Download Combined temperature lidar for measurements in the troposphere

Combined temperature lidar for measurements in
the troposphere, stratosphere, and mesosphere
Andreas Behrendt, Takuji Nakamura, and Toshitaka Tsuda
We describe the performance of a combined Raman lidar. The temperature is measured with the
rotational Raman technique and with the integration technique simultaneously. Additionally measured
parameters are particle extinction and backscatter coefficients and water vapor mixing ratio. In a
previous stage of the system, instrumental problems restricted the performance. We describe how we
rebuilt the instrument and overcame these restrictions. As a result, the measurement time for the same
spatial resolution and accuracy of the rotational Raman temperature measurements is reduced by a
factor of ⬃4.3, and their range could be extended for the first time to the upper stratosphere. © 2004
Optical Society of America
OCIS codes: 010.1110, 010.3640, 280.3640, 010.7030, 290.5860.
1. Introduction
Integration technique lidar systems1 evaluate and
validate middle-atmospheric temperature measurements from satellites and are core instruments in the
network for the detection of stratospheric change.
To ensure the quality of the measured data, sources
of possible errors have been investigated in detail.2,3
The integration technique uses the assumption that
the observed atmospheric column is in hydrostatic
equilibrium and that the detected backscatter signal
is due to molecular scattering only. The latter point
restricts the measurement range to heights above the
stratospheric aerosol layer if an elastic-backscatter
signal is used. By extraction of a molecular backscatter signal from the lidar return, e.g., the vibrational Raman signal of N2, the measurement range of
the integration technique can be extended further
downward to the lower stratosphere at the cost of a
lower signal intensity.4 – 6 However, this augmentation works only when the particle extinction of the
signals is negligible compared with the molecular ex-
When this research was performed, all the authors were with the
Radio Science Center for Space and Atmosphere, Kyoto University,
611-0011 Uji, Kyoto, Japan. A Behrendt 共[email protected]兲 is now with the Institut für Physik und Meteorologie, Universität Hohenheim, Garbenstrasse 30, D-70599 Stuttgart,
Germany.
Received 25 April 2003; revised manuscript received 24 October
2003; accepted 9 February 2004.
0003-6935兾04兾142930-10$15.00兾0
© 2004 Optical Society of America
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APPLIED OPTICS 兾 Vol. 43, No. 14 兾 10 May 2004
tinction. This prerequisite is not always fulfilled in
the lower stratosphere.
In contrast with the integration technique, the rotational Raman technique allows for temperature
measurements without assumptions on the state of
the atmosphere and the measurements are insensitive to the presence of particles to a much higher
degree.7 Several instrumental realizations of rotational Raman temperature lidar have to be made that
yielded step-by-step higher resolution, increased
range, and兾or higher reliability of the data also inside
particle-loaded height regions.8 –15 Simultaneous
measurements with the rotational Raman technique
and integration technique have been performed previously with night-long integration.16,17 Because of
the higher intensities of the rotational Raman signals
of the lidar described here and the extraction of signals with more pronounced temperature sensitivity,
the data of the two techniques can be compared with
the Raman lidar of the Radio Science Center for
Space and Atmosphere 共RASC, Kyoto, Japan兲 with
much higher resolution than before, for example,
hourly in the upper stratosphere, which is highly
beneficial for the study of atmospheric waves.
The RASC lidar is located at the middle and upper
共MU兲 radar observatory 共34.8 °N, 136.1 °E兲 in Shigaraki, Japan. The MU radar is operated at 46.5 MHz
with 50-kW average transmission power and is used as
a mesosphere–stratosphere–troposphere radar and an
incoherent scatter radar.18,19 With this instrument,
atmospheric dynamics can be studied between 2- and
500-km altitude, however, there is an observational
gap between 25 and 60 km at which the radar signals
Fig. 1. Setup of the RASC lidar: BD, beam dump; PD, photodiode; BSM, beam-steering mirror. The laser output is synchronized to a
chopper blade that protects the high-altitude channel PMT used for the integration technique temperature measurements from the intense
lower-altitude signal.
are extremely weak. To fill this gap, a Rayleigh lidar
was set up at the MU observatory in 2000. In 2001,
we redesigned the system and implemented rotational
Raman channels for the measurement of temperature
profiles in the troposphere and stratosphere. Additional measurement parameters of the instrument
were particle extinction coefficient, particle backscatter coefficient, and water vapor mixing ratio measured
with the Raman lidar technique. A detailed description of the system in the previous stage provides information on its performance.15
Here we describe how we rebuilt this instrument
and how we overcame instrumental problems. The
performance of the new instrument is illustrated
with measurement examples. First, we implemented new narrowband interference filters with
higher peak transmittance compared with the previously used filters. Second, the parameters of these
filters were chosen in such a way that they extract
rotational Raman signals with higher temperature
sensitivity than before. Third, new data acquisition
electronics now store the detected lidar signals in
both the photon-counting mode and the analog mode
simultaneously. Before, nonlinear effects hindered
the full-power acquisition of the rotational Raman
signals from altitudes below ⬃10 km, and we therefore had to implement 25% neutral-density filters in
front of the rotational Raman signal detectors to take
measurements at this height region. Now we acquire the unattenuated signals throughout the whole
free troposphere, i.e., starting at approximately
1.6-km height above the system at which point the
laser beam fully enters the field of view of the receiving telescope. A third major change was the separation of the elastic signals from low and high
altitudes. In addition to the improved resolution
and range of the rotational Raman temperature measurements, it is shown that measurements in thin
clouds are possible with the present setup without
the need to correct for elastic-signal intrusion.
2. System Setup
A schematic overview of the RASC lidar is shown in
Fig. 1. Table 1 lists the technical data of the system.
As a transmitter we used an injection-seeded
frequency-doubled Nd:YAG laser that emits light at a
wavelength of 532.25 nm 共in vacuum兲 with ⬃600-mJ
pulse energy at a repetition frequency of 50 Hz. The
primary mirror of the Cassegrainian receiving telescope has a diameter of 0.82 m and the secondary
telescope mirror has a diameter of 0.25 m, which
gives a free telescope area of ⬃0.48 m2. The data
acquisition system 共Licel GbR, Berlin, Germany兲 allows for simultaneous detection of the signals in the
analog mode and in the photon-counting mode. This
feature improves the dynamic range of the signal
acquisition 共see Section 3兲. A chopper protects the
high-altitude elastic channel from the intense lowaltitude signals. The chopper blade rotates with 100
Hz and has two openings. The beam is focused on
the outer edge of the openings 共at a distance of approximately 5 cm to the center兲. The control unit of
the chopper gives a trigger signal of 200 Hz, which is
frequency divided by 4 and adequately delayed for
synchronization of the flash lamps and Q switch of
the laser.
The concept of the RASC lidar to extract the rotational Raman signals follows the design that was
developed for the Raman lidar of GKSS Research
Center, Geesthacht, Germany.14 Characteristic of
this design is a sequential mount of the elastic channel and the two rotational Raman channels that results in high receiver throughput and high
suppression of the elastic-backscatter signal in the
rotational Raman channels 共⬎7 orders of magnitude兲.
The tilted mounting of the filters allows one to tune
the filter passbands by changing the angles of incidence 共AOIs兲 of the light on these filters. For the
first measurements of the RASC lidar, we used the
narrowband filters 共BS3–BS5, Fig. 2兲 of the GKSS
10 May 2004 兾 Vol. 43, No. 14 兾 APPLIED OPTICS
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Table 1. Technical Data of the RASC Lidar with the Recent Upgrades
Laser
Type
Model
Wavelength
Pulse energy
Pulse repetition rate
Beam divergence
Transmitter optics
Geometry
Expansion factor
Beam divergence of the expanded laser beam
Receiver optics
Geometry
Primary mirror diameter
Telescope focal length
Field of view
Dispersion system 共further details see Table 2兲
Type
Channels
Chopper
Model
Detectors
Type
Models
Data acquisition system
Model
Analog mode
Photon-counting mode
Minimum 共typical兲 height resolution
Height range
Raman lidar. These components were now exchanged for new components with improved performance. The transmission versus wavelength of BS3
to BS5 is shown in Fig. 3. When the filters were
exchanged, the injection seeder of our laser was also
replaced. After the replacement we found that the
laser output wavelength ␭0 changed from 532.11 to
532.25 nm 共in vacuum兲. In Fig. 3 the transmission
curves of the GKSS filters are plotted with the same
shift of ⫹0.14 nm to show the filter transmissions
relative to ␭0. The optical properties of the components employed for the signal separation after the
upgrade are given in Table 2. The manufacturer of
the filters, Barr Associates, Westford, Massachusetts,
achieved nearly double the peak transmission of the
combination of BS4a and BS4b 共from 0.38 to 0.72兲 for
the new components and also improved the peak
transmission of BS5 共from 0.75 to 0.87兲. The transmission of BS3 is almost the same as before with 0.83
and 0.82, respectively. When the center wave2932
APPLIED OPTICS 兾 Vol. 43, No. 14 兾 10 May 2004
Frequency-doubled Nd:YAG, injection seeded
Continuum Powerlite 9050
532.25 nm 共in vacuum兲
⬃600 mJ
50 Hz
⬍ 500 ␮rad 共full angle兲
Galilean telescope
8
⬍ 60 ␮rad 共full angle兲
Cassegrainian telescope
820 mm
8000 mm
1.5 mrad 共for the measurements shown here, selectable兲
Filter polychromator
1. Elastic backscatter, low altitude
2. H2O vibrational Raman, 1. Stokes
3. Elastic backscatter, high altitude
4. Lower quantum number pure rotational Raman,
anti-Stokes
5. Higher quantum number pure rotational Raman,
anti-Stokes
6. Na D2 resonance 共for a collocated Na resonance lidar,
removed for the measurements shown here兲
HMS-Elektronik 220 with head 220A and blade 220兾02B
Photomultipliers in analog and single photon-counting
mode simultaneously
Hamamatsu Photonics R943-02, water cooled 共channel 2兲
Electron tubes 9863兾350 共channel 3兲
Electron tubes 9893兾350 共channels 1, 4, and 5兲
Licel Optical Transient Recorder TR16–160
12 bit, 16.66 MHz
250 MHz
9 m 共72 m兲
147.5 km
lengths 共CWLs兲 of the GKSS filters were chosen, we
focused on high performance of the lidar in the condensation temperature regimes of polar stratospheric
clouds 共⬃190 K兲.20 Our aim was to take measurements throughout the troposphere and the stratosphere with the RASC lidar. Therefore we
performed new model calculations for the statistical
temperature uncertainty of rotational Raman temperature measurements at ⬃240 K.
We calculated the rotational Raman spectrum of
dry air 共78.1% N2 and 20.9% O2; the contribution of
H2O is negligible even for water vapor saturation兲 for
␭0 ⫽ 532.25 nm. The formula for the intensities and
positions of the rotational Raman lines can be found
in Ref. 21. Assuming Poisson statistics, the 1-␴ uncertainty of the measured number of photon counts N
is given by
⌬N ⫽ 冑N ,
(1)
Fig. 3. Transmission versus wavelength for beam splitters BS3,
BS4a⫹BS4b, and BS5 used to extract the elastic signal and rotational Raman signals NRR1 and NRR2, respectively. The laser
wavelength ␭0 of 532.25 nm is marked. The transmission data of
the filters owned by GKSS Research Center, Geesthacht, Germany, which we used until October 2001, are shown for comparison 共with CWLs increased by 0.14 nm; see text兲.
Fig. 2. Setup of the RASC lidar polychromator: L1–L9, lenses;
IFa and IFb, interference filters; BS1–BS5, beam splitters; ND,
neutral-density filters; M, mirror; PMT1–PMT5, photomultiplier
tubes for the signals indicated. The Na resonance channel with
BSx, IFx, Lx, and PMTx belongs to a collocated lidar; BSx was
removed for the measurements shown here.
which, for the uncertainty of the rotational Raman
temperature measurements, yields
⌬T ⫽
⫽
冉
1
⳵T
1
⫹
Q
⳵Q
N RR1 N RR2
冉
冊
1兾2
⳵N RR2 1
⳵N RR1 1
⫺
⳵T N RR1
⳵T N RR2
冊冉
⫺1
1
1
⫹
N RR1 N RR2
冊
1兾2
,
(2)
where
Q⫽
N RR2
N RR1
(3)
is the ratio of the number of photon counts NRR1 and
NRR2 of lower and higher rotational quantum number
transition channels. Q is the measurement parameter that yields the atmospheric temperature profile
after calibration of the system. Figure 4 shows the
temperature dependence of NRR1, NRR2, and Q calcu-
lated for the parameters of the RASC lidar. We approximated the differential ratio in Eq. 共2兲 with
⳵T
T2 ⫺ T1
⬇
.
⳵Q Q共T 2兲 ⫺ Q共T 1兲
(4)
The transmission curves of the interference filters
used are very steep (Fig. 3). Therefore we decided to
use a peak transmission of ␶ ⫽ 1 and an out-of-band
transmission of ␶ ⫽ 0 for this simulation.
The results for T1 ⫽ 235 K and T2 ⫽ 240 K 共Fig. 5兲
lead to a change of the CWL of the second rotational
Raman channel, which is now 528.76 nm instead of
529.34 nm. The change of the CWL of the first rotational Raman channel 共531.14 nm instead of 530.84
nm兲 was possible because of the improved out-of-band
blocking of BS4a and BS4b, which allows one to place
the passbands of these components closer to the laser
line. With this setting the RASC lidar extracts rotational Raman signals for the given FWHM of the
filters at 240 K, which are closer to the optimum. In
the present setting, ⌬TRASC ⫽ 1.14 ⫻ ⌬Toptimum,
where ⌬TRASC is the measurement uncertainty for
the RASC lidar for measurements at 240 K and
⌬Toptimum is the lowest measurement uncertainty
possible with the given filter bandwidths at the same
temperature. ⌬Toptimum is found for 240 K at
CWLRR1 ⫽ 531.66 nm and CWLRR2 ⫽ 528.66 nm.
The optimum CWL can be chosen only for the RR2
channel and not for the RR1 channel. With CWLRR1
Table 2. Optical Properties of the Filter Polychromatora
Wavelength
共nm兲
660
532.25
531.1
528.5
a
Parameter
BS1
BS2
BS3
BS4a ⫹ BS4b
Combined
BS5
AOI (deg)
CWL (nm)
FWHM (nm)
45
45
4.8
532.34
0.80
5.0
531.14
0.65
7.2
528.76
1.10
␶ ⬇ 0.9
␶ ⬇ 0.9
␳ ⬇ 0.05
␶ ⬇ 0.9
␶ ⬇ 0.9
␳ ⬎ 0.95
␶ ⬇ 0.85
␶ ⬇ 0.85
␶ ⬇ 0.85
␶ ⫽ 0.82
␳ ⫽ 0.11
␳ ⬎ 0.95
␳ ⬎ 0.96
␶ ⬍ 10⫺6
␶ ⬍ 10⫺6
␶ ⫽ 0.72
␳ ⬎ 0.96
␶ ⬍ 2 䡠 10⫺4
␶ ⫽ 0.87
IFa ⫹ IFb
Combined
3.5 & 0
660.65
1.2
␶ ⫽ 0.48
␶ ⬍ 10⫺8
␶, transmission; ␳, reflectivity. Transmission values ␶ ⬍ 10⫺3 are estimates from the manufacturers
.
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Fig. 4. 共a兲 Calculated intensity of the two pure rotational Raman
signals NRR1 and NRR2 versus temperature T for the new interference filters. 共b兲 Signal ratio Q, which serves for the temperature
measurement of the atmosphere, and weighted sum Nref, which is
used as the Raman reference signal. 共c兲 Relative change of Nref
with temperature. Vertical lines mark the range of atmospheric
temperatures of the measurement example shown in Fig. 6.
⫽ 531.66 nm, the blocking of the elastic backscatter
signal would be too low. The ratio of statistical measurement uncertainties of the present setting by use
of the new filters and the previous setting, for which
the GKSS filters were used, is ⌬TRASC兾⌬TGKSS ⫽
1.14兾1.46 ⫽ 0.78 共Fig. 5兲. According to the first measurements of thin clouds 共see below兲, the present filter settings can be kept and corrections of the RR1
Fig. 5. Dependence of the statistical measurement uncertainty
⌬T 共relative units兲 on the CWL of both rotational Raman channels
for a temperature of 240 K. For the calculation, the filter transmission curves were approximated by rectangular filter passbands
with widths of 0.6 and 1.2 nm for the first and second rotational
Raman channels, respectively. The calculated step width was
0.025 nm. Values are given relative to the minimum error near
CWLRR1 ⫽ 531.7 nm and CWLRR2 ⫽ 528.7 nm 共䊐兲. The CWLs
of the GKSS filters and the RASC filters are marked G and R,
respectively.
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APPLIED OPTICS 兾 Vol. 43, No. 14 兾 10 May 2004
signal discussed before15 are no longer necessary
with the new filters.
Another change to the previous setup was made for
the water vapor Raman channel, which was employed to measure the water vapor mixing ratio with
the Raman technique.22,23 We exchanged the broadband color filter, IFa, for a narrowband interference
filter with 1.2 nm FWHM to improve suppression of
both the elastic signal cross talk to the water vapor
signals and the background noise. We also adjusted
the passband of filter IFa by selecting the AOI.
With a 3.5° AOI the CWL becomes 660.65 nm and the
temperature dependence of the extracted water vapor
Raman signal becomes negligible.24 The tilted
mount of this and other narrowband components
共BS3–BS5兲 provides the possibility to adjust the CWL
共deviation between the specified and the actual filter
CWL and future changes of the laser wavelength兲.
We obtained a Raman reference signal, which is
proportional to the molecular density of the atmosphere and which is essentially temperature independent, with the rotational Raman signals by way of
Nref 共z兲 ⫽ NRR1 共z兲 ⫹ c NRR2 共z兲, where Nref, NRR1, and
NRR2 are the intensity of the reference signal and the
rotational Raman signals, respectively; c is a constant; and z denotes height. We determined c by
calculating the temperature dependence of the rotational Raman signals taking the spectral characteristics of the receiver into account. We found the
ratio of rotational Raman signal efficiencies experimentally by comparing the theoretical and experimental calibration functions for temperature
measurements. With this method we derived a rotational Raman reference signal for measurement of
the backscatter and extinction coefficients25 with a
very small temperature dependence: With the new
interference filters the intensity of the reference signal varies by less than ⫾0.5% for temperatures between 200 and 300 K 关Fig. 4共c兲兴. For cases when
these variations are not negligible compared with the
statistical uncertainty of the data, a further correction of the reference signal can be made by fitting the
curve in Fig. 4共c兲 and eliminating even this small
dependence with the measured temperature profile.
With the interference filters used before, c was 0.1
共15兲 whereas with the new filters c is 0.6. The increase is due to both the higher transmission of the
RR1 filters 共0.72 instead of 0.38兲 and to the change of
the filter CWLs and thus the temperature dependence of the signals.
As the elastic signal from low altitudes is blocked
with the chopper at PMT 3, it is detected in a separate
branch of the receiver with PMT 1. A glass plate
共BS1兲 reflects a small fraction of the total signal intensity for this channel, which is further weakened
by neutral-density filters.
In the previous stage of the RASC lidar, the total
polychromator throughput of the receiver was ⬃30,
70, 33, and 64% for the water vapor Raman channel,
the elastic channel, and the two rotational Raman
channels, respectively 共for operation without BSx, a
beam splitter was used for the collocated sodium li-
dar, which has ⬃90% transmission兲.15 With the
changes described in this paper, the same parameters 共excluding the effect of BS1, the beam splitter
added for the low-altitude elastic channel, which also
has ⬃90% transmission兲 became 46, 70, 58, and 68%,
respectively. Thus, with the new filters, the efficiency of the signal separation was significantly improved by 76% for the first rotational Raman channel
and by 53% for the water vapor Raman channel.
3. Measurement Examples
Typical nighttime measurements taken with the new
instrument are shown in Fig. 6. A total of 72 data
files with a 1-min integration time and a height resolution of 72 m were summed for these plots. The
analog data were scaled to fit the photon-counting
rates. After correcting the photon-counting signals
for dead-time effects26 and subtracting the background, the photon-counting and analog signals are
nearly parallel up to count rates of ⬃200 MHz. The
maximum count rate to which the analog detection
signals are equivalent is ⬃2 GHz. To determine the
dead time of each channel, the photon-counting signals were corrected with different values for the dead
time and compared with the analog data. Minimum
differences yielded dead-time values of 4.0 ns for the
first and second rotational Raman channels and 5.3
ns for the low- and high-altitude elastic channels,
respectively. With these values, differences between the two detection modes are less than ⫾1% for
the rotational Raman channels down to a 2-km
height above sea level 共the lidar altitude is 385 m兲,
less than ⫾5% for the elastic low-altitude signal down
to 7 km, and less than ⫾5% for the elastic highaltitude channel down to 3 km. With the data acquisition electronics used before, the dead times of
the receiver were significantly higher 共⬃12 ns兲.15
In Fig. 6 the lidar rotational Raman temperature
data and the measurements of a nearby radiosonde
are in close agreement in the troposphere. The
tropopause is located near 16.5-km height in both
profiles. In the stratosphere, larger differences between the measured temperatures are probably
caused by the larger amplitudes of wave disturbances
that increase with height. The statistical uncertainty of the rotational Raman temperature data is
below ⫾1 K up to 17.7-km 共21.9-km兲 height for a
height resolution ⌬z of 72 m 共360 m兲 and below ⫾2 K
up to 27.8 km for ⌬z ⫽ 1080 m. Nonlinearities of the
photon-counting data, which were not successfully
compensated by the dead-time correction by use of
the model of a paralyzable detector, can be seen below
3-km height. Temperature values calculated with
the analog rotational Raman data are closer to the
radiosonde data than rotational Raman temperature
data of the photon-counting data. Analog rotational
Raman data and radiosonde data agree well down to
⬃2-km height, i.e., ⬃1.6 km above the lidar. For
lower altitudes, different overlap functions between
the laser beam and the field of view of the telescope
cause deviations. To obtain rotational Raman temperature data down to these altitudes, we chose a
Fig. 6. Temperature measured with the RASC Raman lidar on 9
and 10 August 2002, 23.15– 00.27 Japan standard time 共JST兲.
Rotational Raman temperature values were derived with analog
共dashed curves兲 and photon-counting signals 共solid curves兲. Lidar
data with a height resolution of 72 m were used up to 15-km height;
the data between 15 and 20 km, 20 and 30 km, and above 30 km
were smoothed with sliding average lengths of 360, 1080, and
2952 m, respectively. Error bars in the top and bottom left panels and the curves in the bottom right panel show the 1-␴ statistical
uncertainty of the rotational Raman temperature measurements
by use of the photon-counting signals. The crosses in the right
panel depict the top height of each averaging length. The
CIRA-86 profile for 35 °N and the month of August and data of a
radiosonde launched in Yonago 共35.4 °N, 133.4 °E兲 at 21.00 JST
are shown for comparison.
field stop diameter of 12 mm for these measurements,
resulting in a receiver field of view of 1.5 mrad 共full
angle兲. A coaxial setting of the outgoing laser beam
and the receiving telescope, instead of the biaxial
design that we use, would allow measurements closer
to the ground. On the other hand, however, a coaxial design would cause an unwanted increase of the
low-range signal intensity also in the high-altitude
elastic channel.
The flash-lamp power and Q switch of the laser are
synchronized by the chopper to control the transmit10 May 2004 兾 Vol. 43, No. 14 兾 APPLIED OPTICS
2935
ted and detected signals. Variations of the chopper
frequency or tropospheric clouds or aerosols sometimes lead to signal-induced noise 共SIN兲 and deviations of a few kelvins of the measurements in the
upper mesosphere. To correct for such effects, the
signal received from altitudes between 100 and 147.5
km are fitted with exp共a⬘ z2 ⫹ b⬘ z ⫹ c⬘兲, where a⬘, b⬘,
c⬘ are fitting coefficients and z is the height. This
background noise profile is subtracted from the data.
To avoid SIN, either the chopper laser synchronization could be changed so that signals from above 30
km are also weakened 共which would also render integration technique measurements at these heights
impossible and therefore make comparisons between
the two temperature measurement techniques difficult兲 or neutral-density filters could be used 共which
would decrease the range of the integration technique data兲. At present, we investigate whether additional gating of PMT3 prevents SIN effects when
the height range of the elastic high-altitude channel
is kept constant.
The algorithm to derive the temperature data with
an integration technique was initialized at 85-km
height by use of the zonal average temperature of the
Committee on Space Research (COSPAR) International Reference Atmosphere (CIRA-86) for 35 °N
and the month of August.27 The lidar data and the
CIRA model are within typical climatological variations and planetary wave activity. The stratopause
is located at ⬃47-km height in both profiles. The
lidar profile shows clearly a wave structure with a
mesospheric inversion layer 共which could be caused
by gravity wave breaking兲 at approximately 75-km
height, where the temperature data measured with
the lidar are up to 16 K above the climatological
mean.
Lidar temperature data derived with the rotational
Raman technique and those of the integration technique coincide at approximately 30-km height. Below 28.5-km height, the influence of the chopper is
seen in the integration temperature data. Between
33- and 40-km height, the data of the two lidar techniques show larger differences but still agree within
the 1-␴ statistical uncertainty of the data.
During the measurement shown in Fig. 6, a cirrus
layer was present above the lidar. The peak backscatter ratio of this cloud layer is ⬃7 at 13.2-km
height 共for 72-min averaging and 72-m height resolution兲. The particle backscatter signal of the cloud
does not affect the accuracy of the lidar temperature
measurement. The total extinction of the cloud is
relatively low; the signals are attenuated by ⬃7%.
The relation among integration time tres, height
resolution zres, and the 1-␴ statistical uncertainty
⌬T of the rotational Raman temperature measurements is shown in Fig. 7. For this plot the pure
background-subtracted rotational Raman signals
were employed. Thus the data of Fig. 7 show the
ideal performance of nighttime measurements
共without contamination of the Sun, the Moon, or
electric light兲. With, e.g., 5-min integration time
and a height resolution of 72 m, ⌬T is ⬍1 K up to
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APPLIED OPTICS 兾 Vol. 43, No. 14 兾 10 May 2004
Fig. 7. Relation among integration time, height resolution, and
1-␴ statistical uncertainty of the temperature measurements with
the rotational Raman technique for the upgraded RASC lidar 共calculated with the background-corrected data of 9 and 10 August
2002, 23.15– 00.27 JST兲.
⬃7-km altitude. For measurements in the stratosphere, an integration time of 1 h results in 1 K
uncertainty for 1080 m averaging at 23-km altitude, whereas for 41-km altitude and 2952-m resolution 1-h integration time yields 5 K uncertainty if
the background signals are negligible. The relations shown in Fig. 7 can be scaled for other resolutions with the approximation that ⌬T is
proportional to 共tres zres兲⫺0.5. Before the upgrades
tres ⫽ 9 min and zres ⫽ 500 m yielded ⌬T ⬍ 1 K up
to z ⫽ 11 km 共Ref. 15兲; after the upgrades tres was
2.1 min for the same combination of zres, ⌬T, and z,
which is a factor of 共4.3兲⫺1 smaller than before.
Equivalently, ⌬T has been improved by a factor of
共4.3兲⫺0.5 ⫽ 0.48 when tres, zres, and z are kept constant.
With the increase of the signal intensity by a factor
of 4 共because we removed the 25% neutral-density
filters兲, the improved setting of the filters that gives a
reduction of ⌬T by a factor of 0.78 at T ⫽ 240 K 共see
Section 2兲 and the higher filter transmissions we
would expect an even greater improvement from the
upgrades if measurements were done with exactly
the same instrumental alignment and optical thickness of the atmosphere.
It is noteworthy that for low-range measurements
⌬T is smaller than 1 K up to z ⫽ 3 km for tres ⫽ 1 min
and zres ⫽ 72 m 共Fig. 7兲. This shows that observations of the atmospheric boundary layer structure
with scanning or airborne rotational Raman lidar are
already feasible with the product of laser power 共30
W兲 and the receiving telescope area 共⬃0.48 m2兲 of the
RASC lidar. Furthermore, if instead of 532 nm, for
example, the third harmonic radiation of Nd:YAG at
355 nm was employed as the primary wavelength, a
further increase of the rotational Raman signal intensity by a factor of approximately 共3兾2兲4 ⫽ ⬃5
Fig. 8. Consecutive temperature profiles measured with the rotational Raman technique 共left兲 and their statistical uncertainty ⌬T 共right兲.
The time and height resolution of the raw data is 1 min and 72 m, respectively; for this plot, the photon-counting data were used and
smoothed with a sliding average window of 7 min and 360 m. The average of the same data is shown in Fig. 6.
would be gained compared with the system described
here provided that the efficiency of the receiver is the
same.21
A good compromise between resolution and accuracy for measurements throughout the troposphere
with the new RASC lidar would be an integration
time of 7 min and a gliding average of 360 m. With
this resolution the new system provides rotational
Raman temperature data with a 1-␴ statistical uncertainty ⬍1 K up to an altitude of 16 km 共Fig. 8兲,
which is also the approximate height of the summer
tropopause above the lidar site.
With the upgrades we have described, the rotational Raman temperature measurements also became feasible inside clouds as can be seen in Fig. 9.
Here a cloud with a mean backscatter ratio of 46.9 ⫾
0.1 at 7.9-km height above sea level was present for
30 min above the lidar 共height resolution of the lidar
data is 360 m, altitude of the lidar is 385 m above sea
level兲. Rotational Raman temperature data and the
measurement of a local radiosonde show differences
of 共⫺1.4 ⫾ 0.3兲 K at 7.9-km height. In the previous
stage of the RASC lidar, a cloud with a backscatter
ratio of 26 yielded deviations of ⫺5 K.15 Whether
the deviations between the lidar and the radiosonde
are due to the temporal and spatial differences of the
sampled air masses, multiple-scattering effects, or
leakage of the elastic signal in the rotational Raman
channels remains to be clarified by more measurements. If there are deviations that result from
elastic-signal leakage, either the AOIs of the rotational Raman channel filters can be increased yielding smaller CWLs and thus higher blocking at ␭0
共Ref. 14兲 or the rotational Raman signals can be corrected for the elastically backscattered fraction.15
Figure 9 also shows the other measured parame-
Fig. 9. Measurements in the presence of a high-altitude cloud layer 共25
September 2002, 21.00 –21.30 JST, i.e., 90,000 laser shots兲: 共a兲 temperature
with the rotational Raman technique, 共b兲 statistical uncertainty of the temperature measurement, 共c兲 backscatter ratio, 共d兲 particle backscatter coefficient ␤par, 共c兲 particle extinction coefficient ␣par, 共f 兲 water vapor mixing ratio.
Measurement data of a radiosonde started at the lidar site 共reaching altitudes of 6.5 and 18.5 km at 21.00 and 21.30 JST, respectively兲, the molecular
backscatter coefficient ␤mol and the molecular extinction coefficient ␣mol are
shown for comparison 共enlarged by a factor of 10兲.
10 May 2004 兾 Vol. 43, No. 14 兾 APPLIED OPTICS
2937
Table 3. Main Properties of the Receiving Channels of the RASC Lidar
Property
CWL 共nm兲
FWHM 共nm兲
Polychromator transmission 共entrance
hole to PMTa兲
Total blocking at 532.25 nm
PMTa efficiency for detected signal
Elastic,
Low Altitude
H2O
Raman
Elastic,
High Altitude
Rotational
Raman 1
Rotational
Raman 2
⬇ 10⫺3
660.65
1.2
0.41
532.34
0.8
0.63
531.14
0.65
0.52
528.76
1.10
0.61
0.13
⬇ 109
0.13
0.13
⬇ 107
0.13
⬇ 107
0.13
a
PMT, photomultiplier tube.
ters of the RASC lidar. At 7.9-km height the particle
backscatter coefficient is 共0.03216 ⫾ 0.00006兲 km⫺1
sr⫺1, the particle extinction coefficient is 共0.431 ⫾
0.007兲 km⫺1, the lidar ratio is 13.4 ⫾ 0.3, and the
water vapor mixing ratio is 共0.83 ⫾ 0.03 g kg⫺1兲.
4. Summary
We have presented the new combined Raman lidar of
RASC and have illustrated the performance of the
system with measurement examples. We have
shown that a combined Raman lidar that emits at one
laser wavelength and has five detection channels allows for simultaneous measurements of the atmospheric temperature profile from the bottom of the
free troposphere to the top of the mesosphere 共from
⬃1.6 to ⬃80 km above the system兲, the particle extinction coefficient; the particle backscatter coefficient, and the water vapor mixing ratio, as well as
combined parameters such as relative humidity and
the extinction-to-backscatter ratio. With the RASC
lidar, comparisons between the temperature data of
the rotational Raman technique and the integration
technique are possible on an hourly basis in the
stratosphere, an altitude range for which only a few
other instruments provide temperature data.
Also, inside a cloud with a backscatter ratio of ⬃47,
the rotational Raman lidar temperature data and the
data of a local radiosonde show only small deviations.
Without clouds, the 1-␴ statistical uncertainty of the
rotational Raman temperature measurements is below 1 K at nighttime, e.g., up to ⬃7-km altitude for
5-min integration time and a height resolution of
72 m. For measurements in the stratosphere, e.g.,
an integration time of 1 h results in 1 K uncertainty
for a 1080 m average at 23-km altitude, whereas for
41-km altitude and 2952-m smoothing 1 h of integration time yields 5 K uncertainty if background contamination of the rotational Raman signals is
negligible. In conclusion, resolution, range, and reliability of the rotational Raman technique could be
improved simultaneously. The performance of the
low-altitude temperature measurements also illustrates the potential to employ the rotational Raman
technique with scanning or with airborne lidar systems.
A. Behrendt is grateful to the Japanese Society for
the Promotion of Science for the award of a fellowship
共00765兲 and a research grant 共12440127兲 that made
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APPLIED OPTICS 兾 Vol. 43, No. 14 兾 10 May 2004
this research possible. This study was partially supported by Monbusho grant in aid 14340140.
References
1. A. Hauchecorne and M. L. Chanin, “Density and temperature
profiles obtained with lidar between 30 and 70 km,” Geophys.
Res. Lett. 7, 565–568 共1981兲.
2. P. Keckhut, A. Hauchecorne, and M. L. Chanin, “A critical
review of the database acquired for the long-term surveillance
of the middle atmosphere by the French Rayleigh lidars,” J.
Atmos. Oceanic Technol. 10, 850 – 867 共1993兲.
3. T. Leblanc, I. S. McDermid, A. Hauchecorne, and P. Keckhut,
“Evaluation and optimization of lidar temperature analysis
algorithms using simulated data,” J. Geophys. Res. 103, D6,
6177– 6187 共1998兲.
4. R. G. Strauch, V. E. Derr, and R. E. Cupp, “Atmospheric temperature measurements using Raman backscatter,” Appl. Opt.
10, 2665–2669 共1971兲.
5. W. P. G. Moskowitz, G. Davidson, D. Sipler, C. R. Philbrick,
and P. Dao, “Raman augmentation for Rayleigh lidar,” in Proceedings of the 14th International Laser Radar Conference.
共Istitute di Ricerca sulle Onde Elettromagnetiche, Comitato
Nayionale per le Scienge, Florence, Italy, 1988兲.
6. P. Keckhut, M. L. Chanin, and A. Hauchecorne, “Stratospheric
temperature measurement using Raman lidar,” Appl. Opt. 29,
5182–5185 共1990兲.
7. J. Cooney, “Measurement of atmospheric temperature profiles
by Raman backscatter,” J. Appl. Meteorol. 11, 108 –112 共1972兲.
8. J. Cooney and M. Pina, “Laser radar measurements of atmospheric temperature profiles by use of Raman rotational backscatter,” Appl. Opt. 15, 602– 603 共1976兲.
9. R. Gill, K. Geller, J. Farina, and J. Cooney, “Measurement of
atmospheric temperature profiles using Raman lidar,” J. Appl.
Meteorol. 18, 225–227 共1979兲.
10. Y. F. Arshinov, S. M. Bobrovnikov, V. E. Zuev, and V. M. Mitev,
“Atmospheric temperature measurements using a pure rotational Raman lidar,” Appl. Opt. 22, 2984 –2990 共1983兲.
11. D. Nedeljkovic, A. Hauchecorne, and M. L. Chanin, “Rotational
Raman lidar to measure the atmospheric temperature from
the ground to 30 km,” IEEE Trans. Geosci. Remote Sens. 31,
90 –101 共1993兲.
12. G. Vaughan, D. P. Wareing, S. J. Pepler, L. Thomas, and V.
Mitev, “Atmospheric temperature measurements made by rotational Raman scattering,” Appl. Opt. 32, 2758 –2764 共1993兲.
13. C. R. Philbrick “Raman lidar measurements of atmospheric
properties,” in Atmospheric Propagation and Remote Sensing
III, W. A. Flood and W. B. Miller, eds., Proc. SPIE 2222,
922–931 共1994兲.
14. A. Behrendt and J. Reichardt, “Atmospheric temperature profiling in the presence of clouds with a pure rotational Raman
lidar by use of an interference-filter-based polychromator,”
Appl. Opt. 39, 1372–1378 共2000兲.
15. A. Behrendt, T. Nakamura, M. Onishi, R. Baumgart, and T.
16.
17.
18.
19.
20.
Tsuda, “Combined Raman lidar for the measurement of atmospheric temperature, water vapor, particle extinction coefficient, and particle backscatter coefficient,” Appl. Opt. 41,
7657–7666 共2002兲.
A. Hauchecorne, M. L. Chanin, P. Keckhut, and D. Nedeljkovic, “LIDAR monitoring of the temperature in the middle
and lower atmosphere,” Appl. Phys. B 55, 29 –34 共1992兲.
U. von Zahn, G. von Cossart, J. Fiedler, K. H. Fricke, G. Nelke,
G. Baumgarten, D. Rees, A. Hauchecome, and K. Adolfsen,
“The ALOMAR Rayeligh兾Mie兾Raman lidar: objectives, configuration, and performance,” Ann. Geophys. 18, 815– 833
共2000兲.
S. Fukao, T. Sato, T. Tsuda, S. Kato, K. Wakasugi, and T.
Makihira, “The MU radar with an active phased array system.
1. Antenna and power amplifiers,” Radio Sci. 20, 1155–1168
共1985兲.
S. Fukao, T. Tsuda, T. Kato, S. Sato, K. Wakasugi, and T.
Makihira, “The MU radar with an active phased array system.
2. In-house equipment,” Radio Sci. 20, 1169 –1176 共1985兲.
A. Behrendt and C. Weitkamp, “Optimizing the spectral parameters of a lidar receiver for rotational Raman temperature
measurements,” in Advances in Laser Remote Sensing: Selected Papers Presented at the 20th International Laser Radar
21.
22.
23.
24.
25.
26.
27.
Conference, A. Dabas, C. Loth, and J. Pelon, eds. 共Edition de
l’Ecole Polytechnique, Palaiseau, France, 2001兲, pp. 113–116.
A. Behrendt and T. Nakamura, “Calculation of the calibration
constant of polarization lidar and its dependency on atmospheric temperature,” Opt. Express, 10, 805– 817 共2002兲,
http:兾兾www.opticsexpress.org.
S. H. Melfi, J. D. Lawrence, and M. P. McCormick, “Observation of Raman scattering by water vapor in the atmosphere,”
Appl. Phys. Lett. 15, 295–297 共1969兲.
J. Cooney, “Remote measurement of atmospheric water vapor
profiles using the Raman component of laser backscatter,”
J. Appl. Meteorol. 9, 182–184 共1970兲.
V. Sherlock, A. Hauchecorne, and J. Lenoble, “Methodology for
the independent calibration of Raman backscatter watervapor lidar systems,” Appl. Opt. 38, 5816 –5837 共1999兲.
A. Ansmann, U. Wandinger, M. Riebesell, C. Weitkamp, and
W. Michaelis, “Independent measurement of extinction and
backscatter profiles in cirrus clouds using a combined Raman
elastic-backscatter lidar,” Appl. Opt. 31, 7113–7131 共1992兲.
D. R. Evans, The Atomic Nucleus 共McGraw-Hill, New York,
1955兲, p. 786.
D. Rees, J. J. Barnett, and K. Labitzke, eds., CIRA 1986, Part
II: Middle Atmosphere Models, Adv. Space Res. 共COSPAR兲
10共12兲 共1990兲.
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