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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Accelerated Training for Large Feedforward Neural Networks

In this paper we introduce a new training algorithm, the scaled variable metric (SVM) method. Our approach attempts to increase the convergence rate of the modified variable metric method. It is also combined with the RBackprop algorithm, which computes the product of the matrix of second derivatives (Hessian) with an arbitrary vector. The RBackprop method allows us to avoid computationally expensive, direct line searches. In addition, it can be utilized in the new, 'predictive' updating technique of the inverse Hessian approximation. We have used directional slope testing to adjust the step size and found that this strategy works exceptionally well in conjunction with the Rbackprop algorithm. Some supplementary, but nevertheless important enhancements to the basic training scheme such as improved setting of a scaling factor for the variable metric update and computationally more efficient procedure for updating the inverse Hessian approximation are presented as well. We summarize by comparing the SVM method with four first- and second- order optimization algorithms including a very effective implementation of the Levenberg-Marquardt method. Our tests indicate promising computational speed gains of the new training technique, particularly for large feedforward networks, i.e., for problems where the training process may be the most laborious.

Stepniewski, Slawomir W.↗
NASA NTRS2017

Remote Sensing of Precipitation from Airborne and Spaceborne Radar

Weather radar measurements from airborne or satellite platforms can be an effective remote sensing tool for examining the three-dimensional structures of clouds and precipitation. This chapter describes some fundamental properties of radar measurements and their dependence on the particle size distribution (PSD) and radar frequency. The inverse problem of solving for the vertical profile of PSD from a profile of measured reflectivity is stated as an optimal estimation problem for single- and multi-frequency measurements. Phenomena that can change the measured reflectivity Z(sub m) from its intrinsic value Z(sub e), namely attenuation, non-uniform beam filling, and multiple scattering, are described and mitigation of these effects in the context of the optimal estimation framework is discussed. Finally, some techniques involving the use of passive microwave measurements to further constrain the retrieval of the PSD are presented.

cloud↗
DOE OSTI2021

Human-automated vehicle interactions

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗
DOE OSTI2021

HUMAN-AUTOMATED VEHICLE INTERACTIONS

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗
DOE OSTI2024

(Doublon) Benchmarking of Different Inverse Point Kinetics Implementations for an Autocorrected Reactimeter Algorithm

In November 2017, the Transient Reactor Test Facility returned to operation. Since that time, many transient test series have been completed, such as the Transient Heatsink Overpower Response capsule (THOR), the Transient Water Irradiation System for TREAT (TWIST), and Sirius. Each has provided valuable data for materials performance and reactor safety that can be applied in future designs. During each experimental series, detector count rates provided important information on the core behavior during transients. However, a limitation of these data is that variations in the neutron distribution during experiments can cause errors when attempting to infer reactivity evolution from detector signals. Neutron physics codes can be used to compute the flux shape variations. However, this is a poor solution when the experimental data is used for code verification, validation and uncertainty quantification. Indeed, if the output of the code is used both as a reference and to correct what the reference is compared to, the circular dependency limits the quality of the verification, validation and uncertainty quantification approach. To overcome this problem, the autocorrected reactimeter algorithm (ACRA) has been developed. This approach infers a time-dependent reactivity evolution by testing different spatial corrections and selecting the one that minimizes reactivity variations when the core is in a frozen configuration (i.e., when there is no variation in parameters affecting reactivity). However, the scope of this method was limited to transients where there were negligible thermal feedback. Indeed, the core is never in a frozen configuration when the fuel temperature varies during the whole transient. This is our motivation for developing an improved version of the ACRA that does not require frozen configurations. To develop this new algorithm, we need a precise and unbiased implementation of the inverse point kinetic equations (IPKEs) as any error in the reactivity evaluation will be propagated into the choice of the optimal spatial correction. Indeed, the previous reactimeter algorithm would use approximations, such as a negligible flux amplitude derivative, to focus on rapidity. For the numerical validation of ACRA, we aim at absolute error under for reactivity derived from signals similar to the one of this study. In this summary, we test eight different IPKE implementations. Each will process a mockup signal built for this study, similar to those that the future ACRA will process. Each reactivity output will be compared to the reference reactivity that has been used to generate the mockup signal. The implementation minimizing the difference with the reference reactivity will be used in the development of a new ACRA formulation.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Exploring the effects of data quality, data worth, and redundancy of CO2 gas pressure and saturation data on reservoir characterization through PEST Inversion

This study examined the impacts of reservoir properties on CO2 migration after subsurface injection and evaluated the possibility of characterizing reservoir properties using CO2 monitoring data such as saturation distribution. The injection reservoir was assumed to be located 1400-1500 m below the ground surface such that CO2 remained in the supercritical state. The reservoir was assumed to contain layers with alternating conductive and resistive properties, which is analogous to actual geological formations such as the Mount Simon Sandstone unit. The CO2 injection simulation used a cylindrical grid setting in which the injection well was situated at the center of the domain, which extended up to 8000 m from the injection well. The CO2 migration was simulated using the PNNL-developed simulator STOMP-CO2e (the water-salt-CO2 module). We adopted a nonlinear parameter estimation and optimization modeling software package, PEST, for automated reservoir parameter estimation. We explored the effects of data quality, data worth, and data redundancy on the detectability of reservoir parameters using CO2 saturation monitoring data, by comparing PEST inversion results using data with different levels of noises, various numbers of monitoring wells and locations, and different data collection spacing and temporal sampling intervals. This study yielded insight into the use of CO2 saturation monitoring data for reservoir characterization and how to design the monitoring system to optimize data worth and reduce data redundancy.

Carbon sequestration↗
DOE OSTI2015

A general theory of interference fringes in x-ray phase grating imaging

Purpose: The authors note that the concept of the Talbot self-image distance in x-ray phase grating interferometry is indeed not well defined for polychromatic x-rays, because both the grating phase shift and the fractional Talbot distances are all x-ray wavelength-dependent. For x-ray interferometry optimization, there is a need for a quantitative theory that is able to predict if a good intensity modulation is attainable at a given grating-to-detector distance. In this work, the authors set out to meet this need. Methods: In order to apply Fourier analysis directly to the intensity fringe patterns of two-dimensional and one-dimensional phase grating interferometers, the authors start their derivation from a general phase space theory of x-ray phase-contrast imaging. Unlike previous Fourier analyses, the authors evolved the Wigner distribution to obtain closed-form expressions of the Fourier coefficients of the intensity fringes for any grating-to-detector distance, even if it is not a fractional Talbot distance. Results: The developed theory determines the visibility of any diffraction order as a function of the grating-to-detector distance, the phase shift of the grating, and the x-ray spectrum. The authors demonstrate that the visibilities of diffraction orders can serve as the indicators of the underlying interference intensity modulation. Applying the theory to the conventional and inverse geometry configurations of single-grating interferometers, the authors demonstrated that the proposed theory provides a quantitative tool for the grating interferometer optimization with or without the Talbot-distance constraints. Conclusions: In this work, the authors developed a novel theory of the interference intensity fringes in phase grating x-ray interferometry. This theory provides a quantitative tool in design optimization of phase grating x-ray interferometers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Improving the Quasi‐Biennial Oscillation via a Surrogate‐Accelerated Multi‐Objective Optimization

Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross-correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate-based inference at a fraction of the computational cost of full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi-criteria trade-off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Asteroid models from photometry and complementary data sources

I discuss inversion methods for asteroid shape and spin reconstruction with photometry (lightcurves) and complementary data sources such as adaptive optics or other images, occultation timings, interferometry, and range-Doppler radar data. These are essentially different sampling modes (generalized projections) of plane-of-sky images. An important concept in this approach is the optimal weighting of the various data modes. The maximum compatibility estimate, a multi-modal generalization of the maximum likelihood estimate, can be used for this purpose. I discuss the fundamental properties of lightcurve inversion by examining the two-dimensional case that, though not usable in our three-dimensional world, is simple to analyze, and it shares essentially the same uniqueness and stability properties as the 3-D case. After this, I review the main aspects of 3-D shape representations, lightcurve inversion, and the inclusion of complementary data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2016

Consistent Evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC Through Comparisons to TCCON

Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (X(sub CO2)) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 parts per million vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to (error(sup 2) equals a(sup 2) plus b(sup 2) divided by n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of 0.3 parts per million, with biases larger than the TCCON predicted bias uncertainty of 0.4 parts per million at many stations. We find that GOSAT and CT2013b under-predict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53 degrees North latitude, MACC over-predicts between 26 and 37 degrees North latitude, and CT2013b under-predicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in June-July-August; there is correlation between 0.2 and 0.8 in the NH, with models showing 10-50 percent the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45-67 degrees North latitude for GOSAT and CT2013b.

ACOS-GOSAT↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2018

Applied Aspects of the Study of Thermodynamic Parameters of Oil Disperse Systems

For intensification of oil refining without using secondary processes, thermodynamic parameters of gas condensate and residual fuel oil blends were calculated from experimental investigations involving inverse gas chromatography. Apparently, from thermodynamic parameters of gas condensate and residual fuel oil blend, it is possible to determine the optimal ratio of the components for increasing the yield of light fractions. It is likely that the new colloidal structure of the blend of the disperse system (residual fuel oil) with the homogeneous system (gas condensates) forms via formation of a semicolloidal system followed by restructurization due to intermolecular interaction and dissolution processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Hybrid eigensolvers for nuclear configuration interaction calculations

We examine and compare several iterative methods for solving large-scale eigenvalue problems arising from nuclear structure calculations. In particular, we discuss the possibility of using block Lanczos method, a Chebyshev filtering based subspace iterations and the residual minimization method accelerated by direct inversion of iterative subspace (RMM-DIIS) and describe how these algorithms compare with the standard Lanczos algorithm and the locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm. Although the RMM-DIIS method does not exhibit rapid convergence when the initial approximations to the desired eigenvectors are not sufficiently accurate, it can be effectively combined with either the block Lanczos or the LOBPCG method to yield a hybrid eigensolver that has several desirable properties. We will describe a few practical issues that need to be addressed to make the hybrid solver efficient and robust.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

High gradient RF test results of S-band and C-band cavities for medical linear accelerators

TERA Foundation has proposed and designed hadrontherapy facilities based on novel linacs, i.e. high gradient linacs which accelerate either protons or light ions. The overall length of the linac, and therefore its cost, is almost inversely proportional to the average accelerating gradient. With the scope of studying the limiting factors for high gradient operation and to optimize the linac design, TERA, in collaboration with the CLIC Structure Development Group, has conducted a series of high gradient experiments. The main goals were to study the high gradient behavior and to evaluate the maximum gradient reached in 3 and 5.7 GHz structures to direct the design of medical accelerators based on high gradient linacs. Lastly, this paper summarizes the results of the high power tests of 3.0 and 5.7 GHz single-cell cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Inverse ZrO 2 /Cu as a highly efficient methanol synthesis catalyst from CO 2 hydrogenation

Enhancing the intrinsic activity and space time yield of Cu based heterogeneous methanol synthesis catalysts through CO 2 hydrogenation is one of the major topics in CO 2 conversion into value-added liquid fuels and chemicals. Here we report inverse ZrO 2 /Cu catalysts with a tunable Zr/Cu ratio have been prepared via an oxalate co-precipitation method, showing excellent performance for CO 2 hydrogenation to methanol. Under optimal condition, the catalyst composed by 10% of ZrO 2 supported over 90% of Cu exhibits the highest mass-specific methanol formation rate of 524 g MeOH kg cat -1 h -1 at 220°C, 3.3 times higher than the activity of traditional Cu/ZrO 2 catalysts (159 g MeOH kg cat -1 h -1 ). In situ XRD-PDF, XAFS and AP-XPS structural studies reveal that the inverse ZrO 2 /Cu catalysts are composed of islands of partially reduced 1–2 nm amorphous ZrO 2 supported over metallic Cu particles. The ZrO 2 islands are highly active for the CO 2 activation. Meanwhile, an intermediate of formate adsorbed on the Cu at 1350 cm -1 is discovered by the in situ DRIFTS. This formate intermediate exhibits fast hydrogenation conversion to methoxy. The activation of CO 2 and hydrogenation of all the surface oxygenate intermediates are significantly accelerated over the inverse ZrO 2 /Cu configuration, accounting for the excellent methanol formation activity observed.

36 MATERIALS SCIENCE↗
DOE OSTI2014

First principles transport coefficients and reaction rates of Ar{sub 2}{sup +} ions in argon for cold plasma jet modeling

Momentum-transfer collision cross-sections and integral collision cross-sections for the collision-induced dissociation are calculated for collisions of ionized argon dimers with argon atoms using a nonadiabatic semiclassical method with the electronic Hamiltonian calculated on the fly via a diatomics-in-molecules semiempirical model as well as inverse-method modeling based on simple isotropic rigid-core potential. The collision cross-sections are then used in an optimized Monte Carlo code for evaluations of the Ar{sub 2}{sup +} mobility in argon gas, longitudinal diffusion coefficient, and collision-induced dissociation rates. A thorough comparison of various theoretical calculations as well as with available experimental data on the Ar{sub 2}{sup +} mobility and collision cross-sections is performed. Good agreement is found between both theoretical approaches and the experiment. Analysis of the role of inelastic processes in Ar{sub 2}{sup +}/Ar collisions is also provided.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Material Design Strategies to Achieve Simultaneous High Power and High Energy Density

ABSTRACT Emerging applications require batteries to have both high energy and high power which are not necessarily compatible. The typical inverse relationship between power and energy in batteries is often due to the slow ion diffusion in electrode materials. While the optimization of current battery technology may be sufficient to fully address this issue, we present here that novel chemistry-focused strategies based on new fundamental understanding of materials may be applied to lead to the development of a new generation of batteries that store energy sufficiently and deliver it rapidly.

energy storage (including batteries and capacitors↗
DOE OSTI2024

A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations

Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote-sensing-based algorithm that quantifies the vertically resolved CCN number concentrations (N CCN ) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the lookup tables developed in this study, based on the observations from the Aerosol Robotic Network, to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors in lidar measurements. The retrieved optically equivalent particle size distributions and aerosol-type-dependent particle composition are utilized to calculate critical diameters using κ-Köhler theory and N CCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in N CCN . The potential of this algorithm is further evaluated by retrieving N CCN using airborne lidar from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and is validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the N CCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar – Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) – measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which helps to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

54 ENVIRONMENTAL SCIENCES↗