height at the center z0 of the height gate considered. Meteors are measured at any height, and therefore the true zonal background wind uðzÞ at the meteor height z is uðzÞ ¼ u0 þ ðzz0 Þ
@u þ , @z
ð1Þ
with u0 as the background zonal wind at z0 and @u=@z as the zonal wind shear at height z0. Therefore, even if hypothetically there should be no real variance of the wind field, an artificial variance deviation uu ¼ uðzÞu0 from the nominal background wind would be measured, which would lead to an apparent variance N 1 X @u 2 u0Shear 2 ¼ ðzi z0 Þ , ð2Þ N1 i ¼ 1 @z with N being the number of meteors within the 2-hourly wind interval. This additional shear-induced apparent variance is calculated using the measured background wind profile in the height gate considered and is then subtracted from the variance derived from the regression analysis according to Hocking (2005). For the meridional component, the calculations are performed accordingly using the 2-hourly mean meridional wind shear. The wind shear calculation has been performed by a polynomial fit to the background winds in the height gate under consideration and the adjacent ones to obtain an estimate of the true shear value at the nominal height. For illustration, Fig. 1 shows an example for the zonal variance u0 2 in July 2008 for the height gate
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Fig. 1. Left panel: correction term for the zonal wind variance according to the vertical wind shear versus vertical wind shear of the mean zonal wind for the height gate of 3-km width around 88 km. The solid line represents theoretical values assuming that the meteor heights are equally distributed and the shear is constant over the height gate. Right panel: vertical distribution of the monthly mean zonal wind and the monthly mean absolute value of the wind shear as well as of the original and corrected zonal wind variance. Graphs are based on July 2008 data.
Fig. 2. Histograms of the background zonal (left panel) and meridional (right panel) wind shear for the height gate of 3-km width around 88 km for July of the years 2005–2009 altogether.
86.5–89.5 km (nominal height 88 km). In the left panel for each 2-hourly wind interval the wind variance u0Shear 2 , which has to be subtracted from u0 2, is displayed versus the background shear @u=@z. The stronger the vertical wind shear, the larger is, generally speaking, the correction term for u0 2. This term, however, also depends on the actual distribution of meteor heights within the height gate considered. The solid line represents theoretical values assuming that the meteor heights are equally distributed and the shear is constant over the height gate. The measured values may differ slightly from this line, depending on the meteor height distribution. On the right-hand panel of Fig. 1 the height profiles of the monthly mean absolute shear 9@u=@z9 as well as the original and the corrected zonal wind variances are presented. The monthly mean vertical zonal wind profile is also shown. Note, however, that tides, which may reach amplitudes of more than 50 m/s in winter (e.g., Arras et al., 2009) are averaged out in this curve, so that its gradient does not equal the wind shear curve. Wind shears owing to tides frequently are much larger than the background shear, so that the presented @u=@z is much larger than the one that would be determined from the background wind profile alone, which is especially the case at the upper height gates. It has to be taken into account that due to low meteor count rates as often appearing in the uppermost and lowermost height
gate (nominal heights 82 and 97 km) no results can be presented for these heights. On an average the correction term for u0 2 calculated for each height gate varies between 50 and 200 m2/s2 in July, which makes the corrected variance varying from about 20 to 220 m2/s2. Larger wind shear values correspond with stronger wind variance correction. Fig. 2 shows a histogram of the July background zonal and meridional 2-hourly wind shear values for the years 2005–2009. For both components the histograms have a Gaussian distribution centered at about 5 m/s/km for @u=@z and 0 m/s/km for @v=@z, respectively. The difference between the two components is due to the strong background zonal wind shear in the summer MLT. The majority of the shear values are found between 10 and 20 m/s/km. The corresponding wind shear corrections on the variances according to Eq. (2) are shown in Fig. 3. It can be seen that small correction values between 0 and 20 m2/s2 appear most frequently. For the meridional component small values are found more frequently than for the zonal component, so that in general the wind shear correction effect on the meridional variances is somewhat weaker than on the zonal variances. We did not apply any correction for the vertical momentum flux estimates u0 w0 and v0 w0 . Besides the practical reasons, i.e. no
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Fig. 3. Histograms of the zonal (left panel) and meridional (right panel) wind shear correction on the variances for the height gate of 3-km width around 88 km for July of the years 2005–2009 altogether.
Fig. 4. Height–time cross-sections for the wind shear corrected zonal (upper panel) and meridional (middle panel) wind variance. Running averages over 28 days shifted by 7 days have each been averaged within the period of 5 years (August 2004–July 2009). The 5-yr mean number of meteors per day summed for the altitude range between 83.5 and 95.5 km is shown in the lowermost panel.
information is available about possible mean vertical wind and wind shear, we may assume that the fluctuations of the horizontal wind due to the background wind profile are uncorrelated with the vertical wind fluctuations.
3. Results 3.1. Seasonal variation of variances The 5-yr mean annual variation and vertical structure of GW variances is displayed in Fig. 4. The data consists of 28-day averages shifted by 7 days, and a 5-yr mean annual cycle has been constructed using the variances from August 2004 to July 2009. For the log-normal-distributed variances according to Baumgaertner and McDonald (2007) medians are used for averaging. Note that the variance values in the upper and lowermost height gates are less reliable than those around 90 km altitude due to lower meteor count rates. The 5-yr mean number of meteors is shown in the lowermost panel of Fig. 4 with
a time step of 1 day presenting the meteor counts after applying all previously described selection criteria for the covered altitude range between 83.5 and 95.5 km. Note that these count rates are much smaller than previously presented ones from this radar (e.g., Arras et al., 2009), owing to the strict selection criteria applied here. The annual cycle of the meteor rates displays the known maximum in early summer and lower count rates during the other seasons. The values vary between about 100 and 1500 meteors per day within the selected 12-km altitude range. Several peaks in-between the diagram arise from meteor showers like the Quadrantids at the beginning of January, the Perseids at the end of July to mid of August, and the Geminids in the first half of December. Examples of meteor count rates at other sites can be found, e.g., in Singer et al. (2004); however, when comparing our results with these data it has to be taken into account that meteor detection depends, among others, on frequency, power, and antenna configuration. The zonal and meridional wind variances in the upper panels of Fig. 4 vary approximately between 100 and 250 m2/s2 and show, more expressed in the zonal component, a semi-annual variation of the GW activity with a mesospheric maximum in summer and a secondary weaker maximum in winter as well as two minima around the equinoxes. Note that around 90 km variances increase with height in winter, while they decrease with height in summer. Assuming saturation of GW, according to linear theory their amplitudes are proportional to the intrinsic phase speed and therefore decrease with increasing eastward background wind above the MLT wind reversal in summer. In contrast in winter at the upper height gates, where GW amplitudes are still increasing due to decrease in density, the variances are larger than in summer. The strong summer maximum of the variances coincides with the maximum vertical background wind gradient which can be seen from the vertical distribution of zonal winds in the upper panel of Fig. 5. Here the 5-yr mean annual prevailing zonal wind cycle is displayed which has been constructed in the same way as in Fig. 4, but instead of medians arithmetic means are calculated as the mean wind is Gaussian-distributed (not shown here). The seasonal variance minima occur during times with small vertical zonal wind shear and generally weak prevailing wind. Qualitatively comparable results for mid-latitudes have been presented by Gavrilov et al. (2002) and Jacobi et al. (2006). A similar method to derive GW variances from meteor radar measurements has been described by Mitchell and Beldon (2009) and has been applied to meteor radar data at Esrange, Sweden (67.91N, 21.11E), in the Arctic by Beldon and Mitchell (2009). There a comparable behavior of the GW activity with the described semi-annual cycle has also been pointed out for polar
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Fig. 5. Height–time cross-sections for the mean zonal wind u (upper panel) and the vertical flux of zonal momentum u0 w0 (lower panel). Running averages over 28 days shifted by 7 days, which have each been averaged within the period of 5 years (August 2004–July 2009), are displayed
least squares fit of mean winds and tides to the measured data. These prevailing winds may differ from our means in the presence of strong tides, especially in winter, since in our case the data points are unevenly distributed in the course of one day, which is owing to the low afternoon meteor count rates and our comparatively strict selection criteria. The measured maximum mean zonal winds are about 40 m/s in both eastward and westward direction. In the meridional wind a southward flow during summer and a northward flow in winter dominate, but with values between 20 and 20 m/s, such that v is weaker than the zonal mean wind. For the momentum fluxes u0 w0 and v0 w0 positive values (yellow) characterize vertical transport of eastward/northward directed momentum and negative values (blue) westward/southward directed momentum flux, respectively. Eastward directed vertical momentum flux occurs mainly where approximately westward zonal wind is prevailing and vice versa. This shows the coupling of u0 w0 and u directly. In general, GW can only propagate upward when they move against the background wind, otherwise they would have encountered a critical line in the mesosphere. If westerly wind is prevailing only westward propagating GW with negative zonal momentum flux can move vertically. The same applies to easterly wind in which only eastward propagating GW with positive zonal momentum flux can reach higher altitudes. If a wave is breaking it imposes its momentum on the background flow and induces a force on it which leads to a wind reversal. This effect can be seen in Fig. 5 as the summer zonal wind reversal occurs above the region of positive values of the GW momentum flux. Further it is noticeable that the vertical flux of meridional momentum v0 w0 has most positive values when v is negative and has maximum negative values for positive v which shows that there exists a coupling in the meridional components as well.
3.3. Interannual variability
Fig. 6. As in Fig. 5, but for the mean meridional wind v (upper panel) and the vertical flux of meridional momentum v0 w0 (lower panel).
latitudes. The variance values shown there, which combine the zonal and the meridional component, are in good agreement with those presented in Fig. 4. 3.2. Seasonal cycle of momentum fluxes Of special interest is the variation of the vertical fluxes of zonal (u0 w0 ) and meridional (v0 w0 ) momentum in combination with the mean zonal (u) and meridional (v) wind, which may give insight into the coupling processes between GW and the background circulation. Figs. 5 and 6 present height–time cross-sections of the background zonal and meridional wind (upper panels) as well as of u0 w0 and v0 w0 (lower panels). In both figures 5-yr mean annual cycles are displayed which have been constructed in the same way as in Fig. 4 with the difference that for the Gaussiandistributed mean winds and momentum fluxes arithmetic means are used for averaging. The mean zonal wind shows the wellknown annual variation in the mesosphere with westward directed wind in the summer mesosphere, eastward winds in the summer lower thermosphere and predominantly eastward directed wind in the winter season as well as a semi-annual oscillation in the lower thermosphere. Note, however, that radar wind climatologies usually show prevailing winds (e.g., Schminder et al., 1997; Jacobi et al., 2007) calculated through a
Fig. 7 displays time series of the 3-monthly mean zonal wind variance u0 2 and the 3-monthly mean prevailing zonal wind u for winter (December through February, DJF), spring (March through May, MAM), summer (June through August, JJA) and fall (September through November, SON) for the height gate 86.5–89.5 km (nominal height 88 km) to show the interannual variability of mean winds and GW. It can be seen that the highest values for both mean wind and variance are measured in summer, and the wind variances also show the smallest year-to-year variability then. In winter and, a bit less pronounced, in fall the variability is strongest which is due to the influence of planetary waves on the middle atmosphere circulation. However, the interannual variability of GW variance especially in winter (DJF) shown may also be partly due to the smaller meteor count rates compared to summer. The winter mean wind and variance curves show a similar behavior with minima in 2005 and 2008 and maxima in 2006 and 2009 which follows from the proportionality of GW amplitudes to the magnitude of the intrinsic phase speed 9c u9 in case of wave saturation (e.g., Fritts et al., 2002). Owing to wave filtering in the mesospheric eastward mean winds, preferably westward propagating GW with negative phase speeds reach the MLT, so that small/large mean wind values will be connected with small/large 9c u9 and thus small/large GW amplitudes. In summer (JJA), a generally (although very weakly) decreasing zonal mean wind trend is connected with an increasing trend of zonal variance. GW in summer are filtered through the mesospheric easterlies; therefore preferably eastward propagating GW with positive phase speed reach the MLT, so that small/large zonal wind values are connected with large/small intrinsic phase speeds and thus large/small GW
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Fig. 7. Interannual variation of the mean zonal wind variance (upper panel) and the mean zonal wind (lower panel) for the height gate of 3-km width around 88 km for winter (DJF), spring (MAM), summer (JJA) and fall (SON). Error bars present the standard deviation of the mean values.
Fig. 8. As in the upper panel of Fig. 7, except for the mean meridional wind variance.
amplitudes. Interpretation of the spring (MAM) and fall (SON) variances is difficult, since these ‘‘seasons’’ in fact represent a mixture of winter and summer circulation, with, in particular for MAM, completely different background circulation during the single months. Fig. 7 shows a general weak tendency towards an increase of GW activity from 2005 through 2009. As discussed above, according to linear theory this may be owing to increasing mean winds, both easterly and westerly, in the mesosphere, the former being connected with decreasing westerly winds in the lower thermosphere. The increasing trend is, except for JJA and DJF, also visible in the meridional variance component shown in Fig. 8. The interannual variability of the meridional variance does not seem to be strongly correlated with the background zonal wind, although the summer and fall v0 2 seem to be weakly
anticorrelated with the mean wind. Note, however, that the described trends are weak, and clearly not significant, so that any conclusion drawn from these should be considered with utmost care.
4. Conclusions The method proposed by Hocking (2005) has been applied to the meteor radar measurements at Collm to determine wind variances caused by short period GW and their related momentum fluxes. The obtained results are in correspondence with literature results. It has been observed that the GW activity in the upper mesosphere has a semi-annual oscillation with the main maximum occurring in summer and a minor one in winter. GW
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amplitudes in winter increase with height, while they decrease with altitude in the summer lower thermosphere above 90 km. Around the equinoxes the GW activity has its minima. The connection between the GW momentum fluxes and the mean horizontal background winds has been pointed out clearly by regarding these components in their seasonal cycles, and considering their vertical profiles. Generally, mean winds and momentum fluxes tend to be anticorrelated, which is due to wind filtering of GW. Here the importance of GW in influencing the dynamics of the middle atmosphere can be shown directly. The interannual variation of mean wind and mean wind variance displays some correspondence of both parameters especially in summer and winter and the GW variances have a tendency to increase between 2005 and 2009.
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Gravity wave momentum fluxes in the MLT—Part I: Seasonal variation at Collm (51.31N, 13.01E) Manja Placke a,n, Gunter Stober a, Christoph Jacobi b a b
Leibniz Institute of Atmospheric Physics at the Rostock University, K¨ uhlungsborn, Germany Institute for Meteorology, University of Leipzig, Stephanstr. 3, 04103 Leipzig, Germany
a r t i c l e in f o
a b s t r a c t
Article history: Received 20 November 2009 Received in revised form 21 May 2010 Accepted 14 July 2010 Available online 1 August 2010
By using a method presented by Hocking (2005), wind variances and gravity wave momentum fluxes in the mesosphere and lower thermosphere have been derived from all-sky interferometric meteor radar wind measurements at Collm, Germany (51.31N, 13.01E), during 2004 through 2009. We present vertical profiles of long-term mean monthly mean variances and momentum fluxes. Gravity wave variances show a semi-annual oscillation around 90 km altitude, and generally increase with height. An exception are the summer months where the maximum is measured around 88 km and amplitudes are decreasing above that height. A comparison of momentum fluxes and mean background winds emphasizes the coupling between gravity waves and the background circulation. 3-monthly means of wind and wind variance for individual seasons show stronger interannual variability in winter than in summer, and an indication for mesosphere wind filtering of gravity waves being responsible for this variability. & 2010 Elsevier Ltd. All rights reserved.
Keywords: Gravity waves Middle atmosphere dynamics Meteor radar
1. Introduction Gravity waves (GW) are vertically and horizontally propagating waves that are associated with the buoyancy restoring force in a stably stratified atmosphere (e.g., Andrews et al., 1987). They are generally forced in the lower atmosphere, for instance through orography, at fronts, jets, through geostrophic adjustment or convective processes (e.g., Pavelin and Whiteway, 2002), and depending on the middle atmosphere circulation, may propagate upward to the mesosphere/lower thermosphere (MLT) region. Owing to the small horizontal phase speed of GW, which is of the order of the background mean flow, GW are very sensitive to wind filtering in the middle atmosphere. Therefore, their amplitudes depend on both the seasonal cycle as well as on planetary waves (e.g., Manson et al., 2003; Jacobi et al., 2006). Through transport and deposition of momentum and energy GW lead to the typical circulation patterns in the MLT such as the lower thermosphere wind reversal and the cold summer mesopause. Hence, the knowledge of GW momentum fluxes is crucial to understand the dynamics and energetics of the MLT as well as the coupling of atmospheric layers from the troposphere to the MLT. To obtain information about GW different kinds of instruments, such as radars (e.g., Manson et al., 2002; Gavrilov et al.,
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Corresponding author. Tel.: +49 38293 68204; fax: + 49 38293 6850. E-mail address: [email protected] (M. Placke).
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2002; Jacobi et al., 2006), lidars (e.g., Rauthe et al., 2008), rocket soundings (e.g., Rapp et al., 2004) or satellites like TIMED (SABER) (e.g., Krebsbach and Preusse, 2007), have been used. Lidar measurements are frequently limited to nighttime and demand cloudless conditions. Rocket soundings can provide detailed information about the vertical structure of the mesosphere, but they are expensive and the data has a strongly limited time resolution. Satellites can measure the potential energy of GW activity globally, but due to their usually coarse global resolution and their limited scanning angle they cannot capture the whole spectrum of GW (Alexander et al., 2002). Medium frequency radars (e.g., Manson et al., 2002) are able to measure wind variance continuously, while recently Hocking (2005) has proposed a method to derive also GW momentum fluxes from all-sky meteor radars. These radars detect the wind-drifted ionized ambipolar plasma trails from meteoroids which enter the Earth’s atmosphere. The Doppler shift of these plasma trails is suitable to determine the radial drift velocity, which is mainly caused by the neutral wind field in the altitude of observation and thus contains also information of GW (and planetary wave) activity. Since meteor radars operate continuously, automatic, and with relatively less expense, they are suitable to monitor GW at long time scales. There have been a number of studies using radar that presented the climatology of GW over some stations at midlatitudes. Most studies use the horizontal wind variances as a proxy for GW activity. Generally, GW amplitudes increase with
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altitude and maximize in winter; however, in some cases a summer maximum at lower MLT altitudes has been reported (Gavrilov et al., 2002; Jacobi et al., 2006; Beldon et al., 2009). Thus, in the lower thermosphere GW maximize during winter, while in the upper mesosphere large amplitudes are reported both for summer and winter. Since meteor radar based climatologies of MLT GW are still rare, we present GW variances and fluxes over Collm, Germany (51.31N, 13.01E), based on the method proposed by Hocking (2005), which has been applied to the wind measurements obtained from the Collm meteor radar operating since summer 2004. The paper is organized as follows. In Section 2, the radar system and data analysis is briefly described, including a description of a correction for background wind shear influence on the variance analysis. In Section 3, the seasonal variation of GW variances and momentum fluxes is presented, including a discussion of their interannual variability. Section 4 concludes the paper by summarizing the obtained results for GW activity and interaction with the background circulation.
2. Collm meteor radar measurements and data analysis An all-sky meteor radar has been operated at Collm (51.31N, 13.01E) continuously since summer 2004 to measure hourly winds and temperatures in the height region between 80 and 100 km (Jacobi et al., 2007; Stober et al., 2008). The radar operates at a frequency of 36.2 MHz and a peak power of 6 kW, a pulse length of about 13 ms and a pulse repetition frequency (PRF) of 2144 Hz with a 4-point coherent integration, resulting in an effective PRF of 536 Hz. The radar includes a 3-element Yagi transmitter antenna and a 5-antenna interferometer, consisting of 2-element Yagi receiving antennas with horizontal distance of 2 or 2.5 wavelengths, respectively, to detect meteor positions. Radial winds for single meteors are detected through the phase progression of the received signal at each receiver. The hourly or 2-hourly mean winds are analyzed through projecting the hourly or 2-hourly mean wind to each radial wind vector in the respective time interval and minimizing the squared differences. Basic features of the data analysis have been described by Hocking et al. (2001). In the present analysis, we disregard meteors that have zenith angles of less than 101 or more than 501 as well as those with a radial drift velocity greater than 200 m/s. The limit to 501 zenith angle is explainable by the antenna pattern of a Yagi antenna (Singer et al., 2004; Stober et al., 2010). Meteors with larger zenith angles are outside the 3-dB beam width so that the derived estimates of height and radial drift velocity have an increased uncertainty. In addition, the distance between the radar and the meteors becomes too large for an exact detection, or even exceeds the radar range. The limitation to 101 off zenith as minimum has been chosen because meteor detections close to the zenith are very rare and partly give imprecise results for the azimuth angle. The used limit for the radial drift velocity avoids the inclusion of rapidly decaying meteors. These meteors tend to produce aliased radial drift velocity estimates of sometimes above 200 m/s. The decay time of the meteor defines the resolution of the Doppler spectrum from which the radial drift velocity is determined. The employed effective PRF does not allow to resolve decay times (lasting from the peak amplitude till the complete fading of the echo into the cosmic noise background) below 10 ms, which would result in an aliasing velocity of approximately 200 m/s. Meteors with such rapid decay times are rare events, but sometimes included in the data and have to be removed. The background wind data evaluation has been carried out for 6 height gates, each of 3 km vertical extent and centered at 82, 85,
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88, 91, 94 and 97 km. As maximum meteor rates are measured at about 90 km altitude (e.g., Stober et al., 2008), the GW variance and momentum flux results with highest accuracy and least data gaps can be obtained near that height. The continuous operation of meteor radars makes it possible to derive wind variances (u0 2, v0 2, w0 2) and GW momentum fluxes (u0 v0 , u0 w0 , v0 w0 ) in the MLT at time scales of 2 h by applying the method proposed by Hocking (2005) to the meteor radar data sets. Here u0 , v0 and w0 are the fluctuating eastward, northward and vertical winds. Note that for the mean second moments overbars (as usually used for labeling mean values) are omitted in our notation. The method includes a least squares fit of the deviation between the measured radial wind velocity and the mean winds projected onto the radial winds. It is assumed that this fluctuation is essentially associated with GW (Hocking, 2005). For determining the demanded parameters with the meteor radar at Collm an averaging interval of 2 h was found to be optimum. In case of shorter time intervals there is too little data extent of meteor events to get reliable results. For longer averaging intervals more than 2 h the temporal resolution is reduced. Furthermore, possible variance owing to the 6- or 8-h tide may affect the results when using too long analysis intervals. For further analysis, data points with 2-hourly horizontal mean winds above 150 m/s and 2-hourly meteor counts less than 30 per height gate were disregarded. The threshold of 150 m/s for the horizontal wind is derived from statistics. There are only a few events where the wind velocity did exceed this value, which are mainly caused by radial drift velocities close to 200 m/s. Importantly, this limit is also in line with the vast data set of high resolution in-situ wind measurements presented by Larsen (2002, see Figs. 2–4 of the same reference). The limitation of the minimum meteor counts is necessary to guarantee calculations with a sufficient number of reasonably uniformly distributed meteors. In case of too few data points as often appearing in the upper and lowermost height gate no results can be presented. Within each height gate in the case of background (prevailing and tidal) wind shear artificial variance is measured owing to the vertical distribution of meteors within the 2-h time interval. This is due to the fact that the calculated mean wind and variance is attributed to the nominal height at the center z0 of the height gate considered. Meteors are measured at any height, and therefore the true zonal background wind uðzÞ at the meteor height z is uðzÞ ¼ u0 þ ðzz0 Þ
@u þ , @z
ð1Þ
with u0 as the background zonal wind at z0 and @u=@z as the zonal wind shear at height z0. Therefore, even if hypothetically there should be no real variance of the wind field, an artificial variance deviation uu ¼ uðzÞu0 from the nominal background wind would be measured, which would lead to an apparent variance N 1 X @u 2 u0Shear 2 ¼ ðzi z0 Þ , ð2Þ N1 i ¼ 1 @z with N being the number of meteors within the 2-hourly wind interval. This additional shear-induced apparent variance is calculated using the measured background wind profile in the height gate considered and is then subtracted from the variance derived from the regression analysis according to Hocking (2005). For the meridional component, the calculations are performed accordingly using the 2-hourly mean meridional wind shear. The wind shear calculation has been performed by a polynomial fit to the background winds in the height gate under consideration and the adjacent ones to obtain an estimate of the true shear value at the nominal height. For illustration, Fig. 1 shows an example for the zonal variance u0 2 in July 2008 for the height gate
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Fig. 1. Left panel: correction term for the zonal wind variance according to the vertical wind shear versus vertical wind shear of the mean zonal wind for the height gate of 3-km width around 88 km. The solid line represents theoretical values assuming that the meteor heights are equally distributed and the shear is constant over the height gate. Right panel: vertical distribution of the monthly mean zonal wind and the monthly mean absolute value of the wind shear as well as of the original and corrected zonal wind variance. Graphs are based on July 2008 data.
Fig. 2. Histograms of the background zonal (left panel) and meridional (right panel) wind shear for the height gate of 3-km width around 88 km for July of the years 2005–2009 altogether.
86.5–89.5 km (nominal height 88 km). In the left panel for each 2-hourly wind interval the wind variance u0Shear 2 , which has to be subtracted from u0 2, is displayed versus the background shear @u=@z. The stronger the vertical wind shear, the larger is, generally speaking, the correction term for u0 2. This term, however, also depends on the actual distribution of meteor heights within the height gate considered. The solid line represents theoretical values assuming that the meteor heights are equally distributed and the shear is constant over the height gate. The measured values may differ slightly from this line, depending on the meteor height distribution. On the right-hand panel of Fig. 1 the height profiles of the monthly mean absolute shear 9@u=@z9 as well as the original and the corrected zonal wind variances are presented. The monthly mean vertical zonal wind profile is also shown. Note, however, that tides, which may reach amplitudes of more than 50 m/s in winter (e.g., Arras et al., 2009) are averaged out in this curve, so that its gradient does not equal the wind shear curve. Wind shears owing to tides frequently are much larger than the background shear, so that the presented @u=@z is much larger than the one that would be determined from the background wind profile alone, which is especially the case at the upper height gates. It has to be taken into account that due to low meteor count rates as often appearing in the uppermost and lowermost height
gate (nominal heights 82 and 97 km) no results can be presented for these heights. On an average the correction term for u0 2 calculated for each height gate varies between 50 and 200 m2/s2 in July, which makes the corrected variance varying from about 20 to 220 m2/s2. Larger wind shear values correspond with stronger wind variance correction. Fig. 2 shows a histogram of the July background zonal and meridional 2-hourly wind shear values for the years 2005–2009. For both components the histograms have a Gaussian distribution centered at about 5 m/s/km for @u=@z and 0 m/s/km for @v=@z, respectively. The difference between the two components is due to the strong background zonal wind shear in the summer MLT. The majority of the shear values are found between 10 and 20 m/s/km. The corresponding wind shear corrections on the variances according to Eq. (2) are shown in Fig. 3. It can be seen that small correction values between 0 and 20 m2/s2 appear most frequently. For the meridional component small values are found more frequently than for the zonal component, so that in general the wind shear correction effect on the meridional variances is somewhat weaker than on the zonal variances. We did not apply any correction for the vertical momentum flux estimates u0 w0 and v0 w0 . Besides the practical reasons, i.e. no
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Fig. 3. Histograms of the zonal (left panel) and meridional (right panel) wind shear correction on the variances for the height gate of 3-km width around 88 km for July of the years 2005–2009 altogether.
Fig. 4. Height–time cross-sections for the wind shear corrected zonal (upper panel) and meridional (middle panel) wind variance. Running averages over 28 days shifted by 7 days have each been averaged within the period of 5 years (August 2004–July 2009). The 5-yr mean number of meteors per day summed for the altitude range between 83.5 and 95.5 km is shown in the lowermost panel.
information is available about possible mean vertical wind and wind shear, we may assume that the fluctuations of the horizontal wind due to the background wind profile are uncorrelated with the vertical wind fluctuations.
3. Results 3.1. Seasonal variation of variances The 5-yr mean annual variation and vertical structure of GW variances is displayed in Fig. 4. The data consists of 28-day averages shifted by 7 days, and a 5-yr mean annual cycle has been constructed using the variances from August 2004 to July 2009. For the log-normal-distributed variances according to Baumgaertner and McDonald (2007) medians are used for averaging. Note that the variance values in the upper and lowermost height gates are less reliable than those around 90 km altitude due to lower meteor count rates. The 5-yr mean number of meteors is shown in the lowermost panel of Fig. 4 with
a time step of 1 day presenting the meteor counts after applying all previously described selection criteria for the covered altitude range between 83.5 and 95.5 km. Note that these count rates are much smaller than previously presented ones from this radar (e.g., Arras et al., 2009), owing to the strict selection criteria applied here. The annual cycle of the meteor rates displays the known maximum in early summer and lower count rates during the other seasons. The values vary between about 100 and 1500 meteors per day within the selected 12-km altitude range. Several peaks in-between the diagram arise from meteor showers like the Quadrantids at the beginning of January, the Perseids at the end of July to mid of August, and the Geminids in the first half of December. Examples of meteor count rates at other sites can be found, e.g., in Singer et al. (2004); however, when comparing our results with these data it has to be taken into account that meteor detection depends, among others, on frequency, power, and antenna configuration. The zonal and meridional wind variances in the upper panels of Fig. 4 vary approximately between 100 and 250 m2/s2 and show, more expressed in the zonal component, a semi-annual variation of the GW activity with a mesospheric maximum in summer and a secondary weaker maximum in winter as well as two minima around the equinoxes. Note that around 90 km variances increase with height in winter, while they decrease with height in summer. Assuming saturation of GW, according to linear theory their amplitudes are proportional to the intrinsic phase speed and therefore decrease with increasing eastward background wind above the MLT wind reversal in summer. In contrast in winter at the upper height gates, where GW amplitudes are still increasing due to decrease in density, the variances are larger than in summer. The strong summer maximum of the variances coincides with the maximum vertical background wind gradient which can be seen from the vertical distribution of zonal winds in the upper panel of Fig. 5. Here the 5-yr mean annual prevailing zonal wind cycle is displayed which has been constructed in the same way as in Fig. 4, but instead of medians arithmetic means are calculated as the mean wind is Gaussian-distributed (not shown here). The seasonal variance minima occur during times with small vertical zonal wind shear and generally weak prevailing wind. Qualitatively comparable results for mid-latitudes have been presented by Gavrilov et al. (2002) and Jacobi et al. (2006). A similar method to derive GW variances from meteor radar measurements has been described by Mitchell and Beldon (2009) and has been applied to meteor radar data at Esrange, Sweden (67.91N, 21.11E), in the Arctic by Beldon and Mitchell (2009). There a comparable behavior of the GW activity with the described semi-annual cycle has also been pointed out for polar
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Fig. 5. Height–time cross-sections for the mean zonal wind u (upper panel) and the vertical flux of zonal momentum u0 w0 (lower panel). Running averages over 28 days shifted by 7 days, which have each been averaged within the period of 5 years (August 2004–July 2009), are displayed
least squares fit of mean winds and tides to the measured data. These prevailing winds may differ from our means in the presence of strong tides, especially in winter, since in our case the data points are unevenly distributed in the course of one day, which is owing to the low afternoon meteor count rates and our comparatively strict selection criteria. The measured maximum mean zonal winds are about 40 m/s in both eastward and westward direction. In the meridional wind a southward flow during summer and a northward flow in winter dominate, but with values between 20 and 20 m/s, such that v is weaker than the zonal mean wind. For the momentum fluxes u0 w0 and v0 w0 positive values (yellow) characterize vertical transport of eastward/northward directed momentum and negative values (blue) westward/southward directed momentum flux, respectively. Eastward directed vertical momentum flux occurs mainly where approximately westward zonal wind is prevailing and vice versa. This shows the coupling of u0 w0 and u directly. In general, GW can only propagate upward when they move against the background wind, otherwise they would have encountered a critical line in the mesosphere. If westerly wind is prevailing only westward propagating GW with negative zonal momentum flux can move vertically. The same applies to easterly wind in which only eastward propagating GW with positive zonal momentum flux can reach higher altitudes. If a wave is breaking it imposes its momentum on the background flow and induces a force on it which leads to a wind reversal. This effect can be seen in Fig. 5 as the summer zonal wind reversal occurs above the region of positive values of the GW momentum flux. Further it is noticeable that the vertical flux of meridional momentum v0 w0 has most positive values when v is negative and has maximum negative values for positive v which shows that there exists a coupling in the meridional components as well.
3.3. Interannual variability
Fig. 6. As in Fig. 5, but for the mean meridional wind v (upper panel) and the vertical flux of meridional momentum v0 w0 (lower panel).
latitudes. The variance values shown there, which combine the zonal and the meridional component, are in good agreement with those presented in Fig. 4. 3.2. Seasonal cycle of momentum fluxes Of special interest is the variation of the vertical fluxes of zonal (u0 w0 ) and meridional (v0 w0 ) momentum in combination with the mean zonal (u) and meridional (v) wind, which may give insight into the coupling processes between GW and the background circulation. Figs. 5 and 6 present height–time cross-sections of the background zonal and meridional wind (upper panels) as well as of u0 w0 and v0 w0 (lower panels). In both figures 5-yr mean annual cycles are displayed which have been constructed in the same way as in Fig. 4 with the difference that for the Gaussiandistributed mean winds and momentum fluxes arithmetic means are used for averaging. The mean zonal wind shows the wellknown annual variation in the mesosphere with westward directed wind in the summer mesosphere, eastward winds in the summer lower thermosphere and predominantly eastward directed wind in the winter season as well as a semi-annual oscillation in the lower thermosphere. Note, however, that radar wind climatologies usually show prevailing winds (e.g., Schminder et al., 1997; Jacobi et al., 2007) calculated through a
Fig. 7 displays time series of the 3-monthly mean zonal wind variance u0 2 and the 3-monthly mean prevailing zonal wind u for winter (December through February, DJF), spring (March through May, MAM), summer (June through August, JJA) and fall (September through November, SON) for the height gate 86.5–89.5 km (nominal height 88 km) to show the interannual variability of mean winds and GW. It can be seen that the highest values for both mean wind and variance are measured in summer, and the wind variances also show the smallest year-to-year variability then. In winter and, a bit less pronounced, in fall the variability is strongest which is due to the influence of planetary waves on the middle atmosphere circulation. However, the interannual variability of GW variance especially in winter (DJF) shown may also be partly due to the smaller meteor count rates compared to summer. The winter mean wind and variance curves show a similar behavior with minima in 2005 and 2008 and maxima in 2006 and 2009 which follows from the proportionality of GW amplitudes to the magnitude of the intrinsic phase speed 9c u9 in case of wave saturation (e.g., Fritts et al., 2002). Owing to wave filtering in the mesospheric eastward mean winds, preferably westward propagating GW with negative phase speeds reach the MLT, so that small/large mean wind values will be connected with small/large 9c u9 and thus small/large GW amplitudes. In summer (JJA), a generally (although very weakly) decreasing zonal mean wind trend is connected with an increasing trend of zonal variance. GW in summer are filtered through the mesospheric easterlies; therefore preferably eastward propagating GW with positive phase speed reach the MLT, so that small/large zonal wind values are connected with large/small intrinsic phase speeds and thus large/small GW
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Fig. 7. Interannual variation of the mean zonal wind variance (upper panel) and the mean zonal wind (lower panel) for the height gate of 3-km width around 88 km for winter (DJF), spring (MAM), summer (JJA) and fall (SON). Error bars present the standard deviation of the mean values.
Fig. 8. As in the upper panel of Fig. 7, except for the mean meridional wind variance.
amplitudes. Interpretation of the spring (MAM) and fall (SON) variances is difficult, since these ‘‘seasons’’ in fact represent a mixture of winter and summer circulation, with, in particular for MAM, completely different background circulation during the single months. Fig. 7 shows a general weak tendency towards an increase of GW activity from 2005 through 2009. As discussed above, according to linear theory this may be owing to increasing mean winds, both easterly and westerly, in the mesosphere, the former being connected with decreasing westerly winds in the lower thermosphere. The increasing trend is, except for JJA and DJF, also visible in the meridional variance component shown in Fig. 8. The interannual variability of the meridional variance does not seem to be strongly correlated with the background zonal wind, although the summer and fall v0 2 seem to be weakly
anticorrelated with the mean wind. Note, however, that the described trends are weak, and clearly not significant, so that any conclusion drawn from these should be considered with utmost care.
4. Conclusions The method proposed by Hocking (2005) has been applied to the meteor radar measurements at Collm to determine wind variances caused by short period GW and their related momentum fluxes. The obtained results are in correspondence with literature results. It has been observed that the GW activity in the upper mesosphere has a semi-annual oscillation with the main maximum occurring in summer and a minor one in winter. GW
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amplitudes in winter increase with height, while they decrease with altitude in the summer lower thermosphere above 90 km. Around the equinoxes the GW activity has its minima. The connection between the GW momentum fluxes and the mean horizontal background winds has been pointed out clearly by regarding these components in their seasonal cycles, and considering their vertical profiles. Generally, mean winds and momentum fluxes tend to be anticorrelated, which is due to wind filtering of GW. Here the importance of GW in influencing the dynamics of the middle atmosphere can be shown directly. The interannual variation of mean wind and mean wind variance displays some correspondence of both parameters especially in summer and winter and the GW variances have a tendency to increase between 2005 and 2009.
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