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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Extending MODIS Deep Blue Aerosol Retrieval Coverage to Cases of Absorbing Aerosols Above Clouds: First Results

Absorbing smoke or mineral dust aerosols above clouds (AAC) are a frequent occurrence in certain regions and seasons. Operational aerosol retrievals from sensors like MODIS omit AAC because they are designed to work only over cloud-free scenes. However, AAC can in principle be quantified by these sensors in some situations (e.g. Jethva et al., 2013; Meyer et al., 2013). We present a summary of some analyses of the potential of MODIS-like instruments for this purpose, along with two case studies using airborne observations from the Ames Airborne Tracking Sunphotometer (AATS; http://geo.arc.nasa.gov/sgg/AATS-website/) as a validation data source for a preliminary AAC algorithm applied to MODIS measurements. AAC retrievals will eventually be added to the MODIS Deep Blue (Hsu et al., 2013) processing chain.

deep blue↗
DOE OSTI2025

A worldwide aerosol phenomenology: Elemental and organic carbon in PM 2.5 and PM 10

Elemental carbon (EC), organic carbon (OC), and particulate matter (PM) concentrations in the inhalable (PM 10 ) and fine (PM 2.5 ) size fractions are measured worldwide, albeit with different analytical methods. These measurements from many researchers were collected and analyzed for Africa, America, Asia, and Europe for 2012–2019. EC/PM, OC/PM, and OC/EC ratios were examined based on region, site type, and season to infer potential sources and impacts. These analyses demonstrate that carbonaceous materials are important PM constituents throughout the world. Mean EC/PM ratios were lowest in PM 10 in Sahelian Africa and Europe (∼0.01), highest (>0.07) in PM 2.5 at urban sites in North America, South America, and Japan. Mean OC/PM ratios were lowest in PM 10 in the Sahel (∼0.06) and in PM 2.5 in China and Thailand (0.10), and highest in central and eastern Europe (∼0.3) and North America (∼0.4). OC/EC ratios were elevated in western and northern Europe, and at regional background sites in North America. EC/PM increased with PM 10 in Thailand, while OC/PM increased with higher PM mass in Thailand, India, and North America, highlighting the specific contribution of carbonaceous aerosols to PM pollution in these regions. At European and North American background sites, OC/EC ratios increased with PM mass. Higher OC/EC ratios in dry periods indicate influence of wildfires, prescribed burns, and secondary aerosol formation. Elevated wintertime EC/PM ratios coincide with residential heating in temperate climate zones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses

Physics-based models typically require an in-depth understanding of a phenomenon and assumptions of the underlying process(es), which are often hard to obtain in practice, whereas data-driven machine learning models learn the structure and patterns in the training data without any prior theoretical assumptions and then use inference to develop useful predictions. A novel machine learning-based algorithm has been previously developed for the prediction of black carbon mass absorption cross-section (MAC BC ) and applied to a variety of different atmospheric environments. In contrast to light scattering theories which require assumptions about the underlying physics, this algorithm uses time-series data of aerosol properties to estimate the temporally-varying MAC BC at 870 nm. Here, we analyze our algorithm and discuss the influence of aerosol optical properties (such as Ångström exponents and single scattering albedo) and chemical composition on the model outputs and the associated accuracy. Additionally, we conduct sensitivity analyses on our models to understand how the predictions change in response to different sets of input variables. Our support vector machine (SVM) for regression model is the least sensitive to variations in the input variables, although all models tend to exhibit a degradation to their accuracy when scattering Ångström exponents are less than one.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Performance of pumped counterflow virtual impactors to study aerosol interactions with laboratory generated warm clouds

Pumped Counterflow Virtual Impactors (PCVI) are designed to separate aerosols based on aerodynamic diameter, which is particularly useful for isolating cloud droplets and ice crystals from smaller particles. However, the PCVI transmission efficiency (TE) values reported in the literature show considerable variability, and little information is available on the TEs for cloud liquid droplets. Here, we determined the optimal flow conditions for PCVI sampling for different activation ratios from the MTU Pi-cloud chamber, highlighting the conditions that maximize droplet residual sampling while minimizing interstitial transmission. Even for a lower limit cloud activation ratio = 1:10, an add flow of 1.5 LPM achieves a residual fraction greater than 0.80, with a droplet TE of ∼15% for typical Pi-Chamber droplets with diameters between 3.5 to 10 µm. This framework can be adapted for field cloud measurements based on the cloud conditions of interest. TE for cloud droplets under flow conditions at which ∼99% of the unactivated submicron particles were removed was substantially lower between 8 and 16% than for supermicron dry particles (∼30 to 40% for 3 µm polystyrene latex spheres). In addition, we conducted a comparison of the performances of three similar PCVI units to assess repeatability for dry aerosols and cloud droplets. All three PCVIs performed similarly in rejecting submicron particles smaller than the desired cutoff diameter. One of the units showed lower TEs due to a misalignment of the internal orifice. We discuss a procedure that improved alignment and performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data

Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Not all types of secondary organic aerosol mix: two phases observed when mixing different secondary organic aerosol types

Abstract. Secondary organic aerosol (SOA) constitutes a large fraction of atmospheric aerosol. To assess its impacts on climate and air pollution, knowledge of the number of phases in internal mixtures of different SOA types is required. Atmospheric models often assume that different SOA types form a single phase when mixed. Here, we present visual observations of the number of phases formed after mixing different anthropogenic and biogenic SOA types. Mixing SOA types generated in environmental chambers with oxygen-to-carbon (O/C) ratios between 0.34 and 1.05, we found 6 out of 15 mixtures of two SOA types to result in two phase particles. We demonstrate that the number of phases depends on the difference in the average O/C ratio between the two SOA types (Δ(O/C)). Using a threshold Δ(O/C) of 0.47, we can predict the phase behavior of over 90 % of our mixtures, with one- and two-phase particles predicted for Δ(O/C)<0.47 and Δ(O/C)≥0.47, respectively. This threshold ΔO/C value provides a simple parameter to predict whether mixtures of fresh and aged SOA form one- or two-phase particles in the atmosphere. In addition, we show that phase-separated SOA particles form when mixtures of volatile organic compounds emitted from real trees are oxidized.

Mahrt, Fabian (ORCID:0000000270596765)↗
DOE OSTI2023

Increased importance of aerosol–cloud interactions for surface PM 2.5 pollution relative to aerosol–radiation interactions in China with the anthropogenic emission reductions

Surface fine particulate matter (PM 2.5 ) pollution can be enhanced by feedback processes induced by aerosol–radiation interactions (ARIs) and aerosol–cloud interactions (ACIs). Many previous studies have reported enhanced PM 2.5 concentrations induced by ARIs and ACIs for episodic events in China. However, few studies have examined the changes in the ARI- and ACI-induced PM 2.5 enhancements over a long period, though the anthropogenic emissions have changed substantially in the last decade. In this study, we quantify the ARI- and ACI-induced PM 2.5 changes for 2013–2021 under different meteorology and emission scenarios using the Weather Research and Forecasting model with Chemistry (WRF-Chem), and we investigate the driving factors behind the changes. Our results show that, in January 2013, when China suffered from the worst PM 2.5 pollution, the PM 2.5 enhancement induced by ARIs in eastern China (5.59 µg m −3 ) was larger than that induced by ACIs (3.96 µg m −3 ). However, the ACI-induced PM 2.5 enhancement showed a significantly smaller decrease ratio (51 %) than the ARI-induced enhancement (75 %) for 2013–2021, making ACIs more important for enhancing PM 2.5 concentrations in January 2021. Our analyses suggest that the anthropogenic emission reductions played a key role in this shift. Owing to only anthropogenic emission reductions, the ACI-induced PM 2.5 enhancement decreased by 43 % in January, which was lower than the decrease ratio of the ARI-induced enhancement (57 %). The relative change in ARI- and ACI-induced PM 2.5 enhancement in July was similar to the pattern observed in January, caused by anthropogenic emission reductions. The primary reason for this phenomenon is that the decrease in ambient PM 2.5 for 2013–2021 caused a disproportionately small decrease in the liquid water path (LWP) and an increase in the cloud effective radius (Re) under the condition of high PM 2.5 concentrations. Therefore, the surface solar radiation attenuation (and, hence, the boundary layer height reduction) caused by ACIs decreased slower than that caused by ARIs. Moreover, the lower decrease ratio of the ACI-induced PM 2.5 enhancement was dominated by the lower decrease ratio of ACI-induced secondary PM 2.5 component enhancement, which was additionally caused by the smaller decrease ratio of the air temperature reduction and the relative humidity (RH) increase. Our findings indicate that, with the decrease in ambient PM 2.5 , the ACI-induced PM 2.5 enhancement inevitably becomes more important. This needs to be considered in the formulation of control policies to meet the national PM 2.5 air quality standard.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

Vertical distribution of aerosol extinction cross section and inference of aerosol imaginary index in the troposphere by lidar technique

The paper reports on vertical profiles of aerosol extinction and backscatter in the troposphere which were obtained from multi zenith angle lidar measurements. It is reported that a direct slant path solution was found to be not possible due to horizontal inhomogeneity of the atmosphere. Attention is given to the use of a regression analysis with respect to zenith angle for a layer integration of the angle dependent lidar equation in order to determine the optical thickness and aerosol extinction-to-backscatter ratio for defined atmospheric layers and the subsequent evaluation of cross-section profiles.

Spinhirne, J. D.↗
NASA NTRS1986

Wavelength dependence of aerosol extinction coefficient for stratospheric aerosols

A simple empirical formula for the wavelength dependence of the aerosol extinction coefficient is proposed. The relationship between the constants in the formula and the variable parameter in the aerosol size distribution is explicitly expressed. Good agreement is found between the extinction coefficients calculated from the proposed formula and that calculated from Mie theory. The proposed expression is shown to be better than the Angstroem formula commonly used by atmospheric scientists.

Yue, Glenn K.↗
NASA NTRS1991

Theory of CW lidar aerosol backscatter measurements and development of a 2.1 microns solid-state pulsed laser radar for aerosol backscatter profiling

The performance and calibration of a focused, continuous wave, coherent detection CO2 lidar operated for the measurement of atmospheric backscatter coefficient, B(m), was examined. This instrument functions by transmitting infrared (10 micron) light into the atmosphere and collecting the light which is scattered in the rearward direction. Two distinct modes of operation were considered. In volume mode, the scattered light energy from many aerosols is detected simultaneously, whereas in the single particle mode (SPM), the scattered light energy from a single aerosol is detected. The analysis considered possible sources of error for each of these two cases, and also considered the conditions where each technique would have superior performance. The analysis showed that, within reasonable assumptions, the value of B(m) could be accurately measured by either the VM or the SPM method. The understanding of the theory developed during the analysis was also applied to a pulsed CO2 lidar. Preliminary results of field testing of a solid state 2 micron lidar using a CW oscillator is included.

Kavaya, Michael J.↗
NASA NTRS1991

Stratospheric aerosol and gas experiment III (SAGE III) aerosol and trace gas measurements for Earth Observing System (EOS)

The SAGE III instrument, the latest in a series of satellite-based instruments employing the self-calibrating solar occultation technique to monitor aerosols and trace gases in the atmosphere, and potential contributions to monitoring global change and other EOS objectives are described. Uses of these data are illustrated with SAGE I and II long-term ozone, aerosol, and water vapor data. The SAGE III instrument will improve the SAM II and SAGE data products with greater overall accuracy, and will provide the ability to extend these measurements over a greater height range. SAGE III will provide long-term self-calibrating global data sets from the midtroposphere to mesosphere, which will contribute greatly to the quantification and understanding of global change.

Mccormick, M. P.↗
NASA NTRS2002

Analysis of Atmospheric Aerosol Data Sets and Application of Radiative Transfer Models to Compute Aerosol Effects

This report is the final report for "Analysis of Atmospheric Aerosol Data Sets and Application of Radiative Transfer Models to Compute Aerosol Effects". It is a bibliographic compilation of 29 peer-reviewed publications (published, in press or submitted) produced under this Cooperative Agreement and 30 first-authored conference presentations. The tasks outlined in the various proposals are listed below with a brief comment as to the research performed. Copies of title/abstract pages of peer-reviewed publications are attached.

Schmid, Beat↗
NASA NTRS2005

Aerosol properties computed from aircraft-based observations during the ACE- Asia campaign: A case study of lidar ratio closure and aerosol radiative effects - 2

For a vertical profile with three distinct layers (marine boundary, pollution and dust), observed during the ACE-Asia campaign, we carried out a comparison between the modeled lidar ratio vertical profile and that obtained from collocated airborne NASA AATS-14 sunphotometer and shipborne Micro-Pulse Lidar (MPL) measurements. Vertically resolved lidar ratio was calculated from two size distribution vertical profiles - one obtained by inversion of sunphotometer-derived extinction spectra, and one measured in-situ - combined with the same refractive index model based on aerosol chemical composition. The aerosol model implies single scattering albedos of 0.78 - 0.81 and 0.93 - 0.96 at 0.523 microns (the wavelength of the lidar measurements), in the pollution and dust layers, respectively. The lidar ratios calculated from the two size distribution profiles have close values in the dust layer; they are however, significantly lower than the lidar ratios derived from combined lidar and sunphotometer measurements, most probably due to the use of a simple nonspherical model with a single particle shape in our calculations. In the pollution layer, the two size distribution profiles yield generally different lidar ratios. The retrieved size distributions yield a lidar ratio which is in better agreement with that derived from lidar/sunphotometer measurements in this layer, with still large differences at certain altitudes (the largest relative difference was 46%). We explain these differences by non-uniqueness of the result of the size distribution retrieval and lack of information on vertical variability of particle refractive index. Radiative transfer calculations for this profile showed significant atmospheric radiative forcing, which occurred mainly in the pollution layer. We demonstrate that if the extinction profile is known then information on the vertical structure of absorption and asymmetry parameter is not significant for estimating forcing at TOA and the surface, while it is of importance for estimating vertical profiles of radiative forcing and heating rates.

Kuzmanoski, Maja↗
NASA NTRS2012

A Fast and Efficient Version of the TwO-Moment Aerosol Sectional (TOMAS) Global Aerosol Microphysics Model

This study develops more computationally efficient versions of the TwO-Moment Aerosol Sectional (TOMAS) microphysics algorithms, collectively called Fast TOMAS. Several methods for speeding up the algorithm were attempted, but only reducing the number of size sections was adopted. Fast TOMAS models, coupled to the GISS GCM II-prime, require a new coagulation algorithm with less restrictive size resolution assumptions but only minor changes in other processes. Fast TOMAS models have been evaluated in a box model against analytical solutions of coagulation and condensation and in a 3-D model against the original TOMAS (TOMAS-30) model. Condensation and coagulation in the Fast TOMAS models agree well with the analytical solution but show slightly more bias than the TOMAS-30 box model. In the 3-D model, errors resulting from decreased size resolution in each process (i.e., emissions, cloud processing wet deposition, microphysics) are quantified in a series of model sensitivity simulations. Errors resulting from lower size resolution in condensation and coagulation, defined as the microphysics error, affect number and mass concentrations by only a few percent. The microphysics error in CN70CN100 (number concentrations of particles larger than 70100 nm diameter), proxies for cloud condensation nuclei, range from 5 to 5 in most regions. The largest errors are associated with decreasing the size resolution in the cloud processing wet deposition calculations, defined as cloud-processing error, and range from 20 to 15 in most regions for CN70CN100 concentrations. Overall, the Fast TOMAS models increase the computational speed by 2 to 3 times with only small numerical errors stemming from condensation and coagulation calculations when compared to TOMAS-30. The faster versions of the TOMAS model allow for the longer, multi-year simulations required to assess aerosol effects on cloud lifetime and precipitation.

Lee, Yunha↗
NASA NTRS2019

Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements

The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.

David M. Giles↗
NASA NTRS2015

Constraining Black Carbon Aerosol over Asia using OMI Aerosol Absorption Optical Depth and the Adjoint of GEOS-Chem

Accurate estimates of the emissions and distribution of black carbon (BC) in the region referred to here as Southeastern Asia (70degE-l50degE, 11degS-55degN) are critical to studies of the atmospheric environment and climate change. Analysis of modeled BC concentrations compared to in situ observations indicates levels are underestimated over most of Southeast Asia when using any of four different emission inventories. We thus attempt to reduce uncertainties in BC emissions and improve BC model simulations by developing top-down, spatially resolved, estimates of BC emissions through assimilation of OMI observations of aerosol absorption optical depth (AAOD) with the GEOS-Chem model and its adjoint for April and October of 2006. Overwhelming enhancements, up to 500%, in anthropogenic BC emissions are shown after optimization over broad areas of Southeast Asia in April. In October, the optimization of anthropogenic emissions yields a slight reduction (1-5%) over India and parts of southern China, while emissions increase by 10-50% over eastern China. Observational data from in situ measurements and AERONET observations are used to evaluate the BC inversions and assess the bias between OMI and AERONET AAOD. Low biases in BC concentrations are improved or corrected in most eastern and central sites over China after optimization, while the constrained model still underestimates concentrations in Indian sites in both April and October, possibly as a. consequence of low prior emissions. Model resolution errors may contribute up to a factor of 2.5 to the underestimate of surface BC concentrations over northern India. We also compare the optimized results using different anthropogenic emission inventories and discuss the sensitivity of top-down constraints on anthropogenic emissions with respect to biomass burning emissions. In addition, the impacts of brown carbon, the formulation of the observation operator, and different a priori constraints on the optimization are investigated. Overall, despite these limitations and uncertainties, using OMI AAOD to constrain BC sources improves model representation of BC distributions, particularly over China.

Aerosols↗