Ann. Geophys., 35, 161–170, 2017
www.ann-geophys.net/35/161/2017/
doi:10.5194/angeo-35-161-2017
© Author(s) 2017. CC Attribution 3.0 License.
Climatology of thermospheric neutral winds over Oukaïmeden
Observatory in Morocco
Mohamed Kaab1 , Zouhair Benkhaldoun1 , Daniel J. Fisher2 , Brian Harding2 , Aziza Bounhir1 , Jonathan J. Makela2 ,
Amine Laghriyeb1 , Khalifa Malki1 , Ahmed Daassou1 , and Mohamed Lazrek1
1 Oukaimeden
Observatory, Laboratoire de Physique des Hautes Energies et Astrophysique, FSSM, Cadi Ayyad University,
Marrakech, Morocco
2 Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
Correspondence to: Mohamed Kaab (
[email protected])
Received: 30 August 2016 – Revised: 5 January 2017 – Accepted: 5 January 2017 – Published: 30 January 2017
Abstract. In order to explore coupling between the thermosphere and ionosphere and to address the lack of data relating to thermospheric neutral winds and temperatures over the
African sector, a new system of instruments was installed
at the Oukaïmeden Observatory located in the high Atlas
Mountains, 75 km south of Marrakesh, Morocco (31.206◦ N,
7.866◦ W, 22.84◦ N magnetic). In this work we present the
first multi-year results of the climatology of meridional and
zonal winds obtained during the period from January 2014 to
February 2016, including observations from 648 nights. The
measurements are obtained using an imaging Fabry–Pérot interferometer, which measures the 630.0 nm emissions caused
by dissociative recombination of O+
2 . The basic climatology
of the winds is as expected, showing zonal winds that are
strongly eastward in the early evening just after sunset with
a speed of 50 to 100 m s−1 decreasing in magnitude, and reversing directions in the local summer months, towards sunrise. The meridional winds are slightly poleward in the early
evening during the local winter, before reversing directions
around 21:00 LT. In the local summer months, the meridional
winds are equatorward for the entire night, reaching a maximum equatorward speed of 75 m s−1 . We compare the observed climatologies of neutral winds to that provided by the
recently updated Horizontal Wind Model (HWM14) in order to validate that model’s predictions of the thermospheric
wind patterns over the eastern portion of Africa. The model
captures much of the features in the observational climatologies. The most notable exception is for the zonal winds during local summer, when the maximum eastward wind in the
observations occurs approximately 4 h later than seen in the
model results.
Keywords. Ionosphere (ionosphere–atmosphere interactions; ionospheric irregularities) – meteorology and
atmospheric dynamics (climatology)
1 Introduction
Space weather is a relatively new field of study and encompasses understanding how the near-space environment responds to forcing from lower-atmosphere weather systems
as well as conditions on the Sun. Although the specific effects of space weather – including power grid failures, communication outages, and navigation errors when using spacebased navigation systems such as the Global Positioning System – are local in nature, understanding and predicting their
occurrence requires a global view of the environment. Recognizing this, the United Nations has sponsored the International Space Weather Initiative (ISWI), a multi-national
program focused on advancing the understanding of space
weather through the global deployment of space weather sensors and the development of competencies within the developing world to work in this relatively new field.
ISWI was created as a follow-up activity after the successful International Heliophysics Year (IHY) in 2007 (Thompson et al., 2009). The ISWI is a program of international collaboration which aims to develop the scientific and experimental tools for understanding and predicting the weather
of the near-Earth space environment. To address the need
for global coverage of measurements, the ISWI has brought
together a variety of instrument providers and hosts from
around the world to build an unprecedented observing net-
Published by Copernicus Publications on behalf of the European Geosciences Union.
162
M. Kaab et al.: Climatology of thermospheric neutral winds over Oukaïmeden Observatory
work for space weather science. In comparison to other regions of the globe, the African sector has historically received the least amount of attention in terms of ground-based
ionospheric/thermospheric measurements. Most studies that
present statistics of space weather effects in the African sector are based upon satellite data (e.g., Huang et al., 2001;
Burke et al., 2004). As pointed out in Makela et al. (2004)
and Makela and Miller (2011), however, ground-based statistics and satellite-based statistics can vary significantly. This
is because a satellite in situ probe only makes measurements
at the location of a spacecraft as it moves through a specific
region at a specific altitude. In contrast, ground-based measurements provide, essentially, a continual monitoring capability of a given region of the upper atmosphere.
Previous studies have revealed that the African sector
presents unique features, not seen in other regions of the
world. For example, Hei et al. (2005) analyzed in situ measurements of topside plasma depletions made by the AE-E
satellite and found that the African sector is dominated by superchannels, unlike the single- and multichannel-dominated
Pacific sector. Furthermore, Yizengaw et al. (2011) noted
that the ionospheric E × B drift velocities were faster in the
American sector than the African sector. These studies do not
mention how the neutral parameters couple with the plasma
to create these features, suggesting that more detailed studies of the thermosphere/ionosphere over Africa are required.
Within the past several years, significant ground-based infrastructure has been deployed as part of the ISWI and other
initiatives to address the deficiency of measurements in the
African sector (e.g., Yizengaw et al., 2013). Despite this
progress, critical measurements of thermospheric winds and
temperatures are almost completely lacking in this region.
It was within the framework of the ISWI that in November 2013, a team of scientists from the University of Illinois
at Urbana-Champaign in the United States deployed a suite
of optical instruments at the Oukaïmeden Observatory in
the Atlas Mountains near Marrakesh, Morocco (31.206◦ N,
7.866◦ W, 22.84◦ N magnetic). They installed an imaging
Fabry–Pérot interferometer (FPI) similar to what is described
in Makela et al. (2009) in order to measure the nighttime thermospheric winds and temperatures. These neutral parameters
are crucial as they are a key driver of plasma motion. Similar systems previously deployed in other parts of the world
have demonstrated their utility in capturing the solar cycle,
seasonal, and daily fluctuations of the neutral winds (e.g.,
Emmert et al., 2006; Meriwether et al., 2011; Brum et al.,
2012; Fisher et al., 2015). Combined with data from existing sites in the American sector and elsewhere, the data from
Africa will play a critical role in understanding large-scale
tidal features in the upper atmosphere as well as understanding longitudinal variability in these larger-scale features. In
this paper, we present a 26-month climatology of the neutral winds as well as a comparison to the recently updated
Horizontal Wind Model (HWM14; Drob et al., 2015) and to
Ann. Geophys., 35, 161–170, 2017
Figure 1. A map of the measurement locations for the Fabry–Pérot
interferometer (FPI) in Oukaïmeden Observatory. Each dot represents a possible 250 km observation point of a single FPI measurement.
prior measurements made in different longitude sectors but
at similar geographic and geomagnetic latitudes.
2 Instrumentation
The FPI is an instrument designed to make highly accurate spectral measurements. The instrument produces a twodimensional ring pattern on a high-quality CCD, which can
be analyzed to produce an estimate of the line shape of an
emission. For the case studied here, the emission of interest,
selected by a narrowband interference filter in the instrument,
is that produced by the dissociative recombination of O+
2,
which naturally occurs in the lower thermosphere peaking at
an altitude of about 250 km (Link and Cogger, 1988). The
lifetime of the associated emission process is long enough
that the emitter thermalizes and, thus, the properties of the
emitted photons are indicative of the local thermosphere.
Therefore, the spectral shape measured by the FPI can be
related to the velocity of the emitting region through a measured Doppler shift, while the temperature can be related to
the broadening of the line shape.
The FPI deployed at the observatory has a relatively small
field of view (< 2◦ ) and so a SkyScanner system is used
to steer the field of view to different locations in the sky.
The SkyScanner is a pointing system mounted inside a large
protective dome. Two mirrors are controlled by a dual-axis
motor system to point the field of view in the desired direction. The SkyScanner is controlled by custom software,
which also commands the instrument to collect data. A typical observation mode cycles through a series of six specified
azimuth and elevation directions (zenith, laser, east, north,
west, and south). The integration time for each exposure of
the sky is determined based on the previous sky exposure in
such a way as to keep the uncertainty less than 10 m s−1 . Typical integration time can range from 30 s to 10 min, depending on the brightness of the emission. The “laser” direction
denotes an observation made of a frequency-stabilized HeNe
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M. Kaab et al.: Climatology of thermospheric neutral winds over Oukaïmeden Observatory
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0Jan
2014
Evolution of solar activity
300
250
200
F10. 7 SFU
Day's number
Monthly observation nights
163
150
100
50
0
Apr
2014
Jul
2014
Oct
2014
Jan
Apr
2015
2015
Month of year
Jul
2015
Oct
2015
Jan
2016
Figure 2. The number of observation nights per month from January 2014 until February 2016. The blue bar denotes the average
number of observations obtained each month over the period of
study.
laser, which is used to monitor the stability of the instrument
and provide an estimate of the instrument’s optical transfer
function (see Makela et al., 2011). The laser observations,
contained with the observations made towards zenith over the
entire night, are used to establish the unknown zero-Doppler
reference. Given the estimated instrument function and zeroDoppler reference, observations in the four cardinal directions can be analyzed to produce estimates of the horizontal
thermospheric neutral winds, using the method described in
Harding et al. (2014). Estimates of the zonal (meridional)
winds are made from the observations to the east and west
(north and south) and are assumed to be representative of that
wind component at the location where the given line-of-sight
intersects the emitting layer, assumed to occur at 250 km altitude. This observing geometry is shown in Fig. 1.
Nov
2010
Aug
2011
May
2012
Feb
2013
Days of year
Nov
2013
Aug
2014
May
2015
Feb
2016
vations presented here were made). Given the excellent observing conditions at the observatory, full coverage of all four
seasons was obtained.
In order to create climatologies, we adopted the method
detailed in Fisher et al. (2015). Following this, we eliminate
all non-physical values in the measured neutral winds that
are not automatically removed from initial processing. For
the zonal winds we removed values greater than 200 m s−1
and less than −100 m s−1 , and for the meridional winds
we removed values greater than 150 m s−1 and less than
−150 m s−1 . We also remove values if the estimated wind
uncertainties are more than 25 m s−1 . Finally, we do not consider the observations taken during cloudy periods, as determined by a collocated cloud sensor.
Then, for each month of observation, we sort and bin the
filtered data into 30 min intervals and, for each interval, calculate a weighted average, vm , and sample variability, e, such
that
3 Data and analysis
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Feb
2010
Figure 3. Evolution of the F10.7 from January 2009 to February 2016 with the period of available data (red).
vm =
After deployment of the instrumentation and an initial testing
period at the end of 2013, useful observations of the instrument began in January 2014. Figure 2 shows the number of
usable observing nights for the 26 months used in this study.
A total of 648 nights were collected, giving a monthly average equal to 24 nights a month (the blue bar in Fig. 2), representing a significant database from which climatological averages will be derived in this study. These nights extend the
initial climatologies presented by Fisher et al. (2015), where
the first year of observations from this site was presented.
The period covered in the present study coincides with a period of decreasing solar activity covering the end of the maximum of solar cycle 24 through its declining phase, as shown
in Fig. 3 (the red bar denotes the time during which obser-
May
2009
N
P
vi .wi
i
N
P
(1)
wi
i
and
e=
N
1 X
(vi − vm )2
N −1 i
!1/2
,
(2)
where vi , wi = 1/σi2 , and N are respectively the value of
the wind (zonal or meridional), its weight (the inverse of its
uncertainty, σi , squared), and the number of measurements in
the given bin. All data for a given month, regardless of year,
were grouped together in this process.
Examining the meridional winds presented in Fig. 4, we
see a clear seasonality, typical of a midlatitude station, in
the climatologies. In the local winter months (November–
February), poleward speeds of approximately 50 m s−1 are
Ann. Geophys., 35, 161–170, 2017
164
M. Kaab et al.: Climatology of thermospheric neutral winds over Oukaïmeden Observatory
Figure 4. Monthly averages of meridional winds from January 2014
until February 2016 (blue). Airglow-weighted model results from
HWM07 (red) and HWM14 (green). Positive values are northward.
seen in the early evening, reversing to equatorward flow
around 21:00 LT. No poleward flow is observed in the local summer months (June–August) post-sunset. Throughout
the year, equatorward winds are observed in the middle of
the night, having the largest magnitudes in the local summer
months (75 m s−1 ), with negligible meridional flow seen in
the middle of the night during the local winter months. The
peak in equatorward flow shifts from about 23:00 LT during
the spring equinox, to 01:00 LT during the summer solstice,
to 02:00 LT during the autumnal equinox. This flow reverses
once again, to poleward, between 01:00 and 03:00 LT during
the local winter months, but does not fully recover for local
summer months, with an equatorward flow of 50 m s−1 seen
at sunrise from April through August.
Measurements of the zonal winds are presented in Fig. 5.
The wind is eastward at the beginning of the night in all seasons and increases for the first several hours after sunset. A
maximum eastward speed of 100 m s−1 is observed at around
21:00 LT in the local winter months. The maximum eastward
flow transitions to later times and smaller amplitudes during the equinox periods and into the local summer, when the
maximum eastward flow of 75 m s−1 is observed around local midnight. After the maximum eastward flow is reached,
a gradual abatement is seen in all months. The wind reverses
to westward around 02:00 LT from April through September,
reaching a maximum westward flow of around 25 m s−1 . A
reversal is not seen during the other months.
Ann. Geophys., 35, 161–170, 2017
Figure 5. Monthly averages of zonal winds from January 2014
until February 2016 (blue). Airglow-weighted model results from
HWM07 (red) and HWM14 (green). Positive values are eastward.
4 Discussion
4.1
Comparison to climatological models
One source of uncertainty in conducting a comparison of the
observed climatologies and the estimated winds provided by
an empirical model is that the altitude of the emitting layer,
and thus the location of the observations, is unknown for the
imaging FPI measurements. Recently, Chartier et al. (2015)
performed a study that simulated the thermospheric observations of an FPI using several different models. For simulations of the low-latitude thermosphere, they concluded
that incorrectly assigning the altitude of the emission layer
could lead to erroneous comparisons and that the largest errors were introduced when the emitting layer was lower than
250 km, due to the larger altitudinal gradients in the winds at
low altitudes.
To investigate this potential effect on the observations presented here, we have simulated the altitudinal profiles of the
630.0 nm emission over the Oukaïmeden Observatory during
the period studied. Standard climatological models (IRI Bilitza et al. (2014) and MSIS Picone et al. (2002)) are used to
provide estimates of the atmospheric constituents required to
calculate the redline volume emission rate specified in Link
and Cogger (1988). The peak altitude of the emitting layer
is then found as a function of hour and day of year. Results
are shown in Fig. 6. It is clear that the standard assumption
that the emitting layer peaks at an altitude of 250 km is not
appropriate most of the time, although one could argue that
“on average” it is an appropriate value. Specifically, the layer
begins the night relatively low in altitude. By midnight, the
peak emission altitude has typically increased by 20–40 km
before decreasing to post-sunset altitudes before dawn. Seawww.ann-geophys.net/35/161/2017/
M. Kaab et al.: Climatology of thermospheric neutral winds over Oukaïmeden Observatory
630.0 nm peak altitude
6
2
Altitude [km]
Local time
4
300
290
280
270
260
250
240
230
220
210
200
0
22
20
18
Jan
2015
Mar
2015
May
2015
Jul
Sep
2015
2015
Day of year
Nov
2015
Jan
2016
Figure 6. Modeled 630.0 nm emission peak altitude over Oukaïmeden Observatory as a function of local time and day of year during
2015. The emission profile was modeled using the volume emission
rate described in Link and Cogger (1988), which requires utilizing
the IRI and MSIS models.
sonally, the layer is highest during the local spring and lowest during local winter. Failing to take these dynamics into
account could bias the comparison results to a model.
In this section, we compare the monthly climatologies to
the thermospheric wind patterns predicted by the Horizontal
Wind Model (HWM). This model was recently updated by
Drob et al. (2015), with one of the main new data sources in
the update being recent FPI data from the American sector.
For this sector, the improvement in specifying the thermospheric winds was significant compared to the previous iteration of HWM (HWM07; Drob et al., 2008). Prior comparisons to the imaging FPI observations in the American sector
to the HWM07 model had shown significant disagreements
(Makela et al., 2012). Since no new ground-based measurements of the thermospheric wind from the African sector
were included in the reformulation of HWM14, the comparisons presented here serve as an independent validation for
that model.
To account for variability due to the peak airglow layer
(mentioned above) in generating monthly statistics, we calculate the airglow-weighted HWM winds for comparison to
the observed FPI wind climatologies. Chartier et al. (2015)
found that this approach produced more satisfactory results
than assuming a fixed altitude of 250 km. Of course, the models used to predict the airglow layer’s volume emission rate
are empirical models, and so the actual day-to-day variability in the layer altitude will not be captured. However, we are
limited by not having actual observations of the layer altitude, or a proxy such as hmF2. The airglow-weighted winds
are estimated by
P
uz az
z
(3)
û = P ,
az
z
where uz are the horizontal winds from HWM at altitude z
and az is the calculated redline volume emission rate from
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165
Link and Cogger (1988) at altitude z. Both HWM and the
models needed for the volume emission rate do vary with
local time and season. All presented HWM data have been
airglow-weighted in order to provide more robust comparisons with our data.
The meridional wind comparison between the FPImeasured monthly climatologies (blue), airglow-weighted
HWM07 winds (red), and airglow-weighted HWM14 winds
(green) is presented in Fig. 4. In general, HWM14 correctly
captures the trends of the meridional wind pattern described
above for the monthly climatologies derived from the observations. That is, the early evening winds in the local winter
months are poleward, reducing in magnitude and eventually
reversing to equatorward near local midnight, then reducing
in magnitude again and reversing to poleward before local
sunrise. In the local summer, the initial meridional winds are
close to zero, or slightly equatorward, in agreement with the
data. Their equatorward magnitude increases towards local
midnight, remaining equatorward through the early morning
hours. This general agreement is improved as compared to
HWM07, especially for August–October, for which HWM07
shows significant disagreement with the observed climatologies. The improvement of HWM14 over HWM07 has been
noted in other studies (Liu et al., 2016).
A closer look reveals several disagreements between the
observations and HWM14. In April and May, the magnitude
of the maximum equatorward flow of HWM14 is underestimated by approximately 30 m s−1 . Conversely, in September
through November, HWM14 overestimates the magnitude of
the maximum equatorward flow by approximately 40 m s−1 .
We also see disagreement in the early evening hours of July
through September, during which the increase in the equatorial wind magnitude occurs earlier in the evening in HWM14
than it does in the observations.
Turning to the zonal winds in Fig. 5, we see general agreement of the measured climatologies and predictions of the
empirical model from September through February. The general trend of strong eastward winds in the early evening followed by a general abatement is seen in both the observations
and HWM14. However, during these months, the model disagrees with the observations immediately before sunrise, predicting westward winds that are about 50 m s−1 stronger than
those observed.
The agreement between the observations and HWM14
predictions of the zonal wind is less satisfactory for the other
months, especially from June through August. During these
months, there appears to be a significant phase shift between
the observed maximum in the eastward wind magnitude and
that predicted by HWM14. This phase shift is approximately
4 h, with the peak seen at around 20:00 LT in the model and
close to midnight in the observations. Similar to the comparisons surrounding the winter solstice discussed above, the
predicted zonal winds are significantly more westward immediately before sunrise during the months surrounding the
summer solstice.
Ann. Geophys., 35, 161–170, 2017
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M. Kaab et al.: Climatology of thermospheric neutral winds over Oukaïmeden Observatory
Zonal wind 04:00 LT
0.45
0.40
0.35
Power
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0
5
10
15
20
Frequency [1 yr -1]
Figure 7. A Lomb–Scargle periodogram of zonal winds over
Oukaïmeden Observatory at 04:00 LT. The squares represent the locations of the annual, semiannual, and terannual harmonics.
To further investigate the long-term variations in the thermospheric winds and their comparisons to the airglowweighted HWM14 neutral winds, we perform an analysis to
extract the annual, semiannual, and terannual variations in
the observations. This analysis is similar to that performed
by Yuan et al. (2013) for observations made at Xinglong station, in central China. Comparisons to those observations are
described below in Sect. 4.2. Here, we choose to look at
three local times over the observation period: pre-midnight
(20:00 LT ± 15 min), midnight (24:00 LT ± 15 min), and
post-midnight (04:00 LT ± 15 min). We first utilize a fast
Lomb–Scargle periodogram to pull out the dominant frequencies from each local time, binning for meridional and
zonal winds independently. Figure 7 shows an example of
the periodogram produced to find the strength of the harmonics in the wind data. Using this power spectrum, the leastsquares model returns a best-fit wave with a period of 1 year.
From this wave, we extract an offset and peak amplitude in
m s−1 as well as phase where the day of year (DOY) reports
the location of maximum amplitude. This single harmonic
is then subtracted from the observational data to get residuals for another Lomb–Scargle analysis. This cycle repeats
for a harmonic fitting for both a 6-month period and then a
4-month period.
We present the daily and seasonal variations in the measured neutral winds against the airglow-weighted HWM14
winds at 20:00, 24:00, and 04:00 LT for meridional and zonal
components in Figs. 8 and 9, respectively. Using the results of this analysis, we list the amplitudes and phases from
the annual, semiannual, and terannual components of the
harmonic reconstruction for both the measured winds and
airglow-weighted HWM14 winds in Table 1. The dominant
variation seen in the observations is the annual variation, with
the exception of the observed zonal winds at 24:00 LT, for
Ann. Geophys., 35, 161–170, 2017
Figure 8. A comparison of measured meridional winds to HWM at
fixed local times of 20:00, 24:00, and 04:00. The black dots represent a binned measurement, the red bars represent monthly averaged
FPI estimate, and the green dots represent the HWM14 airglowweighed estimate. Positive values are northward.
Figure 9. A comparison of measured zonal winds to HWM at fixed
local times of 20:00, 24:00, and 04:00. The black dots represent a
binned measurement, the red bars represent monthly averaged FPI
estimate, and the green dots represent the HWM14 airglow-weighed
estimate. Positive values are eastward.
which all three components have nearly equal amplitudes.
The phase of the observed annual variation is nearly constant
for the meridional wind (ranging from day of year 344–355),
with a bit more variation seen for the zonal component (ranging from day of year 8–47). The analysis of the HWM14 results also shows a dominant annual variation. However, disagreements in the amplitude of about 20 m s−1 and phase of
up to almost 50 days between the observations and modeling results are seen. For the amplitude, there is no consistent
biasing between the observations and model (that is, the amplitude of the modeled annual variation can be either larger
or smaller than the observations). However, the phase for
the meridional (zonal) wind is always later (earlier) for the
model.
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M. Kaab et al.: Climatology of thermospheric neutral winds over Oukaïmeden Observatory
167
Table 1. The annual, semiannual, and terannual harmonic fits to the meridional and zonal winds for the Moroccan FPI data, HWM07, and
HWM14 at 20:00, 24:00, and 04:00 LT. The amplitude is in m s−1 and the phase (in parentheses) is in DOY.
LT
Period (year)
Meridional wind
FPI
HWM07
HWM14
FPI
HWM07
HWM14
20
1
1/2
1/3
11.8 (344)
2.4 (45)
7.4 (6)
12.9 (301)
5.9 (81)
2.0 (76)
29.9 (8)
3.3 (114)
1.0 (121)
29.9 (23)
7.6 (130)
5.4 (70)
20.2 (199)
19.0 (147)
0.8 (4)
25.7 (359)
15.9 (146)
1.5 (90)
24
1
1/2
1/3
37.8 (346)
5.1 (52)
9.4 (64)
27.9 (324)
14.7 (93)
1.3 (98)
24.4 (24)
8.9 (148)
0.5 (117)
7.9 (47)
8.3 (28)
8.1 (76)
14.6 (100)
14.1 (56)
1.1 (16)
46.6 (359)
6.4 (105)
0.5 (20)
04
1
1/2
1/3
39.1 (355)
9.2 (84)
3.6 (4)
28.0 (344)
27.0 (91)
1.1 (40)
36.0 (356)
18.7 (104)
1.1 (14)
36.2 (8)
4.7 (8)
4.9 (66)
53.2 (362)
6.6 (54)
0.9 (32)
59.6 (359)
3.2 (156)
0.5 (6)
Turning to the terannual variations, we note that as intended, HWM does not fit this 4-month variation. However, the observations indicate that the terannual variation is
more significant than the semiannual variation for the early
evening meridional wind and of equal importance for the
zonal winds. Looking at the phases, we see that there are
also significant disagreements between the observations and
models for these components. This could be a limitation of
the analysis technique used, as the amplitudes of these components are on the order of the uncertainties in the measurements, however, Liu et al. (2016) found that, regionally and
seasonally, HWM14 underestimates the neutral winds when
compared to GOCE satellite measurements.
4.2
Zonal wind
Comparison to prior observations
Since there are such a limited number of Fabry–Pérot observatories, the database containing FPI measurements is
somewhat sparse, making it difficult to find direct corollaries. However, there are three prior studies that present
data from sites with roughly comparable latitudes as Morocco (31.206◦ N, 7.866◦ W, 22.84◦ N magnetic) and so
should share characteristics with the observations presented
here. Fisher et al. (2015) presented FPI data from Pisgah
Astronomical Research Institute (PARI; 35.2◦ N, 2.85◦ W,
47.63◦ N magnetic). This site is at nearly the same geographic latitude but a very different geomagnetic latitude and
longitude sector. Second, there is the work of Brum et al.
(2012), which presents a 30-year climatology of thermospheric winds for observations made above Arecibo Observatory in Puerto Rico (18.35◦ N, 66.75◦ W, 31.10◦ N magnetic). This site has a much closer geomagnetic latitude to
Oukaïmeden Observatory but is not close geographically in
latitude or longitude. Third, is prior work from Yuan et al.
(2013) which compared the FPI data from Xinglong station
in central China (40.2◦ N, 117.4◦ E, 35.57◦ N magnetic) to
HWM07. Though this site is not as close to Oukaïmeden in
either geographic or geomagnetic coordinates as either PARI
www.ann-geophys.net/35/161/2017/
or Arecibo, it is applicable to use as a comparison of how
the harmonic analysis at two sites changes between HWM07
and HWM14.
Figure 11 of Fisher et al. (2015) compared the first year
of FPI data over the Oukaïmeden Observatory to coincident
measurements taken from PARI. The extension of analysis
of data from Morocco presented here confirms the initial climatologies presented in this earlier paper. To summarize the
comparison presented by Fisher et al. (2015), good agreement was found between the two sites; both sets of observations showed eastward winds all year in the early evening
that decay towards a westward reversal before sunrise. They
also showed general agreement in the meridional winds being equatorward in local summer and poleward followed by
an equatorward reversal during the local winter. However,
the magnitude of the winds were shown to be slightly different for the two sites, with the local midnight equatorward
winds being stronger over PARI than Morocco. For the zonal
winds, differences were seen in the amplitude of the maximum eastward flow (with the winds typically being faster
over Morocco) as well as the timing of the reversal from eastward to westward flow (with the reversal taking place 2 h earlier in local time over PARI). Fisher et al. (2015) concluded
that, in line with the prior hypothesis presented by Wu et al.
(2014), some of these differences were likely attributable to
differences in the geomagnetic latitudes of the two sites. This
prompts the need to compare the data from Oukaïmeden with
data from a site at a similar geomagnetic latitude.
The Arecibo Observatory was chosen because it is closer
in geomagnetic latitude to the Oukaïmeden Observatory.
Brum et al. (2012) presented an analysis of 30 years of neutral wind measurements made from this site. For comparison,
we use results of the seasonal variations in the meridional
and zonal winds presented in their Fig. 8. First looking at the
meridional data, we see general agreement in all four seasons. The first point of agreement is the flow throughout the
local winter and equinox nights; winds start northward and
Ann. Geophys., 35, 161–170, 2017
168
M. Kaab et al.: Climatology of thermospheric neutral winds over Oukaïmeden Observatory
Table 2. The annual, semiannual, and terannual harmonic fits to the meridional and zonal winds for the Xinglong FPI data (after Table 1 of
Yuan et al., 2013), HWM07, and HWM14 at local midnight (24:00 LT). The amplitude is in m s−1 and the phase (in parentheses) is in DOY.
LT
24:00
Period (year)
1
1/2
1/3
Meridional wind
FPI
HWM07
HWM14
FPI
HWM07
HWM14
7.0 (238)
3.3 (151)
2.9 (21)
15.6 (235)
12.5 (175)
0.8 (119)
18.9 (290)
7.5 (87)
0.5 (57)
23.0 (2)
5.9 (18)
0.4 (102)
17.6 (256)
21.5 (165)
0.8 (16)
56.2 (272)
4.2 (138)
0.5 (100)
turn southward, reaching a maximum around local midnight,
and finally reversing again before sunrise. While the general pattern is shared between the two sites, the poleward-toequatorward and equatorward-to-poleward reversals (except
when they do not occur in the June solstice) occur earlier and
later, respectively over Oukaïmeden. The magnitudes are in
agreement for the equinoxes, but during the winter period,
the meridional winds over Morocco flow poleward for most
of the night, with only slight reversals to equatorward, which
is in contrast to Arecibo having a 40 m s−1 equatorward wind
around local midnight.
Next looking at the zonal measurements from Arecibo, we
again see general agreement in all four seasons. The winds
start eastward, increasing to a maximum value before local
midnight, and then slowly decay to 0 m s−1 (and even reversing in June). The magnitude of peak eastward flow over
Arecibo is nearly always 80 m s−1 , while the data collected
from Oukaïmeden vary more with season, maximizing at
110 m s−1 during March equinox and minimizing at 60 m s−1
during September equinox. The timing of this maximum is
also different between the two sites; during the equinoxes,
it occurs roughly 1 h earlier over Oukaïmeden than Arecibo.
Again, as with the comparison to HWM14 presented above,
the summer winds are different in this regard; winds measured over Arecibo have a maximum eastward wind 2 h before the maximum over Oukaïmeden. After local midnight,
the magnitude and timings of the winds are in good agreement. It should be noted that some of the disagreements
described above can be attributed to the differences in solar flux condition between the Morocco observations (see
Fig. 3) and those presented in Fig. 8 of Brum et al. (2012).
Specifically, our results were collected during the transition
between a weak solar maximum and solar minimum conditions, whereas the Brum et al. (2012) results in their Fig. 8
were presented for only low and high solar flux conditions.
Yuan et al. (2013) compared the FPI measurements from
Xinglong, China, at midnight local time to HWM07 by fitting three harmonics (annual, semiannual, and terannual) to
the winds. We have expanded their Table 1 pertaining to the
250 km altitude to include a harmonic analysis comparison
to the airglow-weighted HWM14 winds in our Table 2. We
have also recalculated the amplitude and phase for HWM07
using airglow-weighting in order to best see how HWM14
has changed from HWM07. We compare this to the HWM
Ann. Geophys., 35, 161–170, 2017
Zonal wind
changes seen over Oukaïmeden (from Table 1) to see how
the different regions are affected. Yuan et al. (2013) found
that the annual variation is dominant in the zonal winds. For
the meridional winds, the amplitudes of the annual component is much smaller, although still roughly double the other
two harmonic components for the meridional winds. The
HWM07 prediction for the amplitude of the zonal wind annual variation was slightly too small, while its prediction was
too large for the meridional wind annual variation. However,
it overemphasized the semiannual variation in both directions. On the other hand, HWM14 seems to have improved
in matching the semiannual variation, as the magnitudes have
been significantly reduced and are much closer to the observations. However, the amplitude of the annual variation is
greatly overestimated for both horizontal wind directions.
Over the Oukaïmeden Observatory, the change from
HWM07 to HWM14 brought similar results. The harmonic
analysis shows that the semiannual component was reduced
in HWM14, compared to HWM07, in all three local times.
Interestingly, the reduction in the semiannual component of
zonal winds at local midnight worsens the accuracy compared to HWM07. The terannual component of the Oukaïmeden data is stronger than in the Xinglong data at local midnight, suggesting that adding a terannual harmonic to the
Horizontal Wind Model could improve the fits in certain
geographic regions. This result also suggests that including FPI data from the Oukaïmeden Observatory in future
HWM implementations could help constrain the components
of the winds and improve the African sector estimates of the
250 km neutral winds.
5 Conclusions
We have presented the first multi-year thermospheric neutral wind climatology obtained in Oukaïmeden Observatory
for a period of 26 months coinciding with a solar maximum
period. This climatology is useful as a validation of upper
atmospheric neutral wind models over the African sector.
HWM14 generally compares well with the meridional and
zonal winds, but significant magnitude and phase differences
remain in certain seasons. The analysis of observed climatologies from different longitude sectors that are close in either geographic or geomagnetic latitude show general simwww.ann-geophys.net/35/161/2017/
M. Kaab et al.: Climatology of thermospheric neutral winds over Oukaïmeden Observatory
ilarities but also allude to the difficulties in parsing out the
control of geographically ordered versus geomagnetically ordered processes. A more comprehensive study of these effects will require additional data sources, such as those acquired from satellite platforms, and the deployment of additional ground-based instruments.
6 Data availability
The LOS wind and neutral temperature data used in
this study are freely available for use in the Madrigal
database http://madrigal.haystack.mit.edu/madrigal/. Please
contact Jonathan J. Makela (
[email protected]) before
using these data. This work uses pyglow, a Python package
that wraps several upper atmosphere climatological models.
The pyglow package is open-sourced and available at https:
//github.com/timduly4/pyglow/. Pyglow pulls the KP, AP and
F10.7 data from NOAA’s National Centers for Environmental Information: https://www.ngdc.noaa.gov/stp/geomag/kp_
ap.html. The website links to a publicly available FTP page
where all of the data can downloaded: ftp://ftp.ngdc.noaa.
gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. Work at the University of Illinois, including for the deployment of the instrumentation to Morocco,
was supported by National Science Foundation CEDAR grants
AGS 11-38998 and AGS 14-52291 as well as by NASA grant
NNX14AD46G. The authors would like to thank the Oukaïmeden
observatory and its staff for their support and assistance to the FPI
operations. The LOS wind and neutral temperature data used in
this study are freely available for use in the Madrigal database.
Please contact Jonathan J. Makela (
[email protected]) before
using these data.
The topical editor, P. J. Erickson, thanks three anonymous referees for help in evaluating this paper.
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