1. Introduction
In this work, we have focused solely on deriving an accurate measurement of AOT, rather than vertical aerosol profiles, as was the goal of previous aerosol lidar work. In sun photometry an approach similar to an elevation-scanning lidar measurement is used for absolute calibration and is called the Langley plot (Adler-Golden and Slusser 2007). For a Langley plot to be valid, the aerosol layer must be horizontally uniform over the angles of measurement. However, this horizontal uniformity only applies to the total column attenuation of the aerosol (from the ground to space), not to the specific vertical structure of the aerosol layers. Rigorously, what is assumed is that the integral of Eq. (1) does not change if an inclined integration path is chosen. This weaker assumption is less affected by turbulent mixing and localized aerosol sources than the assumption of complete stratification for all altitudes required to recover attenuation and backscatter profiles from a multiangle measurement (Adam et al. 2007; Hamilton 1969). Since in sun photometry the Langley plot method relies on the earth rotation for elevation scanning, this makes it only practical for the instrument calibration in daytime conditions with a clear atmosphere that is stable over a long period of time.
In this paper we ask the questions, if the same weaker (integral) uniformity assumption can be used to recover AOT from an elevation-scanning lidar measurement and if it would result in precision comparable to a sun photometer measurement. Because we are only interested in the AOT (total column), the analysis can be made under the less restrictive assumption that the total column integral of αa, Eq. (1), is constant with the lidar pointing angle. No assumption with respect to βa is required other than that the measurement altitude z1 can be chosen such that βa(z1) makes a negligible contribution to the total β. This results in a measurement technique that is insensitive to small-scale fluctuations in aerosol scattering caused by turbulence or local fluctuations in boundary layer height. Our results indicate that indeed lidar AOT derived with this approach has at least a comparable precision to the sun photometry.
2. Measurement concept
To select z1 we note that in the absence of large volcanic eruptions, concentrations of stratospheric aerosols are normally exceedingly small in comparison to concentrations of tropospheric aerosols. It is therefore safe to assume that the entire vertical column density of aerosols is contained in the altitude range starting from the ground and extending to just above the tropopause. This makes the altitude of a few kilometers above the tropopause a good choice for z1. At our location the tropopose is located around 10–12 km; thus, we have chosen z1 = 15 km in our measurements.
The distinct benefit of this measurement over a Langley plot with a sun photometer is that, unlike the sun elevation angle, the lidar elevation scan can be performed quickly, giving more credence to the assumption of constancy (in the integral sense) of the aerosol layer in time and horizontal direction during the measurement.
To quantify the performance of this approach, we performed a 2-week measurement campaign in El Segundo, California, with an elevation-scanning 355-nm lidar that was adapted to this experiment. A collocated Cimel 318 sun photometer collected data simultaneously, and the comparison of these two datasets is the subject of this paper.
3. Experiment
a. Sun photometer
One of the two collocated instruments used in this comparison was a Cimel 318 sun photometer. The sun photometer used is the model standard for the Aerosol Robotic Network AERONET (Holben et al. 1998; NASA 2014). Both calibration and data processing of the sun photometer are done by NASA’s AERONET program. The data used for this study are level 2.0 according to the AERONET classification (NASA 2014).
b. Lidar system and measurement
We adapted an existing 355-nm lidar system to perform this measurement. The actual system used was significantly larger than what would have been ideal; thus, the signal had to be attenuated to avoid saturation. Parameters of a more appropriate system are discussed in the conclusions section below.
The transmitter is based on a frequency-tripled Nd:YAG laser (Spectra Physics model GCR-290), with approximately 370 mJ at 355 nm of transmit energy and a 30-Hz repetition rate (11-W average power). The receiver has an aperture of 0.76 m in diameter, a field stop aperture of 320-μrad in diameter, and a bandpass filter with a 0.3-nm full width at half maximum (FWHM). The signal was further attenuated with neutral density filters to allow photon counting with a photomultiplier (PMT). The angular pointing of the system was calibrated against local landmarks and verified by pointing at the moon. Both were performed with an alignment camera. We estimate the angular pointing accuracy to be better than 0.5°.
The measurement campaign was conducted between 13 and 28 February 2013 in El Segundo. Since we are located about 4 km from the coast, we chose to point our lidar in the azimuth direction approximately parallel to the local coastline of 337° (northwest). Five elevation angles were chosen such that the resulting points are equally spaced on the airmass axis: 80°, 55.9°, 44.1°, 35.8°, and 29.5°. The lidar return signal from 15 km (±0.5 km) altitude was averaged for 3 min (laser-on time) at each elevation angle. Because of communication overheads and manual moving of the lidar elevation angle, the minimum time between successive data points was 5–6 min and the duration of a complete elevation scan was approximately 30 min. To enable comparison with the sun photometer, measurements were performed in the daytime. Therefore, the background was subtracted and then the signal was normalized by laser power as monitored by a photodiode. The typical signal was approximately 4–50 photoelectron counts per laser shot in the 15-km (±0.5 km) altitude range, depending on the elevation angle. This corresponds to 22 000–270 000 photoelectron counts per data point. The background counts ranged from essentially zero after sunset up to 15–30 counts per laser shot in the middle of the day.
4. Lidar data analysis
As expected from Eq. (10), the range-corrected signal S(θ) depends linearly on the air mass 1/sin(θ). A representative lidar elevation scan (data point) is shown in Fig. 1, along with the linear fit to the data. The slope found by linear regression is 1.268 ± 0.029 (1σ), which corresponds to τa + τm(z1) = 0.634 ± 0.015 (1σ). Both angular pointing and the shot noise of the PMT are plotted as error bars. However, the shot noise is smaller than the data point symbols and is obscured by them. The spread of the data points appear to be wider than the error bars, suggesting that uncertainties in the fit are dominated by the variability of the atmosphere rather than the uncertainties associated with measurements at the individual angles.
Representative single measurement showing a range-corrected lidar signal S(θ) vs air mass 1/sin(θ). The data were taken at 2252 UTC 26 Feb 2013 in El Segundo, z1 = 15 km, and the coefficient of determination of the regression is R2 = 0.9984.
Citation: Journal of Atmospheric and Oceanic Technology 32, 7; 10.1175/JTECH-D-14-00183.1
Representative single measurement showing a range-corrected lidar signal S(θ) vs air mass 1/sin(θ). The data were taken at 2252 UTC 26 Feb 2013 in El Segundo, z1 = 15 km, and the coefficient of determination of the regression is R2 = 0.9984.
Citation: Journal of Atmospheric and Oceanic Technology 32, 7; 10.1175/JTECH-D-14-00183.1
Representative single measurement showing a range-corrected lidar signal S(θ) vs air mass 1/sin(θ). The data were taken at 2252 UTC 26 Feb 2013 in El Segundo, z1 = 15 km, and the coefficient of determination of the regression is R2 = 0.9984.
Citation: Journal of Atmospheric and Oceanic Technology 32, 7; 10.1175/JTECH-D-14-00183.1
A large fraction of the total attenuation is due to molecular Rayleigh scattering. We used a Rayleigh 355-nm scattering cross section of 2.7589 × 10−26 cm2 from Bodhaine et al. (Bodhaine et al. 1999) to obtain a Rayleigh contribution of τm(z1) = 0.522 for z1 = 15 km. We assumed a vertical temperature profile of the standard atmosphere and took the same ground-level pressure as in the AERONET analysis of the sun photometer data. Our choice of pressure was driven by the desire to be as consistent as possible in the analysis of the data from the two instruments. However, we have also tried the analysis with the surface pressure available from a local National Weather Service weather station (available from NOAA NCDC 2013), and we saw essentially the same results in the regression between the time-coincident data from the two instruments.
The other two potential contributors to molecular attenuation are atmospheric NO2 and ozone. Ozone produces a small correction to the sun photometer measurement at 340 nm (on the order of 0.007). Because the column of ozone is predominantly located in the altitudes above 15 km and the absorption cross section for ozone is 10 times smaller at 355 than at 340 nm (Sander et al. 2011), no ozone correction has been applied to the lidar data. Note that even in highly polluted environments, where the integrated tropospheric column of ozone may reach 50–60 DU (Fishman et al. 1996), the resulting correction to the lidar-derived AOT would be less than 0.0005. On the other hand, since NO2 is mostly found below 15 km (Schaub et al. 2006), the lidar AOT was corrected for it. For this comparison experiment, our primary concern was to account for NO2 consistently between the lidar and the sun photometer. Therefore, we used total column concentrations employed by the AERONET algorithm (Holben et al. 1998; NASA 2014) and applied them to the lidar data. We assumed an NO2 absorption cross section of 4.562 × 10−19 cm2 at 355 nm (Harwood and Jones 1994). This resulted in a typical calculated NO2 absorption contribution of 0.0085 to the total optical thickness. Thus, subtracting these molecular contributions from the total optical thickness gives the lidar AOTs reported in the following section.
5. Results
We conducted a 2-week measurement campaign between 13 and 28 February 2013 in El Segundo, simultaneously collecting data with our elevation-scanning lidar and sun photometer. Several lidar data points (elevation scans) were collected on some days, while none were collected on some others due to weather and other operational constraints. A total of 31 elevation scans were collected. One representative day of measurements (26 February) is shown in Fig. 2. Both lidar AOT and 340- and 380-nm AOT from the sun photometer are shown. Since the values of the sun photometer AOT at the two wavelengths differ by less than 20%, we chose to use a linear interpolation between the two wavelengths to obtain an estimate of AOT at 355 nm for direct comparison with lidar AOT. The standard deviation of the slope found in Fig. 1 is shown as a representative error (1σ).
Time series of lidar AOT at 355 nm and sun photometer AOT at 340 and 380 nm taken at 26 Feb 2013 in El Segundo; sunset at 0147 UTC (dashed line).
Citation: Journal of Atmospheric and Oceanic Technology 32, 7; 10.1175/JTECH-D-14-00183.1
Time series of lidar AOT at 355 nm and sun photometer AOT at 340 and 380 nm taken at 26 Feb 2013 in El Segundo; sunset at 0147 UTC (dashed line).
Citation: Journal of Atmospheric and Oceanic Technology 32, 7; 10.1175/JTECH-D-14-00183.1
Time series of lidar AOT at 355 nm and sun photometer AOT at 340 and 380 nm taken at 26 Feb 2013 in El Segundo; sunset at 0147 UTC (dashed line).
Citation: Journal of Atmospheric and Oceanic Technology 32, 7; 10.1175/JTECH-D-14-00183.1
Time-coincident AOT at 355 nm from the two instruments obtained in this manner are plotted in Fig. 3 as a scatterplot. A linear regression, τalidar = (1.00 ± 0.17)τaphot + (0.025 ± 0.019) (1σ), is also shown. The coefficient of determination of this regression is 55%. Again, the standard deviation of the slope found in Fig. 1 is shown as a representative error (1σ). Since the AOT during the campaign stayed in a very narrow range, we consider this to be a good correlation between the two datasets. The fitted slope is also very close to the one-to-one correspondence between the two datasets. The offset, on the other hand, shows statistically significant difference from zero, but it still is within expected uncertainties.
Scatterplot of lidar AOT vs time-coincident sun photometer AOT at 355 nm for the campaign from 13 to 28 Feb 2013 in El Segundo; R2 = 0.55.
Citation: Journal of Atmospheric and Oceanic Technology 32, 7; 10.1175/JTECH-D-14-00183.1
Scatterplot of lidar AOT vs time-coincident sun photometer AOT at 355 nm for the campaign from 13 to 28 Feb 2013 in El Segundo; R2 = 0.55.
Citation: Journal of Atmospheric and Oceanic Technology 32, 7; 10.1175/JTECH-D-14-00183.1
Scatterplot of lidar AOT vs time-coincident sun photometer AOT at 355 nm for the campaign from 13 to 28 Feb 2013 in El Segundo; R2 = 0.55.
Citation: Journal of Atmospheric and Oceanic Technology 32, 7; 10.1175/JTECH-D-14-00183.1
There are potentially three sources of the discrepancy between the two measurements: instrument calibration, Rayleigh optical thickness accuracy, and aerosol layer horizontal nonuniformity. The elevation-scanning lidar AOT measurement is inherently self-calibrating. The sun photometer, on the other hand, depends on periodic absolute calibrations with a transfer standard at NASA on an approximately annual basis. Stability of this absolute calibration is probably the largest source of uncertainty and could explain the 0.025 offset between the two measurements. Holben et al. (1998) reported up to 26% annual decay rate of filter transmission for the two UV channels (380 and 340 nm). They note that this stability was expected to improve with a move to filters made by ion-assisted deposition. However, for our 380- and 340-nm filters, we frequently see 5%–10% annual drifts, which correspond to 0.05–0.1 AOT errors. AERONET performs postdeployment calibration and reanalyzes the data by interpolating between pre- and postdeployment calibrations on an approximately annual basis. The reanalysis assumes linear filter degradation with time. Since the degradation is not necessarily linear, residual offsets remain and get added to the absolute accuracy of the standard, which is used in the calibration. The sun photometer data reported here are corrected with the postdeployment calibration, and the agreement is understandably better. However, the need for the postdeployment recalibration also presents a challenge for rapid measurement campaigns. Clearly, a self-calibrating method such as the lidar approach reported here would be beneficial for such campaigns as well.
In addition to the calibration stability, there are other potential sources of discrepancy between the sun photometer and elevation-scanning lidar data. In the ultraviolet, the contribution to the measured value of τa + τm(z1) from Rayleigh scattering is much larger than the aerosol contribution, so even a small discrepancy in the Rayleigh cross section can be significant. We chose to use the best available literature value for the cross section (Bodhaine et al. 1999) because the elevation-scanning lidar is an absolute measurement, while the sun photometer measurement is relative to its calibration. Also, all the molecular corrections applied to both sets of raw data, most notably Rayleigh optical thickness and the linear interpolation between the 380- and 340-nm sun photometer channels, are potential contributors to the discrepancy. The offset of 0.025 is only 5% of the calculated Rayleigh optical thickness, so only a small deviation in the Rayleigh correction could account for a significant portion of the offset.
The final potential source of discrepancy is the assumption of horizontal uniformity in the local atmosphere. This point is exacerbated by the coastal location of the measurement. Throughout our measurement campaign, the lidar azimuth pointing was fixed at 337°, which is approximately parallel to the coastline, while the sun photometer derives AOT from direct sun measurement and therefore follows the sun throughout the day, thus pointing in roughly opposite azimuthal direction. As a result, the two instruments may sample somewhat different atmosphere at any given time. However, the data showed no significant correlation with the time of day, which might be expected if this had a large impact on the comparison.
6. Conclusions
We have shown that high-accuracy AOT measurements can be made with an elevation-scanning lidar, even in relatively clear conditions. As the data show, by assuming only horizontal constancy of AOT [independence on elevation angle of the integral in Eq. (9)], we avoided the inaccuracies associated with the assumption of complete aerosol stratification of the conventional multiangle aerosol approach (Adam et al. 2007). The technique has several inherent advantages over the current standard sun photometer method of measuring AOT. The lidar method described here can be made at any time of day (or night) and in any azimuth direction. In addition, the lidar measurement is absolute and thus requires no lengthy pre- and postdeployment calibration. On the other hand, in its current implementation, the lidar method only measures AOT at a single wavelength. While it is possible to expand the lidar system with other wavelengths in the UV and visible, obtaining the same degree of spectral coverage as is achieved by the sun photometer is certainly not very feasible. We therefore see elevation-scanning lidar as a complementary method to the sun photometer technique.
As mentioned above, the lidar used in this measurement is not representative of the required system parameters. The system can be significantly scaled down without loss of accuracy. In fact, it is easy to imagine a compact micropulse system based on the tripled output of an ytterbium fiber laser (Di Teodoro et al. 2014). For daytime measurement the main challenge would be balancing background light suppression measures with available transmitter pulse energy. High pulse energy presents a challenge for fiber lasers. However, the very relaxed spatial resolution requirement of this measurement permits pulses as long as a few microseconds, thus lowering peak power in the fiber. Nevertheless, additional spectral filtering—for example, with an etalon as was done on CALIPSO (Zaun et al. 2004) and a reduction in the system field of view—would be needed to build a micropulse lidar for daytime operation. Without further commentary possible system parameters are presented in Table 1 for illustration purposes. It should be noted that the parameters in the table are obtained by scaling to match signal and noise collected in our present measurement, rather than rigorously examining the required signal. Noting that such a laser transmitter can easily operate at a few kilohertz repetition rate, the whole elevation angle scan can be accomplished in just a few minutes.
Possible system specification.
Acknowledgments
This work was supported by The Aerospace Corporation’s Sustained Experimentation and Research for Program Applications and Independent Research and Development programs and NASA Contract NNG11VH00B.
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