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Airborne lidar measurements have been made of polar stratospheric clouds (PSCs) during the Airborne Arctic Stratospheric Expedition in January-February 1989. These show the existence of a systematic relationship between the backscatter depolarization ratio and the (aerosol + molecular)/molecular backscatter ratio. The data are consistent with a two population PSC particle model.
The micropulse lidar (MPL) is a ground-based, optical, remote-sensing system designed primarily to determine the altitude of clouds; however, it is also used for detection of atmospheric aerosols. The physical principle is the same as for radar. Pulses of energy are transmitted into the atmosphere; the energy scattered back to the transceiver is collected and measured as a time-resolved signal, thereby detecting clouds and aerosols in real time. From the time delay between each outgoing pulse and the backscattered signal, the distance to the scatterer is inferred. Post-processing of the lidar return characterizes the extent and properties of aerosols or other particles in a region.
The change of polarization properties of scattered light strongly depends on the non-spherisity of target particles. It should be helpful information for the study of stratospheric aerosols to know the non-spherisity of stratospheric aerosol particles. The change of the total backscatter depolarization ratio of the stratospheric aerosol layer measured at Syowa Station (69.00 deg S, 39.35 deg E), Antarctica is described.
Optical properties are computed for fractal-like aggregate black carbon (BC) aerosols coated with different substances. Two models are used for these aerosols: (i) the coated aggregate model (model I), where coating is added to the voids and surroundings of BC fractal-like aggregates; and (ii) the closed-cell aggregate model (model II), where coating is added concentrically to each monomer of BC fractal-like aggregates. Our results favor choosing the coated aggregate model (model I) to simulate scattering and absorption by coated BC aerosols because this model is morphologically more realistic, and because this model yields mass absorption cross section (MAC) and backscattering linear depolarization ratio (LDR) values that are consistent with field measurements. Moreover the corresponding computed degree of linear polarization (DoLP) and LDR values are very sensitive to changes in the coating volume fraction (fvol) and the coating refractive index (m). With the same absorbing BC core, the MAC value increases steadily with increasing fvol and m values. For example, using a density of 1.8 g/cm 3 and BC refractive index 1.95 + i0.79, the calculated MAC values for uncoated BC aggregates range from 6.2 to 6.8 m 2 /g at 0.55 µm. When coating material is applied to the BC aggregates, the calculated MAC values for model I particles increase to between 9.8 and 13.2 m 2 /g (depending on m) when fvol = 87.5%. The backscattering LDR values also tend to increase with the increasing m values for the shapes and sizes considered in our study. For model I particles, the backscattering LDR values span a wide range of 4.2–27.8% at a wavelength of 0.35 µm at fvol = 87.5% when m increases from 1.33 to 1.55. Our results are relevant to analyses of polarimetric and lidar observations of smoke particles, especially when these particles undergo hygroscopic growth.
Knowledge of particle sizes and number densities of polar stratospheric clouds (PSCs) is highly important, because they are critical parameters for the modeling of the ozone chemistry of the stratosphere. In situ measurements of PSC particles are rare. the main instrument for the accumulation of PSC data are lidar systems. Therefore the derivation of some microphysical properties of PSCS from the optical parameters measured by lidars would be highly beneficial for ozone research. Inversion of lidar data obtained in the presence of PSCs formed from crystalline particles type 11 and the various nitric acid tri Ydrrate (NAT) types cannot be easily accomplished, because a suitable scattering theory for small faceted crystals has not been readily available tip to now. As a consequence, the T-matrix method is commonly used for the interpretation of these PSC lidar data. Here the assumption is made that the optical properties of an ensemble of spheroids resemble those of crystalline PSCs, and microphysical properties of the PSC are inferred from the optical signatures of the PSC at two or more wavelengths. The problem with the T-matrix approach is that the assumption of spheroidal instead of faceted particles can lead to dramatically wrong results: Usually cloud particle properties are deduced from analysis of lidar profiles of backscatter ratio and depolarization ratio. The particle contribution to the backscatter ratio is given by the product of the particle number density and the backscattering cross section. The latter is proportional to the value of the particle's scattering phase function at 180 degrees scattering angle. At 180 degrees however, the phase functions of rough, faceted crystals and of spheroids with same maximum dimension differ by a factor of 6. From this it follows that for a PSC consisting of faceted crystals, the particle number density is underestimated by roughly the same factor if spheroidal particles are unrealistically assumed. We are currently developing a retrieval technique for determining the microphysical parameters of crystalline PSCs that takes into account the faceted shape of the PSC particles. This approach utilizes finite-difference time-domain (FDTD) calculations of particle optical properties. The accuracy and the free choice of the shape of the scattering particle make the FDTD technique a promising tool for the inversion of PSC lidar data. A first comparison of FDTD and T-matrix calculations will be presented.
We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) to develop a machine learning regression methodology to infer PM2.5 concentrations at the surface and aloft. These airborne HSRL measurements were acquired over major metropolitan regions in the United States and Asia during more than 170 flights since 2010. Hourly surface PM2.5 measurements from the EPA air quality system and similar networks in other countries acquired within 10 km and 15 minutes of these near-surface HSRL measurements are used to train models that compute PM2.5 concentrations from the HSRL measurements. We examine several regression methods and find that exponential Gaussian Process algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. Model performance varies significantly depending on various combinations of HSRL aerosol measurements (e.g., aerosol backscatter, extinction, depolarization, backscatter color ratios, lidar ratios, aerosol optical thickness) and retrievals (e.g., mixed layer height, aerosol type) used in the regressions. Models that use near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS errors around 4 mg/m3 and correlation coefficients above 0.9. HSRL measurements were often acquired when the aircraft flew systematic “raster-scan” patterns for several hours over these cities. These flight patterns enabled measurements of the spatial, temporal, and vertical variabilities in the distributions of aerosol backscatter and aerosol intensive properties and allowed us to derive the corresponding variabilities in PM2.5 concentrations. We present examples of such variabilities over urban areas in the United States as well as Asia. We describe also how the distribution of surface PM2.5 varies with aerosol type and use these retrievals to examine model simulations of surface PM2.5 in these metropolitan regions. We also discuss how this methodology may be applied to measurements from satellite lidars such as CALIOP on CALIPSO and ATLID on EarthCARE.
Cloud optical properties, such as extinction-to-backscatter ratio and depolarization ratio, have a significant impact on the accuracy of cloud extinction retrievals from lidar systems because parameterizations of these variables are often used in non-ideal conditions to determine cloud type and optical depth. Statistics and trends of these optical parameters were analyzed for four years, 2003-2007, of Cloud Physics Lidar data during five projects of varying geographic locations. Extinction-to-backscatter ratio (at 532 nm) was derived by calculating the transmission loss through the cloud layer, while depolarization ratio was computed using the parallel and perpendicular polarized 1064 nm channels. The majority of the cloud layers yielded an S-ratio between 10 and 40 sr with the S-ratio frequency distribution centered at 25 sr for ice clouds, 21 sr for mixed phase clouds, and 11 sr for water clouds. On average for ice clouds, S ratio slightly decreased with decreasing temperature, while depolarization ratio increased significantly as temperatures decreased. Trends for water and mixed phase clouds were also observed. Ultimately, these observed trends in optical properties as a function of temperature and geographic location will improve current parameterizations of extinction-to-backscatter ratio, which consequently increases accuracy in cloud optical depth and radiative forcing estimates.
This paper provides a thorough modeling-based overview of the scattering and radiative properties of a wide variety of morphologically complex carbonaceous aerosols. Using the numerically-exact superposition T-matrix method, we examine the absorption enhancement, absorption Angstroem exponent (AAE), backscattering linear depolarization ratio (LDR), and scattering matrix elements of black-carbon aerosols with 11 different model morphologies ranging from bare soot to completely embedded soot-sulfate and soot-brown carbon mixtures. Our size-averaged results show that fluffy soot particles absorb more light than compact bare-soot clusters. For the same amount of absorbing material, the absorption cross section of internally mixed soot can be more than twice that of bare soot. Absorption increases as soot accumulates more coating material and can become saturated. The absorption enhancement is affected by particle size, morphology, wavelength, and the amount of coating. We refute the conventional belief that all carbonaceous aerosols have AAEs close to 1.0. Although LDRs caused by bare soot and certain carbonaceous particles are rather weak, LDRs generated by other soot-containing aerosols can reproduce strong depolarization measured by Burton et al. for aged smoke. We demonstrate that multi-wavelength LDR measurements can be used to identify the presence of morphologically complex carbonaceous particles, although additional observations can be needed for full characterization. Our results show that optical constants of the host/coating material can significantly influence the scattering and absorption properties of soot-containing aerosols to the extent of changing the sign of linear polarization. We conclude that for an accurate estimate of black-carbon radiative forcing, one must take into account the complex morphologies of carbonaceous aerosols in remote sensing studies as well as in atmospheric radiation computations.
Atmospheric aerosols have high spatiotemporal variations due to their diverse sources including windblown dust, wildfires, volcanic eruptions, and anthropogenic emissions. These aerosols also have strong diurnal emission characteristics that influence long-range transport, air quality, Earth’s radiation budget, biogeochemical cycles, and weather forecasting. While current sensors can measure the steady-state of global 3D cloud and aerosol properties, we lack the observational datasets to discover the diurnal variability of aerosols, even at seasonal and regional scales, because these missions utilize sun synchronous orbits. An Aerosols and Clouds, Convection and Precipitation (ACCP) observing system has been established as part of NASA’s new Earth System Observatory (ESO) to fulfill the science needs presented in the 2017 Earth Science Decadal Survey. The selected ACCP architecture encompasses sensors in two orbit planes, one of which utilizes an inclined orbit (nominally ~55 degrees at a 407 km altitude) to advance our understanding of aerosol and cloud properties and target the dynamics of the cloud and aerosol processes on sub-daily time scales. The anticipated instrumentation suite for the ACCP Inclined Orbit Project is a Stereo Camera System, Polarimeter, Microwave Radiometer, Two-Frequency Radar, and Backscatter Lidar. The polarimeter is expected to have multiple wavelength bands and viewing angles to enable accurate retrievals of aerosol microphysical and optical properties. The backscatter lidar will provide vertical profiles of attenuated backscatter and depolarization ratio at two wavelengths, with better daytime signal-to-noise ratio than previous space-based lidar systems to improve aerosol detection/typing and estimates of aerosol optical properties. The stereo camera system, while designed primarily for clouds, will also provide aerosol plume heights and air motions at the plume heights. This invited presentation will provide (1) an overview of the ACCP Inclined Orbit Project, (2) a description of the new sensor capabilities and orbital characteristics, and (3) a preview of the potential for new synergistic aerosol inversion techniques.
Clouds and aerosols are one of the largest uncertainties in understanding and forecasting the Earth’s changing climate system. The type and height of aerosols are important factors in determining the top-of-atmosphere (TOA) radiation budget, either direct reflection of solar radiation back to space and/or absorption of solar radiation. In addition to their impact on the Earth’s climate system, aerosols near the surface from wildfires, man-made pollution events, and dust storms are hazardous to human health. The phase and height of clouds also play a critical role in determining the role of clouds in the Earth’s climate system. Cirrus clouds in the upper troposphere can induce a significant daytime TOA warming effect, while liquid water clouds near the surface cause a large corresponding cooling effect. Lidar measurements provide accurate vertically resolved information about clouds and aerosols, including complex multi-layer scenes where passive sensors are challenged and at night, when passive sensors are unable to measure cloud and aerosol properties. The Cloud-Aerosol Transport System (CATS) is a lidar instrument that operated for 33 months on the International Space Station (ISS) at the 1064 nm wavelength to measure attenuated total backscatter and depolarization ratio. These fundamental measurements are used to derive “vertical feature mask” cloud and aerosol products, including layer top/base heights, layer geometrical thickness, aerosol type, and cloud phase. While space-based lidar systems like CATS provide cloud and aerosol vertical distributions that improve our understanding of the climate system, averaging of the daytime data from these sensors is required, at the expense of spatial resolution, to improve the daytime signal-to noise (SNR) and thus atmospheric layer detection. This presentation shows results from machine learning (ML) techniques that, when applied to CATS data: 1. improve the 1064 nm SNR 2. enable detection of atmospheric features during daytime with a horizontal resolution of 350 m or 5 km (compared to the 60 km required for standard CATS data products) 3. increase the number of atmospheric layers detected in the CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination, cloud phase, and aerosol typing compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. The ML tools described in this paper can facilitate the development of smaller, low-cost lidar systems in the future and enable real-time accessibility of lidar data products from future lidar systems for monitoring and forecasting of hazardous events.
- We will analyze the volcanic plume progression, spread and properties using space-based observations - OMPS LP, SAGE III/ISS, CALIPSO, TROPOMI - OMPS LP and SAGE III/ISS are limb instruments that measure the aerosol vertical profiles globally, while CALIPSO is a LIDAR that measures the backscatter and depolarization ratio of the aerosol profiles. -TROPOMI is a UV instrument that measures SO2 and absorbing aerosol total column - Volcanic plume in the upper stratosphere - The main plume in middle stratosphere - Volcanic aerosol transport to SH/NH - Volcanic aerosol optical properties
CALIOP nighttime measurements of lidar backscatter, color and depolarization ratios during the summer of 2007 are used to study transatlantic dust properties downwind of Saharan sources, and to examine the interaction of clouds and dust. We discuss the following findings: (1) while lidar backscatter doesn't change much with altitude in the Saharan Air Layer (SAL), depolarization and color ratios both increase with altitude in the SAL; (2) lidar backscatter and color ratio increase as dust is transported westward in the SAL; (3) the vertical lapse rate of dust depolarization ratio increases within SAL as plumes move westward; (4) nearby clouds barely affect the backscatter and color ratio of dust volumes within SAL but not so below SAL. Finally, (5) the odds of CALIOP finding dust below SAL next to clouds are about 2/3 of those far away from clouds. This feature, together with an apparent increase in depolarization ratio near clouds, indicates that particles in some dusty volumes lose asphericity in the humid air near clouds, and cannot be identified by CALIPSO as dust.
Mineral dust has a significant and uncertain role in the direct aerosol radiative forcing of climate. Spaceborne lidars such as CALIOP help reduce these uncertainties through vertical profile measurements of aerosol optical properties. One current limitation to the accurate retrieval of aerosol extinction and optical depth from CALIOP is the assumed relationship between the aerosol extinction to aerosol backscatter (i.e. the extinction-to-backscatter ratio, also referred to here as the lidar ratio or Sa). This problem is especially acute at 1064 nm, where few estimates of the lidar ratio exist. This study uses a dataset of eight underflights of CALIOP during August 2010 by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL) to study Saharan dust transported across the Atlantic Ocean. The standard HSRL profile products include aerosol backscatter coefficients and depolarization ratios at both 532 nm and 1064 nm, and aerosol extinction coefficients (and therefore also lidar ratios) at 532 nm only. In this work, we further derive estimates of aerosol lidar ratios and extinction coefficients at 1064 nm via application of a two-wavelength technique that uses the 532 nm aerosol backscatter coefficients and the 1064 nm attenuated total backscatter profile. Summary statistics of the dust lidar ratio and depolarization at 532 nm and 1064 nm from these eight flights are presented. Implications for the CALIOP dust and polluted dust aerosol types and lidar ratio selection are discussed. In addition to the two-wavelength retrievals of lidar ratio at 1064 nm, a demonstration case of a 1064 nm lidar ratio retrieval over the ocean from CALIOP using the CloudSat measurement of surface scattering cross section as a constraint is presented (Josset et al, 2010).
We use CALIOP nighttime measurements of lidar backscatter, color and depolarization ratios, as well as particulate retrievals during the summer of 2007 to study transatlantic dust properties downwind of Saharan sources, and to examine the influence of nearby clouds on dust. Our analysis suggests that (1) under clear skies, while lidar backscatter and color ratio do not change much with altitude and longitude in the Saharan Air Layer (SAL), depolarization ratio increases with altitude and decreases westward in the SAL (2) the vertical lapse rate of dust depolarization ratio, introduced here, increases within SAL as plumes move westward (3) nearby clouds barely affect the backscatter and color ratio of dust volumes within SAL but not so below SAL. Moreover, the presence of nearby clouds tends to decrease the depolarization of dust volumes within SAL. Finally, (4) the odds of CALIOP finding dust below SAL next to clouds are about of those far away from clouds. This feature, together with an apparent increase in depolarization ratio near clouds, indicates that particles in some dust volumes loose asphericity in the humid air near clouds, and cannot be identified by CALIPSO as dust.
First results of an intercomparison measurement campaign between three aerosol lidar instruments and in-situ backscatter sondes performed at Table Mountain Facility (34.4 deg N, 117.7 deg E, 2280 m asl) in February-March 1997 are presented. During the campaign a total of 414 hours of lidar data were acquired by the Aerosol-Temperature-Lidar (ATL, Goddard Space Flight Center) the Mobile-aerosol-Raman-Lidar (MARL, Alfred Wegener Institute), and the TMF-Aerosol-Lidar (TAL, Jet Propulsion Laboratory), and four backscatter sondes were launched. From the data set altitude profiles of backscatter ratio and volume depolarization of stratospheric background aerosols at altitudes between 15 and 25 km and optically thin high-altitude cirrus clouds at altitudes below 13 km are derived. On the basis of a sulfuric acid aerosol model color ratio profiles obtained from two wavelength lidar data are compared to the corresponding profiles derived from the sonde observations. We find an excellent agreement between the in-situ and ATL lidar data with respect to backscatter and color ratio. Cirrus clouds were present on 16 of 26 nights during the campaign. Lidar observations with 17 minute temporal and 120-300 m spatial resolution indicate high spatial and temporal variability of the cirrus layers. Qualitative agreement is found between concurrent lidar measurements of backscatter ratio and volume depolarization.
Beam attenuation coefficient, c, provides an important optical index of plankton standing stocks, such as phytoplankton biomass and total particulate carbon concentration. Unfortunately, c has proven difficult to quantify through remote sensing. Here, we introduce an innovative approach for estimating c using lidar depolarization measurements and diffuse attenuation coefficients from ocean color products or lidar measurements of Brillouin scattering. The new approach is based on a theoretical formula established from Monte Carlo simulations that links the depolarization ratio of sea water to the ratio of diffuse attenuation Kd and beam attenuation C (i.e., a multiple scattering factor). On July 17, 2014, the CALIPSO satellite was tilted 30Âdeg off-nadir for one nighttime orbit in order to minimize ocean surface backscatter and demonstrate the lidar ocean subsurface measurement concept from space. Depolarization ratios of ocean subsurface backscatter are measured accurately. Beam attenuation coefficients computed from the depolarization ratio measurements compare well with empirical estimates from ocean color measurements. We further verify the beam attenuation coefficient retrievals using aircraft-based high spectral resolution lidar (HSRL) data that are collocated with in-water optical measurements.
Development work is underway at the Goddard Space Flight Center to construct a depolarization measuring atmospheric lidar receiver. The Atmospheric Lidar (AL) is tentatively scheduled to fly on the Space Shuttle in a late-1999 time frame. The AL will fly in conjunction with the Shuttle Laser Altimeter (SLA) and the Infrared Spectral Imaging Radiometer (ISIR) to provide a comprehensive package of atmospheric aerosol and cloud information. The AL operates in conjunction with the SLA laser transmitter and measures profiles of atmospheric backscatter at 532 nm. The receiver system discriminates between the parallel and perpendicular polarizations of the backscattered signal, thus providing depolarization ratios for scattering from clouds. The lidar receiver also provides cloud height and thickness measurements to complement the brightness temperature measurements generated by the ISIR thermal imager. The function of the AL is twofold. The primary function is to provide range-resolved measurements of atmospheric aerosol backscatter and depolarization ratio at 532 nm with 75 m vertical resolution. The scientific purpose of these measurements is to determine composition of clouds based on the depolarization ratio (i.e cloud content is water or ice), to determine cloud height and thickness, and to gain further understanding of the global distribution of aerosols. This information, when coupled with the cloud brightness measured by the ISIR thermal imager will provide a significant amount of information on cloud composition and radiative effects, particularly for cirrus and sub-visual cirrus clouds. A secondary function of the AL is to serve as an in-space test bed for lidar technology advancements, including a fully fiber-coupled receiver and photon counting from space. In addition, the data obtained by the AL will be used to develop software for the Geoscience Laser Altimeter System (GLAS) flight mission.
This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.