Winds measured by lidar from the Aeolus satellite are
compared with winds measured by two ground-based radars – MARA in Antarctica
(70.77∘ S, 11.73∘ E) and ESRAD (67.88∘ N,
21.10∘ E) in Arctic Sweden – for the period 1 July–31 December
2019. Aeolus is a demonstrator mission to test whether winds measured by
Doppler lidar from space can have sufficient accuracy to contribute to
improved weather forecasting. A comprehensive programme of calibration and
validation has been undertaken following the satellite launch in 2018, but,
so far, direct comparison with independent measurements from the Arctic or
Antarctic regions have not been made. The comparison covers heights from the
low troposphere to just above the tropopause. Results for each radar site
are presented separately for Rayleigh (clear) winds, Mie (cloudy) winds,
sunlit (“summer”) and non-sunlit (“winter”) seasons, and ascending and descending satellite tracks. Horizontally projected line-of-sight (HLOS) winds from
Aeolus, reprocessed using baseline 2B10, for passes within 100 km of the
radar sites, are compared with HLOS winds calculated from 1 h averaged
radar horizontal wind components. The agreement in most data subsets is very
good, with no evidence of significant biases (<1 m s-1).
Possible biases are identified for two subsets (about -2 m s-1 for
the Rayleigh winds for the descending passes at MARA and about 2 m s-1 for the Mie winds for the ascending passes at ESRAD, both in winter), but these are only marginally significant. A robust
significant bias of about 7 m s-1 is found for the Mie winds for the ascending tracks at MARA in summer. There is also some evidence for increased
random error (by about 1 m s-1) for the Aeolus Mie winds at MARA in
summer compared to winter. This might be related to the presence of sunlight
scatter over the whole of Antarctica as Aeolus transits across it during
summer.
Introduction
The Aeolus satellite is a European Space Agency (ESA) mission which aims to demonstrate the possibility of providing global wind measurements throughout
the troposphere and lower stratosphere using Doppler wind lidar, with good
enough accuracy for use in assimilations for numerical weather prediction.
Aeolus carries a single instrument – the Atmospheric Laser Doppler
Instrument (ALADIN) – with two detectors to analyse the backscattered laser
light from atmospheric molecules (Rayleigh scatter) and cloud/aerosol
particles (Mie scatter), respectively (Stoffelen et al., 2005; ESA, 2008;
Reitebuch, 2012). The line-of-sight component of the wind is calculated from
the Doppler shift of the backscattered light. Accurate measurement of the
Doppler shift of the backscattered light requires careful calibration of the
detectors, and a comprehensive process of calibration and validation was
planned for the mission. More details on the lidar performance and sources
of systematic and random errors are described in Reitebuch et al. (2020) and
Rennie and Isaksen (2020). Dedicated campaigns for comparative measurements
by airborne lidar and radiosonde profiling took place early in the mission
(Baars et al., 2020; Lux et al., 2020; Witschas et al., 2020). Comparisons
with ground-based radar and lidar observations, regular meteorological
measurements, and global assimilations have also been undertaken both for
specific locations (Khaykin et al., 2020; Guo et al., 2021) and on a global
scale (Martin et al., 2021; Rennie and Isaksen, 2020). Particularly the
dedicated campaign and global-scale studies have led to several changes in
the data processing as the instrument performance in the space environment
became better understood. For example, corrections have had to be made for
“hot pixels”, which are increased dark current rates for specific ALADIN ACCD (accumulation charge coupled device) detector pixels (Weiler et al., 2020) and for biases in line-of-sight winds of up to 5 m s-1, which were found to differ between ascending and descending nodes and between
different geographic regions (Martin et al., 2021; Rennie and Isaksen,
2020). These variable biases have been found to be largely caused by varying
temperature gradients across the instrument's mirror, and, after application
of corrections for the mirror effects, the biases have been reduced
considerably (to <2 m s-1), sufficient for Aeolus winds to be
able to improve weather forecasts (Rennie and Isaksen, 2020). Random errors
have been found to be larger than the initial goal (1–2 m s-1) due to
reduced laser power and signal losses in the receiver path (Reitebuch et
al., 2020). They have been found to be 4–5 m s-1 (Rayleigh) or 3 m s-1 (Mie), at least up to February 2020 (Rennie and Isaksen, 2020). So
far, validation for the polar regions has been based on the ECMWF global
assimilation model (Rennie and Isaksen, 2020). However, very few standard
upper-air meteorological measurements (radiosondes, aircraft in situ
sensors) are available in the polar regions, so the model's accuracy is not
well known in those regions. There is also a risk that different cloud
conditions, surface reflectivities and summer daylight in these regions
could lead to different performance of the lidar measurements. At the same
time, accurate lidar measurements over the polar regions would be a
particular asset to global weather forecasting and climate monitoring as
these regions are so poorly covered by other observations. Validation of the
Aeolus winds against direct independent wind measurements at polar latitudes
offers a possibility to begin to see to what extent polar conditions might
affect measurements, particularly whether scattered sunlight effects are as
theoretically predicted, since the long summer days result in ALADIN
measurements being made in full sunlight in those regions even with a
dawn–dusk orbit.
Overview of measurement platformsAeolus
The Aeolus satellite was launched on 22 August 2018 and lies in a dawn–dusk
sun-synchronous orbit (inclination 97∘), at a height of about 320 km and with a period of about 90 min. The laser is pointed downwards at
35∘ from nadir. In most atmospheric conditions, magnitudes of
vertical winds are small so that the Doppler shift of the backscattered
light is mainly determined by the line-of-sight component of the horizontal
wind. The laser is directed approximately perpendicular to the orbit track,
towards the night hemisphere, so as to minimize background scatter due to
sunlight. During the morning (descending) part of the track the azimuth of
the target-to-laser direction is about 100∘, and during the
evening (ascending) part the azimuth is about 260∘, so that the
Doppler shift of the backscattered signal is mainly due to zonal winds. At
high latitudes the azimuth changes gradually and, as a consequence,
meridional winds have more effect on the Doppler shift. Measurements are typically made using 18–20 laser pulses (3 km of track distance), with
returns integrated before height profiles of scattering characteristics and
Doppler shift are extracted. In the early part of the mission, 30 of these
measurements were then analysed as a group to pick separate valid Mie
(cloudy) and Rayleigh (clear) returns, leading to profiles of line-of-sight
wind corresponding to about an 87 km horizontal track (Reitebuch et al., 2020). The group length for Mie winds was later reduced to 14 km (horizontal averaging lengths smaller than 14 km are also possible) to improve the
impact of the retrieved Aeolus winds on numerical weather prediction (Rennie
and Isaksen, 2020).
Processed data including the mirror correction (baseline 2B10) have been
available for new observations since April 2020; however, a different
algorithm for the mirror correction was introduced in October 2020 (baseline
2B11). A period of homogeneous reprocessed data is also available using
baseline 2B10 from July–December 2019. The move to baseline 2B10 and higher
has been found to make considerable improvements to biases generally (Martin
et al., 2021; Rennie and Isaksen, 2020), so it is most relevant to compare
with these baselines. Because of long lead times for data transfer from
Antarctica, at the time of writing, the most recent data available from MARA
were 31 December 2019, so we focus on the time interval 1 July–31 December
2019.
MARA radar
The MARA wind-profiler radar is located at the Indian Antarctic station,
Maitri, at 70.77∘ S, 11.73∘ E. It has been operational
at that site since February 2014 (earlier located at other sites in Antarctica; see, e.g., Kirkwood et al., 2007; Mihalikova and Kirkwood, 2013).
It operates continuously except for occasional breaks for repairs and
maintenance and measures three components of wind in the
troposphere/lowermost stratosphere at heights from about 500 m to 12 km,
with interlaced measurement modes (1 min each) giving a height resolution
of 75, 150 and 600 m. The radar operates at a frequency of 54.5 MHz, and
signal is scattered back from irregularities in the refractive index of the
air. The radar beam is vertical, with a beam width of about 12∘,
corresponding to a 2000 m diameter of the measurement volume at 10 km
height. Horizontal wind is derived from the horizontal motion of the
diffraction pattern of the scattered signal across the antenna field
(Briggs, 1994), while vertical wind is directly derived from the Doppler
shift of the received backscatter. The strength of the scattered signal
(from refractive index gradients in the air) is strongly dependent on the
gradient of potential temperature (Kirkwood et al., 2010). This often leads
to a gap in coverage in the upper troposphere (approx. 6–9 km altitude)
where the temperature gradient is adiabatic. This restricts the heights
available for comparison with Aeolus; however, the continuous operation
allows comparison with any close Aeolus pass over the site (passes within
100 km occur about four times each week) over long periods of time. The
location of MARA and the typical tracks for the four Aeolus orbits passing
close by each week are shown in Fig. 1. The accuracy of wind measurements
made by MARA has been assessed by comparison with regular radiosondes from a
nearby station, over several months in 2014 (Belova et al., 2021). No
significant biases (<0.25 m s-1) were found and random
differences (standard deviation <4 m s-1, for 1 h averaged
winds) were low enough to indicate the possibility of a useful comparison with
Aeolus.
Maps showing the locations of the MARA (a) and ESRAD (b) radars and typical Aeolus measurement tracks for orbits passing within 100 km of the respective radar. Red are ascending tracks, blue descending. At MARA four tracks per week and at ESRAD three per week pass close to the radars.
ESRAD
The ESRAD wind-profiler radar is located at Esrange, near Kiruna in Arctic
Sweden, at 67.88∘ N, 21.10∘ E. It has been in continuous
operation at the same site since 1996 (Chilson et al., 1999). ESRAD operates
with a vertical beam, with a beamwidth of about 4∘, corresponding
to a 700 m diameter for the measuring volume at 10 km height. It is operated
in a similar fashion to MARA, with interlaced measurement modes with height
resolutions of 75, 150 and 900 m. Since 2019, for better comparison with
Aeolus, high signal-to-noise-ratio (SNR) measurements with 900 m resolution
have occupied 4 min of each 8 min cycle. ESRAD operates at 52 MHz, and, like MARA, the signals have low SNR in the upper troposphere. However,
ESRAD has higher power (about 1.5–2 times the power of MARA in 2019/2020) and a much larger antenna field (about 5 times the area of MARA), allowing
better height coverage, from about 500 to 14 000 m, with fewer data gaps in
the upper troposphere. The accuracy of the wind measurements made by ESRAD
has been assessed by comparison with 28 radiosondes launched at the same
site, during the period January 2017–August 2019 and with the regional NWP (numerical weather prediction) model HARMONIE-AROME for the period September 2018–May 2019 (Belova et al., 2021). These show a systematic underestimate of wind speed by about 8 % in zonal wind and 25 % in meridional wind at ESRAD, most likely due to
non-random noise which cannot be easily removed. However, since the
radiosonde and model comparisons cover the same time frame as the Aeolus
mission, a correction for the underestimate can be applied before comparison
with Aeolus. ESRAD–sonde random differences, after correction for the
systematic underestimate (standard deviation <5 m s-1, for 1 h
averaged winds) are again low enough to indicate the possibility of a useful
comparison with Aeolus. The location of ESRAD and the typical measurement
paths for the three Aeolus orbits which each week pass within 100 km of ESRAD
are shown in Fig. 1.
Polar plots of azimuth and angular distance (∘) from the radars of mean locations of all Aeolus wind profiles used for comparison. Mie profiles are shown in red for ascending tracks and blue is for descending ones. The along-track distance included in each Mie profile is about 14 km (0.13∘). Rayleigh profiles are shown in green for ascending tracks and light blue for descending. The along-track distance included in each Rayleigh profile is about 87 km (0.78∘). There are up to 14 (very sparsely populated) Mie cloudy profiles within 100 km radius of the radar along each orbit track. These are combined to make a single profile for comparison with the single radar profile, as detailed in the text.
Description of the wind dataAeolus HLOS winds
We use the Aeolus Level 2B (L2B) data product, with the 2B10 baseline which
includes a bias correction for mirror temperature variations. Rayleigh clear
and Mie cloudy winds are used. We use the period 1 July–31 December 2019
where a consistent reanalysed dataset is available. Aeolus HLOS (horizontal
projection of the line-of-sight component) Mie and Rayleigh winds, where the
distance between the mean position of a measurement and the radar was 100 km
or less, have been used for comparison. Rayleigh winds are accumulated and
averaged over 87 km, and we use only the measurement closest to the radar
even though, sometimes, there is more than one with a mean position within 100 km. We reject Rayleigh HLOS winds with an error estimate >8 m s-1 (and if the validity flag is 0). Aeolus range–bin settings vary between different latitude bands and are changed from time to time to
address different scientific or mission objectives. For the period July to
mid-October, the height coverage for Rayleigh winds over the MARA site was
up to 24 or 27 km (the Aeolus height bins also adjust to the ground
elevation, which varies from sea level to almost 3000 m between 100 km north
and south of MARA), with a height resolution between 500 and 2000 m; from
mid-October until December it was up to 17 or 20 km (apart from 2 weeks at the beginning of November when height coverage was only up to 14–17 km), with a height resolution between 500 and 1000 m. For the period of July to the end of October, the height coverage for Rayleigh winds over the ESRAD site was up to 24 km, with a height resolution between 500 and 2000 m; from
mid-October to December it was up to 20 km, with a height resolution between
500 and 1000 m (apart from 2 weeks at the beginning of November when
height coverage was only up to 14 km).
Mie wind profiles are provided for 14 km (or shorter) accumulation lengths,
so there are generally several Mie profiles within 100 km of the radar.
The number of valid Mie wind measurements in each profile is very small.
Between 1 and 14 Mie wind profiles were found for each pass (mean 11), with
on average only one valid Mie wind per profile at MARA and two at ESRAD. At MARA a
third of passes and at ESRAD 5 % of passes had no valid Mie winds. Valid Mie winds were found only below 11 km height at MARA and 13 km at ESRAD. Since the Mie wind data are so sparse, we average all profiles within 100 km of the
radar to make a single profile, using the same height bins as the Rayleigh
wind profile (closest to the radar) during the same pass. Before averaging,
we reject Mie HLOS winds with an error estimate >5 m s-1 (and if
the validity flag is 0). The rejection thresholds for Mie and Rayleigh winds
were chosen as recommended by Rennie and Isaksen (2020). Those authors found
appropriate thresholds subjectively based on a compromise between the number
of observations that pass quality control and the overall quality of the
dataset. (Note that the closest Rayleigh clear profile is chosen before
quality criteria are applied.) The locations of all available Aeolus wind
profiles, containing at least one valid wind measurement during the
comparison period, are shown in Fig. 2.
MARA data
The MARA radar operates in three different modes, with different height coverage
and resolution, interlaced, with 1 min for each mode (Table 1). The
length of the radar pulse determines the height resolution. Shorter
pulses/a smaller height resolution are aimed at the lower altitudes where the
scattered signal is strong. Longer pulses, with a wider height resolution, are
needed for the upper troposphere where the scattered signal is much weaker.
The scattered signal is intrinsically highly variable in time and the 1 min wind estimates show considerable random variation. The Aeolus measurements which we compare with correspond to an 87 km (Rayleigh), or up to 200 km (Mie) distance along the satellite track and may be located up to 100 km from the radar, so there is no sense in comparing instantaneous measurements. We instead use averages from 30 min before to 30 min after the satellite passage. We also average to the same height intervals as the corresponding Rayleigh wind profile.
Main characteristics of the radar operating modes used in the comparison. MARA (ESRAD) is located at 117 m (295 m) above the mean sea level. Note that due to technical problems, the mode
fca_4500 did not operate at MARA from 24 June–2 September 2019.
Radar wind estimates are quality checked before averaging. Checks include
the absence of non-atmospheric echoes, a high enough signal-to-noise ratio
(>1) and mathematically successful fitting of the wind. For example, in a 1 h ×1 km (vertically) averaging bin, there are radar wind estimates from up to sixty 1 min time intervals and 20 heights at
MARA. Some of these estimates will be invalid because of a low signal-to-noise
ratio or because the full-correlation method used to derive the wind has
been unsuccessful, which particularly affects the radar measurements with the shortest height resolution. In practice, between 100–400 estimates are
averaged in typical height bins below 5 km altitude but only 10–20 above
that. The large numbers of individual measurements in the lower troposphere
lead to low values for the uncertainties in the mean values (standard error
of the mean), typically <0.5 m s-1 for MARA. In order to
further reject uncertain measurements, we reject averaged winds if the
standard error of the mean is >2 m s-1.
ESRAD data
The ESRAD radar operated in four different modes, with different height
coverage and resolution, interlaced, with 1 or 2 min for each mode.
Three of these provided good enough quality data for use in this comparison
(Table 1). The mode “fcx_aeolus” was implemented, with
shorter height coverage, which allows faster repetition of the radar pulses,
to try to retrieve more valid wind estimates from the upper troposphere for
comparison with Aeolus. As at MARA, we use averages from 30 min before
to 30 min after the satellite pass and average to the same height
intervals as the corresponding Rayleigh wind profile.
As at MARA, ESRAD radar wind estimates are quality checked before averaging.
At ESRAD, for example, in a 1 h ×1 km (vertically) averaging bin,
there are radar wind estimates from up to twenty-two 2 min intervals and eight heights at ESRAD. In practice, between 20–70 radar wind estimates are
averaged for height bins in the lower troposphere (below 6 km) and 10–20 at
higher altitudes at ESRAD. Uncertainties in the mean values (standard error
of the mean) are typically less than 1 m s-1 for ESRAD. As with MARA,
we reject averaged winds if the standard error of the mean is >2 m s-1. We also correct ESRAD wind components for the systematic
underestimate found in comparison with radiosondes and reanalysis (Belova et
al., 2021): 8 % in zonal wind and 25 % in meridional wind. Radar winds
are measured from time delays between signals received on different sections
of the radar antenna array as the diffraction pattern of the scattered radio
waves is advected by the wind. The baseline for determining the zonal
component is longer than that for the meridional one, and the receivers for
the different parts of the array are not equally susceptible to non-random
noise. This leads to underestimates of the wind speed which differ between
the two components (Belova et al., 2021).
ERA5 data
To provide a further check, we also compare Aeolus and radar winds with the ECMWF reanalysis ERA5
(Hersbach et al., 2020). ERA5 winds were taken from the closest grid point
to each radar location (68.00∘ N, 21.00∘ E for ESRAD;
70.75∘ S, 11.75∘ E for MARA) and the closest hour to
each Aeolus pass. Wind profiles are interpolated to the centre heights of
the corresponding Aeolus Rayleigh wind profile height bins. The comparison
is made using only those heights at which valid wind measurements are
available for Aeolus Rayleigh clear and radar (or Aeolus Mie cloudy and
radar). Note that neither ESRAD nor MARA data nor Aeolus data were
assimilated by ECMWF for the period studied (July–December 2019).
Intercomparisons
To compare radar and Aeolus winds, we first calculate what the HLOS wind
should be according to the zonal (U) and meridional (V) wind components
measured by the radar. (We could in principle include the vertical wind
component measured by the radar, but this was found to be negligible in the
1 h averages.)
HLOSradar=-Uradar⋅sin(φAeolus)-Vradar⋅cos(φAeolus),
where φAeolus is the azimuth from the laser scattering volume to the satellite.
Scatter plots of Aeolus Rayleigh HLOS winds against HLOS winds according to MARA radar data. Red crosses indicate measurements made on ascending tracks and blue crosses are used for descending tracks. Dashed red and blue lines show fitted regression lines. Black dashed line indicates equality. Heights indicated are the lowest and highest where valid winds are available for comparison. (a) The Antarctic winter period 1 July–23 September 2019; (b) summer 24 September–31 December 2019.
Aeolus Rayleigh–MARA HLOS wind comparison.
Aeolus Rayleigh vs. MARASummer Winter 24 September–31 December 2019 1 July–23 September 2019 AscendDescendAllAscendDescendAllAltitudes, km0.8–11 km 1.5–7.6 km N points83891724757104Correlation0.810.710.820.760.750.81Slope,1.21.11.10.90.90.8795 % conf. interval[11.3][0.91.3][1.01.2][0.71.1][0.71.1][0.81.0]Intercept, m s-10.6-0.50.0-0.2-1.4-0.8Bias, m s-10.00.00.0-0.5-2.0-1.390 % conf. interval[-1.11.0][-1.11.0][-0.80.7][-2.01.0][-3.1-0.9][-2.2-0.4]SD, m s-15.76.05.86.15.15.6
To quantify the differences between radar and Aeolus winds, we also compute
mean differences (bias) and the standard deviation (SD) of the differences
as
2bias=1N⋅∑i=1NHLOSAeolus,i-HLOSradar,i,3SD=1N-1∑i=1NHLOSAeolus,i-HLOSradar,i-bias2.
The HLOS winds measured by the radars for every Aeolus collocation event
were calculated according to Eq. (1) using the zonal and meridional
winds averaged as described in Sect. 3. All data from 1 July to 31 December 2019 were divided into two seasons: sunlit (“summer”, 1 July–23 September at ESRAD, 24 September–31 December at MARA) with 12–24 h direct sunlight and non-sunlit (“winter”) covering the rest of the time. Comparison between the Aeolus HLOS Rayleigh/Mie winds and HLOS winds measured by the radars has
been made for each season separately. We computed correlation, a linear fit
of Aeolus on the radar winds, a bias defined as the mean Aeolus–radar
difference (Eq. 2) and the standard deviation (SD) of the difference (Eq. 3). In order to evaluate the uncertainties of the results, we estimated the confidence intervals for the slope of the fit and for the bias. For the calculation of the altitude profiles of the bias and SD, all Aeolus and
radar wind data were collected into 1 km height bins.
Aeolus Mie–MARA HLOS wind comparison.
Summer Winter 24 September–31 December 2019 1 July–23 September 2019 AscendDescendAscendDescendAltitudes, km1.5–8.3 1.6–5.5 N points33351710Correlation0.630.720.730.70Slope,1.01.31.11.295 % conf. interval[0.51.4][0.81.7][0.61.7][0.22.2]Intercept, m s-16.5-2.40.4-1.2Bias, m s-16.6-0.5-1.00.990 % conf. interval[4.88.6][-2.41.3][-3.41.4][-2.44.1]SD, m s-16.86.55.75.6Aeolus vs. MARA
In Fig. 3 the comparison of Aeolus Rayleigh and MARA winds is presented.
The data for the descending orbits are marked in blue and those for the ascending
orbits in red. We also plot there the linear fits of Aeolus on MARA winds as
dashed lines. The comparison results are presented in Table 2. We see a very
good agreement between the Aeolus Rayleigh and MARA HLOS winds for both
seasons and pass directions: the slope of the fit is not significantly
different from 1 and the bias is close to 0, with one exception: for the
descending orbits in winter there is a bias of about -2 m s-1. However, the standard deviations of the Aeolus–radar differences are relatively large (5–6 m s-1). Since no large differences were found between the ascending and descending orbits, we made calculations for all overpasses as well.
Height profiles in 1 km bins of (a) the number of comparison
points available and (b) mean value (bias) and standard deviation of the differences between Aeolus Rayleigh HLOS winds and MARA-derived HLOS winds for the Antarctic winter period 1 July–23 September 2019. Red lines and shading are for ascending tracks and blue is for descending ones. Solid lines in (b) show the bias, with the shaded areas corresponding to the 90 % confidence interval. Dashed lines in (b) show the standard deviation.
The same as Fig. 4. but for Aeolus Rayleigh HLOS winds and MARA-derived HLOS winds for the Antarctic summer period 24 September–31 December 2019.
The behaviour of the bias and standard deviation of the Aeolus–radar
differences as a function of height is shown in Figs. 4 and 5. In both
figures it can be seen that the 90 % confidence intervals for both
ascending and descending orbits largely overlap and, at most heights,
overlap the zero line.
The same as Fig. 3 but for Aeolus Mie HLOS winds against MARA.
The same as Fig. 4 but for Aeolus Mie HLOS winds against MARA (winter).
The same as Fig. 5 but for Aeolus Mie HLOS winds against MARA (summer).
Figures 6–8 and Table 3 show the results of the comparison for Aeolus Mie
winds. The height coverage and the number of valid Mie data points is small,
especially for the winter season, which leads to high uncertainties. The
biases are small except for the ascending passes in summer where the bias is
substantial: 6.6 m s-1. This deviation is clearly seen in Fig. 6, and in the height-resolved plot in Fig. 8, between 2.5 and 4.5 km, it is
systematically significantly well above zero. We note that the bias only
appears for the ascending track, and only for Mie winds, not for Rayleigh winds.
Closer examination of the data (not shown here) also shows that the bias
affects both the closer tracks to the north-east of the radar and those to
the south-west (see Fig. 2).
In Fig. 9 we show monthly average biases between MARA and Aeolus wind
measurements and also a comparison with ERA5 winds (for the closest hour and
closest grid point to the MARA location). There is clearly a close agreement
between MARA and ERA5 and very similar biases between Aeolus and ERA5 as
between Aeolus and MARA. The small negative bias seen for Rayleigh wind
measurements for winter descending orbits appears only in August and is
barely significant at the 90 % confidence limit in that month. Note also
that the confidence limits for the biases are wider in winter due to fewer
comparison points. In Fig. 9, the large positive bias seen for Mie wind
measurements for summer ascending orbits appears in both October and
November, in comparison with both MARA and ERA5, and is clearly significant
at the 90 % confidence limit.
Month-by-month mean values of biases in HLOS winds (solid lines) and 90 % confidence limits (shaded areas) at MARA. Red is for ascending tracks and blue is for descending ones. (a) Aeolus Rayleigh minus MARA, (b) Aeolus Mie
minus MARA, (c) Aeolus Rayleigh minus ERA5, (d) Aeolus Mie minus ERA5, (e) MARA minus ERA5 at available Aeolus Rayleigh comparison times/heights and (f) MARA minus ERA5 at available Aeolus Mie comparison times/heights.
Scatter plots of Aeolus Rayleigh HLOS winds against HLOS winds according to ESRAD radar data. Red crosses indicate measurements made on ascending tracks and blue crosses are for descending tracks. Dashed red and blue lines show fitted regression lines. Black dashed line indicates equality. Heights indicated are the lowest and highest where valid winds are available for comparison. (a) The Arctic summer period 1 July–23 September 2019; (b) winter 24 September–31 December 2019.
Aeolus Rayleigh–ESRAD HLOS wind comparison.
Aeolus Rayleigh vs. ESRADSummer Winter 1 July–23 September 2019 24 September–31 December 2019 AscendDescendAllAscendDescendAllAltitudes, km2.5–12.9 2.4–12.9 N points7918426399206305Correlation0.930.910.920.840.820.88Slope,1.01.01.01.00.91.095 % conf. interval[0.91.1][1.01.1][1.01.1][0.91.1][0.81.0][0.91.0]Intercept, m s-1-0.2-0.5-0.5-0.2-1.2-0.6Bias, m s-1-0.3-0.5-0.4-0.2-0.5-0.490 % conf. interval[-1.00.5][-1.10.1][-1.00.1][-1.10.6][-1.10.1][-1.00.2]SD, m s-13.84.84.55.25.25.2
The random differences between MARA and Aeolus HLOS winds (SD) are 5.7–6.8 m s-1 for summer and 5.1–6.1 m s-1 for winter and similar for Mie and Rayleigh winds. This is much bigger than the standard error for the average radar winds themselves (2 m s-1) and somewhat more than found comparing MARA and radiosonde winds (4 m s-1, Belova et al., 2021). Some of this will be due to the expected random errors of the Aeolus winds (4–5 m s-1 for Rayleigh winds, 3 m s-1 for Mie winds) and the difference in location of MARA and Aeolus measurements (see Fig. 2).
The distance between the observations can be up to 100 km, with the largest
spread of locations for the Mie measurements, and is more than in most cases
in a comparison with radiosondes. However, the slightly higher random
differences in summer compared to winter for Mie winds suggest higher Aeolus
random errors in summer, since the distances from MARA do not vary between
the seasons and weather systems are more variable, which would lead to more
spatial difference in winter rather than in summer. However, given the
short length of the analysed time interval, it is possible that this is not
a summer/winter effect but just a result of a small number of individual
weather systems.
Aeolus vs. ESRAD
The results of the comparison between Aeolus and ESRAD are presented in
Figs. 10–16 and Tables 4 and 5. In general, there are significantly more
valid data points for Rayleigh, as well for Mie winds, than in comparison
with MARA, and height coverage is also extended. The results for Rayleigh
winds are summarized in Table 4. The slopes of the linear fits are about 1,
and the biases are 0, within the uncertainties. Again, since there are no
large differences in bias or slope between ascent and descent, we also
calculate the values for both sets together, and the results similarly show no significant deviation. The height profile of the biases in Figs. 11 and 12 shows apparently significant bias at a few heights but nothing
systematically at all heights.
Height profiles in 1 km bins of (a) the number of comparison
points available and (b) mean value (bias) and standard deviation of the differences between Aeolus Rayleigh HLOS winds and ESRAD-derived HLOS winds for the Arctic summer period 1 July–23 September 2019. Red lines and
shading are for ascending tracks and blue is for descending ones. Solid lines in (b) show the bias, with the shaded areas corresponding to the 90 % confidence interval. Dashed lines in (b) show the standard deviation.
The same as Fig. 11 but for Aeolus Rayleigh HLOS winds and ESRAD-derived HLOS winds for the Arctic winter period 24 September–31 December 2019.
For Mie winds (Figs. 13–15, Table 5), the number of available comparisons is
small but higher than at MARA, and the height coverage is better. The slopes
of the regression lines are close to 1, and the bias is not significantly
different from 0, except in the case of the ascending orbits in winter
when the average bias is found to be 2.4 m s-1. In Fig. 15 we can see
that this bias is systematically positive at all heights, although the
number of data points is very small and the significance is marginal.
Aeolus Mie–ESRAD HLOS wind comparison.
Aeolus Mie vs. ESRADSummer Winter 1 July–23 September 2019 24 September–31 December 2019 AscendDescendAscendDescendAltitudes, km2.3–10.9 2.5–11.4 N points36753759Correlation0.760.900.910.85Slope,0.80.81.00.995 % conf. interval[0.71.0][0.70.9][0.91.1][0.81.0]Intercept0.50.22.30.5Bias, m s-10.50.72.40.990 % conf. interval[-0.81.8][-0.31.8][1.33.4][-0.22.1]SD, m s-14.75.53.95.2
The same as Fig. 10 but for Aeolus Mie HLOS winds against ESRAD.
As Fig. 11 but for Aeolus Mie HLOS winds against ESRAD (summer).
As Fig. 12 but for Aeolus Mie HLOS winds against ESRAD (winter).
Month-by-month mean values of biases in HLOS winds (solid lines) and 90 % confidence limits (shaded areas) at ESRAD. Red is for ascending tracks and blue is for descending ones. (a) Aeolus Rayleigh minus ESRAD, (b) Aeolus Mie
minus ESRAD, (c) Aeolus Rayleigh minus ERA5, (d) Aeolus Mie minus ERA5, (e) ESRAD minus ERA5 at available Aeolus Rayleigh comparison times/heights and (f) ESRAD minus ERA5 at available Aeolus Mie comparison times/heights.
The random differences between ESRAD and Aeolus HLOS winds (SD) are 3.8–5.5 m s-1 for summer and 3.9–5.2 m s-1 for winter and are similar for Mie and Rayleigh winds. This is again much bigger than the standard error for the average radar winds themselves (2 m s-1) but close to that found when comparing ESRAD and radiosonde winds (4 m s-1, Belova et al., 2021). Some of this will be due to the expected random errors of the Aeolus winds together with the distance of up to 100 km between the observations. The random differences are indeed slightly smaller for the ascending paths than the descending ones and, as shown in Fig. 2, the Aeolus measurements are closer to the radar site on the ascending conjunctions.
In Fig. 16 we show monthly average biases between ESRAD and Aeolus wind
measurements and also a comparison with ERA5 winds (for the closest hour and
closest grid point to the ESRAD location). There is clearly a close
agreement between ESRAD and ERA5 and very similar biases between Aeolus and
ERA5 as between Aeolus and ESRAD. This confirms no biases for Rayleigh wind
measurements significant at the 90 % confidence level. For Mie wind
measurements the moderate positive bias seen for winter ascending orbits
appears significant in both October and November, in comparison with both
ESRAD and ERA5. It can also be noted that there is no seasonal change in the
agreement between ESRAD and ERA5. So, this may be a genuine bias which has
not been successfully removed in the Aeolus data processing. However, given
the rather short time interval for the comparison, we cannot rule out the
possibility that it is due to spatial differences between the winds at ESRAD
and at the Aeolus measurement locations.
Summary and conclusion
The aim of this study was to assess the accuracy of winds measured by lidar
from the Aeolus satellite at polar latitudes by comparison with independent
measurements made by two ground-based wind-profiler radars: MARA near the
coast of Queen Maud Land in Antarctica and ESRAD in Arctic Sweden. The
radars make their observations at fixed locations (within a radius of a few
100 m); hence, exactly co-located measurements with Aeolus are not possible.
Aeolus Rayleigh (clear sky) measurements are averaged along a considerable
accumulation length (87 km) and, although Aeolus Mie (cloudy) measurements
are in principle averaged over shorter distances (14 km or less), the
latter are in practice found to be sparse at the radar locations since they
depend on the presence of suitable clouds or aerosols. For comparison with
the radar, we therefore use the Rayleigh (clear) wind profile closest to the
radar for any pass within 100 km and average all Mie (cloudy) winds
registered within 100 km of the radar on that pass. We use temporal averaging from 30 min before to 30 min after the satellite pass for
the radar winds, which may provide, to some extent, a proxy for the spatial
averaging intrinsic to the Aeolus measurements. We separated the datasets at
each radar site into ascending and descending passes and into summer and
winter seasons as they might show different behaviour. The agreement between
Aeolus and radar winds is generally very good. The slopes of Aeolus-on-radar
wind regression lines do not differ significantly from 1, and correlation
coefficients are between 0.63 and 0.93. The random differences are a
combination of Aeolus observation error, representativeness error and radar
wind random error. The values we observe (4–7 m s-1) are in most cases about what could be expected from the known level of random error for Aeolus winds (4–5 m s-1 for Rayleigh, 3 m s-1 for Mie) and spatial/temporal differences between radar and Aeolus measurements.
There is a particular interest in possible biases since these have been
found to be a problem in the earlier stages of Aeolus validation but have
been much reduced by more recent data processing methods (including the 2B10
baseline which is used here). The only clear systematic bias found in this
comparison is 6.6 [4.88.6] m s-1 for Mie (cloudy) winds at MARA for
ascending passes in summer (where the values inside the square brackets are
the 90 % confidence limits). This bias also appears when the Aeolus Mie
winds are compared with ECMWF ERA5 reanalysis. Summer is the season when the
effect of scattered sunlight from the ice cap is at a maximum and can affect the
satellite for some minutes as it crosses Antarctica before passing the MARA
site. There is also some indication that the random errors of Mie winds may
be about 1 m s-1 more in summer than in winter at MARA.
There are further small biases with marginal significance. At MARA, there is
a bias -2.0[-3.1-0.9] m s-1 for the Rayleigh descending passes in winter, but
this is not systematically significant over an extended height range. At
ESRAD, there appears to be a larger bias – 2.4 [1.33.4] m s-1 for the Mie ascending passes in winter – but this is based on a very small number of comparison
points.
In summary, the agreement between radar and Aeolus winds is generally very
good. For 13 out of 16 subdivisions of the data (Rayleigh/Mie,
ascending/descending tracks, summer/winter, Arctic/Antarctic) we find no
evidence for any bias in the Aeolus winds (<1 m s-1). For a
further two subdivisions (specified in the previous paragraph) there may be
a significant bias, but more data will be needed to establish whether this is
truly the case. We find robust evidence for a large bias of about 7 m s-1 in only one case – summer, Mie winds for the ascending tracks over MARA in Antarctica. This should be looked into further when more recent data become available from MARA.
Data availability
ESRAD data are available from Peter Voelger on motivated request. MARA data can be obtained on reasonable request from Sourav Chatterjee. Aeolus data are publicly available at the ESA Aeolus Online Dissemination System (https://earth.esa.int/eogateway/missions/aeolus/data, last access: 10 January 2021, ESA, 2021).
Author contributions
SK, EB and PV develop and maintain the software and data processing for ESRAD. They developed the codes for the radar–Aeolus comparison and conducted the data analysis. SC and KS provided the MARA data. All co-authors discussed the comparison methods and results. EB and SK prepared the paper with contribution from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Aeolus data and their application (AMT/ACP/WCD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
This work was supported by the Swedish National Space Agency (grant nos.
125/18 and 279/18). ESRAD operation and maintenance is provided by Esrange
Space Center of Swedish Space Corporation. The team members at Maitri
station for the 38th Indian scientific expedition to Antarctica (ISEA) and
the Antarctic logistics division at NCPOR (India) are acknowledged for
providing necessary support for the operation of MARA. Figure 1 is plotted
using “M_Map: A mapping package for MATLAB”, version 1.4m, by
Pawlowicz (2020, https://www.eoas.ubc.ca/~rich/map.html, last access: 22 February 2021).
Financial support
This research has been supported by the Swedish National Space Agency (grant nos. 125/18 and 279/18).
Review statement
This paper was edited by Oliver Reitebuch and reviewed by three anonymous referees.
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