Lidar Measurements Research Articles

Characterization of the aerosol vertical distributions and their impacts on warm clouds based on multi-year ARM observations

Aerosol vertical distribution plays a crucial role in cloud development and thus precipitation since both aerosol indirect and semi-direct effects significantly depend on the relative position of aerosol layer in reference to cloud, but its precise influence on cloud remains unclear. In this study, we integrated multi-year Raman Lidar measurements of aerosol vertical profiles from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) facility with available Value-Added Products of cloud features to characterize aerosol vertical distributions and their impacts on warm clouds over the continental and marine ARM atmospheric observatories, i.e., Southern Great Plains (SGP) and Eastern North Atlantic (ENA). A unimodal seasonal distribution of aerosol optical depths (AODs) with a peak in summer is found at upper boundary layer over SGP, while a bimodal distribution is observed at ENA for the AODs at lower levels with a major winter-spring maximum. The diurnal mean of upper-level AOD at SGP shows a maximum in the early evening. According to the relative positions of aerosol layers to clouds we further identify three primary types of aerosol vertical distribution, including Random, Decreasing, and Bottom. It is found that the impacts of aerosols on cloud may or may not vary with aerosol vertical distribution depending on environmental conditions, as reflected by the wide variations of the relations between AOD and cloud properties. For example, as AOD increases, the liquid water paths (LWPs) tend to be reduced at SGP but enhanced at ENA. The relations of cloud droplet effective radius with AOD largely depend on aerosol vertical distributions, particularly showing positive values in the Random type under low-LWP condition (<50 g m−2). The distinct features of aerosol-cloud interactions in relation to aerosol vertical distribution are likely attributed to the continental-marine contrast in thermodynamic environments and aerosol conditions between SGP and ENA.

Collaborative development of the Lidar Processing Pipeline (LPP) for retrievals of atmospheric aerosols and clouds

Abstract. Atmospheric lidars can simultaneously measure clouds and aerosols with high temporal and spatial resolution and hence help understand cloud–aerosol interactions, which are the source of major uncertainties in future climate projections. However, atmospheric lidars are typically custom-built, with significant differences between them. In this sense, lidar networks play a crucial role as they coordinate the efforts of different groups, provide guidelines for quality-assured routine measurements and opportunities for side-by-side instrument comparisons, and enforce algorithm validation, all aiming to homogenize the physical retrievals from heterogeneous instruments in a network. Here we provide a high-level overview of the Lidar Processing Pipeline (LPP), an ongoing, collaborative, and open-source coordinated effort in Latin America. The LPP is a collection of tools with the ultimate goal of handling all the steps of a typical analysis of lidar measurements. The modular and configurable framework is generic enough to be applicable to any lidar instrument. The first publicly released version of the LPP produces data files at levels 0 (raw and metadata), 1 (averaging and layer mask), and 2 (aerosol optical properties). We assess the performance of the LPP through quantitative and qualitative analyses of simulated and measured elastic lidar signals. For noiseless synthetic 532 nm elastic signals with a constant lidar ratio (LR), the root mean square error (RMSE) in aerosol extinction within the boundary layer is about 0.1 %. In contrast, retrievals of aerosol backscatter from noisy elastic signals with a variable LR have an RMSE of 11 %, mostly due to assuming a constant LR in the inversion. The application of the LPP for measurements in São Paulo, further constrained by co-located AERONET data, retrieved a lidar ratio of 69.9 ± 5.2 sr at 532 nm, in agreement with reported values for urban aerosols. Over the Amazon, analysis of a 6 km thick multi-layer cirrus found a cloud optical depth of about 0.46, also in agreement with previous studies. From this exercise, we identify the need for new features and discuss a roadmap to guide future development, accommodating the needs of our community.

A first global height-resolved cloud condensation nuclei data set derived from spaceborne lidar measurements

Abstract. We present a global multiyear height-resolved data set of aerosol-type-specific cloud condensation nuclei concentrations (nCCN) estimated from the spaceborne lidar aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. For estimating nCCN, we apply the recently introduced Optical Modelling of the CALIPSO Aerosol Microphysics (OMCAM) algorithm to the CALIPSO Level 2 Aerosol Profile product. The estimated nCCN are then gridded into a uniform latitude–longitude grid of 2∘×5∘, a vertical grid of resolution 60 m from the surface to an altitude of 8 km, and a temporal resolution of 1 month. The data span a total of 186 months, from June 2006 to December 2021. In addition, we provide a 3D aerosol-type-specific climatology of nCCN produced using the complete time series. We further highlight some potential applications of the data set in the context of aerosol–cloud interactions. The complete data set can be accessed at https://doi.org/10.1594/PANGAEA.956215 (Choudhury and Tesche, 2023).

Digital twin of wind farms via physics-informed deep learning

The spatiotemporal flow field in a wind farm determines the wind turbines’ energy production and structural fatigue. However, it is not obtainable by the current measurement, modeling, and prediction tools in wind industry. Here we propose a novel data and knowledge fusion approach to create the first digital twin for onshore/offshore wind farm flow system, which can predict the in situ spatiotemporal wind field covering the entire wind farm. The digital twin is developed by integrating the Lidar measurements, the Navier–Stokes equations, and the turbine modeling using actuator disk method, via physics-informed neural networks. The design enables the seamless integration of Lidar measurements and turbine operating data for real-time flow characterization, and the fusion of flow physics for retrieving unmeasured wind field information. It thus addresses the limitations of existing wind prediction approaches based on supervised machine learning, which cannot achieve such prediction because the training targets are not available. Case studies of a wind farm under typical operating scenarios (i.e. a greedy case, a wake-steering case, and a partially-operating case) are carried out using high-fidelity numerical experiments, and the results show that the developed digital twin achieves very accurate mirroring of the physical wind farm, capturing detailed flow features such as wake interaction and wake meandering. The prediction error for the flow fields, on average, is just 4.7% of the value range. With the accurate flow field information predicted, the digital twin is expected to enable brand new research across wind farm lifecycle including monitoring, control, and load assessment.

Lidar attenuation coefficient in the global oceans: insights from ICESat-2 mission.

The attenuation coefficient of natural waters plays a significant role in our understanding of hydrology from both the oceanographic and biological point of view. The advent of near-continuous observations by sophisticated space-based lidars now offers an unprecedented opportunity to characterize attenuation coefficients over open oceans on global and regional scales. At present, however, literature reports of lidar-derived attenuation coefficient estimates (klidar, m-1) in oceanic waters are very limited. In this study, we present a global survey of klidar derived from ATLAS/ICESat-2 nighttime measurements. Our results augment the existing passive sensor ocean color data set with a new diurnal component and extend the record to now include previously unavailable polar nighttime observations. The values of ATLAS measured klidar at 532 nm are between 0.045 and 0.39 m-1 with the higher values (>0.15 m-1) correlated with coastal waters and sea ice covered oceans. The average klidar in clearest oligotrophic ocean gyres is ∼0.058 ± 0.012 m-1 at 532 nm. The results reported here demonstrate the feasibility of using ATLAS/ICESat-2 lidar measurements for global klidar studies, which will in turn provide critical insights that enable climate models to correctly describe the amount of light present under sea ice, and for heat deposition studies in the upper ocean.

Arctic Cloud‐Base Ice Precipitation Properties Retrieved Using Bayesian Inference

AbstractCloud‐climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long‐lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud‐base properties a more appealing target for inference and evaluation of model simulations. Here I describe an inverse model for the estimation of cloud base ice precipitation properties over Utqiagvik, North Slope of Alaska, using the synthesis of ground‐based radar and lidar measurements. By leveraging a Markov Chain Monte Carlo algorithm as the core of the inverse model, a wide range of particle size distributions are sampled, and different combinations of ice habit models are examined, both of which are typically fixed in other retrieval methods. Results show intriguing links between different cloud base thermodynamic and ice precipitation properties. Apparent ice number concentration enhancements at temperatures of −5 and −15°C suggest possible secondary ice production (SIP). The analysis alludes to an overestimation of SIP occurrence and intensity, especially in studies relying only on radar or lidar measurements. Finally, reflectivity‐dependent ice precipitation rate and ice water content parameterizations are presented.

Extension of Methane Emission Rate Distribution for Permian Basin Oil and Gas Production Infrastructure by Aerial LiDAR.

Aerial LiDAR measurements at 7474 oil and gas production facilities in the Permian Basin yield a measured methane emission rate distribution extending to the detection sensitivity of the method, 2 kg/h at 90% probability of detection (POD). Emissions are found at 38.3% of facilities scanned, a significantly higher proportion than reported in lower-sensitivity campaigns. LiDAR measurements are analyzed in combination with measurements of the heavy tail portion of the distribution (>600 kg/h) obtained from an airborne solar infrared imaging spectrometry campaign by Carbon Mapper (CM). A joint distribution is found by fitting the aligned LiDAR and CM data. By comparing the aerial samples to the joint distribution, the practical detection sensitivity of the CM 2019 campaign is found to be 280 kg/h [256, 309] (95% confidence) at 50% POD for facility-sized emission sources. With respect to the joint model distribution and its confidence interval, the LiDAR campaign is found to have measured 103.6% [93.5, 114.2%] of the total emission rate predicted by the model for equipment-sized emission sources (∼2 m diameter) with emission rates above 3 kg/h, whereas the CM 2019 campaign is found to have measured 39.7% [34.6, 45.1%] of the same quantity for facility-sized sources (150 m diameter) above 10 kg/h. The analysis is repeated with data from CM 2020-21 campaigns with similar results. The combined distributions represent a more comprehensive view of the emission rate distribution in the survey area, revealing the significance of previously underreported emission sources at rates below the detection sensitivity of some emissions monitoring campaigns.

Doppler Lidar Measurements of Wind Variability and LLJ Properties in Central Oklahoma during the August 2017 Land–Atmosphere Feedback Experiment

Abstract Low-level jets (LLJs) are an important nocturnal source of wind energy in the U.S. Great Plains. An August 2017 lidar-based field-measurement campaign [the Land–Atmosphere Feedback Experiment (LAFE)] studied LLJs over the central SGP site in Oklahoma and found nearly equal occurrences of the usual southerly jets and postfrontal northeasterly jets—typically rare during this season—for an opportunity to compare the two types of LLJs during this month. Southerly winds were stronger than the northeasterlies by more than 4 m s−1 on average, reflecting a significantly higher frequency of winds stronger than 12 m s−1. The analysis of this dataset has been expanded to other SGP Doppler-lidar sites to quantify the variability of winds and LLJ properties between sites of different land use. Geographic variations of winds over the study area were noted: on southerly wind nights, the winds blew stronger at the highest, westernmost sites by 2 m s−1, whereas on the northeasterly flow nights, the easternmost sites had the strongest wind speeds. Lidar measurements at 5 sites during August 2017, contrasted to the 2016–21 summertime data, revealed unusual wind and LLJ conditions. Temporal hodographs using hourly averaged winds at multiple heights revealed unorganized behavior in the turbulent stable boundary layer (SBL) below the jet nose. Above the nose, some nights showed veering qualitatively similar to inertial oscillation (IO) behavior, but at amplitudes much smaller than expected for an IO, whereas other nights showed little veering. Vertical hodographs had a linear shape in the SBL, indicating little directional shear there, and veering above, resulting in a hook-shaped hodograph with height. Significance Statement Doppler-lidar measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) were used to quantify the variability of winds and low-level jet (LLJ) properties between five sites of different land use and wind regimes across this area. Knowledge of wind and LLJ structure and dynamics is important for many applications, and strong southerly LLJ winds at night are an important resource for wind energy. The analysis of multiyear (2016–21) summertime LLJ parameters provided insight into the LLJ climatology in this part of the Great Plains.

Self-Supervised Depth Correction of Lidar Measurements From Map Consistency Loss

Depth perception is considered an invaluable source of information in the context of 3D mapping and various robotics applications. However, point cloud maps acquired using consumer-level <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">light detection and ranging</i> sensors (lidars) still suffer from bias related to local surface properties such as measuring beam-to-surface incidence angle. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned depth sensors error while preserving geometric and map consistency details. Despite the effort, depth correction of lidar measurements is still an open challenge mainly due to the lack of clean 3D data that could be used as ground truth. In this paper, we introduce two novel point cloud map consistency losses, which facilitate self-supervised learning on real data of lidar depth correction models. Specifically, the models exploit multiple point cloud measurements of the same scene from different view-points in order to learn to reduce the bias based on the constructed map consistency signal. Complementary to the removal of the bias from the measurements, we demonstrate that the depth correction models help to reduce localization drift. Additionally, we release a data set that contains point cloud scans captured in an indoor corridor environment with precise localization and ground truth mapping information <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{1}$</tex-math></inline-formula> .

A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines

Wind turbine aerodynamic interactions within wind farms lead to significant energy losses. Optimizing the flow between turbines presents a promising solution to mitigate these losses. While analytical models offer a fundamental approach to understanding aerodynamic interactions, further development and refinement of these models are imperative. We propose a simplified analytical model that combines the Gaussian wake model and the cylindrical vortex induction model to evaluate the interaction between wake and induction zones in 3.5 MW wind turbines with 328 m spacing. The model’s validation is conducted using field data from a nacelle-mounted LiDAR system on the downstream turbine. The ‘Direction to Hub’ parameter facilitates a comparison between the model predictions and LiDAR measurements at distances ranging from 50 m to 300 m along the rotor axis. Overall, the results exhibit reasonable agreement in flow trends, albeit with discrepancies of up to 15° in predicting peak interactions. These deviations are attributed to the single-hat Gaussian shape of the wake model and the absence of wake expansion consideration, which can be revisited to improve model fidelity. The ‘Direction to Hub’ parameter proves valuable for model validation and LiDAR calibration, enabling a detailed flow analysis between turbines. This analytical modeling approach holds promise for enhancing wind farm efficiency by advancing our understanding of turbine interactions.

Validation activities of Aeolus wind products on the southeastern Iberian Peninsula

Abstract. In 2018, the European Space Agency launched the first Doppler wind lidar system into space, providing wind observation profiles from the lower stratosphere down to the surface in two different channels based on the scene classification: cloudy or clear. A statistical validation campaign of Aeolus wind products has been performed with a ground-based Doppler lidar system and radiosondes at the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS)–Andalusian Global ObseRvatory of the Atmosphere (AGORA) facility in Granada (Spain). The validation activities with the automatic ground-based lidar system lasted from July 2019 to the orbit shift of June 2021. Aeolus data from two different processing baselines (10 and 11) were validated with 30 min averages of coincident ground-based lidar measurements, using a 100 km horizontal spatial collocation criterion. This resulted in 109 collocations and a mean observation distance from the ground-based lidar system of ∼50 km. The comparison did not raise any significant over- or underestimation of Aeolus horizontal line-of-sight wind speed during that period for the Rayleigh-clear and Mie-cloudy configurations. However, the ground-based lidar measurements were limited to the lower 3.5 km of the atmosphere and, consequently, the obtained results. Multiple analyses were performed varying the criteria of maximum distance and the average period for the ground-based lidar measurements in order to confirm the reliability of the criteria considered. A separate study was performed with Aeolus products after the orbit shift (baseline 12) with different collocation criteria (mean observation distance of ∼75 km, to the station), from July 2021 to May 2022. A set of seven radiosondes were launched with the aim of increasing their coincidence in space and time with the satellite overpass (∼30 min before). The radiosondes could provide full vertical coverage of Aeolus profiles (from the surface up to ∼20 km above sea level), and the comparison did not yield any significant over- or underestimation of the Rayleigh-clear wind speed, while the Mie-cloudy wind speed was significantly overestimated. Multiple analyses were performed in order to test how the spatiotemporal collocation of the radiosonde affected the results. Radiosondes not ideally collocated were proven to still be useful for comparison with the Rayleigh-clear observations but not with the Mie-cloudy observations.

Validation protocol for the evaluation of space-borne lidar particulate back-scattering coefficient bbp

Introduction: Space-borne lidar measurements from sensors such as CALIOP were recently used to retrieve the particulate back-scattering coefficient, bbp, in the upper ocean layers at a global scale and those observations have a strong potential for the future of ocean color with depth-resolved observations thereby complementing the conventional ocean color remote sensed observations as well as overcoming for some of its limitations. It is critical to evaluate and validate the space-borne lidar measurements for ocean applications as CALIOP was not originally designed for ocean applications. Few validation exercises of CALIOP were published and each exercise designed its own validation protocol. We propose here an objective validation protocol that could be applied to any current and future space-borne lidars for ocean applications.Methods: We, first, evaluated published validation protocols for CALIOP bbp product. Two published validation schemes were evaluated in our study, by using in-situ measurements from the BGC-Argo floats. These studies were either limited to day- or nighttime, or by the years used or by the geographical extent. We extended the match-up exercise to day-and nighttime observations and for the period 2010–2017 globally. We studied the impact of the time and distance differences between the in-situ measurements and the CALIOP footprint through a sensitivities study. Twenty combinations of distance (from 9-km to 50-km) and time (from 9 h to 16 days) differences were tested.Results &amp;amp; Discussion: A statistical score was used to objectively selecting the best optimal timedistance windows, leading to the best compromise in term of number of matchups and low errors in the CALIOP product. We propose to use either a 24 h/9 km or 24 h/15 km window for the evaluation of space-borne lidar oceanic products.

Investigating an indirect aviation effect on mid-latitude cirrus clouds – linking lidar-derived optical properties to in situ measurements

Abstract. Aviation has a large impact on the Earth's atmosphere and climate by various processes. Line-shaped contrails and contrail cirrus clouds lead to changes in the natural cirrus cloud cover and have a major contribution to the effective radiative forcing from aviation. In addition, aviation-induced aerosols might also change the microphysical properties and optical properties of naturally formed cirrus clouds. Latter aerosol–cloud interactions show large differences in the resulting effective radiative forcing, and our understanding on how aviation-induced aerosols affect cirrus cloud properties is still poor. Up to now, observations of this aviation-induced aerosol effect have been rare. In this study, we use combined airborne lidar and in situ ice cloud measurements to investigate differences in the microphysical and optical properties of naturally formed cirrus clouds, which formed in regions that are highly affected by aviation-induced aerosol emissions and, of those, which formed in regions rather unaffected by aviation. Urbanek et al. (2018) showed that those cirrus clouds, which are more affected by aviation-induced soot emission, are characterized by larger values of the particle linear depolarization ratio (PLDR). In this follow-on study we relate collocated lidar measurements performed aboard HALO during the ML-CIRRUS mission of the particle linear depolarization ratio with in situ cloud probe measurements of the number concentration and effective diameter of the ice particles. In situ measurements for both cloud types (high-PLDR-mode – aviation-affected – and low-PLDR-mode – pristine – cirrus) can be reliably compared in a temperature range between 210 and 215 K. Within this temperature range we find that high-PLDR-mode cirrus clouds tend to show larger effective ice particle diameters with a median value of 61.4 compared to 50.7 µm for low-PLDR-mode pristine cirrus clouds. Larger effective ice particles in aviation-influenced (high-PLDR-mode) cirrus are connected to lower ice particle number concentration with a median value of 0.05 compared to 0.11 cm−3 (low-PLDR-mode), which evolved in more pristine regions with only little impact from aviation. We suspect that a suppression of homogeneous ice formation by the heterogeneously freezing soot aerosol particles included in the areas affected by air traffic is the cause of the reduced ice crystal concentrations.

GROUNDED: A localizing ground penetrating radar evaluation dataset for learning to localize in inclement weather

Mapping and localization using surface features is prone to failure due to environment changes such as inclement weather. Recently, Localizing Ground Penetrating Radar (LGPR) has been proposed as an alternative means of localizing using underground features that are stable over time and less affected by surface conditions. However, due to the lack of commercially available LGPR sensors, the wider research community has been largely unable to replicate this work or build new and innovative solutions. We present GROUNDED, an open dataset of LGPR scans collected in a variety of environments and weather conditions. By labeling these data with ground truth localization from an RTK-GPS/Inertial Navigation System, and carefully calibrating and time-synchronizing the radar scans with ground truth positions, camera imagery, and lidar data, we enable researchers to build novel localization solutions that are resilient to changing surface conditions. We include 108 individual runs totaling 450 km of driving with LGPR, GPS, odometry, camera, and lidar measurements. We also present two new evaluation benchmarks for 1) localizing in weather and 2) multi-lane localization, to enable comparisons of future work supported by the dataset. Additionally, we present a first application of the new dataset in the form of LGPRNet: an inception-based CNN architecture for learning localization that is resilient to changing weather conditions. The dataset can be accessed at http://lgprdata.com .

Spatio-Temporal Fusion of LiDAR and Camera Data for Omnidirectional Depth Perception

Object recognition and depth perception are two tightly coupled tasks that are indispensable for situational awareness. Most autonomous systems are able to perform these tasks by processing and integrating data streaming from a variety of sensors. The multiple hardware and sophisticated software architectures required to operate these systems makes them expensive to scale and operate. This paper implements a fast, monocular vision system that can be used for simultaneous object recognition and depth perception. We borrow from the architecture of a start-of-the-art object recognition system, YOLOv3, and extend its architecture by incorporating distances and modifying its loss functions and prediction vectors to enable it to multitask on both tasks. The vision system is trained on a large database acquired through the coupling of LiDAR measurements with complementary 360-degree camera to generate a high-fidelity labeled dataset. The performance of the multipurpose network is evaluated on a test dataset consisting of a total of 7,634 objects collected on a different road network. When compared with ground truth LiDAR data, the proposed network achieves a mean absolute percentage error rate of 11% on the passenger car within 10 m and a mean error rate of 7% or 9% on the truck within 10 m and beyond 10 m, respectively. It was also observed that adding a second task (depth perception) to the modeling network improved the accuracy of object detection by about 3%. The proposed multipurpose model can be used for the development of automated alert systems, traffic monitoring, and safety monitoring.

Multi-campaign ship and aircraft observations of marine cloud condensation nuclei and droplet concentrations

In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns: ACTIVATE; NAAMES; CAMP2EX; ORACLES; SOCRATES; MARCUS; and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, represent a range of clean to polluted conditions in various climate regimes. With the extensive range of environmental conditions sampled, this data collection is ideal for testing satellite remote detection methods of CDNC and CCN in marine environments. Remote measurement methods are vital to expanding the available data in these difficult-to-reach regions of the Earth and improving our understanding of aerosol-cloud interactions. The data collection includes particle composition and continental tracers to identify potential contributing CCN sources. Several of these campaigns include High Spectral Resolution Lidar (HSRL) and polarimetric imaging measurements and retrievals that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.

Particle Microphysical Parameters and the Complex Refractive Index from 3β + 2α HSRL/Raman Lidar Measurements: Conditions of Accurate Retrieval, Retrieval Uncertainties and Constraints to Suppress the Uncertainties

We study retrieval methods in regard to their potential to accurately retrieve particle microphysical parameters (PMP) from 3β + 2α HSRL/Raman lidar measurements. PMPs estimated with these methods are number, surface-area and volume concentrations, the effective radius, and complex refractive index of the investigated particle size distribution (PSD). The 3β + 2α optical data are particle backscatter coefficients at 355, 532 and 1064 nm and extinction coefficients at 355 and 532 nm. We present results that are fundamental for our understanding of how uncertainties of the optical data convert into uncertainties of PMPs. PMPs can only be retrieved with preset accuracy if the input optical data are accurate to at least eight significant digits, i.e., 10−6%. Such measurement accuracy cannot be achieved by currently existing lidar measurement techniques and the fact that atmospheric conditions are not static during lidar observations. Our analysis of the results derived with the novel approach shows that (a) the uncertainty of the retrieved surface-area concentration increases proportionally to the measurement uncertainty of the extinction coefficient at 355 nm, (b) the uncertainty of the effective radius is inversely proportional to the measurement uncertainty of the extinction-related Ångström exponent, (c) the uncertainty of volume concentration is close to the one of the effective radius, and (d) the uncertainty of number concentration is proportional to the inverse of the square value of the uncertainty of the effective radius. The complex refractive index (CRI) cannot be estimated without introducing extra constraints, even if measurement uncertainties of the optical data are as low as 1−3%. We tested constraints and their impact on the solution space, and in how far these constraints could allow us to restrict the retrieval uncertainties. For example, we used information about relative humidity that can be measured with Raman lidar. Relative humidity is an important piece of information that allows for more accurate aerosol typing and thus plays a vital role in any kind of aerosol characterization. The measurement example we used in this study shows that such a constraint can reduce the retrieval uncertainty of single scattering albedo (SSA) to as low as ±0.01–±0.025 (at 532 nm), on the condition that the uncertainty of the input optical data stays below 15%. The results will be used for uncertainty analysis of data products provided by future versions of the Tikhonov Advanced Regularization Algorithm (TiARA). This algorithm has evolved into a standard tool for the derivation of microphysical particle properties from multiwavelength High-Spectral-Resolution Lidar (HSRL)/Raman lidar operated in Europe, East Asia, and the US.

Effects of boundary layer dynamics and meteorology on ultrafine particle formation and growth

It is currently not well understood how the planetary boundary layer (PBL) dynamics affects the formation and growth of ultrafine particles in the atmosphere, because of the limited co-located aerosol size distributions and lidar measurements. Here we show, from long-term in-situ observations, a clear link between boundary layer processes and surface aerosol dynamics. Aerosol size distributions in the diameter range from 7 to 300 nm were measured with a scanning mobility particle sizer (SMPS) at the University of Alabama Huntsville campus in 2016, 2017, and 2022. The backscatter of larger aerosols was measured using a co-located lidar ceilometer. Meteorological surface parameters including temperature, relative humidity, station pressure, wind speed and direction, solar radiation, and precipitation were also measured. New particle formation (NPF) and long growth (over 12 h) events occurred most frequently during the winter, unlike many other locations where NPF events occur more frequently in spring and fall, indicating that cooler temperatures play important roles in ultrafine particle formation in the warm subtropical environment. During the summer with strong solar radiation, there were more short-time growth events (less than 6 h), likely associated with convection within the boundary layer. Our observations also show cold fronts affect the measured aerosol size distributions because the combination of precipitation scavenging and cleaner air can provide thermodynamic conditions favorable for NPF and aerosol growth. These results highlight the importance of meteorology on aerosol formation and growth.

Rotor equivalent wind speed prediction based on mechanism analysis and residual correction using Lidar measurements

Efficient power generation and reduced operational cost are crucial to make wind energy a successful market player. To achieve satisfactory performance in wind turbine technology, it is essential to accurately predict the rotor equivalent wind speed (REWS). Since traditional wind transducers are difficult to measure multiple-point wind speed, related researches on predicting REWS are lacking. In this paper, a hybrid REWS prediction framework is proposed, which combines mechanism analysis and data-driven technique, aided by Lidar measurements. The framework employs the mechanism modeling and time delay analysis to achieve preliminary REWS prediction, but the accuracy is restricted by the quantity of Lidar measurement points. To enhance the prediction accuracy, a residual prediction model based on the data-driven technique is created to correct the mechanism prediction. Specifically, a novel combined modeling approach based on empirical mode decomposition and sample entropy algorithms is presented. Through reconstructing the input data of the combined model into high, middle, and low frequency components and exploring the prediction laws of different frequency components obtained by typical machine learning models, the combined residual prediction model is established. Additionally, an enhanced whale optimization algorithm named WOAmM is presented to optimize the parameters of the combined model. The proposed method is validated through a four-beam Lidar measurement scenario simulated by BLADED software. The experimental results indicate that the combined model outperforms each single model and the mechanism prediction, and improves the REWS prediction accuracy by up to75.04%, 60.92%, 51.85%, and 57.52% in the four datasets, respectively. The proposed method could facilitate the utilization of Lidar in improving the performance of wind turbines.

Seasonal distribution of cirrus cloud characteristics and their rapid descent from polarization lidar measurements at the west coast of India

Seasonal distribution of cirrus cloud characteristics and their rapid descent from polarization lidar measurements at the west coast of India