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Abstract
A well-designed filter assembly is incorporated to an earlier mobile Rayleigh Doppler Lidar developed at University of Science and Technology of China (USTC) for wind measurement round the clock. The filter assembly consists of two cascaded Fabry-Perot Etalons (FPEs) and a narrow-band interference filter (IF), which are optimized to filter out strong solar background radiation during daytime. The high resolution FPE is mainly used to compress the whole bandwidth of the filter assembly, whereas the low resolution FPE with relatively large free spectral range (FSR) is primarily used to block the unwanted periodic transmission peaks of high resolution FPE arising within the narrow-band IF passband. Some test experiments are carried out and demonstrate that the filter assembly have an overall peak transmission of 33.32% with a bandwidth of 2.41 pm at 355 nm. When applying it to the USTC mobile Rayleigh Doppler Lidar, the daytime background is only 3% or less than before. Consequently, the detectable altitude during daytime increases to ∼51 km with wind velocity accuracy of ±7.6 m/s.
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
Doppler Wind Lidar (DWL) has been proven as powerful, accurate and reliable measurement technique for remote sensing of atmospheric wind velocities from ground level up to the metal layer about 120 km altitude. However, the presence of strong solar background light is still a significant problem for a direct-detection Doppler Lidar operating during daytime, which greatly limits its capability for atmospheric dynamics research. Several kinds of ultra-narrowband filters have been adopted for dealing with solar background reduction, such as molecular absorption filter (MAF) [1], Faraday anomalous dispersion optical filter (FADOF) [2,3], and single or cascaded Fabry-Perot Etalons (FPEs) [4]. Usually, the MAF and FADOF are specifically for those Lidars employ laser wavelength located at some molecular absorption lines or alkali-metal atom’s resonance lines, and thus don’t have the versatility.
Single or cascaded FPEs have been proven widely used for filtering of solar background light in the visible, near-IR and UV spectral ranges [5]. Inspired by technologies of passively measuring wind developed for the High-Resolution Doppler Imager (HRDI) [6], Fischer et al. firstly built up an incoherent Doppler Lidar capable of measuring winds and aerosols from the planetary boundary layer through the stratospheric aerosol layer during both daytime and nighttime by means of a multiple FPEs in combination with a narrow-band filter for solar background reduction [7]. However, the system can only measure Doppler shift from aerosol particles and not suitable for molecular detection. In the Rayleigh/Mie/Raman (RMR) Lidar at ALOMAR observatory, high solar background suppression was achieved by utilizing dielectric interference filters together with single or double FPEs at wavelengths 1064, 532 and 355 nm [8–10]. The cascaded FPEs designed for detection of molecular backscatter signal during daytime have an overall bandwidth of 0.3 pm at 532 nm. After equipped to the Doppler wind channel (532 nm detection branch), the RMR Lidar can perform wind observations throughout 24 hours at any time of the year. Even though the FPEs design considerations in RMR Lidar are presented in the cited literatures above, as a matter of fact, the exhaustive specifications design and optimization process are missing. McKay et al. proposed a general method for design and performance analysis of FPEs as solar background filters by maximizing signal to background ratio enhancement [11]. But the practical limitations on FPE plate smoothness and parallelism sets an upper limits to the capability of cascaded FPE filters to suppress the solar background. In the Aeolus airborne demonstrator (A2D), the polarization dependent Rayleigh spectrometer composed by sequential FPEs, together with a 2.6 nm bandwidth solar background filter blocks most of the background natural light, enabling a measurement altitude up to 12 km in daytime conditions [12–16]. Similarly, Shen et al. proposed a high spectral resolution Lidar (HSRL) based on triple FPEs and similar polarization discrimination technique for multi atmospheric parameters detection, which shows significant enhancement of daytime measurement capability [17]. Recent publication reveals that cascaded FPEs can also be incorporated to an iron temperature and wind Lidar working at near-UV 386 nm with a bandwidth of 2 pm, which allows nearly background-free observations during daylight [18]. Such application is somewhat different with double-edge technique based direct-detection Lidars, as a third FPE is required to discriminate the Doppler shift for the latter one. Satellite Doppler Lidar is thought to be the most promising candidate to meet the requirements on global wind profile observations with high vertical resolution, precision, and accuracy. Compared with ground-based and airborne Lidar systems, the space-borne Lidar suffers much severer daylight filtering problems because the Lidar signals decreases as the inverse square of detection range, whereas the solar background is independent of detection range for lower Earth orbit. Careful consideration has been taken in the first space-borne wind Lidar ADM-Aeolus. Besides employing narrow bandwidth background blocking filter and polarization dependent optical receiver, the satellite is flown with the ALADIN instrument pointing toward the anti-sun direction with a quite small field of view of its telescope (∼19µrad), which significantly ameliorate the daytime performance, but cannot be easily transplanted to other platforms [12,19].
In summary, solar background reduction by using cascaded FPEs for extending daytime wind measurement capability at 355nm is only theoretically analyzed by McKay et al. and has not been implemented in a double-edge technique based direct-detection Lidar system yet. In this work, we focus on extending the daytime wind measurement capability of an earlier developed Doppler Lidar system at USTC. A briefly review of this Lidar instrument setup is given in section 2. In section 3, the detailed optimization of the cascaded FPEs filter assembly is represented. Calibration of frequency response function for wind retrieval is displayed in section 4. Experiments carried out to demonstrate the Lidar’s capability for daytime run is shown in section 5. A brief conclusion is made in section 6.
2. Instrument setup
The USTC mobile Rayleigh Doppler Lidar, which has been reported by Dou et al. in detail [20], is based on the double edge technique, as shown in Fig. 1. The main idea that the Doppler shift is retrieved is by using Fabry-Perot interferometer as optical frequency discriminators to realize differentially measuring the imbalance of the double edge channels. Unlike the previous couple of frequency stabilized laser with scanable Fabry-Perot interferometer, here, a tunable laser source in combination with a Fabry-Perot etalon with fixed cavity length is used to simplify the frequency locking and calibration process of the Lidar system. The Lidar system employs a commercial available Q-switched, diode-pumped Nd:YAG laser operating at frequency tripled 355 nm, which guarantee a more stable power output with 100 Hz pulse repetition and approximate 200 mJ energy per pulse. By injection of a narrow linewidth (<20 kHz) fiber laser, the power laser makes stable master frequency in the slave cavity and single longitudinal mode operation. The seed laser is a 100 mW turn-key single frequency distributed feedback (DFB) fiber laser system with active wavelength control and wide-range thermal wavelength tunability up to 700 pm at 1064 nm. The thermo-electrical temperature controllers (TECs) not only stabilizes the operation of the laser desensitizing it to the environmental temperature fluctuations, but also makes it possible to achieve considerable tuning of the center wavelength by changing the operating temperature of the laser. Besides, the laser also comes with a fast piezo-electric tuning capability, where the laser wavelength can be modulated externally at kHz modulation bandwidth to lock it to a stable reference.
Fig. 1. Schematic setup of mobile Rayleigh Doppler Lidar with cascaded Fabry-Perot interferometers for daytime observation.
After power amplification by the power laser, the laser pulses are emitted to the atmosphere via a 15X expander to compress the beam divergence to 60 µrad. The backscatter of atmospheric molecules is collected by a 1 m diameter telescope with field view (FOV) of 90 µrad. A multimode fiber is used to guide the Lidar signal to the optical receiver to perform frequency discrimination. In this work, a dual-channel Fabry-Perot etalon is used to lock the transmitting laser frequency and to discriminate the Doppler frequency shift by time division multiplex, which is realized by optical contacting a non-polarization beam splitter assembly onto the front plate of the FPE [21]. The reference signal used for frequency locking goes through one incident path onto the double edge channel of the FPE firstly, and then the Lidar backscatter goes through another incident path onto the double edge channel of the FPE to discriminate the Doppler frequency shift [22]. For daytime operation, an ultra-narrow band filter assembly is incorporated to the optical path to block the strong background sunlight radiation. The detailed parameters optimization of the FPEs is given in the next section. Originally, frequency locking of emitted laser is achieved by actively control of cavity length of a Fabry-Perot Interferometer, which is also employed as the frequency discriminator in the Lidar system [20,23]. Here, thinking about we got another two cascade FPEs into the optical receiver, a tunable seed laser and a FPE with fixed cavity length is adopted for frequency discrimination to simplify the frequency locking and calibration of the system. The key parameters of this Lidar system are summarized in Table 1.
Table 1. Key parameters of this Doppler Lidar system
3. Optimization of cascaded Fabry-Perot etalons
Lidar operation during the daytime requires that the solar background be greatly reduced. For a Rayleigh Doppler Lidar system, the Rayleigh backscatter is Doppler-broadened with full width at half maximum (FWHM) bandwidth on the order of ∼1.5 pm at 355 nm. A Lidar system by employing only these dielectric filters (usually with bandwidths of 0.15∼0.5 nm) would not work well in sunlight, even though the field of view of the telescope is compressed to smaller than 100 µrad. Narrower spectral bandwidth filters are still needed for solar background reduction. In this work, this has been accomplished through the use of cascaded air-spaced FPEs in combination with a narrowband interference filter, as shown in Fig. 2. The high resolution FPE is mainly used to compress the whole bandwidth of the filter assembly, whereas the low resolution FPE with relatively large free spectral range (FSR) is primary to block the unwanted periodic transmission peaks of high resolution FPE arising within the narrow-band IF passband. Compared with solid-spaced etalons, the air-spaced ones have the advantages not only of desensitization to temperature fluctuations, but can be tunable, permitting precise centering of the passband at the desired wavelength of the Lidar signal [24,25].
Fig. 2. Transmission versus wavelength plot for ± 0.2 nm centered at the laser wavelength: (a) Transmission curve of narrowband interference filter (blue dash line), high resolution FPE (cyan line), low resolution FPE (red line) and spectral resolved Rayleigh scattering intensity (pink shadow); (b) Spectral response of three filter elements combined in series.
3.1 Effective finesses of Fabry-Perot etalons
The transmission of a FPE illuminated with white light is given approximately by ${\pi \mathord{\left/ {\vphantom {\pi {({2{F_R}} )}}} \right.} {({2{F_R}} )}}$, where ${F_R}$ is the reflectance finesse, given in turn by the expression ${{\pi R} \mathord{\left/ {\vphantom {{\pi R} {({1 - R} )}}} \right.} {({1 - R} )}}$, where R is the reflectance of the etalon reflective coating. As can be seen that high reflectance finesse (high $R$) is essential for good solar background attenuation, but conflicts with high signal transmission. In practice, the value of R is basically limited by plate defects. A defect finesse ${F_D}$ is defined to quantify the effect of plate defects in broadening the passband of a FPE. The plate defects may be made up of three distinct types: spherical bowing, surface irregularities, and departure from parallelism. Usually, all three defects occur simultaneously and the overall defect finesse ${F_D}$ can be given approximately by
where ${F_{Ds}}$, ${F_{DG}}$, and ${F_{Dp}}$ are three types of plate defects mentioned above, respectively [26]. The peak transmission of a FPE is given approximately by the ratio of the effective finesse, ${F_E}$, to the reflectance finesse ${F_R}$. The effective finesse can be described as a combination of the reflectance finesse with the defect finesse, which can be determined by a measurement of FWHM of transmission peaks with coatings of very high reflectivity: So, high peak transmission occurs only when the defect finesse is much larger than the reflectance finesse. In this work, the ${F_R}$ is chosen to be about ∼24, indicating that the solar background attenuation for a single etalon could be as much as a factor ∼15. However, the attenuation will be somewhat less than this, owning to residual transmittance through the parasitic transmittance maxima. The defect finesse is generally specified as a fraction of characterization wavelength (usually 633 nm) ${\lambda \mathord{\left/ {\vphantom {\lambda k}} \right.} k}$, where k is a roughness factor corresponding to etalon polishing smoothness. Nowadays, the best possible etalon plates would offer a defect finesse on the order of ∼24 at 355 nm. Thus it follows that FPEs for solar background filters at 355 nm must have the highest possible defect finesse, or either the signal transmittance or the attenuation of the solar background will suffer. From Eq. (2), one can easily get the value of effective finesse ${F_E}$ ∼17 in this work, tending to the limits of what can be practically constructed.3.2 Plate spacing of Fabry-Perot etalons
The FPE is characterized by periodic transmittance maxima. Those orders of transmittance maxima other than the one centered within the passband of the IF must be suppressed for ideal solar background reduction. The adjacent maxima of a FPE is separated by the free spectral range (FSR), given by ${c \mathord{\left/ {\vphantom {c {2l}}} \right.} {2l}}$ in frequency unit ($\textrm{c}$ being the speed of light and l the etalon plate spacing). For a single FPE with given effective finesse, higher spectral resolution accompanies with smaller FSR, indicating more orders of arising within the interference filter transmission passband; whereas lower spectral resolution comes with wider FWHM of zero order transmittance maxima, resulting higher bandwidth for solar background to pass through. They both leads to high white light transmittance. In this work, the plate spacing optimization is performed simultaneously and is on a basis that the signal to noise ratio (SNR) of Lidar return signal reaches maximum in sunlight, by modeling the expected response of the atmosphere as a function of altitude and taking the spectral response of the filter assembly into account.
For a single wavelength pulsed Lidar system, the received photons at altitude r is given by the Lidar equation [27]:
Fig. 3. Contour of simulated daytime SNR (signal to noise ratio) of Lidar return from 30 km altitude along with FSR1 (the low resolution FPE) and FSR2 (the high resolution FPE). The accumulating time is 2 min. The cross point of the red dashed lines indicated the Lidar working point chosen for this work.
It’s obviously that the SNR is on diagonally symmetric distribution. The blue fringes, which are related to smaller SNR, appears where there are overlapped transmission peaks between the high and low resolution FPEs, resulting high transmittance of solar background radiation. The working point chosen for this Lidar instrument has been labeled at the cross point of the two red dashed lines. The reason we select this working point rather than those with larger SNR is that while the FSR of the high resolution FPE is much narrower than the bandwidth of Rayleigh spectrum, the large SNR is then contributed by several orders of transmittance maximum. As a result, enormous systematic error may occur when performing Doppler shift discrimination by using backscatter with more than one order of transmittance maximum in spectrum. Therefore necessary limitations are required to be put forward before optimizing FSRs for the FPEs based on the simulated SNR. Considering that the bandwidth of the IF is 150 pm, the low resolution FPE, which is basically used for blocking extra orders of the high resolution FPE transmission peak arising within the passband of the IF, is limited to have a FSR comparable to 150 pm, i.e. the bandwidth of IF. The FSR of high resolution FPE is set to be larger than 33 pm, corresponding to a bandwidth of ∼2 pm, just a little wider than the distance between transmission peak of the double-edge channels to guarantee most of Rayleigh backscatter passing it through. Finally, The FSR1 and FSR2 are choose to be 147.74 pm and 33.58 pm, respectively. By substituting the optimized FSR into $d = {c \mathord{\left/ {\vphantom {c {(2nFSR)}}} \right.} {(2nFSR)}}$, one can get the plate spacing of FPEs together with other specifications of the solar filter component summarized in Table 2. It should be noted here that the optimization result is not the only choice for solar background reduction.
Table 2. Specifications of FPEs and narrowband IF
4. Calibration of the Lidar system
Nighttime wind measurement based on double edge technique has been reported in detail by several groups [29–32]. The line of sight (LOS) wind are retrieved on a basis that by using kinds of frequency discriminators, the Doppler frequency shift detection can be converted to detection of the intensity imbalance between the double edge channels. This approach is quantified by a so-called frequency response function:
4.1 Derivate the frequency response function of the Lidar system
The Lidar system frequency response function can be derived from Fabry-Perot interferometry theory [34]. The general equation for transmission through an ideal FPE (assuming parallel, perfectly flat plates with no surface defects, thus having broadening caused only from reflectivity of the surface coatings) is given by [35,36]
In our Lidar system, a multimode fiber is used to guide the Rayleigh backscatter received by the telescope to the optical receiver. Such configuration not only reduces the field of view of the telescope, but also helps the post fiber optics collimates the beam to be normal incidence to the FPEs. Assuming that the incident illumination uniformly distributes on the FPE plates with a divergence ${\theta _0}$, the transmission function can be rewritten by integrating over angle and normalized as
For nighttime observation, there is only one FPE (i.e., FPE3 in Fig. 1) used in the optical receiver for frequency discrimination. Just like the derivation above in Eqs. (10) and (11), the real transmission of atmospheric molecules backscatter on FPE3 is a convolution of the FPE transmission and the spectrum of Rayleigh backscatter:
4.2 Characterization of the Lidar system calibration
In order to get an accurate frequency response function for wind retrieval, the Lidar system must be calibrated, which is done on a basis that scan the FPEs over wavelength, fit it and then calculate the frequency response function based on the equations deduced above. The laser used here is an injection seeded Nd:YAG pulsed laser working at wavelength tripled 355 nm. The seed laser is a tunable diode-pumped fiber laser with linewidth ∼56 kHz and wavelength tuning capability of larger than 700 pm at 1064 nm. The minimum tuning step is about ∼1 pm at 1064 nm, corresponding to ∼0.33 pm at 355 nm. During the scanning, the laser wavelength is monitored by a wavelength meter and stabilized by putting it into a temperature controlled container. Furthermore, the thermo-electrical temperature controllers (TECs) of the fiber laser not only makes it possible to achieve considerable tuning of the center wavelength by changing the operating temperature of the laser, but also stabilizes the operation of the laser desensitizing it to the environmental temperature fluctuations. The laser source is firstly attenuated by an integrating sphere, and then coupled and guided to the FPE surface by a 200 µm diameter multimode fiber. At the fiber end, a cylindrical lens is used to expand and collimate the light beam to ∼20 mm diameter with 0.9 mrad full angle divergence, which is compatible with the maximum acceptance angles of the FPEs. At the transmittance bottom area, the scanning step is set to be 3 pm at 355 nm. Whereas at the passband area, the scanning steps are set to be 0.33 pm and 0.9 pm for the high and low resolution FPEs, respectively. The scanned raw data is fitted by a plate defect broadened Airy function depicted in Eq. (11). Figure 4 represents typical scanning results of the high and low resolution FPEs’ transmission curves and their fitting results.
Fig. 4. Transmission curves of (a) high resolution and (b) low resolution FPEs scanned by a tunable diode pumped fiber laser individually. The red plot represents the raw data, and the blue line is fitted result. The residuals between them are also presented.
The result shows that the high resolution FPE has a transmission peak of about 55.12% at central wavelength 354.73118 nm and bandwidth of 2.27 pm, which is slightly wider than what we expect. Besides the laser linewidth broaden effect, this degradation of the FPE performance maybe comes of both the angular divergence and the unnormal incidence of the illumination beam. Conversely, the low resolution FPE has a much higher transmission peak of 65.14% at central wavelength 354.73004 nm, and a bandwidth of 8.36 pm, which is comparable with the designed value, as shown in Fig. 4(b). It should be noted that there is a small deviation (1.14 pm) between the fitted central wavelengths of the FPEs, which will not affect too much on the filter assembly performance, because the passband of high resolution FPE is so narrow that for the low resolution FPE, the overall transmittance almost not change. The absolute residual between the measured and fitted results has also been plotted. In Figs. 4(a) and 4(b), the deviations for the high resolution FPE seems to be larger than that for the low resolution FPE. We hold the point that it’s the imperfect parallelism between the etalon plates rather than the frequency drift cause the deviations. On the one hand, a precise wavelength meter (High Finesse, WSU 2) has been utilized for monitoring the actual laser wavelength/frequency during each scanning process, which allows measurement of the absolute frequency with an accuracy of 2 MHz by further calibrating to a He-Ne laser. On the other hand, it’s reasonable that the high resolution FPE is more sensitive to the plate defects introduced by departure from parallelism as it have a larger cavity spacing. Even though the deviations may lead to fitting uncertainties, they won’t affect the wind measurement accuracy too much because almost all the Rayleigh backscatter pass through the FPEs via the central region of the passband, where good agreement can be found between the measured and fitted results for both FPEs.
However, the initial scanning results of individual FPE shows good agreement with our design, which motivates us to move further assessment of the performance of the cascaded FPEs. The experiments setup is all the same with the layout for individual FPE scanning. The FPEs are installed in series so that the collimated normal incident light can pass them through in sequences. To avoid the effects of the inter-etalon reflections, a weak absorber (∼5%) is placed in between the etalons, which is the simplest and most effective approach. The scanning results of cascaded FPEs’ Transmission curve fitted with Eq. (12) together with the adjacent double sideband are shown in Fig. 5.
Fig. 5. Scanning (red plot) and fitted (blue light) results of cascaded FPEs’ Transmission curves are given along with the absolute residual.
The fitting result shows that the adjacent sideband is exactly 33.6 pm far away from the central transmission peak. Taking the contribution of the weak absorber into account, the transmission peak value 33.32% is approximate the product of two individual transmission peak 35.91%. The cascaded FPEs have a passband width of 2.41 pm, which is comparable to the theoretical value. Furthermore, the central wavelength of the cascaded FPEs locates at 354.73109 nm, right at the center of two individual FPE as we expected. All the excellent performance of both individual and cascaded FPEs demonstrate the capability to build a stable and robust filter system.
Besides accurate spectral response of cascaded FPEs filters, to get the frequency response function as depicted in Eqs. (19) and (20), the temperature profile related Rayleigh backscatter spectrum is required. Usually, the temperature profile can be given by local atmosphere models. However, errors may occur when the temperature profile is biased. So during daily observation, the temperature profile over 30 km altitude is calibrated by a vertical-pointing Lidar system in terms with Rayleigh integration technique, whereas at the altitude below 30 km, the temperature profile is obtained from local radiosonde sounding. As the frequency response of IF and the frequency discriminator FPE have been described in many literatures, it isn’t a significant problem for calibration now and won’t be discussed here. Figure 6 shows the calibrated frequency response functions for nighttime and daytime observation, respectively.
Fig. 6. The calibrated frequency response functions for (a) nighttime and (b) daytime wind measurement.
Both the nighttime and daytime frequency response function are depicted with the same color bar. As can be seen that, on the one hand, there is no significant changes between the daytime and nighttime frequency response functions as well as their sensitivities no matter whether the radial wind is large or not, indicating that the well calibrated optical receiver incorporating FPEs filter assembly does not degrade the performance of wind sounding apparently. On the other hand, as the altitude increased and temperature decreased, the amplitude changed of daytime frequency response function is smaller than that of the nighttime, giving the evidence that daytime frequency response function is not as sensitive to temperature biases as the nighttime measurement. Even though, the wind measurement accuracy during daytime is still worse than the results of nighttime due to the lower signal to noise ratio, which would be discussed in next section.
5. Demonstration of daytime measurement capability
The well-designed cascaded FPEs filter assembly is initially installed to an earlier developed mobile Rayleigh Doppler Lidar system at University of Science and Technology of China. The Lidar system consists of three independent Lidars: one aims vertically for temperature measurement, another two are placed orthogonally with a zenith angle of 30 degrees for horizontal wind sounding. Since built up at 2013, the Lidar system has run several winter-over campaigns and has made contribution to stratospheric gravity waves research [40,41]. However, due to limitation of weak capability for daytime observation, it can only go through those waves with period no longer than 10∼12 hours. For planetary waves, tides and gravity waves with long periods, which play an important role on the momentum and energy transfer, turbulence and general circulation in the stratosphere, the Lidar cannot give a full picture of the perturbations. This problem has been solved by implementation of this ultra-narrow solar background filter, which enables meaningful observations to be made round the clock with altitude range from 15 km to ∼60 km during night and to ∼50 km during daytime. To demonstrate the wind measurement capability of this Lidar, comparison experiments are carried out at various environment situations. We focus on the solar background reduction with the emphasis that what the signal to noise ratio limited detectable altitude can be reached under daytime circumstances.
Figure 7 shows an inter-comparison results of Lidar backscatter from nighttime without FPEs (green line), daytime without FPEs (blue line), and daytime with FPEs (red line). The results are 5 min, i.e. 30000 pulses, averaged with laser energy ∼200 mJ per pulse. During nighttime, the main background noise source is from the star in field of view as well as the dark counts of the PMTs (typical ∼80 counts per second). It’s so weak that filters with bandwidth narrower than ∼1 nm is not required. Conversely for daytime, as can be seen from blue line Fig. 7, the Lidar backscatter gradually submerged into the noise from only about 35 km altitude. It should be noticed that the actual solar contribution to the measured daytime background is ∼1.6 MHz/Bin, which yields an average sky brightness of about ∼0.04 $W \cdot {m^2} \cdot s{r^{ - 1}} \cdot n{m^{ - 1}}$, comparable to the modeled solar irradiation used above (0.025 $W \cdot {m^2} \cdot s{r^{ - 1}} \cdot n{m^{ - 1}}$) for calculating the throughput of the Lidar background. To determine the actual performance of the cascaded FPEs, comparison experiments are carried out just a few minutes later. As shown in Fig. 7 red line, the averaged solar background decreased to ∼0.05 MHz/Bin, i.e. more than 96% of the solar background was reduced. Even though the received Lidar backscatter also decreased to only ∼30% of that without FPEs in the optical path, it’s acceptable because the detectable altitude reached up to ∼50 km during daytime situations.
Fig. 7. Inter-comparison of Lidar backscatter under different situations: nighttime without FPEs are represented with green line; daytime without FPEs with blue line and daytime with FPEs with red line.
First field experiments are carried out on October 8, 2018 to highlight the Lidar’s capability for both daytime and nighttime wind measurements. Typical horizontal wind measurements results are shown in Figs. 8(a) and 8(b). Actually, we have got only one FPEs cascaded filter assembly for solar background reduction nowadays. So in the actual practice, the two orthogonally placed Lidars worked sequentially with the same optical receiver at daytime hours. The two Lidars were of course seeded by the same seed laser to guarantee identical frequency response.
Fig. 8. Comparison of horizontal wind speed between Lidar and radiosonde measurement during (a) nighttime and (b) daytime.
Figure 8(a) shows data taken at nighttime hours with vertical resolution of 0.2 and 1 km at altitudes below and above 40 km, respectively. To get a higher signal to noise ratio, the wind profile was accumulated by 30 minutes, generating wind measurement error of ± 7.96 m/s at 60 km altitudes. Figure 8(b) shows data taken at daytime hours with the same temporal and spatial resolution as above. For nighttime observation, the wind error is about ± 7.96 m/s at 60 km altitude, whereas the altitude where wind error reaches ± 7.6 m/s at daytime is about 51 km. Here, only the statistical standard error of wind measurement are considered. According to the theory of double-edge technique from Flesia and Korb [32], the statistical standard error $\varepsilon$ can be estimated by
where $SNR$ and $\Theta $ are the signal to noise ratio and the sensitivity for the double-edge measurement, respectively. The sensitivity $\Theta $ is defined as the fractional change in the frequency response function for a unit wind velocity change. In Fig. 6, one can easily find that the sensitivity $\Theta $ for nighttime is slightly larger than that for daytime. Assuming the shot noise or Poisson noise are the dominate noise source and follows the Poisson statistics, the SNR is then inversely proportional to the square root of photon counts accumulated by the detector. Generally, the daytime measurement has a relatively small SNR compared with the nighttime measurement. Therefore, the overall error bar of daytime is larger than the nighttime. In comparison, both the daytime and nighttime wind profiles are plot together with local radiosonde sounding results (red plots in Figs. 8(a) and 8(b)). Unfortunately, due to the terrain obstacles, the radiosondes can only decode the telemetry up to about 20 km altitude as they make their ascents. Anyway, the residuals to the radiosonde have been analyzed statistically. The histograms presented in Fig. 9 summarize the wind difference in the altitude range from 13 km to 20 km for daytime and nighttime observations. The mean velocity differences are −0.08 m/s and 0.43 m/s for daytime and nighttime, respectively. Whereas the standard deviations of velocity are 1.4 m/s and 1.52 m/s, respectively. Even though the residuals of daytime measurement is a litter larger than that of the nighttime, the inter-comparison results of Lidar and radiosondes measurements show good agreement, which demonstrates the Lidar’s capability for accurate wind measurements at both daytime and nighttime. Furthermore, from the decomposed meridional and zonal wind, we saw obvious wave-structures existing at altitudes from 32 km to 41 km with vertical wavelength of about 4.5 km, which is very typical stratospheric gravity wave features captured by kinds of observations.Fig. 9. Histograms of the velocity residuals between lidar and radiosonde measurement for (a) nighttime and (b) daytime. Red lines are the Gaussian fit results to the data.
6. Conclusions and future works
The capability of an earlier developed mobile Rayleigh Doppler Lidar system incorporating a well-designed filter assembly for both daytime and nighttime wind measurements has been demonstrated in this work. The design and optimization of the FPE parameters, the calibration of the system frequency response are given in detail. Field experiments show that for a given wind accuracy of ± 7.6 m/s, the detectable altitudes can be reached at daytime and nighttime situation with 30 min integration period are 51 km and 60 km, respectively. The spatial and temporal coverage of the Doppler Lidar system makes it a powerful tool for stratospheric waves study. The technique offers a practicable way for Rayleigh Lidar to realize daytime observation. The advantages include not only the overall filter performance, but also inexpensive to build, which makes it very attractive technique for future development of both ground-based and space-borne Lidar systems.
Funding
National Natural Science Foundation of China (41774193).
References
1. Z. S. Liu, B. Y. Liu, Z. G. Li, Z. A. Yan, S. H. Wu, and Z. B. Sun, “Wind measurements with incoherent Doppler lidar based on iodine filters at night and day,” Appl. Phys. B: Lasers Opt. 88(2), 327–335 (2007). [CrossRef]
2. K. H. Kwon, C. S. Gardner, D. C. Senft, F. L. Roesler, and J. Harlander, “Daytime lidar measurements of tidal winds in the mesospheric sodium layer at Urbana, Illinois,” J. Geophys. Res. 92(A8), 8781–8786 (1987). [CrossRef]
3. W. Huang, X. Chu, B. P. Williams, S. D. Harrell, J. Wiig, and C.-Y. She, “Na double-edge magneto-optic filter for Na lidar profiling of wind and temperature in the lower atmosphere,” Opt. Lett. 34(2), 199–201 (2009). [CrossRef]
4. W. H. Hunt, M. A. Vaughan, K. A. Powell, and C. Weimer, “CALIPSO lidar description and performance assessment,” J. Atmos. Ocean. Technol. 26(7), 1214–1228 (2009). [CrossRef]
5. U. Von Zahn, G. Von Cossart, J. Fiedler, K. H. Fricke, and G. Nelke, “The ALOMAR Rayleigh / Mie / Raman lidar : objectives, configuration, and performance,” Ann. Geophys. 18(7), 815–833 (2000). [CrossRef]
6. W. R. Skinner, D. A. Gell, A. R. Marshall, P. B. Hays, J. F. Kafkalidis, and D. R. Marsh, “High-resolution Doppler imager: instrument performance from late 1991 to mid-1999,” In Proceedings of Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research III, AM Larar, ed. (International Society for Optics and Photonics, 1999), pp. 304–315.
7. K. W. Fischer, M. J. Mcgill, and T. D. Irgang, “Visible wavelength Doppler lidar for measurement of wind and aerosol profiles during day and night,” Opt. Eng. 34(2), 499–511 (1995). [CrossRef]
8. D. Rees, U. Von Zahn, G. Von Cossart, G. Nelke, K. H. Fricke, and N. D. Lloyd, “The Doppler wind and temperature system of the ALOMAR Lidar,” in Proceedings of 13th ESA Symposium on European Rocket and Balloon Programmes and Related Research, B. KaldeichSchurmann, ed. (EUROPEAN SPACE AGENCY, 1997), pp. 121–128.
9. D. Rees, U. Von Zahn, G. Von Cossart, K. H. Fricke, W. Eriksen, and J. A. McKay, “Daytime Lidar measurements of the stratosphere and mesosphere at the ALOMAR observatory,” Adv. Space Res. 26(6), 893–902 (2000). [CrossRef]
10. G. Baumgarten, “Doppler Rayleigh/Mie/Raman lidar for wind and temperature measurements in the middle atmosphere up to 80 km,” Atmos. Meas. Tech. 3(6), 1509–1518 (2010). [CrossRef]
11. J. A. McKay, “Single and tandem Fabry-Perot etalons as solar background filters for lidar,” Appl. Opt. 38(27), 5851–5858 (1999). [CrossRef]
12. O. Reitebuch, C. Lemmerz, E. Nagel, Y. Durand, M. Endemann, F. Fabre, and M. Chaloupy, “The airborne demonstrator for the direct-detection doppler wind lidar ALADIN on ADM-Aeolus. Part I: Instrument design and comparison to satellite instrument,” J. Atmos. Ocean. Technol. 26(12), 2501–2515 (2009). [CrossRef]
13. O. Lux, C. Lemmerz, F. Weiler, U. Marksteiner, B. Witschas, S. Rahm, A. Schäfler, and O. Reitebuch, “Airborne wind lidar observations over the North Atlantic in 2016 for the pre-launch validation of the satellite mission Aeolus,” Atmos. Meas. Tech. 11(6), 3297–3322 (2018). [CrossRef]
14. U. Marksteiner, C. Lemmerz, O. Lux, S. Rahm, A. Schäfler, B. Witschas, and O. Reitebuch, “Calibrations and wind observations of an airborne direct-detection wind LiDAR supporting ESA’s Aeolus mission,” Remote Sens. 10(12), 2056 (2018). [CrossRef]
15. U. Marksteiner, O. Reitebuch, S. Rahm, I. Nikolaus, C. Lemmerz, and B. Witschas, “Airborne direct-detection and coherent wind lidar measurements along the east coast of Greenland in 2009 supporting ESA’s Aeolus mission,” Proc. SPIE 8182, 81820J (2011). [CrossRef]
16. U. Marksteiner, Airborne Wind Lidar Observations for the Validation of the ADM-Aeolus Instrument (Technische Universität München, 2013).
17. F. Shen, C. Xie, C. Qiu, and B. Wang, “Fabry–Perot etalon-based ultraviolet trifrequency high-spectral-resolution lidar for wind, temperature, and aerosol measurements from 0.2 to 35 km altitude,” Appl. Opt. 57(31), 9328–9340 (2018). [CrossRef]
18. J. Höffner and J. Lautenbach, “Daylight measurements of mesopause temperature and vertical wind with the mobile scanning iron lidar,” Opt. Lett. 34(9), 1351–1353 (2009). [CrossRef]
19. L. Francou, E. Rugi Grond, S. Blum, and C. Voland, “Optical analysis and performance verification on ALADIN spectrometers,” Proc. SPIE 10566, 1056624 (2017). [CrossRef] .
20. X. Dou, Y. Han, D. Sun, H. Xia, Z. Shu, R. Zhao, M. Shangguan, and J. Guo, “Mobile Rayleigh Doppler lidar for wind and temperature measurements in the stratosphere and lower mesosphere,” Opt. Express 22(S5), A1203–A1221 (2014). [CrossRef]
21. J. Zheng, D. Sun, T. Chen, R. Zhao, Y. Han, Z. Li, A. Zhou, and N. Zhang, “Scanning Rayleigh Doppler Lidar for Wind Profiling Based on Non-polarized Beam Splitter Cube Optically Contacted FPI,” Curr. Opt. Photonics 2(2), 195–202 (2018). [CrossRef]
22. N. Zhang, D. Sun, Y. Han, C. Chen, Y. Wang, J. Zheng, H. Liu, F. Han, A. Zhou, and L. Tang, “Zero Doppler correction for Fabry–Pérot interferometer-based direct-detection Doppler wind LIDAR,” Opt. Eng. 58(5), 054101 (2019). [CrossRef]
23. H. Xia, D. Sun, Y. Yang, F. Shen, J. Dong, and T. Kobayashi, “Fabry-Perot interferometer based Mie Doppler lidar for low tropospheric wind observation,” Appl. Opt. 46(29), 7120–7131 (2007). [CrossRef]
24. D. Rees, P. A. Rounce, T. R. Hicks, N. K. Reay, P. D. Atherton, and F. J. Mulligan, “A stable, rugged, capacitance-stabilised piezoelectric scanned Fabry-Perot etalon,” J. Phys. E: Sci. Instrum. 14(11), 1320–1325 (1981). [CrossRef]
25. T. R. Hicks, N. K. Reay, and P. D. Atherton, “The application of capacitance micrometry to the control of Fabry-Perot etalons,” J. Phys. E: Sci. Instrum. 17(1), 49–55 (1984). [CrossRef]
26. T. R. Atherton, P. D. Reay, N. K. Ring, and J. and Hicks, “Tunable Fabry -Perot filters,” Opt. Eng. 20(6), 206806 (1981). [CrossRef]
27. R. M. Measures, Laser Remote Sensing: Fundamentals and Applications (Wiley-Interscience, 1984).
28. M. J. McGill, W. D. Hart, J. a McKay, and J. D. Spinhirne, “Modeling the performance of direct-detection Doppler lidar systems including cloud and solar background variability,” Appl. Opt. 38(30), 6388–6397 (1999). [CrossRef]
29. M. L. Chanin, A. Garnier, A. Hauchecorne, and J. Porteneuve, “A Doppler lidar for measuring winds in the middle atmosphere,” Geophys. Res. Lett. 16(11), 1273–1276 (1989). [CrossRef]
30. C. Flesia, C. Korb, and C. Hirt, “Double-edge molecular measurement of lidar wind profiles at 355 nm,” Opt. Lett. 25(19), 1466–1468 (2000). [CrossRef]
31. C. Souprayen, A. Garnier, A. Hertzog, A. Hauchecorne, and J. Porteneuve, “Rayleigh-Mie Doppler wind lidar for atmospheric measurements. I. Instrumental setup, validation, and first climatological results,” Appl. Opt. 38(12), 2410–2421 (1999). [CrossRef]
32. C. Flesia and C. L. Korb, “Theory of the double-edge molecular technique for Doppler lidar wind measurement,” Appl. Opt. 38(3), 432–440 (1999). [CrossRef]
33. H. Xia, X. Dou, M. Shangguan, R. Zhao, and D. Sun, “Stratospheric temperature measurement with scanning Fabry-Perot interferometer for wind retrieval from mobile Rayleigh Doppler lidar,” Opt. Express 22(18), 21775–21789 (2014). [CrossRef]
34. M. J. McGill, “Multiple Fabry-Perot interferometers in an incoherent Doppler lidar,” Opt. Eng. 36(1), 139–145 (1997). [CrossRef]
35. J. M. Vaughan, The Fabry–Perot Interferometer The Fabry-Perot Interferometer: History, Theory, Practice and Applications (Taylor & Francis Group, 1989).
36. G. Hernandez, Fabry-Perot Interferometers (Cambridge University Press, 1988).
37. W. R. Skinner, P. B. Hays, and V. J. Abreu, “Optimization of a triple etalon interferometer,” Appl. Opt. 26(14), 2817–2827 (1987). [CrossRef]
38. A. Dabas, M. L. Denneulin, P. Flamant, C. Loth, A. Garnier, and A. Dolfi-Bouteyre, “Correcting winds measured with a Rayleigh Doppler lidar from pressure and temperature effects,” Tellus, Ser. A Dyn. Meteorol. Oceanogr. 60(2), 206–215 (2008). [CrossRef]
39. B. Witschas, C. Lemmerz, and O. Reitebuch, “Horizontal lidar measurements for the proof of spontaneous Rayleigh–Brillouin scattering in the atmosphere,” Appl. Opt. 51(25), 6207–6219 (2012). [CrossRef]
40. R. Zhao, X. Dou, D. Sun, X. Xue, J. Zheng, Y. Han, T. Chen, G. Wang, and Y. Zhou, “Gravity waves observation of wind field in stratosphere based on a Rayleigh Doppler lidar,” Opt. Express 24(6), A581–591 (2016). [CrossRef]
41. R. Zhao, X. Dou, X. Xue, D. Sun, Y. Han, C. Chen, J. Zheng, Z. Li, A. Zhou, Y. Han, G. Wang, and T. Chen, “Stratosphere and lower mesosphere wind observation and gravity wave activities of the wind field in China using a mobile Rayleigh Doppler lidar,” J. Geophys. Res.: Space Phys. 122(8), 8847–8857 (2017). [CrossRef]
