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A lidar system has been built to measure atmospheric-density fluctuations and the temperature in the upper stratosphere, the mesosphere, and the lower thermosphere, measurements that are important for an understanding of climate and weather phenomena. This lidar system, the Purple Crow Lidar, uses two transmitter beams to obtain atmospheric returns...
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... receiving telescope is a 2.65-m-diameter liquid mirror, shown schematically in Fig. 2. The liquid- mirror technology offers the advantages of a large mirror at a fraction of the cost of a conventional glass telescope. The equipment and materials needed to build the 2.65-m-diameter mirror cost approximately $30,000, albeit with a considerable amount of labor costs extra! A comprehensive technical review of the ...
Citations
... In early 2003, Hickson built the 6-m Large Zenith Telescope (LZT) to extend LM technology to larger apertures (see Fig. 2(b) and Hickson et al., 2007). Liquid mirrors have also been used by atmospheric scientists for LIDAR applications (Sica et al., 1995;Wuerker, 2002). For instance, the LZT provided unprecedented spatial and temporal resolution for studying the structure and dynamics of the Earth's mesosphere and lower thermosphere. ...
The present article is based upon an invited talk delivered at the occasion of the inauguration of the 4m International Liquid Mirror Telescope (ILMT) which took place in Devasthal (ARIES, Uttarakhand, India) on 21st March 2023. We present hereafter a short history of the liquid mirror telescopes and in particular of the 4m ILMT which is the first liquid mirror telescope entirely dedicated to astrophysical observations. We discuss a few preliminary scientific results and illustrate some direct CCD images taken during the first commissioning phase of the telescope. We invite the reader to refer to the series of ILMT poster papers published in these proceedings of the third BINA workshop for more details about the instrument, operation, first observations, performance and scientific results.
... In atmospheric temperature measurement, the error transfer formula is used to calculate the uncertainty [12], where the main source is considered to be the random statistical uncertainty of detected photon counts [13][14][15]. Some publications have also given expressions for the atmospheric temperature uncertainty owing to the ancillary temperature uncertainty [16], but there was confusion between the concepts of error and measurement uncertainty. Other reports intentionally introduced initial errors to investigate the effect of ancillary temperature uncertainty on temperature retrieval, although specific analytical expressions were not given [17][18][19]. ...
Measurement uncertainty is an extremely important parameter for characterizing the quality of measurement results. In order to measure the reliability of atmospheric temperature detection, the uncertainty needs to be evaluated. In this paper, based on the measurement models originating from the Chanin-Hauchecorne (CH) method, the atmospheric temperature uncertainty was evaluated using the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method (MCM) by considering the ancillary temperature uncertainty and the detection noise as the major uncertainty sources. For the first time, the GUM atmospheric temperature uncertainty framework was comprehensively and quantitatively validated by MCM following the instructions of JCGM 101: 2008 GUM Supplement 1. The results show that the GUM method is reliable when discarding the data in the range of 10–15 km below the reference altitude. Compared with MCM, the GUM method is recommended to evaluate the atmospheric temperature uncertainty of Rayleigh lidar detection in terms of operability, reliability, and calculation efficiency.
... The PCL is a Rayleigh-Raman lidar which has been operational since 1992. Details about PCL instrumentation can be found in Sica et al. (1995). From 1992 to 2010, the lidar was located at the Delaware Observatory ( 2. A low-gain Rayleigh (LR) channel that detects the backscattered counts from 25 to 110 km altitude (this channel is optimized to detect counts at lower altitudes where the high-intensity back-scattered counts can saturate the detector and cause non-linearity in the observed signal; thus, using the low-gain channel, at lower altitudes, the signal remains linear) (vertical resolution: 7 m). ...
While it is relatively straightforward to automate the processing of lidar signals, it is more
difficult to choose periods of “good” measurements to process. Groups use various ad hoc
procedures involving either very simple (e.g. signal-to-noise ratio) or more complex procedures
(e.g. Wing et al., 2018) to perform a task that is easy to train humans to perform but is time-consuming. Here, we use machine learning techniques to train the machine to sort the measurements
before processing. The presented method is generic and can be applied to most lidars. We test the
techniques using measurements from the Purple Crow Lidar (PCL) system located in London,
Canada. The PCL has over 200 000 raw profiles in Rayleigh and Raman channels available for
classification. We classify raw (level-0) lidar measurements as “clear” sky profiles with strong
lidar returns, “bad” profiles, and profiles which are significantly influenced by clouds or
aerosol loads. We examined different supervised machine learning algorithms including the random
forest, the support vector machine, and the gradient boosting trees, all of which can successfully
classify profiles. The algorithms were trained using about 1500 profiles for each PCL channel,
selected randomly from different nights of measurements in different years. The success rate of identification for all the channels is above 95 %. We also used the t-distributed stochastic embedding (t-SNE) method, which is an unsupervised algorithm, to cluster our lidar profiles. Because the t-SNE is a data-driven method in which no labelling of the training set is needed, it is an attractive algorithm to find anomalies in lidar profiles. The method has been tested on several nights of measurements from the PCL measurements. The t-SNE can successfully
cluster the PCL data profiles into meaningful categories. To demonstrate the use of the technique,
we have used the algorithm to identify stratospheric aerosol layers due to wildfires.
... The PCL is a Rayleigh-Raman lidar which has been operational since 1992. Details about PCL instrumentation can be found in Sica et al. (1995). From 1992 to 2010, the lidar was located at the Delaware Observatory ( The Rayleigh channels are used for atmospheric temperature retrievals, and the water vapour and nitrogen channels are used to retrieve water vapour mixing ratio. . ...
Abstract. While it is relatively straightforward to automate the processing of lidar signals, it is more difficult to choose periods of "good" measurements to process. Groups use various ad hoc procedures involving either very simple (e.g. signal-to-noise ratio) or more complex procedures (e.g. Wing et al., 2018) to perform a task which is easy to train humans to perform but is time consuming. Here, we use machine learning techniques to train the machine to sort the measurements before processing. The presented methods is generic and can be applied to most lidars. We test the techniques using measurements from the Purple Crow Lidar (PCL) system located in London, Canada. The PCL has over 200,000 raw scans in Rayleigh and Raman channels available for classification. We classify raw (level-0) lidar measurements as "clear" sky scans with strong lidar returns, "bad" scans, and scans which are significantly influenced by clouds or aerosol loads. We examined different supervised machine learning algorithms including the random forest, the support vector machine, and the gradient boosting trees, all of which can successfully classify scans. The algorithms where trained using about 1500 scans for each PCL channel, selected randomly from different nights of measurements in different years. The success rate of identification, for all the channels is above 95 %. We also used the t-distributed Stochastic Embedding (t-SNE) method, which is an unsupervised algorithm, to cluster our lidar scans. Because the t-SNE is a data driven method in which no labelling of training set is needed, it is an attractive algorithm to find anomalies in lidar scans. The method has been tested on several nights of measurements from the PCL measurements.The t-SNE can successfully cluster the PCL data scans into meaningful categories. To demonstrate the use of the technique, we have used the algorithm to identify stratospheric aerosol layers due to wildfires.
... The limiting factor in the white lamp calibration technique is the degree to which we know the molecular cross sections, which have uncertainties on the order of 5 % (Avila et al., 2004;Venable et al., 2011). While internal calibration offers many advantages, it is impractical for many systems, such as lidars that use multiple mirrors (Dinoev et al., 2013;Godin-Beekmann et al., 2003) or large-aperture mirrors such as the rotating liquid mercury mirror of the University of Western Ontario's Purple Crow Lidar (Sica et al., 1995). ...
Raman lidars have been designated as potential candidates for trend studies by the Network for the Detection of Atmospheric Composition Change (NDACC) and GCOS (Global Climate Observing System) Reference Upper Air Network (GRUAN); however, for such studies improved calibration techniques are needed as well as careful consideration of the calibration uncertainties. Trend determinations require frequent, accurate, and well-characterized measurements. However, water vapour Raman lidars produce a relative measurement and require calibration in order to transform the measurement into a mixing ratio, a conserved quantity when no sources or sinks for water vapour are present. Typically, the calibration is done using a reference instrument such as a radiosonde. We present an improved trajectory technique to calibrate water vapour Raman lidars based on the previous work of Whiteman et al. (2006), Leblanc and Mcdermid (2008), Adam et al. (2010), and Herold et al. (2011), who used radiosondes as an external calibration source and matched the lidar measurements to the corresponding radiosonde measurement. However, they did not consider the movement of the radiosonde relative to the air mass and fronts. Our trajectory method is a general technique which may be used for any lidar and only requires that the radiosonde report wind speed and direction. As calibrations can be affected by a lack of co-location with the reference instrument, we have attempted to improve their technique by tracking the air parcels measured by the radiosonde relative to the field of view of the lidar. This study uses GRUAN Vaisala RS92 radiosonde measurements and lidar measurements taken by the MeteoSwiss RAman Lidar for Meteorological Observation (RALMO), located in Payerne, Switzerland, from 2011 to 2016 to demonstrate this improved calibration technique. We compare this technique to the traditional radiosonde–lidar calibration technique which does not involve tracking the radiosonde and uses the same integration time for all altitudes. Both traditional and our trajectory methods produce similar profiles when the water vapour field is homogeneous over the 30 min calibration period. We show that the trajectory method reduces differences between the radiosonde and lidar by an average of 10 % when the water vapour field is not homogeneous over a 30 min calibration period. We also calculate a calibration uncertainty budget that can be performed on a nightly basis. The calibration uncertainty budget includes the uncertainties due to phototube paralysis, aerosol extinctions, the assumption of the Ångström exponent, and the radiosonde. The study showed that the radiosonde was the major source of uncertainty in the calibration at 4 % of the calibration value. This trajectory method showed small improvements for RALMO's calibration but would be more useful for stations in different climatological regions or when non-co-located radiosondes are the only available calibration source.
... Receivers are typically equipped with mechanical or electro-optical choppers in order to prevent detection of elastic echoes from lower levels, primarily the troposphere, which may overload 15 detectors and induce non-linear responses (signal-induced noise) in the upper level signals, typically those collected from the stratosphere and mesosphere (Di Girolamo et al., 1994). The use of mechanical choppers, usually located just below the telescope focus (Sica et al., 1995), imposes the implementation of separate lidar receivers for the purpose of achieving a successful simultaneous exploitation of the rotational Raman and the integration techniques. A simpler optical design solution can be considered in case of use of a moderate power laser source and a smaller aperture telescope. ...
... In this regard, it 20 is to be pointed out that performing accurate temperature measurements through the integration lidar technique imposes the use of lidar systems with large values of the power-aperture (PA) product. Values of the PA product for temperature lidars exploiting the integration technique are usually in excess of 10 Wm 2 (Hauchercorne et al., 1992), with values for specific systems in excess of 50 Wm 2 (Sica et al., 1995). The Raman lidar system considered in the present paper is characterized by a PA product not exceeding 1 Wm 2 . ...
The Raman Lidar system BASIL entered the International Network for the Detection of Atmospheric Composition Change (NDACC) in 2012. Since then measurements were carried out routinely on a weekly basis. This manuscript reports specific measurement results from this effort, with a dedicated focus on temperature and water vapour profile measurements. The main objective of this research effort is to provide a characterization of the system performance. Measurements illustrated in this manuscript demonstrate the ability of BASIL to perform measurements of the temperature profile up to 50 km and of the water vapour mixing ratio profile up to 15 km, when considering an integration time of 2 h and a vertical resolution of 150 m, with measurement bias not exceeding 0.1 K and 0.1 g kg−1, respectively. Relative humidity profiling capability up to the tropopause is also demonstrated by combining simultaneous temperature and water vapour profile measurements.
Raman lidar measurements are compared with measurements from additional instruments, such as radiosondings and satellite sensors (IASI and AIRS), and with model re-analyses data (ECMWF and ECMWF-ERA). Comparisons in this paper cover the altitude region up to 15 km for water vapour mixing ratio and up to 50 km for the temperature. We focused our attention on four selected case studies collected during the first 2 years of operation of the system (November 2013–October 2015). Comparisons between BASIL and the different sensor/model data in terms of water vapour mixing ratio indicate a mean absolute/relative bias of −0.024 g kg−1 (or −3.9 %), 0.346 g kg−1 (or 37.5 %), 0.342 g kg−1 (or 36.8 %), −0.297 g kg−1 (or −25 %), −0.381 g kg−1 (or −31 %), when compared with radisondings, IASI, AIRS, ECMWF, ECMWF-ERA, respectively. For what concerns the comparisons in terms of temperature measurements, these reveal a mean absolute bias between BASIL and the radisondings, IASI, AIRS, ECMWF, ECMWF-ERA of −0.04, 0.48, 1.99, 0.14, 0.62 K, respectively. Based on the available dataset and benefiting from the circumstance that the Raman lidar BASIL could be compared with all other sensor/model data, it was possible to estimate the absolute bias of all sensors/datasets, this being 0.004 g kg−1/0.30 K, 0.021 g kg−1/−0.34 K, −0.35 g kg−1/0.18 K, −0.346 g kg−1/−1.63 K, 0.293 g kg−1/−0.16 K and 0.377 g kg−1/0.32 K for the water vapour mixing ratio/temperature profile measurements carried out by BASIL, the radisondings, IASI, AIRS, ECMWF, ECMWF-ERA, respectively.
... The PCL is a Rayleigh-Raman lidar that was located at the Delaware Observatory (42.52 • N , 81.23 • W) near the University of Western Ontario in London, Canada, from 1992 to 2010 (Sica et al., 1995Argall et al., 2000). In 2012, the PCL was moved to the Environmental Sciences Western Field Station (43.07 • N, 81.33 • W; 275 m of altitude). ...
... The PCL receiver is a liquid mercury mirror with a diameter of 2.65 m. From 1994 to 1998, the PCL used a single detection channel (the high-level Rayleigh (HLR) channel) over the range of 30 to 110 km (Sica et al., 1995). In 1999, a low-level Rayleigh (LLR) channel was added, which is nearly linear above 25 km . ...
Hauchecorne and Chanin (1980) developed a robust method to calculate middle-atmosphere
temperature profiles using measurements from Rayleigh-scatter lidars. This
traditional method has been successfully used to greatly improve our
understanding of middle-atmospheric dynamics, but the method has some
shortcomings regarding the calculation of systematic uncertainties and the
vertical resolution of the retrieval. Sica and Haefele (2015) have shown that the
optimal estimation method (OEM) addresses these shortcomings and allows
temperatures to be retrieved with confidence over a greater range of heights
than the traditional method. We have calculated a temperature climatology
from 519 nights of Purple Crow Lidar Rayleigh-scatter measurements using an
OEM. Our OEM retrieval is a first-principle retrieval in which the forward
model is the lidar equation and the measurements are the level-0 count
returns. It includes a quantitative determination of the top altitude of the
retrieved temperature profiles, the evaluation of nine systematic plus random
uncertainties, and the vertical resolution of the retrieval on a
profile-by-profile basis. Our OEM retrieval allows for the vertical
resolution to vary with height, extending the retrieval in altitude 5 to
10 km higher than the traditional method. It also allows the comparison of
the traditional method's sensitivity to two in-principle equivalent methods
of specifying the seed pressure: using a model pressure seed versus using a
model temperature combined with the lidar's density measurement to calculate
the seed pressure. We found that the seed pressure method is superior to
using a model temperature combined with the lidar-derived density. The
increased altitude capability of our OEM retrievals allows for a comparison
of the Rayleigh-scatter lidar temperatures throughout the entire altitude
range of the sodium lidar temperature measurements. Our OEM-derived Rayleigh
temperatures are shown to have improved agreement relative to our previous
comparisons using the traditional method, and the agreement of the
OEM-derived temperatures is the same as the agreement between existing sodium
lidar temperature climatologies. This detailed study of the calculation of
the new Purple Crow Lidar temperature climatology using the OEM establishes
that it is both highly advantageous and practical to reprocess existing
Rayleigh-scatter lidar measurements that cover long time periods, during
which time the lidar may have undergone several significant equipment
upgrades, while gaining an upper limit to useful temperature retrievals
equivalent to an order of magnitude increase in power-aperture product due to
the use of an OEM.
... The last factor is the most limiting due to its current uncertainties on the order of 10% (Penney and Lapp, 1976). While internal calibration offers many advantages, it is impractical for many systems, such 25 as lidars that use multiple mirrors (Dinoev et al., 2013;Godin-Beekmann et al., 2003) or large-aperture mirrors such as the rotating liquid mercury mirror of The University of Western Ontario's Purple Crow Lidar (Sica et al., 1995). ...
Lidars are well-suited for trend measurements in the upper troposphere and lower stratosphere, particularly for species such as water vapour. Trend determinations require frequent, accurate and well-characterized measurements. However, water vapour Raman lidars produce a relative measurement and require calibration in order to transform the measurement into physical units. Typically, the calibration is done using a reference instrument such as a radiosonde. We present an improved trajectory technique to calibrate water vapour Raman lidars based on the previous work of Whiteman et al. (2006), Leblanc and Mcdermid (2008), and Adam et al. (2010) who used radiosondes as an external calibration source, and matched the lidar measurements to the corresponding radiosonde measurement. However, they did not consider the movement of the radiosonde. As calibrations can be affected by a lack of co-location with the reference instrument, we have attempted to improve their technique by tracking the air parcels measured by the radiosonde relative to the field-of-view of the lidar. This study uses GCOS Reference Upper Air Network (GRUAN) Vaisala RS92 radiosonde measurements and lidar measurements from the MeteoSwiss RAman Lidar for Meteorological Observation (RALMO), located in Payerne, Switzerland to demonstrate this improved calibration technique. We compare this technique to traditional radiosonde-lidar calibration techniques which do not involve tracking the radiosonde. Both traditional and our trajectory methods produce similar profiles when the water vapour field is homogeneous over the 30min calibration period. We show that the trajectory method more accurately reproduces the radiosonde profile when the water vapour field is not homogeneous over a 30min calibration period. We also calculate a calibration uncertainty budget that can be performed on a nightly basis. We include the contribution of the radiosonde measurement uncertainties to the total calibration uncertainty, and show that on average the uncertainty contribution from the radiosonde is 4%. We also calculate the uncertainty in the calibration due to the uncertainty in the lidar's counting system, caused by phototube paralyzation, and found it to be an average of 0.3% for our system. This trajectory method allows a more accurate calibration of a lidar, even when non-co-located radiosondes are the only available calibration source, and also allows additional nights to be used for calibration that would otherwise be discarded due to variability in the water vapour profile.
... This was confirmed by another work, where they concluded that iodine absorption filter provided better performance than the Fabry-Perot interferometer [10]. Some other interesting works included, density and temperature measurements over a large height range using a large liquid-mercury mirror, this large mercury mirror leads to higher power due to bigger collection solid angle [11]. It was shown that by using two Iodine filters kept at two different temperatures, it is possible to extract the temperature information by taking a ratio of these signals [12]. ...
... The three resonances corresponding to the values of 1,2 and 3 are close to each other and form a single line when the thermal broadening is accounted for. Similarly, the transitions corresponding to (4,5,6), (7,8,9) and (10,11,12) are seen as a single line on the transmission plot. The gap between the respective two lines observed on the transmission plot due to 85 Rb (7,8,9) and (10,11,12) is 3 GHz and 87 Rb (1,2,3) and (4,5,6) is 6.8 GHz. ...
... Similarly, the transitions corresponding to (4,5,6), (7,8,9) and (10,11,12) are seen as a single line on the transmission plot. The gap between the respective two lines observed on the transmission plot due to 85 Rb (7,8,9) and (10,11,12) is 3 GHz and 87 Rb (1,2,3) and (4,5,6) is 6.8 GHz. Therefore, there are 4 total spectral features observed on the transmission plots of the natural rubidium vapor filter. ...
This paper discusses calculations related with the backscattered number of photons for a laser shot down at different frequencies from an aircraft. We show profiles for Rayleigh-Brillouin backscattered signals from different heights in atmosphere. A measurement technique is investigated that uses a rubidium vapor filter maintained at different temperatures. The transmission profiles of rubidium vapor filters are studied to maximize the measurement performance. This is further investigated to determine atmospheric temperature, wind velocity and H2O levels from Rayleigh-Brillouin backscattered signals. The width of the Rayleigh-Brillouin backscattered signal is proportional to the temperature from the height of scattering, the Mie scattering peaks from particle scattering are shifted in frequency corresponding to the wind velocity along the direction of laser beam and the tails of the Rayleigh-Brillouin backscattered signals are broadened depending upon the concentration of the H2O in atmosphere.
... The PCL is a Rayleigh-Raman lidar which was located at the Delaware Observatory (42.52 • N , 81.23 • W ) near The Uni-5 versity of Western Ontario in London, Canada from 1992 to 2010 (Sica et al., 1995Argall et al., 2000). (Sica et al., 1995). ...
... The PCL is a Rayleigh-Raman lidar which was located at the Delaware Observatory (42.52 • N , 81.23 • W ) near The Uni-5 versity of Western Ontario in London, Canada from 1992 to 2010 (Sica et al., 1995Argall et al., 2000). (Sica et al., 1995). In 10 1999, a Low Level Rayleigh (LLR) channel was added, which is nearly linear above 25 km . ...
Hauchecorne and Chanin (1980) developed a robust method to calculate middle atmosphere temperature profiles using measurements from Rayleigh-scatter lidars. This traditional method has been successfully used to greatly improve our understanding of middle atmospheric dynamics, but the method has some shortcomings in regard to the calculation of systematic uncertainties and vertical resolution of the retrieval. Sica and Haefele (2015) have shown the Optimal Estimation Method (OEM) addresses these shortcomings and allows temperatures to be retrieved with confidence over a greater range of heights than the traditional method. We have developed a temperature climatology from Purple Crow Lidar (PCL) Rayleigh-scatter measurements on 519 nights using an OEM. Our OEM retrieval is a first-principle retrieval where the forward model is the lidar equation and the measurements are the level 0 count returns. It includes a quantitative determination of the top altitude of the retrieval, the evaluation of 9 systematic plus random uncertainties, and vertical resolution of the retrieval on a profile-by-profile basis. By using the calculated averaging kernels our new retrieval extends our original climatology by an additional 5 to 10 km in altitude relative to the traditional method. The OEM statistical uncertainty makes the largest contribution in the uncertainty budget. However, significant contributions are also made from the systematic uncertainties, in particular the uncertainty due to choosing a tie-on pressure as required by the assumption of hydrostatic equilibrium, mean molecular mass variations with height, and ozone absorption cross section uncertainty. The vertical resolution of the PCL climatology is 1 km up to about 90 km and then increases to about 3 km around 100 km. The new PCL temperature climatology is compared with three sodium lidar climatologies. The comparison between the PCL and sodium lidar climatologies shows improved agreement relative to the climatology generated using the method of Hauchecorne and Chanin, that is the PCL climatology is as similar to the sodium lidar climatologies as the sodium lidar climatologies are to each other. The height-extended OEM-derived climatology is highly insensitive to the choice of an a priori temperature profile, in the sense that the a priori temperature profile contributes much less uncertainty than the statistical uncertainty.
