Lidar Retrievals Research Articles

Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021

Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021

Geometric Factor Correction Algorithm Based on Temperature and Humidity Profile Lidar

Due to the influence of geometric factors, the temperature and humidity profile of lidar’s near-field signal was warped when sensing the air environment. In order to perform geometric factor correction on near-field signals, this article proposes different correction solutions for the Mie and Raman scattering channels. Here, the Mie scattering channel used the Raman method to invert the aerosol backscatter coefficient and correct the extinction coefficient in the transition zone. The geometric factor was the ratio of the measured signal to the forward-computed vibration Raman scattering signal. The aerosol optical characteristics were reversed using the corrected echo signal, and the US standard atmospheric model was added to the missing signal in the blind zone, reflecting the aerosol evolution process. The stability and dependability of the proposed algorithm were validated by the consistency between the visibility provided by the Environmental Protection Agency and the visibility acquired via lidar retrieval data. The near-field humidity data were supplemented by the interpolation method in the Raman scattering channel to reflect the water vapor transfer process in the temporal dimension. The measured transmittance curve of the filter, the theoretical normalized spectrum, and the sounding data were used to compute the delay geometric factor. The temperature was retrieved and the near-field signal distortion issue was resolved by applying the corrected quotient of the temperature channel. The proposed algorithm exhibited robustness and universality, enhancing the system’s detection accuracy compared to the temperature and humidity data constantly recorded by the probes in the meteorological gradient tower, which have a high correlation with the lidar observation data. The comparison between lidar data and instrument monitoring data showed that the proposed algorithm could effectively correct distorted echo signals in the transition zone, which was of great value for promoting the application of lidar in the meteorological monitoring of the urban canopy layer.

Conducting volcanic ash cloud exercises: practising forecast evaluation procedures and the pull-through of scientific advice to the London VAAC

The London Volcanic Ash Advisory Centre (VAAC) provides forecasts on the expected presence of volcanic ash in the atmosphere to mitigate the risk to aviation. It is fundamentally important that operational capability is regularly tested through exercises, to guarantee an effective response to an event. We have developed exercises which practise the pull-through of scientific advice into the London VAAC, the forecast evaluation process, and the decision-making procedures and discussions needed for generating the best possible forecasts under real-time conditions. London VAAC dispersion model forecasts are evaluated against observations. To test this capability in an exercise, we must create observation data for a hypothetical event. We have developed new methodologies for generating and using simulated satellite and lidar retrievals. These simulated observations enable us to practise our ability to interpret, compare, and evaluate model output and observation data under real-time conditions. Forecast evaluation can benefit from an understanding of how different choices of model setup (input parameters), model physics, and driving meteorological data impact the predicted extent and concentration of ash. Through our exercises, we have practised comparing output from model simulations generated using different models, model setups, and meteorological data, supplied by different institutions. Our exercises also practise the communication and interaction between Met Office (UK) scientists supporting the London VAAC and external experts, enabling knowledge exchange and discussions on the interpretation of model output and observations, as we strive to deliver the best response capability for the aviation industry and stakeholders. In this paper, we outline our exercise methodology, including the use of simulated satellite and lidar observations, and the development of the strategy to compare output generated from different modelling systems. We outline the lessons learnt, including the benefits and challenges of conducting exercises which practise our ability to provide scientific advice for an operational response at the London VAAC.

Comparison of scanning aerosol lidar and in situ measurements of aerosol physical properties and boundary layer heights

Abstract. The spatiotemporal distribution of aerosol particles in the atmosphere has a great impact on radiative transfer, clouds, and air quality. Modern remote sensing methods, as well as airborne in situ measurements by unpiloted aerial vehicles (UAV) or balloons, are suitable tools to improve our understanding of the role of aerosol particles in the atmosphere. To validate the measurement capabilities of three relatively new measurement systems and to bridge the gaps that are often encountered between remote sensing and in situ observation, as well as to investigate aerosol particles in and above the boundary layer, we conducted two measurement campaigns and collected a comprehensive dataset employing a scanning aerosol lidar, a balloon-borne radiosonde with the Compact Optical Backscatter Aerosol Detector (COBALD), an optical particle counter (OPC) on a UAV, and a comprehensive set of ground-based instruments. The extinction coefficients calculated from near-ground-level aerosol size distributions measured in situ are well correlated with those retrieved from lidar measurements, with a slope of 1.037 ± 0.015 and a Pearson correlation coefficient of 0.878, respectively. Vertical profiles measured by an OPC-N3 on a UAV show similar vertical particle distributions and boundary layer heights to lidar measurements. However, the sensor, OPC-N3, shows a larger variability in the aerosol backscatter coefficient measurements, with a Pearson correlation coefficient of only 0.241. In contrast, the COBALD data from a balloon flight are well correlated with lidar-derived backscatter data from the near-ground level up to the stratosphere, with a slope of 1.063 ± 0.016 and a Pearson correlation coefficient of 0.925, respectively. This consistency between lidar and COBALD data reflects the good data quality of both methods and proves that lidar can provide reliable and spatial distributions of aerosol particles with high spatial and temporal resolutions. This study shows that the scanning lidar has the capability to retrieve backscatter coefficients near the ground level (from 25 to 50 m above ground level) when it conducts horizontal measurement, which is not possible for vertically pointing lidar. These near-ground-level retrievals compare well with ground-level in situ measurements. In addition, in situ measurements on the balloon and UAV validated the scanning lidar retrievals within and above the boundary layer. The scanning aerosol lidar allows us to measure aerosol particle distributions and profiles from the ground level to the stratosphere with an accuracy equal to or better than in situ measurements and with a similar spatial resolution.

The implementation of dust mineralogy in COSMO5.05-MUSCAT

Abstract. Mineral dust aerosols are composed of a complex assemblage of various minerals depending on the region in which they originated. Given the different mineral composition of desert dust aerosols, different physicochemical properties and therefore varying climate effects are expected. Despite the known regional variations in mineral composition, chemical transport models typically assume that mineral dust aerosols have uniform composition. This study adds, for the first time, mineralogical information to the mineral dust emission scheme used in the chemical transport model COSMO–MUSCAT. We provide a detailed description of the implementation of the mineralogical database, GMINER (Nickovic et al., 2012), together with a specific set of physical parameterizations in the model's mineral dust emission module, which led to a general improvement of the model performance when comparing the simulated mineral dust aerosols with measurements over the Sahara region for January–February 2022. The simulated mineral dust aerosol vertical distribution is tested by a comparison with aerosol lidar measurements from the lidar system PollyXT, located at Cape Verde. For a lofted mineral dust aerosol layer on 2 February at 05:00 UTC the lidar retrievals yield a dust mass concentration peak of 156 µg m−3, while the model calculates the mineral dust peak at 136 µg m−3. The results highlight the possibility of using the model with resolved mineral dust composition for interpretation of the lidar measurements since a higher absorption in the UV–Vis wavelengths is correlated with particles having a higher hematite content. Additionally, the comparison with in situ mineralogical measurements of dust aerosol particles shows that more of them are needed for model evaluation.

Understanding the Roles of Aerosols and Clouds in Environment, Meteorology and Climate with Advanced Lidar Remote Sensing Techniques

This Special Issue lists nine publications, covering the topics of advanced atmospheric lidar techniques, lidar retrievals, and lidar applications. The investigations listed here are diverse, but they are all focused on atmospheric lidars. Some urgent issues, for instance low blind zone detection and polarization detection at a near-infrared wavelength band, were discussed and explored. The results are helpful for extending atmospheric lidar applications. In terms of lidar retrievals, a planetary boundary layer height retrieval and an automatic lidar retrieval for aerosol optical properties were investigated in some of the publications, which can strengthen the atmospheric lidar capabilities. For lidar applications, a detailed analysis of the evolution of stratospheric aerosol and dust–cloud interactions was presented. In this Editorial, the articles published within this Special Issue are reviewed to highlight their innovative contributions and main research findings.

Wind-driven emissions of coarse-mode particles in an urban environment

Abstract. Quantifying surface–atmosphere exchange rates of particles is important for understanding the role of suspended particulate matter in radiative transfer, clouds, precipitation, and climate change. Emissions of coarse-mode particles with a diameter greater than 0.5 µm provide giant cloud condensation nuclei and ice nuclei. These emissions are critical for understanding the evolution of cloud microphysical properties yet remain poorly understood. Here we introduce a new method that uses lidar retrievals of the elastic backscatter and Doppler velocity to obtain surface number emissions of particles with a diameter greater than 0.53 µm. The technique is applied to study particle number fluxes over a 2-month period from 1 June to 10 August 2022 during the TRACER campaign at an urban site near Houston, TX, USA. We found that all the observed fluxes were positive (upwards), indicating particle emission from the surface. The fluxes followed a diurnal pattern and peaked near noon local time. Flux intensity varied through the 2 months with multi-day periods of strong fluxes and multi-day periods of weak fluxes. Emission particle number fluxes peaked near ∼ 100 cm−2 s−1. The daily averaged emission fluxes correlated with friction velocity and were anticorrelated with surface relative humidity. The emission flux can be parameterized as F= 3000 u*4, where u* is the friction velocity in m s−1 and the emission flux F is in cm−2 s−1. The u* dependence is consistent with emission from wind-driven erosion. Estimated values for the mass flux are in the lower range of literature values from non-urban sites. These results demonstrate that urban environments may play an important role in supplying coarse-mode particles to the boundary layer. We anticipate that quantification of these emissions will help constrain aerosol–cloud interaction models that use prognostic aerosol schemes.

Satellite Lidar Measurements as a Critical New Global Ocean Climate Record

The year 2023 marked the tenth anniversary of the first published description of global ocean plankton stocks based on measurements from a satellite lidar. Diverse studies have since been conducted to further refine and validate the lidar retrievals and use them to discover new characteristics of plankton seasonal dynamics and marine animal migrations, as well as evaluate geophysical products from traditional passive ocean color sensors. Surprisingly, all of these developments have been achieved with lidar instruments not designed for ocean applications. Over this same decade, we have witnessed unprecedented changes in ocean ecosystems at unexpected rates and driven by a multitude of environmental stressors, with a dominant factor being climate warming. Understanding, predicting, and responding to these ecosystem changes requires a global ocean observing network linking satellite, in situ, and modeling approaches. Inspired by recent successes, we promote here the creation of a lidar global ocean climate record as a key element in this envisioned advanced observing system. Contributing to this record, we announce the development of a new satellite lidar mission with ocean-observing capabilities and then discuss additional technological advances that can be envisioned for subsequent missions. Finally, we discuss how a potential near-term gap in global ocean lidar data might, at least partially, be filled using on-orbit or soon-to-be-launched lidars designed for other disciplinary purposes, and we identify upcoming needs for in situ support systems and science community development.

Lidar ratio calculations from in situ aerosol optical, microphysical and chemical measurements: Observations at puy de Dôme, France and analysis with CALIOP

The ratio between lidar extinction and backscatter coefficients, also known as lidar ratio (LR), is an important parameter in atmospheric aerosol studies. In this paper, we propose a method to determine the 532 nm LR using in situ measurements performed over the 2015–2016 period at the puy de Dôme (PUY) station, central France, located at about 1465 m altitude. This method uses a Mie code with the measured aerosols size distribution and refractive index determined from aerosols optical measurements as inputs. The LR values obtained have been compared to LR calculated also with a Mie code but with refractive index determined from the measured aerosol chemical composition. A good correlation is observed for the period 2015–2016 with an agreement which increases to 99% after a significant imaginary part refractive index reduction, corresponding to much less carbonated particles than initially estimated. >50% of the LR values calculated at the station are within the 60–80 sr range under ambient atmospheric conditions. A statistical comparison with the CALIOP spatial lidar retrieval gives a good agreement at the location of PUY between retrieved values (62 sr) with a negligible bias and a dispersion indicative of a similar variability of LR (about 14 sr). The influence of air mass history on the LR has also been studied using backward trajectory analysis and CALIOP aerosol types along the trajectories, identifying five source regions. For continental, smoke and polluted dust aerosols types, CALIOP and PUY LR show a good agreement. For dusty and mainly clean marine aerosols, the differences observed between both suggest that air masses coming from the Atlantic Ocean sector at altitudes lower than 2.5 km, have experienced mixing with continental air masses during their travel increasing their LR before reaching the PUY station.

Estimation of the Seawater Lidar Ratio by MODIS: Spatial–Temporal Characteristics and Ecological Significance

The lidar ratio of seawater is an essential quantity related to both lidar retrieval and water constituent. However, few studies discuss its spatial–temporal characteristics and ecological significance, which limits its applications in lidar remote sensing and marine science. This paper investigates the spatial–temporal characteristics and ecological significance of the lidar ratio of seawater using satellite passive remote sensing, which is validated by in situ measurements. Spatially, nearshore lidar ratio values are higher than offshore, mainly owing to the high concentration of colored dissolved organic matter in nearshore water. Temporally, the lidar ratio in each hemisphere exhibits lower values in summer than in winter due to the annual boom–bust cycle of phytoplankton. Furthermore, the variability patterns of the lidar ratio are nearly consistent with those of the chlorophyll-to-carbon ratio, implying the high ecological significance of phytoplankton physiology. These findings will provide the foundation for the application of lidar ratio in marine science and lidar remote sensing.

Aerosol first indirect effect of African smoke at the cloud base of marine cumulus clouds over Ascension Island, southern Atlantic Ocean

Abstract. The interactions between aerosols and clouds are among the least understood climatic processes and were studied over Ascension Island. A ground-based UV polarization lidar was deployed on Ascension Island, which is located in the stratocumulus-to-cumulus transition zone of the southeastern Atlantic Ocean, to infer cloud droplet sizes and droplet number density near the cloud base of marine boundary layer cumulus clouds. The aerosol–cloud interaction (ACI) due to the presence of smoke from the African continent was determined during the monsoonal dry season. In September 2016, a cloud droplet number density ACIN of 0.3 ± 0.21 and a cloud effective radius ACIr of 0.18 ± 0.06 were found, due to the presence of smoke in and under the clouds. Smaller droplets near the cloud base makes them more susceptible to evaporation, and smoke in the marine boundary layer over the southeastern Atlantic Ocean will likely accelerate the stratocumulus-to-cumulus transition. The lidar retrievals were tested against more traditional radar–radiometer measurements and shown to be robust and at least as accurate as the lidar–radiometer measurements. The lidar estimates of the cloud effective radius are consistent with previous studies of cloud base droplet sizes. The lidar has the large advantage of retrieving both cloud and aerosol properties using a single instrument.

A Unified Formulation for the Computation of the Six-Degrees-of-Freedom-Motion-Induced Errors in Floating Doppler Wind LiDARs

This work presents an analytical formulation to assess the six-degrees-of-freedom-motion-induced error in floating Doppler wind LiDARs (FDWLs). The error products derive from the horizontal wind speed bias and apparent turbulence intensity. Departing from a geometrical formulation of the FDWL attitude and of the LiDAR retrieval algorithm, the contributions of the rotational and translational motion to the FDWL-measured total error are computed. Central to this process is the interpretation of the velocity–azimuth display retrieval algorithm in terms of a first-order Fourier series. The obtained 6 DoF formulation is validated numerically by means of a floating LiDAR motion simulator and experimentally in nearshore and open-sea scenarios in the framework of the Pont del Petroli and IJmuiden campaigns, respectively. Both measurement campaigns involved a fixed and a floating ZephIRTM 300 LiDAR. The proposed formulation proved capable of estimating the motion-induced FDWL horizontal wind speed bias and returned similar percentiles when comparing the FDWL with the fixed LiDAR. The estimations of the turbulence intensity increment statistically matched the FDWL measurements under all motional and wind scenarios when clustering the data as a function of the buoy’s mean tilt amplitude, mean translational-velocity amplitude, and mean horizontal wind speed.

Validation of the TROPOMI/S5P aerosol layer height using EARLINET lidars

Abstract. The purpose of this study is to investigate the ability of the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) to derive accurate geometrical features of lofted aerosol layers, selecting the Mediterranean Basin as the study area. Comparisons with ground-based correlative measurements constitute a key component in the validation of passive and active satellite aerosol products. For this purpose, we use ground-based observations from quality-controlled lidar stations reporting to the European Aerosol Research Lidar Network (EARLINET). An optimal methodology for validation purposes has been developed and applied using the EARLINET optical profiles and TROPOMI aerosol products, aiming at the in-depth evaluation of the TROPOMI aerosol layer height (ALH) product for the period 2018 to 2022 over the Mediterranean Basin. Seven EARLINET stations were chosen, taking into consideration their proximity to the sea, which provided 63 coincident aerosol cases for the satellite retrievals. In the following, we present the first validation results for the TROPOMI/S5P ALH using the optimized EARLINET lidar products employing the automated validation chain designed for this purpose. The quantitative validation at pixels over the selected EARLINET stations illustrates that the TROPOMI ALH product is consistent with the EARLINET lidar products, with a high correlation coefficient R=0.82 (R=0.51) and a mean bias of -0.51±0.77 km and -2.27±1.17 km over ocean and land, respectively. Overall, it appears that aerosol layer altitudes retrieved from TROPOMI are systematically lower than altitudes from the lidar retrievals. High-albedo scenes, as well as low-aerosol-load scenes, are the most challenging for the TROPOMI retrieval algorithm, and these results testify to the need to further investigate the underlying cause. This work provides a clear indication that the TROPOMI ALH product can under certain conditions achieve the required threshold accuracy and precision requirements of 1 km, especially when only ocean pixels are included in the comparison analysis. Furthermore, we describe and analyse three case studies in detail, one dust and two smoke episodes, in order to illustrate the strengths and limitations of the TROPOMI ALH product and demonstrate the presented validation methodology. The present analysis provides important additions to the existing validation studies that have been performed so far for the TROPOMI S5P ALH product, which were based only on satellite-to-satellite comparisons.

Retrieval of Aerosol Microphysical Properties from Multi-Wavelength Mie–Raman Lidar Using Maximum Likelihood Estimation: Algorithm, Performance, and Application

Lidar plays an essential role in monitoring the vertical variation of atmospheric aerosols. However, due to the limited information that lidar measurements provide, ill-posedness still remains a big challenge in quantitative lidar remote sensing. In this study, we describe the Basic algOrithm for REtrieval of Aerosol with Lidar (BOREAL), which is based on maximum likelihood estimation (MLE), and retrieve aerosol microphysical properties from extinction and backscattering measurements of multi-wavelength Mie–Raman lidar systems. The algorithm utilizes different types of a priori constraints to better constrain the solution space and suppress the influence of the ill-posedness. Sensitivity test demonstrates that BOREAL could retrieve particle volume size distribution (VSD), total volume concentration (Vt), effective radius (Reff), and complex refractive index (CRI = n − ik) of simulated aerosol models with satisfying accuracy. The application of the algorithm to real aerosol events measured by LIlle Lidar AtmosphereS (LILAS) shows it is able to realize fast and reliable retrievals of different aerosol scenarios (dust, aged-transported smoke, and urban aerosols) with almost uniform and simple pre-settings. Furthermore, the algorithmic principle allows BOREAL to incorporate measurements with different and non-linearly related errors to the retrieved parameters, which makes it a flexible and generalized algorithm for lidar retrieval.

Numerical investigation on the accuracy of size information of fractal soot aerosols retrieved by lidar: Optical property, morphology effect, and parameterization scheme

Lidar is effective in retrieving size distribution of atmospheric particles such as soot aerosols in real-time based on Mie scattering theory. However, the fundamental spherical shape assumption of Mie theory is obviously discrepant with the actual situations of atmospheric soot particles which are fractal and mixed with coating materials. In this study, the effects of fractal shapes and morphological parameters such as fractal dimension (Df), volume fraction (Vf), and particle radius (r) of soot aerosols on the size information retrieved by lidar based on Mie theory were investigated from the numerical aspect, closed-cell and coated-aggregate models were employed to represent soot aerosols thinly and heavily coated by non-absorbing materials, respectively. Optical properties of fractal soot particles were calculated at first, then backscattering and extinction properties were employed to retrieve backscattering equivalent radius (rbk) and extinction equivalent radius (rext) based on Mie theory, just like the lidar retrieval. Results revealed that optical properties of spherical and fractal models differ significantly with the variation of volume equivalent radius (rvol). Radius retrieval errors based on extinction cross-section are much smaller than that based on backscattering cross-section. The retrieval accuracy of coated-aggregate is higher than bare fractal aggregate and closed-cell models. Retrieved size distribution parameters mean radius are generally underestimated while standard deviation are overestimated for both models. Furthermore, parameterization schemes connecting rext and rvol of soot were constructed, retrieval errors are effectively reduced from about 25% to 10%, which is meaningful for the improvement of size retrieval accuracy of soot using lidar observations.

Extending water vapor measurement capability of photon-limited differential absorption lidars through simultaneous denoising and inversion

Abstract. The micropulse differential absorption lidar (MPD) was developed at Montana State University (MSU) and the National Center for Atmospheric Research (NCAR) to perform range-resolved water vapor (WV) measurements using low-power lasers and photon-counting detectors. The MPD has proven to produce accurate WV measurements up to 6 km altitude. However, the MPD's ability to produce accurate higher-altitude WV measurements is impeded by the current standard differential absorption lidar (DIAL) retrieval methods. These methods are built upon a fundamental methodology that algebraically solves for the WV using the MPD forward models and noisy observations, which exacerbates any random noise in the lidar observations. The work in this paper introduces the adapted Poisson total variation (PTV) specifically for the MPD instrument. PTV was originally developed for a ground-based high spectral resolution lidar, and this paper reports on the adaptations that were required in order to apply PTV on MPD WV observations. The adapted PTV method, coined PTV-MPD, extends the maximum altitude of the MPD from 6 to 8 km and substantially increases the accuracy of the WV retrievals starting above 2 km. PTV-MPD achieves the improvement by simultaneously denoising the MPD noisy observations and inferring the WV by separating the random noise from the non-random WV. An analysis with 130 radiosonde (RS) comparisons shows that the relative root-mean-square difference (RRMSE) of WV measurements between RS and PTV-MPD exceeds 100 % between 6 and 8 km, whereas the RRMSE between RS and the standard method exceeds 100 % near 3 km. In addition, we show that by employing PTV-MPD, the MPD is able to extend its useful range of WV estimates beyond that of the ARM Southern Great Plains Raman lidar (RRMSE exceeding 100 % between 3 and 4 km); the Raman lidar has a power-aperture product 500 times greater than that of the MPD.

Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties

Lidar techniques present a distinctive ability to resolve vertical structure of optical properties within the upper water column at both day- and night-time. However, accuracy challenges remain for existing lidar instruments due to the ill-posed nature of elastic backscatter lidar retrievals and multiple scattering. Here we demonstrate the high performance of, to the best of our knowledge, the first shipborne oceanic high-spectral-resolution lidar (HSRL) and illustrate a multiple scattering correction algorithm to rigorously address the above challenges in estimating the depth-resolved diffuse attenuation coefficient Kd and the particulate backscattering coefficient bbp at 532 nm. HSRL data were collected during day- and night-time within the coastal areas of East China Sea and South China Sea, which are connected by the Taiwan Strait. Results include vertical profiles from open ocean waters to moderate turbid waters and first lidar continuous observation of diel vertical distribution of thin layers at a fixed station. The root-mean-square relative differences between the HSRL and coincident in situ measurements are 5.6% and 9.1% for Kd and bbp, respectively, corresponding to an improvement of 2.7–13.5 and 4.9–44.1 times, respectively, with respect to elastic backscatter lidar methods. Shipborne oceanic HSRLs with high performance are expected to be of paramount importance for the construction of 3D map of ocean ecosystem.

This is FAST: multivariate Full-permutAtion based Stochastic foresT method—improving the retrieval of fine-mode aerosol microphysical properties with multi-wavelength lidar

Despite the small size and ubiquitous presence, fine-mode aerosols play a vital role in climate change and human health, especially in highly populated regions. Hitherto, some studies have been carried out to retrieve fine-mode aerosol microphysical properties from multi-wavelength lidar measurements. However, there has been a dearth of lidar-based methods with quality retrieval and high time efficiency to adequately quantify the impacts of fine-mode aerosols on Earth's energy budget. The reasons for the gap involve the limitations in current approaches and the nature of the under-determined problem with insufficient information of three backscattering coefficients (β) and two extinction coefficients (α), typically known as the 3β + 2α configuration. Furthermore, the latest lidars, especially for spaceborne and airborne applications, are inherently difficult to perform routine diurnal 3β + 2α observations with high resolution and require high processing speed for massive data. To overcome these conundrums, we developed a novel unsupervised machine learning method—multivariate Full-permutAtion based Stochastic foresT method (dubbed the FAST method)—to improve the retrieval of fine-mode aerosol microphysical properties. To the best of knowledge, this work is the first time that machine learning algorithms are employed in attempts to retrieve the aerosol microphysical properties with stand-alone multi-wavelength lidar data. The major merits of the FAST method include 1) high accuracy of fine-mode aerosol products with the typical 3β + 2α configuration, 2) acceptable performances for fewer input optical channels, and 3) high processing speed for large volume data. Comprehensive simulations have been conducted to investigate the error characteristic of the FAST method under different conditions. We also applied the FAST method to the airborne lidar data acquired during the NASA DISCOVER-AQ field campaign. The retrievals of the FAST method provide high resolution time-height distributions of fine-mode aerosol microphysical properties at 20-s temporal resolution and 45-m vertical resolution. In situ measurements are compared with multi-wavelength lidar retrievals showing good agreements. We achieved 0.010, 0.014 and 0.016 in terms of mean absolute difference for retrieved 532-nm single-scattering albedo with 3β + 2α, 3β + 1α, and 2β + 1α configurations, respectively. The proposed method is expected to represent an important step toward improving microphysical retrievals from multi-wavelength lidar data, especially for airborne and spaceborne lidar missions.

Exploring the Scientific Utility of Combined Spaceborne Lidar and Lightning Observations of Thunderstorms

AbstractApproximately 8 months of colocated spaceborne lidar and lightning observations were analyzed in a pathfinder study to understand the advantages and challenges of using these combined observations to understand thunderstorms. Data from the Lightning Imaging Sensor (LIS) and the Cloud‐Aerosol Transport System (CATS) lidar were used when they overlapped on the International Space Station during March–October 2017. Using simple matching criteria, 8246 LIS flashes occurred within 25 km of the CATS ground track. CATS cloud top heights near these flashes showed similar behavior with latitude when compared with a spaceborne radar‐based climatology, but the lidar cloud tops were approximately 2‐km higher than 20‐dBZ radar echo tops. CATS cloud phase near LIS flashes was consistent with ice‐ or mixed‐phase more than 90% of the time, showing the value of using lightning observations to validate lidar‐based feature masks. In addition, correlations between a proxy for LIS flash rate and CATS ice water path, cloud optical depth, and cloud top height were low (0.38–0.42) but positive and highly statistically significant (>99%), suggesting that lidar retrievals of cloud properties can be meaningfully compared with lightning observations despite lidar's known inability to penetrate deeply into optically thick clouds like thunderstorms. Finally, CATS was used to help diagnose LIS false alarms due to surface‐based glint. The false alarm rate was approximately 0.1%, which demonstrated the excellent performance of the surface glint filter in the LIS processing code. The results suggest that fruitful scientific insights can be expected from larger combined lidar/lightning datasets.

ICESAT-2 Lidar retrievals for a smoke and clouds case: estimates in the south america region

The present study aims to estimate the height of smoke aerosols and clouds from the Advanced Topographic Laser Altimeter System (ATLAS) instrument sensor onboard the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). The study was conducted during the dry season on September 14th of 2020, when there was a large number spot of biomass burning fires and smoke aerosol layers over the Amazon and the South American region. Most often, the smoke plumes move with southward winds, along the east of the Andes. During this period occasionally polar cold fronts move from the south, meeting the smoke aerosol layers. Initially, daily images from the polar orbit satellites such as Terra, Aqua, NOAA and Suomi were used to identify the spatial location of smoke plumes and clouds over the region. Then, ICESat-2 tracks were analyzed to evaluate their presence in the study area. The results show that ICESat-2 data retrieval is able to estimate the height of the aerosol layer and the top of the cloud. Some tracks show clearly higher clouds, with tops reaching about 12-13 km, when there is the presence of a cold front or deep convective cumulonimbus. Over clear sky regions where smoke is observed, estimated heights from the lidar are on the order of 3-4 km. Local profiles of temperature obtained from radiosondes show inversion layers at similar heights as detected by the lidar sensor. These inversion layers seem to be associated with the warming effect of the absorbing biomass burning aerosols.