Search Results
3+2 + X : what is the most useful depolarization input for retrieving microphysical properties of non-spherical particles from lidar measurements using the spheroid model of Dubovik et al. (2006)?
The typical multiwavelength aerosol lidar data set for inversion of optical to microphysical parameters is composed of three backscatter coefficients (β) at 355, 532, and 1064 nm and two extinction coefficients (α) at 355 and 532 nm. This data combination is referred to as a 3β C 2α or 3 + 2 data set. This set of data is sufficient for retrieving some important microphysical particle parameters if the particles have spherical shape. Here, we investigate the effect of including the particle linear depolarization ratio (δ) as a third input parameter for the inversion of lidar data. The inversion algorithm is generally not used if measurements show values of d that exceed 0.10 at 532 nm, i.e. in the presence of nonspherical particles such as desert dust, volcanic ash, and, under special circumstances, biomass-burning smoke. We use experimental data collected with instruments that are capable of measuring d at all three lidar wavelengths with an inversion routine that applies the spheroidal light-scattering model of Dubovik et al. (2006) with a fixed axis-ratio distribution to replicate scattering properties of non-spherical particles. The inversion gives the fraction of spheroids required to replicate the optical data as an additional output parameter. This is the first systematic test of the effect of using all theoretically possible combinations of d taken at 355, 532, and 1064 nm as input in the lidar data inversion. We find that depolarization information of at least one wavelength already provides useful information for the inversion of optical data that have been collected in the presence of non-spherical mineral dust particles. However, any choice of d will give lower values of the single-scattering albedo than the traditional 3 + 2 data set. We find that input data sets that include d355 give a spheroid fraction that closely resembles the dust ratio we obtain from using β532 and d532 in a methodology applied in aerosol-type separation. The use of d355 in data sets of two or three d? reduces the spheroid fraction that is retrieved when using d532 and d1064. Use of the latter two parameters without accounting for d355 generally leads to high spheroid fractions that we consider not trustworthy. The use of three d instead of two δ, including the constraint that one of these is measured at 355 nm does not provide any advantage over using 3 + 2 + d355 for the observations with varying contributions of mineral dust considered here. However, additional measurements at wavelengths different from 355 nm would be desirable for application to a wider range of aerosol scenarios that may include non-spherical smoke particles, which can have values of d355 that are indistinguishable from those found for mineral dust. We therefore conclude that - depending on measurement capability - the future standard input for inversion of lidar data taken in the presence of mineral dust particles and using the spheroid model of Dubovik et al. (2006) might be 3+2Cδ355 or 3 + 2 + δ355 + δ532. © 2019 The Author(s).
EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product
We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) aboard the International Space Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter measurements within a 50km distance between the ground station and the ISS overpass and as close in time as possible, typically with the starting time or stopping time of the EARLINET performed measurement time window within 90min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are analyzed here to demonstrate the performance of the CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions, CATS is in good agreement with EARLINET, independent of daytime and nighttime conditions. CATS low negative biases are observed, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds; these are biases which may lead to systematic deviations and slight underestimations of the total aerosol optical depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low negative biases in CATS backscatter coefficient profiles, of the order of 6.1%, indicate the good nighttime performance of CATS. During daytime, a reduced signal-to-noise ratio by solar background illumination prevents retrievals of weakly scattering atmospheric layers that would otherwise be detectable during nighttime, leading to higher negative biases, of the order of 22.3%. © Author(s) 2019.
Cloud-Aerosol Transport System (CATS) 1064 nm calibration and validation
The Cloud-Aerosol Transport System (CATS) lidar on board the International Space Station (ISS) operated from 10 February 2015 to 30 October 2017 providing range-resolved vertical backscatter profiles of Earth's atmosphere at 1064 and 532 nm. The CATS instrument design and ISS orbit lead to a higher 1064 nm signal-to-noise ratio than previous space-based lidars, allowing for direct atmospheric calibration of the 1064 nm signals. Nighttime CATS version 3-00 data were calibrated by scaling the measured data to a model of the expected atmospheric backscatter between 22 and 26 km a.m.s.l. (above mean sea level). The CATS atmospheric model is constructed using molecular backscatter profiles derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data and aerosol scattering ratios measured by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The nighttime normalization altitude region was chosen to simultaneously minimize aerosol loading and variability within the CATS data frame, which extends from 28 to −2 km a.m.s.l. Daytime CATS version 3-00 data were calibrated through comparisons with nighttime measurements of the layer-integrated attenuated total backscatter (iATB) from strongly scattering, rapidly attenuating opaque cirrus clouds. The CATS nighttime 1064 nm attenuated total backscatter (ATB) uncertainties for clouds and aerosols are primarily related to the uncertainties in the CATS nighttime calibration technique, which are estimated to be ∼9 %. Median CATS V3-00 1064 nm ATB relative uncertainty at night within cloud and aerosol layers is 7 %, slightly lower than these calibration uncertainty estimates. CATS median daytime 1064 nm ATB relative uncertainty is 21 % in cloud and aerosol layers, similar to the estimated 16 %–18 % uncertainty in the CATS daytime cirrus cloud calibration transfer technique. Coincident daytime comparisons between CATS and the Cloud Physics Lidar (CPL) during the CATS-CALIPSO Airborne Validation Experiment (CCAVE) project show good agreement in mean ATB profiles for clear-air regions. Eight nighttime comparisons between CATS and the PollyXT ground-based lidars also show good agreement in clear-air regions between 3 and 12 km, with CATS having a mean ATB of 19.7 % lower than PollyXT. Agreement between the two instruments (∼7 %) is even better within an aerosol layer. Six-month comparisons of nighttime ATB values between CATS and CALIOP also show that iATB comparisons of opaque cirrus clouds agree to within 19 %. Overall, CATS has demonstrated that direct calibration of the 1064 nm channel is possible from a space-based lidar using the atmospheric normalization technique.
Year-round stratospheric aerosol backscatter ratios calculated from lidar measurements above northern Norway
We present a new method for calculating backscatter ratios of the stratospheric sulfate aerosol (SSA) layer from daytime and nighttime lidar measurements. Using this new method we show a first year-round dataset of stratospheric aerosol backscatter ratios at high latitudes. The SSA layer is located at altitudes between the tropopause and about 30 km. It is of fundamental importance for the radiative balance of the atmosphere. We use a state-of-the-art Rayleigh-Mie-Raman lidar at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) station located in northern Norway (69N, 16E; 380ma.s.l.). For nighttime measurements the aerosol backscatter ratios are derived using elastic and inelastic backscatter of the emitted laser wavelengths 355, 532 and 1064nm. The setup of the lidar allows measurements with a resolution of about 5 min in time and 150 m in altitude to be performed in high quality, which enables the identification of multiple sub-layers in the stratospheric aerosol layer of less than 1 km vertical thickness. We introduce a method to extend the dataset throughout the summer when measurements need to be performed under permanent daytime conditions. For that purpose we approximate the backscatter ratios from color ratios of elastic scattering and apply a correction function. We calculate the correction function using the average backscatter ratio profile at 355nm from about 1700 h of nighttime measurements from the years 2000 to 2018. Using the new method we finally present a year-round dataset based on about 4100 h of measurements during the years 2014 to 2017. © Author(s) 2019.
A new description of probability density distributions of polar mesospheric clouds
In this paper we present a new description of statistical probability density functions (pdfs) of polar mesospheric clouds (PMCs). The analysis is based on observations of maximum backscatter, ice mass density, ice particle radius, and number density of ice particles measured by the ALOMAR Rayleigh-Mie-Raman lidar for all PMC seasons from 2002 to 2016. From this data set we derive a new class of pdfs that describe the statistics of PMC events that is different from previous statistical methods using the approach of an exponential distribution commonly named the g distribution. The new analysis describes successfully the probability distributions of ALOMAR lidar data. It turns out that the former g-function description is a special case of our new approach. In general the new statistical function can be applied to many kinds of different PMC parameters, e.g., maximum backscatter, integrated backscatter, ice mass density, ice water content, ice particle radius, ice particle number density, or albedo measured by satellites. As a main advantage the new method allows us to connect different observational PMC distributions of lidar and satellite data, and also to compare with distributions from ice model studies. In particular, the statistical distributions of different ice parameters can be compared with each other on the basis of a common assessment that facilitates, for example, trend analysis of PMC. © Author(s) 2019.
Separation of the optical and mass features of particle components in different aerosol mixtures by using POLIPHON retrievals in synergy with continuous polarized Micro-Pulse Lidar (P-MPL) measurements
The application of the POLIPHON (POlarization-LIdar PHOtometer Networking) method is presented for the first time in synergy with continuous 24/7 polarized Micro-Pulse Lidar (P-MPL) measurements to derive the vertical separation of two or three particle components in different aerosol mixtures, and the retrieval of their particular optical properties. The procedure of extinction-to-mass conversion, together with an analysis of the mass extinction efficiency (MEE) parameter, is described, and the relative mass contribution of each aerosol component is also derived in a further step. The general POLIPHON algorithm is based on the specific particle linear depolarization ratio given for different types of aerosols and can be run in either 1-step (POL-1) or 2 steps (POL-2) versions with dependence on either the 2- or 3-component separation. In order to illustrate this procedure, aerosol mixing cases observed over Barcelona (NE Spain) are selected: a dust event on 5 July 2016, smoke plumes detected on 23 May 2016 and a pollination episode observed on 23 March 2016. In particular, the 3-component separation is just applied for the dust case: a combined POL-1 with POL-2 procedure (POL-1/2) is used, and additionally the fine-dust contribution to the total fine mode (fine dust plus non-dust aerosols) is estimated. The high dust impact before 12:00 UTC yields a mean mass loading of 0.6±0.1 g m'2 due to the prevalence of Saharan coarse-dust particles. After that time, the mean mass loading is reduced by two-thirds, showing a rather weak dust incidence. In the smoke case, the arrival of fine biomass-burning particles is detected at altitudes as high as 7 km. The smoke particles, probably mixed with less depolarizing non-smoke aerosols, are observed in air masses, having their origin from either North American fires or the Arctic area, as reported by HYSPLIT back-trajectory analysis. The particle linear depolarization ratio for smoke shows values in the 0.10-0.15 range and even higher at given times, and the daily mean smoke mass loading is 0.017±0.008 g m'2, around 3 % of that found for the dust event. Pollen particles are detected up to 1.5 km in height from 10:00 UTC during an intense pollination event with a particle linear depolarization ratio ranging between 0.10 and 0.15. The maximal mass loading of Platanus pollen particles is 0.011±0.003 g m'2, representing around 2 % of the dust loading during the higher dust incidence. Regarding the MEE derived for each aerosol component, their values are in agreement with others referenced in the literature for the specific aerosol types examined in this work: 0.5±0.1 and 1.7±0.2 m2 g'1 are found for coarse and fine dust particles, 4.5±1.4 m2 g'1 is derived for smoke and 2.4±0.5 m2 g'1 for non-smoke aerosols with Arctic origin, and a MEE of 2.4±0.8 m2 g'1 is obtained for pollen particles, though it can reach higher or lower values depending on predominantly smaller or larger pollen grain sizes. Results reveal the high potential of the P-MPL system, a simple polarization-sensitive elastic backscatter lidar working in a 24/7 operation mode, to retrieve the relative optical and mass contributions of each aerosol component throughout the day, reflecting the daily variability of their properties. In fact, this procedure can be simply implemented in other P-MPLs that also operate within the worldwide Micro-Pulse Lidar Network (MPLNET), thus extending the aerosol discrimination at a global scale. Moreover, the method has the advantage of also being relatively easily applicable to space-borne lidars with an equivalent configuration such as the ongoing Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on board NASA CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) and the forthcoming Atmospheric Lidar (ATLID) on board the ESA EarthCARE mission.
Raman LIDAR for UHECR experiments: an overview of the L'Aquila (Italy) lidar station experience for the retrieval of quality-assured data
L'Aquila (Italy) lidar station is part of the EARLINET (European Aerosol Research Lidar Network) since its beginning in the 2000. In the EARLINET community great efforts are devoted to the quality-assurance of the aerosol optical properties inserted in the database. To this end, each lidar station performed intercomparisons with reference instruments, a series of internal hardware checks in order to assess the quality of their instruments and exercises to test the algorithms used to retrieve the aerosol optical parameters. In this paper we give an overview of our experience within EARLINET qualityassurance (QA) program, which was adopted for the Raman lidar (RL) operated in the AUGER Observatory. This program could be systematically adopted for the lidar systems needed for the current and upcoming UHECR experiments, like CTA (Cherenkov Telescope Array).