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Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests that both root mean square error (RMSE) and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not outperform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud-free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly developed modeling system contains the necessary ingredients for assimilation of radiances in the ultraviolet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from the TROPOspheric Monitoring Instrument (TROPOMI), the Ozone Mapping and Profiler Suite (OMPS), and eventually the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.

Current cloud and aerosol identification methods for multispectral radiometers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), employ multichannel spectral tests on individual pixels (i.e., fields of view). The use of the spatial information in cloud and aerosol algorithms has been primarily through statistical parameters such as nonuniformity tests of surrounding pixels with cloud classification provided by the multispectral microphysical retrievals such as phase and cloud top height. With these methodologies there is uncertainty in identifying optically thick aerosols, since aerosols and clouds have similar spectral properties in coarse-spectral-resolution measurements. Furthermore, identifying clouds regimes (e.g., stratiform, cumuliform) from just spectral measurements is difficult, since low-altitude cloud regimes have similar spectral properties. Recent advances in computer vision using deep neural networks provide a new opportunity to better leverage the coherent spatial information in multispectral imagery. Using a combination of machine learning techniques combined with a new methodology to create the necessary training data, we demonstrate improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types. The labeled training dataset was created from an adapted NASA Worldview platform that provides an efficient user interface to assemble a human-labeled database of cloud and aerosol types. The convolutional neural network (CNN) labeling accuracy of aerosols and cloud types was quantified using independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and MODIS cloud and aerosol products. By harnessing CNNs with a unique labeled dataset, we demonstrate the improvement of the identification of aerosols and distinct cloud types from MODIS and VIIRS images compared to a per-pixel spectral and standard deviation thresholding method. The paper concludes with case studies that compare the CNN methodology results with the MODIS cloud and aerosol products.

Tropical cyclones (TCs) are among the most destructive natural phenomena with huge societal and economic impact. They form and evolve as the result of complex multiscale processes and nonlinear interactions. Even today the understanding and modeling of these processes is still lacking. A major goal of NASA is to bring the wealth of satellite and airborne observations to bear on addressing the unresolved scientific questions and improving our forecast models. Despite their significant amount, these observations are still underutilized in hurricane research and operations due to the complexity associated with finding and bringing together semicoincident and semicontemporaneous multiparameter data that are needed to describe the multiscale TC processes. Such data are traditionally archived in different formats, with different spatiotemporal resolution, across multiple databases, and hosted by various agencies. To address this shortcoming, NASA supported the development of the Jet Propulsion Laboratory (JPL) Tropical Cyclone Information System (TCIS)—a data analytic framework that integrates model forecasts with multiparameter satellite and airborne observations, providing interactive visualization and online analysis tools. TCIS supports interrogation of a large number of atmospheric and ocean variables, allowing for quick investigation of the structure of the tropical storms and their environments. This paper provides an overview of the TCIS’s components and features. It also summarizes recent pilot studies, providing examples of how the TCIS has inspired new research, helping to increase our understanding of TCs. The goal is to encourage more users to take full advantage of the novel capabilities. TCIS allows atmospheric scientists to focus on new ideas and concepts rather than painstakingly gathering data scattered over several agencies.

The South China Sea (SCS) is a receptor of numerous natural and anthropogenic aerosol species from throughout greater Asia. A combination of several developing countries, archipelagic and peninsular terrain, a strong Asian monsoon climate, and a host of multi-scale meteorological phenomena make the SCS one of the most complex aerosol–meteorological systems in the world. However, aside from the well-known biomass burning emissions from Indonesia and Borneo, the current understanding of aerosol sources is limited, especially in remote marine environments. In September 2011, a 2-week research cruise was conducted near Palawan, Philippines, to sample the remote SCS environment. Size-segregated aerosol data were collected using a Davis Rotating Uniform size-cut Monitor (DRUM) sampler and analyzed for concentrations of 28 elements measured via X-ray fluorescence (XRF). Positive matrix factorization (PMF) was performed separately on the coarse, fine, and ultrafine size ranges to determine possible sources and their contributions to the total elemental particulate matter mass. The PMF analysis resolved six sources across the three size ranges: biomass burning, oil combustion, soil dust, a crustal–marine mixed source, sea spray, and fly ash. Additionally, size distribution plots, time series plots, back trajectories and satellite data were used in interpreting factors. The multi-technique source apportionment revealed the presence of biogenic sources such as soil dust, sea spray, and a crustal–marine mixed source. Anthropogenic sources were also identified: biomass burning, oil combustion, and fly ash. Mass size distributions showed elevated aerosol concentrations towards the end of the sampling period, which coincided with a shift of air mass back trajectories to southern Kalimantan. Covariance between coarse-mode soil dust and fine-mode biomass burning aerosols were observed. Agreement between the PMF and the linear regression analyses indicates that the PMF solution is robust. While biomass burning is indeed a key source of aerosol, this study shows the presence of other important sources in the SCS. Identifying these sources is not only key for characterizing the chemical profile of the SCS but, by improving our picture of aerosol sources in the region, also a step forward in developing our understanding of aerosol–meteorology feedbacks in this complex environment.

Lofted mineral dust over data-sparse regions presents considerable challenges to satellite-based remote sensing methods and numerical weather prediction alike. The Southwest Asia domain is replete with such examples, with its diverse array of dust sources, dust mineralogy, and meteorologically-driven lofting mechanisms on multiple spatial and temporal scales. A microcosm of these challenges occurred over 3–4 August 2016 when two dust plumes, one lofted within an inland dry air mass and another embedded within a moist air mass, met over the Southern Arabian Peninsula. Whereas conventional infrared-based techniques readily detected the dry air mass dust plume, they experienced marked difficulties in detecting the moist air mass dust plume, which only became apparent when visible reflectance revealed it crossing over an adjacent dark water background. In combining information from numerical modelling, multi-satellite/multi-sensor observations of lofted dust and moisture profiles, and idealized radiative transfer simulations, we develop a better understanding of the environmental controls of this event, characterizing the sensitivity of infrared-based dust detection to column water vapor, dust vertical extent, and dust optical properties. Differences in assumptions of dust complex refractive index translate to variations in the sign and magnitude of the split-window brightness temperature difference commonly used for detecting mineral dust. A multi-sensor technique for mitigating the radiative masking effects of water vapor via modulation of the split-window dust-detection threshold, predicated on idealized simulations tied to these driving factors, is proposed and demonstrated. The new technique, indexed to independent-sensor description of the surface-to-500 mb atmospheric column moisture, reveals parts of the missing dust plume embedded in the moist air mass, with best performance over land surfaces.

The NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) project included goals related to aerosol particle life cycle in convective regimes. Using the University of Wisconsin High Spectral Resolution Lidar system at Huntsville, Alabama, USA, and the NASA DC-8 research aircraft, we investigate the altitude dependence of aerosol, water vapor and Altocumulus (Ac) properties in the free troposphere from a canonical 12 August 2013 convective storm case as a segue to a presentation of a mission-wide analysis. It stands to reason that any moisture detrainment from convection must have an associated aerosol layer. Modes of covariability between aerosol, water vapor and Ac are examined relative to the boundary layer entrainment zone, 0 ∘C level, and anvil, a region known to contain Ac clouds and a complex aerosol layering structure (Reid et al., 2017). Multiple aerosol layers in regions warmer than 0 ∘C were observed within the planetary boundary layer entrainment zone. At 0 ∘C there is a proclivity for aerosol and water vapor detrainment from storms, in association with melting level Ac shelves. Finally, at temperatures colder than 0 ∘C, weak aerosol layers were identified above Cumulus congestus tops (∼0 and ∼-20 ∘C). Stronger aerosol signals return in association with anvil outflow. In situ data suggest that detraining particles undergo aqueous-phase or heterogeneous chemical or microphysical transformations, while at the same time larger particles are being scavenged at higher altitudes leading to enhanced nucleation. We conclude by discussing hypotheses regarding links to aerosol emissions and potential indirect effects on Ac clouds.

This study characterizes impacts of peat-forest (PF) smoke on an urban environment through carbonaceous profiles of >260 daily PM2.5 samples collected during 2012, 2013 and 2015. Organic carbon (OC) and elemental carbon (EC) comprising eight carbonaceous fractions are examined for four sample groups – non-smoke-dominant (NSD), smoke-dominant (SD), episodic PM2.5 samples at the urban receptor, and near-source samples collected close to PF burning sites. PF smoke introduced much larger amounts of OC than EC, with OC accounting for up to 94% of total carbon (TC), or increasing by up to 20 times in receptor PM2.5. SD PM2.5 at the receptor site and near-source samples have OC3 and EC1 as the dominant fractions. Both sample classes also exhibit char-EC >1.4 times of soot-EC, characterizing smoldering-dominant PF smoke, unlike episodic PM2.5 at the receptor site featuring large amounts of pyrolyzed organic carbon (POC) and soot-EC. Relative to the mean NSD PM2.5 at the receptor, increasing strength of transboundary PF smoke enriches OC3 and OC4 fractions, on average, by factors of >3 for SD samples, and >14 for episodic samples. A peat-forest smoke (PFS) indicator, representing the concentration ratio of (OC2+OC3+POC) to soot-EC, shows a temporal trend satisfactorily correlating with an organic marker (levoglucosan) of biomass burning. The PFS indicator systematically differentiates influences of PF smoke from source to urban receptor sites, with a progressive mean of 3.6, 13.4 and 20.1 for NSD, SD and episodic samples respectively at the receptor site, and 54.7 for the near-source PM2.5. A PFS indicator of ≥5.0 is proposed to determine dominant influence of transboundary PF smoke on receptor urban PM2.5 in the equatorial Asia with ∼90% confidence. Assessing >2900 hourly OCEC data in 2017–2018 supports the applicability of the PFS indicator to evaluate hourly impacts of PF smoke on receptor urban PM2.5 in the Maritime Continent.

Monte Carlo and Morris screening techniques are used to examine the relative sensitivity of deep convective simulations to changes in initial conditions (IC) versus changes to microphysical parameters (MP). IC are perturbed using a set of empirical orthogonal function–principal component pairs obtained from a database of tropical soundings, while MP are perturbed consistent with their range of realistic values. Monte Carlo experiments provide a broad overview of parameter–output response, while Morris screening techniques identify the most significant influences on specific model output variables. Changes to MP produce a similar order-of-magnitude response in convective hydrologic cycle, dynamics, and latent heating as changes to IC. Changes in IC appear to have a larger effect on radiative fluxes than perturbations to MP. The distribution of low-level latent heating reveals that changes in MP have a larger influence on cold pool properties than changes to the environment. The dominant effects are produced by a subset of cloud MP and thermodynamic structure functions, indicating perturbation of a subset of the control factors may be used to produce most of the variability in a short-term convective-scale ensemble forecast. The most influential MP are the autoconversion threshold, the rain particle size distribution intercept, and the ice particle fall speed parameters. The most influential EOFs are those that correspond to variability in lower- to midtropospheric temperature and water vapor, as well as zonal low-level shear. The results have implications for both the understanding of what influences convective development and the design of ensemble-based prediction and data assimilation systems.

We present an algorithm for retrieving aerosol layer height (ALH) and aerosol optical depth (AOD) for smoke and dust over vegetated land and water surfaces from measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). Our algorithm uses EPIC atmospheric window bands to determine AOD and then takes advantage of oxygen A and B bands to derive ALH. We applied this algorithm on several dust and smoke events. Validation shows our results are of high accuracy.

Using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Bidirectional Reflectance Distribution Function (BRDF) with the U.S. Department of Agriculture&rsquo, s National Agricultural Statistics Service&rsquo, s Cropland Data Layer (CDL), the daily albedo of homogenous agricultural fields was derived for 51 common United States field crops by wavelength, sky-type, day of year, crop, and hardiness zone from 2015&ndash, 2018. This study suggests that crop growth can result in changes in reflectivity up to a factor of 2 at most wavelengths and is unique per crop type in timing and range. Additionally, broadband impacts were studied and found to be less conspicuous than the individual wavelengths, but still significant. The results were used to evaluate a common method of cropland albedo estimation, normalized difference vegetation index (NDVI) as a proxy for albedo, and this method was found to have some significant limitations dependent on wavelength and date. Finally, a database of surface albedo variations as a function of growing period is constructed for common field crops to the United States (as well as additional land-cover types). This database can be used to aid both satellite remote-sensing applications and long-term weather modeling efforts by providing a method for parameter adjustments based on crop driven albedo changes, including changes in cropland composition related to commodity markets and other external factors.

Previous studies have noted a relationship between African dust and Atlantic tropical cyclone (TC) activity. However, due to the limitations of past dust analyses, the strength of this relationship remains uncertain. The emergence of aerosol reanalyses, including the Navy Aerosol Analysis and Prediction System (NAAPS) aerosol optical depth (AOD) reanalysis, NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and ECMWF Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA), enables an investigation of the relationship between African dust and TC activity over the tropical Atlantic and Caribbean in a consistent temporal and spatial manner for 2003–2018. Although June–July–August (JJA) 550 nm dust AOD (DAOD) from all three reanalysis products correlates significantly over the tropical Atlantic and Caribbean, the difference in DAOD magnitude between products can be as large as 60 % over the Caribbean and 20 % over the tropical North Atlantic. Based on the three individual reanalyses, we have created an aerosol multi-reanalysis consensus (MRC). The MRC presents overall better root mean square error over the tropical Atlantic and Caribbean compared to individual reanalyses when verified with ground-based AErosol RObotic NETwork (AERONET) AOD measurements. Each of the three individual reanalyses and the MRC have significant negative correlations between JJA Caribbean DAOD and seasonal Atlantic accumulated cyclone energy (ACE), while the correlation between JJA tropical North Atlantic DAOD and seasonal ACE is weaker. Possible reasons for this regional difference are provided. A composite analysis of 3 high-JJA-Caribbean-DAOD years versus 3 low-JJA-Caribbean-DAOD years reveals large differences in overall Atlantic TC activity. We also show that JJA Caribbean DAOD is significantly correlated with large-scale fields associated with variability in interannual Atlantic TC activity including zonal wind shear, mid-level moisture, and sea surface temperature (SST), as well as the El Niño–Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), implying confounding effects of these factors on the dust–TC relationship. We find that seasonal Atlantic DAOD and the AMM, the leading mode of coupled Atlantic variability, are inversely related and intertwined in the dust–TC relationship. Overall, DAOD in both the tropical Atlantic and Caribbean is negatively correlated with Atlantic hurricane frequency and intensity, with stronger correlations in the Caribbean than farther east in the tropical North Atlantic.

Current cloud and aerosol identification methods for multi-spectral radiometers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), employ multi-channel spectral tests on individual pixels (i.e. field of views). The use of the spatial information in cloud and aerosol algorithms has been primarily statistical parameters such as non-uniformity tests of surrounding pixels with cloud classification provided by the multi-spectral microphysical retrievals such as phase and cloud top height. With these methodologies there is uncertainty in identifying optically thick aerosols, since aerosols and clouds have similar spectral properties in coarse spectral-resolution measurements. Furthermore, identifying clouds regimes (e.g. stratiform, cumuliform) from just spectral measurements is difficult, since low-altitude cloud regimes have similar spectral properties. Recent advances in computer vision using deep neural networks provide a new opportunity to better leverage the coherent spatial information in multi-spectral imagery. Using a combination of machine learning techniques combined with a new methodology to create the necessary training data we demonstrate improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types. The training labeled dataset was created from an adapted NASA Worldview platform that provides an efficient user interface to assemble a human labeled database of cloud and aerosol types. The Convolutional Neural Network (CNN) labeling accuracy of aerosols and cloud types was quantified using independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and MODIS cloud and aerosol products. By harnessing CNNs with a unique labeled dataset, we demonstrate the improvement of the identification of aerosol and distinct cloud types from MODIS and VIIRS images compared to a per-pixel spectral and standard deviation thresholding method. The paper concludes with case studies that compare the CNN methodology results with the MODIS cloud and aerosol products.

Satellite observations and model simulations cannot, by themselves, give full insight into the complex relationships between aerosols and clouds. This is especially true over Southeast Asia, an area that is particularly sensitive to changes in precipitation yet poses some of the world's largest observability and predictability challenges. We present a new collocated dataset, the Curtain Cloud-Aerosol Regional A-Train dataset, or CCARA. CCARA includes collocated satellite observations from Aqua's Moderate-resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the Navy Aerosol Analysis and Prediction System (NAAPS). The CCARA dataset is designed with the capability to investigate aerosol–cloud relationships in regions with limited aerosol retrievals due to high cloud amounts by leveraging the NAAPS model reanalysis of aerosol concentration in these regions. This combined aerosol and cloud dataset provides coincident and vertically resolved cloud and aerosol observations for 2006–2016. Using the model reanalysis aerosol fields from the NAAPS and coincident cloud liquid effective radius retrievals from MODIS (cirrus contamination using CALIOP), we investigate the first aerosol indirect effect in Southeast Asia. We find that, as expected, aerosol loading anti-correlates with cloud effective radius, with maximum sensitivity in cumulous mediocris clouds with heights in the 3–4.5 km level. The highest susceptibilities in droplet effective radius to modeled perturbations in particle concentrations were found in the more remote and pristine regions of the western Pacific Ocean and Indian Ocean. Conversely, there was much less variability in cloud droplet size near emission sources over both land and water. We hypothesize this is suggestive of a high aerosol background already saturated with cloud condensation nuclei even during the relatively clean periods, in contrast to the remote ocean regions, which have periods where the aerosol concentrations are low enough to allow for larger droplet growth.

This work reports the first ground-based atmospheric measurements of 26 halocarbons in Singapore, an urban-industrial city-state in Southeast (SE) Asia. A total of 166 whole air canister samples collected during two intensive 7 Southeast Asian Studies (7SEAS) campaigns (August-October 2011 and 2012) were analyzed for C1-C2 halocarbons using gas chromatography-electron capture/mass spectrometric detection. The halocarbon dataset was supplemented with measurements of selected non-methane hydrocarbons (NMHCs), C1-C5 alkyl nitrates, sulfur gases and carbon monoxide to better understand sources and atmospheric processes. The median observed atmospheric mixing ratios of CFCs, halons, CCl4 and CH3CCl3 were close to global tropospheric background levels, with enhancements in the 1-17% range. This provided the first measurement evidence from SE Asia of the effectiveness of Montreal Protocol and related national-scale regulations instituted in the 1990s to phase-out ozone depleting substances (ODS). First- and second-generation CFC replacements (HCFCs and HFCs) dominated the atmospheric halocarbon burden with HFC-134a, HCFC-22 and HCFC-141b exhibiting enhancements of 39-67%. By combining near-source measurements in Indonesia with receptor data in Singapore, regionally transported peat-forest burning smoke was found to impact levels of several NMHCs (ethane, ethyne, benzene, and propane) and short-lived halocarbons (CH3I, CH3Cl, and CH3Br) in a subset of the receptor samples. The strong signatures of these species near peat-forest fires were potentially affected by atmospheric dilution/mixing during transport and by mixing with substantial urban/regional backgrounds at the receptor. Quantitative source apportionment was carried out using positive matrix factorization (PMF), which identified industrial emissions related to refrigeration, foam blowing, and solvent use in chemical, pharmaceutical and electronics industries as the major source of halocarbons (34%) in Singapore. This was followed by marine and terrestrial biogenic activity (28%), residual levels of ODS from pre-Montreal Protocol operations (16%), seasonal incidences of peat-forest smoke (13%), and fumigation related to quarantine and pre-shipment (QPS) applications (7%).

This work focuses on the chemical characterization of fine aerosol particles (PM2.5) collected from a rural remote island of the Bay of Bengal (Bhola, Bangladesh) from April to August, 2013. PM2.5 particle-loaded filters were analyzed for organic carbon (OC), elemental carbon (EC), water-soluble ions, and selected saccharides (levoglucosan, mannosan, galactosan, arabitol, and mannitol). The average PM2.5 mass was 15.0 ± 6.9 μg m−3. Organic carbon and elemental carbon comprised roughly half of the analyzed components. Organic carbon was the predominant contributor to total carbon (TC) and accounting for about 28% of PM2.5 mass. Secondary organic carbon (SOC) was inferred to be ~ 26% of OC. The sum of ions comprised ~ 27% of PM2.5 mass. The contribution of sea salt aerosol was smaller than expected for a sea-near site (17%), and very high chloride depletion was observed (78%). NssSO4 2− was a dominant ionic component with an average concentration of 2.0 μg m−3 followed by Na+, NH4 +, and nssCa2+. The average concentration of arabitol and mannitol was 0.11 and 0.14 μg m−3, respectively, while levoglucosan and its stereoisomers (mannosan and galactosan) were bellow detection limit. NH4 +/SO4 2− equivalent ratio was 0.30 ± 0.13 indicating that secondary inorganic aerosol is not the main source of SO4 2−. Enrichment factor (EF) analysis showed that SO4 2− and NO3 − were enriched in atmospheric particles compared to sea aerosol and soil indicating their anthropogenic origin. Higher OC/EC ratio (3.70 ± 0.88) was a good indicator of the secondary organic compounds formation. Other ratios (OC/EC, K+/EC, nssSO4 2−/EC) and correlation analysis suggested mixed sources for carbonaceous components. Arabitol and mannitol both showed strong correlation with EC having R 2 value 0.89 and 0.95, respectively. Air mass trajectories analysis showed that concentrations of soil and anthropogenic species were lower for air masses originating from the sea (May–August) and were higher when air came from land (April).

By combining Collection 6 Moderate Resolution and Imaging Spectroradiometer (MODIS) and Version 22 Multi-angle Imaging Spectroradiometer (MISR) aerosol products with Cloud and Earth's Radiant Energy System (CERES) flux products, the aerosol optical thickness (AOT, at 0.55 µm) and shortwave (SW) aerosol radiative effect (SWARE) trends are studied over ocean for the near-full Terra (2000–2015) and Aqua (2002–2015) data records. Despite differences in sampling methods, regional SWARE and AOT trends are highly correlated with one another. Over global oceans, weak SWARE (cloud-free SW flux) and AOT trends of 0.5–0.6 W m−2 (−0.5 to −0.6 W m−2) and 0.002 AOT decade−1 are found using Terra data. Near-zero AOT and SWARE trends are also found for using Aqua data, regardless of the angular distribution models (ADMs) used. Regionally, positive AOT and cloud-free SW flux (negative SWARE) trends are found over the Bay of Bengal, the Arabian Sea, the Arabian/Persian Gulf and the Red Sea, while statistically significant negative trends are derived over the Mediterranean Sea and the eastern US coast. In addition, the global mean instantaneous SW aerosol direct forcing efficiencies are found to be ∼ −60 W m−2 AOT−1, with corresponding SWARE values of ∼ −7 W m−2 from both Aqua and Terra data, again regardless of CERES ADMs used. Regionally, SW aerosol direct forcing efficiency values of ∼ −40 W m−2 AOT−1 are found over the southwest coast of Africa where smoke aerosol particles dominate in summer. Larger (in magnitude) SW aerosol direct forcing efficiency values of −50 to −80 W m−2 AOT−1 are found over several other dust- and pollutant-aerosol-dominated regions. Lastly, the AOT and SWARE trends from this study are also intercompared with aerosol trends (such as active-based ones) from several previous studies. Findings suggest that a cohesive understanding of the changing aerosol skies can be achieved through the analysis of observations from both passive- and active-based analyses, as well as from both narrowband and broadband datasets.

We presented an algorithm for inferring aerosol layer height (ALH) and optical depth (AOD) over ocean surface from radiances in oxygen A and B bands measured by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory orbiting at Lagrangian-1 point. The algorithm was applied to EPIC imagery of a two-day dust outbreak over the North Atlantic Ocean. Retrieved ALHs and AODs were evaluated against counterparts observed by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network. The comparisons showed 71.5% of EPIC-retrieved ALHs were within ±0.5 km of those determined from CALIOP and 74.4% of EPIC AOD retrievals fell within a ±(0.1+10%) envelope of MODIS retrievals. This study demonstrates the potential of EPIC measurements for retrieving global aerosol height multiple times daily, which are essential for evaluating aerosol profile simulated in climate models and for better estimating aerosol radiative effects.

Data assimilation of AERONET and MODIS Aerosol Optical Thickness (AOT) for aerosol forecasting was tested within the Navy Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting is currently comprised of a deterministic NAAPS simulation coupled to NAVDAS-AOD, a 2-dimensional variational data assimilation system, for MODIS AOT assimilation. An ensemble version of NAAPS (ENAAPS) coupled to an Ensemble Adjustment Kalman Filter (EAKF) from DART was recently developed, allowing for a range of data assimilation and forecasting experiments to be run with deterministic NAAPS and ENAAPS. The main findings are that the EAKF, with its flow dependent error covariances, makes better use of sparse observations such as AERONET AOT. Assimilating individual AERONET observations in the 2DVar can increase the analysis errors when observations are located in high AOT gradient regions. By including AERONET with MODIS AOT assimilation, the magnitudes of peak aerosol events (AOT > 1) were better captured with improved temporal variability, especially in India and Asia where aerosol prediction is a challenge. Assimilating AERONET AOT with MODIS had little impact on the 24-hour forecast skill compared to MODIS assimilation only, but differences were found downwind of AERONET sites. The 24-hour forecast skill was approximately the same for forecasts initialized with analyses from AERONET AOT assimilation alone compared to MODIS assimilation, particularly in regions where the AERONET network is dense; including the United States and Europe, indicating AERONET could serve as a backup observation network for over-land synoptic scale aerosol events.

This paper describes the second part of a series of investigation to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from the future hyperspectral and geostationary satellite sensors such as Tropospheric Emissions: Monitoring of POllution (TEMPO). The information content in these hyperspectral measurements is analyzed for 6 principal components (PCs) of surface spectra and a total of 14 aerosol parameters that describe the columnar aerosol volume V total , fine-mode aerosol volume fraction, and the size distribution and wavelength-dependent index of refraction in both coarse and fine mode aerosols. Forward simulations of atmospheric radiative transfer are conducted for 5 surface types (green vegetation, bare soil, rangeland, concrete and mixed surface case) and a wide range of aerosol mixtures. It is shown that the PCs of surface spectra in the atmospheric window channel could be derived from the top-of-the-atmosphere reflectance in the conditions of low aerosol optical depth (AOD ≤ 0.2 at 550 nm), with a relative error of 1%. With degree freedom for signal analysis and the sequential forward selection method, the common bands for different aerosol mixture types and surface types can be selected for aerosol retrieval. The first 20% of our selected bands accounts for more than 90% of information content for aerosols, and only 4 PCs are needed to reconstruct surface reflectance. However, the information content in these common bands from each TEMPO individual observation is insufficient for the simultaneous retrieval of surface’s PC weight coefficients and multiple aerosol parameters (other than V total ). In contrast, with multiple observations for the same location from TEMPO in multiple consecutive days, 1–3 additional aerosol parameters could be retrieved. Consequently, a self-adjustable aerosol retrieval algorithm to account for surface types, AOD conditions, and multiple-consecutive observations is recommended to derive aerosol parameters and surface reflectance simultaneously from TEMPO.

Particulate matter (PM) pollution from China is transported eastward to Korea and Japan and has been suggested to influence surface air quality on the West Coast of the United States. However, remote sensing studies have been inconclusive as to recent trends in Chinese emissions and transport. We reconciled different passive remote sensing points of view and found that while aerosol optical thickness (AOT) as an indicator of particulate pollution has increased from the start of the observation period (2000) to 2006–2007 from the main Chinese coastal outflow regions, since then there has been a 10–20% decrease in AOT (with respect to 2007). Reductions were observed in spring, summer, and fall seasons. No improvement in exported PM pollution is found for the winter season.

As part of the Southeast United States based Studies of Emissions & Atmospheric Composition, Clouds & Climate Coupling by Regional Surveys (SEAC4RS), and collinear with part of the Southeast Atmosphere Study (SAS), the University of Wisconsin High Spectral Resolution Lidar (UW-HSRL) system was deployed to the University of Alabama from June 19th through November 4th, 2013. With a collocated Aerosol Robotic NETwork (AERONET) sun photometer, a nearby Chemical Speciation Network (PM2.5) measurement station, and near daily ozonesonde releases for the August-September SEAC4RS campaign, the site allowed the region's first comprehensive diurnal monitoring of aerosol particle vertical structure. A 532 nm lidar ratio of 55 sr provided good closure between aerosol backscatter and AERONET Aerosol Optical Thickness (AOT). A principle component analysis was performed to identify key modes of variability in aerosol backscatter. “Fair weather” days exhibited classic planetary boundary layer (PBL) structure of a mixed layer accounting for ~50% of AOT and an entrainment zone providing another 25%. An additional 5-15% of variance is gained from the lower free troposphere from either convective detrainment or frequent intrusions of Western United States biomass burning smoke. Generally aerosol particles were contained below the 0o C level, a common level of stability in convective regimes. However, occasional strong injections of smoke to the upper troposphere were also observed, accounting for the remaining 10-15% variability in AOT. Examples of these common modes of variability in frontal and convective regimes are presented, demonstrating why AOT often has only a weak relationship to surface PM2.5 concentration.

Using nighttime observations from Visible Infrared Imager Radiometer Suite (VIIRS) Day/Night band (DNB), the characteristics of artificial light sources are evaluated as functions of observation conditions, and incremental improvements are documented on nighttime aerosol retrievals using VIIRS DNB data on a regional scale. We find that the standard deviation of instantaneous radiance for a given artificial light source is strongly dependent upon the satellite viewing angle but is weakly dependent on lunar fraction and lunar angle. Retrieval of nighttime aerosol optical thickness (AOT) based on the novel use of these artificial light sources is demonstrated for three selected regions (United States, Middle East and India) during 2015. Reasonable agreement is found between nighttime AOTs from the VIIRS DNB and temporally adjacent daytime AOTs from the AErosol RObotic NETwork (AERONET) as well as from coincident nighttime AOT retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), indicating the potential of this method to begin filling critical gaps in diurnal AOT information at both regional and global scales. Issues related to cloud, snow and ice contamination during the winter season, as well as data loss due to the misclassification of thick aerosol plumes as clouds, must be addressed to make the algorithm operationally robust.

Due to instrument sensitivities and algorithm detection limits, Level 2 (L2) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 532 nm aerosol extinction profile retrievals are often populated with retrieval fill values (RFVs), which indicate the absence of detectable levels of aerosol within the profile. In this study, using four years (2007–2008 and 2010–2011) of CALIOP Version 3 L2 aerosol data, the occurrence frequency of daytime CALIOP profiles containing all RFVs (all-RFV profiles) is studied. In the CALIOP data products, the aerosol optical thickness (AOT) of any all-RFV profile is reported as being zero, which may introduce a bias in CALIOP-based AOT climatologies. For this study, we derive revised estimates of AOT for all-RFV profiles using collocated Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and, where available, Aerosol Robotic Network (AERONET) data. Globally, all-RFV profiles comprise roughly 71 % of all daytime CALIOP L2 aerosol profiles (i.e., including completely attenuated profiles), accounting for nearly half (45 %) of all daytime cloud-free L2 aerosol profiles. The mean collocated MODIS DT (AERONET) 550 nm AOT is found to be near 0.06 (0.08) for CALIOP all-RFV profiles. We further estimate a global mean aerosol extinction profile, a so-called noise floor, for CALIOP all-RFV profiles. The global mean CALIOP AOT is then recomputed by replacing RFV values with the derived noise floor values for both all-RFV and non-all-RFV profiles. This process yields an improvement in the agreement of CALIOP and MODIS over-ocean AOT.

In this proof-of-concept paper, we apply a bulk-mass-modeling method using observations from the NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument for retrieving particulate matter (PM) concentration over the contiguous United States (CONUS) over a 2-year period (2008–2009). Different from previous approaches that rely on empirical relationships between aerosol optical depth (AOD) and PM2.5 (PM with particle diameters less than 2.5 µm), for the first time, we derive PM2.5 concentrations, during both daytime and nighttime, from near-surface CALIOP aerosol extinction retrievals using bulk mass extinction coefficients and model-based hygroscopicity. Preliminary results from this 2-year study conducted over the CONUS show a good agreement (r2∼0.48; mean bias of −3.3 µg m−3) between the averaged nighttime CALIOP-derived PM2.5 and ground-based PM2.5 (with a lower r2 of ∼0.21 for daytime; mean bias of −0.4 µg m−3), suggesting that PM concentrations can be obtained from active-based spaceborne observations with reasonable accuracy. Results from sensitivity studies suggest that accurate aerosol typing is needed for applying CALIOP measurements for PM2.5 studies. Lastly, the e-folding correlation length for surface PM2.5 is found to be around 600 km for the entire CONUS (∼300 km for western CONUS and ∼700 km for eastern CONUS), indicating that CALIOP observations, although sparse in spatial coverage, may still be applicable for PM2.5 studies.

This study characterizes the impacts of transported peat-forest (PF) burning smoke on an urban environment and evaluates associated source burning conditions based on carbon properties of PM2.5 at the receptor site. We developed and validated a three-step classification that enables systematic and more rapid identification of PF smoke impacts on a tropical urban environment with diverse emissions and complex atmospheric processes. This approach was used to characterize over 300 daily PM2.5 data collected during 2011–2013, 2015 and 2019 in Singapore. A levoglucosan concentration of ≥0.1 μg/m3 criterion indicates dominant impacts of transported PF smoke on urban fine aerosols. This approach can be used in other ambient environments for practical and location-dependent applications. Organic carbon (OC) concentrations (as OC indicator) can be an alternate to levoglucosan for assessing smoke impacts on urban environments. Applying the OC concentration indicator identifies smoke impacts on ∼80% of daily samples in 2019 and shows an accuracy of 51–86% for hourly evaluation. Following the systematic identification of urban PM2.5 predominantly affected by PF smoke in 2011–2013, 2015 and 2019, we assessed the concentration ratio of char-EC/soot-EC as an indicator of smoldering- or flaming-dominated burning emissions. When under the influence of transported PF smoke, the mean concentration ratio of char-EC to soot-EC in urban PM2.5 decreased by >70% from 8.2 in 2011 to 2.3 in 2015 but increased to 3.8 in 2019 (p

Ship-based measurements of aerosol and cloud condensation nuclei (CCN) properties are presented for 2 weeks of observations in remote marine regions of the South China Sea/East Sea during the southwestern monsoon (SWM) season. Smoke from extensive biomass burning throughout the Maritime Continent advected into this region during the SWM, where it was mixed with anthropogenic continental pollution and emissions from heavy shipping activities. Eight aerosol types were identified using a k-means cluster analysis with data from a size-resolved CCN characterization system. Interpretation of the clusters was supplemented by additional onboard aerosol and meteorological measurements, satellite, and model products for the region. A typical bimodal marine boundary layer background aerosol population was identified and observed mixing with accumulation mode aerosol from other sources, primarily smoke from fires in Borneo and Sumatra. Hygroscopicity was assessed using the κ parameter and was found to average 0.40 for samples dominated by aged accumulation mode smoke; 0.65 for accumulation mode marine aerosol; 0.60 in an anthropogenic aerosol plume; and 0.22 during a short period that was characterized by elevated levels of volatile organic compounds not associated with biomass burning impacts. As a special subset of the background marine aerosol, clean air masses substantially scrubbed of particles were observed following heavy precipitation or the passage of squall lines, with changes in observed aerosol properties occurring on the order of minutes. Average CN number concentrations, size distributions, and κ values are reported for each population type, along with CCN number concentrations for particles that activated at supersaturations between 0.14 and 0.85 %.

The largest 7 Southeast Asian Studies (7SEAS) operation period within the Maritime Continent (MC) occurred in the August–September 2012 biomass burning season. Included was an enhanced deployment of Aerosol Robotic Network (AERONET) sun photometers, multiple lidars, and field measurements to observe transported smoke and pollution as it left the MC and entered the southwest monsoon trough. Here we describe the nature of the overall 2012 southwest monsoon (SWM) and biomass burning season to give context to the 2012 deployment. The MC in 2012 was in a slightly warm El Niño/Southern Oscillation (ENSO) phase and with spatially typical burning activity. However, overall fire counts for 2012 were 10 % lower than the Reid et al. (2012) baseline, with regions of significant departures from this norm, ranging from southern Sumatra (+30 %) to southern Kalimantan (−42 %). Fire activity and monsoonal flows for the dominant burning regions were modulated by a series of intraseasonal oscillation events (e.g., Madden–Julian Oscillation, or MJO, and boreal summer intraseasonal oscillation, or BSISO). As is typical, fire activity systematically progressed eastward over time, starting with central Sumatran fire activity in June related to a moderately strong MJO event which brought drier air from the Indian Ocean aloft and enhanced monsoonal flow. Further burning in Sumatra and Kalimantan Borneo occurred in a series of significant events from early August to a peak in the first week of October, ending when the monsoon started to migrate back to its wintertime northeastern flow conditions in mid-October. Significant monsoonal enhancements and flow reversals collinear with tropical cyclone (TC) activity and easterly waves were also observed. Islands of the eastern MC, including Sulawesi, Java, and Timor, showed less sensitivity to monsoonal variation, with slowly increasing fire activity that also peaked in early October but lingered into November. Interestingly, even though fire counts were middling, resultant AERONET 500 nm aerosol optical thickness (AOT) from fire activity was high, with maximums of 3.6 and 5.6 in the Sumatra and Kalimantan source regions at the end of the burning season and an average of ∼ 1. AOTs could also be high at receptor sites, with a mean and maximum of 0.57 and 1.24 in Singapore and 0.61 and 0.8 in Kuching Sarawak. Ultimately, outside of the extreme 2015 El Niño event, average AERONET AOT values were higher than any other time since sites were established. Thus, while satellite fire data, models, and AERONET all qualitatively agree on the nature of smoke production and transport, the MC's complex environment resulted in clear differences in quantitative interpretation of these datasets.

The largest 7 Southeast Asian Studies (7SEAS) operations period within the Maritime Continent (MC) occurred in the August–September 2012 biomass burning season. Data included were observations aboard the M/Y Vasco, dispatched to the Palawan Archipelago and Sulu Sea of the Philippines for September 2012. At these locations, the Vasco observed MC smoke and pollution entering the southwest monsoon (SWM) monsoonal trough. Here we describe the research cruise findings and the finer-scale aerosol meteorology of this convectively active region. This 2012 cruise complemented a 2-week cruise in 2011 and was generally consistent with previous findings in terms of how smoke emission and transport related to monsoonal flows, tropical cyclones (TC), and the covariance between smoke transport events and the atmosphere's thermodynamic structure. Biomass burning plumes were usually mixed with significant amounts of anthropogenic pollution. Also key to aerosol behavior were squall lines and cold pools propagating across the South China Sea (SCS) and scavenging aerosol particles in their path. However, the 2012 cruise showed much higher modulation in aerosol frequency than its 2011 counterpart. Whereas in 2011 large synoptic-scale aerosol events transported high concentrations of smoke into the Philippines over days, in 2012 measured aerosol events exhibited a much shorter-term variation, sometimes only 3–12 h. Strong monsoonal flow reversals were also experienced in 2012. Nucleation events in cleaner and polluted conditions, as well as in urban plumes, were observed. Perhaps most interestingly, several cases of squall lines preceding major aerosol events were observed, as opposed to 2011 observations where these lines largely scavenged aerosol particles from the marine boundary layer. Combined, these observations indicate pockets of high and low particle counts that are not uncommon in the region. These perturbations are difficult to observe by satellite and very difficult to model. Indeed, the Navy Aerosol Analysis and Prediction System (NAAPS) simulations captured longer period aerosol events quite well but largely failed to capture the timing of high-frequency phenomena. Ultimately, the research findings of these cruises demonstrate the real world challenges of satellite-based missions, significant aerosol life cycle questions such as those the future Aerosol/Clouds/Ecosystems (ACE) will investigate, and the importance of small-scale phenomena such as sea breezes, squall lines, and nucleation events embedded within SWM patterns in dominating aerosol life cycle and potential relationships to clouds.

Temporal variability in the vertical distribution of aerosol optical thickness (AOT) derived from the 0.532 µm aerosol extinction coefficient is described using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations over 8.5 years (June 2006 to December 2014). Temporal variability of CALIOP column-integrated AOT is largely consistent with total column AOT trends from several passive satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, and the Sea-viewing Wide Field-of-view Sensor. Globally, a 0.0002 AOT per year positive trend in deseasonalized CALIOP total column AOT for daytime conditions is attributed to corresponding changes in near-surface (i.e., 0.0–0.5 km or 0.5–1.0 km above ground level (agl)) aerosol particle loading, while a −0.0006 AOT per year trend during nighttime is attributed to elevated (i.e., 1.0–2.0 km or >2.0 km agl) aerosols. Regionally, increasing daytime CALIOP AOTs are found over Southern Africa and India, mostly due to changes in aerosol loading at the 1.0–2.0 km and 0.0–0.5 km agl layers, respectively. Decreasing daytime CALIOP AOTs are observed over Northern Africa, Eastern U.S., and South America (due mostly to elevated aerosol loading), while the negative CALIOP AOT trends found over Eastern China, Europe, and Western U.S. are due mostly to aerosol layers nearer the surface. To our knowledge, this study is the first to provide both a globally comprehensive estimation of the temporal variation in aerosol vertical distribution and an insight into passive sensor column AOT trends in the vertical domain.

This paper describes the first part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from a newly developed hyperspectral instrument, the GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO), by taking full advantage of available hyperspectral measurement information in the visible bands. We describe the theoretical framework of an inversion algorithm for the hyperspectral remote sensing of the aerosol optical properties, in which major principal components (PCs) for surface reflectance is assumed known, and the spectrally dependent aerosol refractive indices are assumed to follow a power-law approximation with four unknown parameters (two for real and two for imaginary part of refractive index). New capabilities for computing the Jacobians of four Stokes parameters of reflected solar radiation at the top of the atmosphere with respect to these unknown aerosol parameters and the weighting coefficients for each PC of surface reflectance are added into the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM), which in turn facilitates the optimization in the inversion process. Theoretical derivations of the formulas for these new capabilities are provided, and the analytical solutions of Jacobians are validated against the finite-difference calculations with relative error less than 0.2%. Finally, self-consistency check of the inversion algorithm is conducted for the idealized green-vegetation and rangeland surfaces that were spectrally characterized by the U.S. Geological Survey digital spectral library. It shows that the first six PCs can yield the reconstruction of spectral surface reflectance with errors less than 1%. Assuming that aerosol properties can be accurately characterized, the inversion yields a retrieval of hyperspectral surface reflectance with an uncertainty of 2% (and root-mean-square error of less than 0.003), which suggests self-consistency in the inversion framework. The next step of using this framework to study the aerosol information content in GEO-TASO measurements is also discussed.

A major continental-scale biomass burning smoke event from 28–30 June 2015, spanning central Canada through the eastern seaboard of the United States, resulted in unforecasted drops in daytime high surface temperatures on the order of 2–5 °C in the upper Midwest. This event, with strong smoke gradients and largely cloud-free conditions, provides a natural laboratory to study how aerosol radiative effects may influence numerical weather prediction (NWP) forecast outcomes. Here, we describe the nature of this smoke event and evaluate the differences in observed near-surface air temperatures between Bismarck (clear) and Grand Forks (overcast smoke), to evaluate to what degree solar radiation forcing from a smoke plume introduces daytime surface cooling, and how this affects model bias in forecasts and analyses. For this event, mid-visible (550 nm) smoke aerosol optical thickness (AOT, τ) reached values above 5. A direct surface cooling efficiency of −1.5 °C per unit AOT (at 550 nm, τ550) was found. A further analysis of European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), United Kingdom Meteorological Office (UKMO) near-surface air temperature forecasts for up to 54 h as a function of Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOT data across more than 400 surface stations, also indicated the presence of the daytime aerosol direct cooling effect, but suggested a smaller aerosol direct surface cooling efficiency with magnitude on the order of −0.25 to −1.0 °C per unit τ550. In addition, using observations from the surface stations, uncertainties in near-surface air temperatures from ECMWF, NCEP, and UKMO model runs are estimated. This study further suggests that significant daily changes in τ550 above 1, at which the smoke-aerosol-induced direct surface cooling effect could be comparable in magnitude with model uncertainties, are rare events on a global scale. Thus, incorporating a more realistic smoke aerosol field into numerical models is currently less likely to significantly improve the accuracy of near-surface air temperature forecasts. However, regions such as eastern China, eastern Russia, India, and portions of the Saharan and Taklamakan deserts, where significant daily changes in AOTs are more frequent, are likely to benefit from including an accurate aerosol analysis into numerical weather forecasts.

The Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field mission based at Ellington Field, Texas, during August and September 2013 employed the most comprehensive airborne payload to date to investigate atmospheric composition over North America. The NASA ER-2, DC-8, and SPEC Inc. Learjet flew 57 science flights from the surface to 20 km. The ER-2 employed seven remote sensing instruments as a satellite surrogate and eight in situ instruments. The DC-8 employed 23 in situ and five remote sensing instruments for radiation, chemistry, and microphysics. The Learjet used 11 instruments to explore cloud microphysics. SEAC4RS launched numerous balloons, augmented Aerosol RObotic NETwork, and collaborated with many existing ground measurement sites. Flights investigating convection included close coordination of all three aircraft. Coordinated DC-8 and ER-2 flights investigated the optical properties of aerosols, the influence of aerosols on clouds, and the performance of new instruments for satellite measurements of clouds and aerosols. ER-2 sorties sampled stratospheric injections of water vapor and other chemicals by local and distant convection. DC-8 flights studied seasonally evolving chemistry in the Southeastern U.S., atmospheric chemistry with lower emissions of NOx and SO2 than in previous decades, isoprene chemistry under high and low NOx conditions at different locations, organic aerosols, air pollution near Houston and in petroleum fields, smoke from wildfires in western forests and from agricultural fires in the Mississippi Valley, and the ways in which the chemistry in the boundary layer and the upper troposphere were influenced by vertical transport in convective clouds.

The aerosol chemistry environment of the Arabian Gulf region is extraordinarily complex, with high concentrations of dust aerosols from surrounding deserts mixed with anthropogenic aerosols originating from a large petrochemical industry and pockets of highly urbanized areas. Despite the high levels of aerosols experienced by this region, little research has been done to explore the chemical composition of both the anthropogenic and mineral dust portion of the aerosol burden. The intensive portion of the United Arab Emirates Unified Aerosol Experiment (UAE2), conducted during August and September 2004 was designed in part to resolve the aerosol chemistry through the use of multiple size-segregated aerosol samplers. The coarse mode mass (derived by subtracting the PM2.5 aerosol mass from the PM10 mass) is largely dust at 76% ± 7% of the total coarse mode mass, but is significantly impacted by anthropogenic pollution, primarily sulfate and nitrate. The PM2.5 aerosol mass also contains a large dust burden, at 38% ± 26%, but the anthropogenic component dominates. The total aerosol burden has significant impact not only on the atmosphere, but also the local population, as the air quality levels for both the PM10 and PM2.5 aerosol masses reached unhealthy levels for 24% of the days sampled.

This paper describes the third part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface spectral reflectance from GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO) instrument. Since the algorithm is designed for future hyperspectral and geostationary satellite sensors, such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), it is applied to GEO-TASO data measured over the same area by different flights as part of the Korea-United Stated Air Quality Study (KORUS-AQ) field campaign in 2016. While GEO-TASO has a spectral sampling interval of ~0.28 nm in the visible, its data is thinned through a band selection approach with consideration of atmospheric transmittance and different surface types, which yields 20 common spectral bands to be used by the algorithm. The algorithm starts with 4 common principal components (PCs) for surface spectral reflectance extracted from various spectral libraries; constraints of surface reflectance and aerosol model parameters are obtained respectively from k-means clustering analysis of the Rayleigh-corrected GEO-TASO spectra and AERONET data. The algorithm then proceeds iteratively with an optimal estimation approach to update PCs and retrieve aerosol optical depth (AOD) from GEO-TASO measured spectra until state vector converges. The comparison of AODs between GEO-TASO retrievals (y) and 7 AERONET (x) sites reveals that the iterative updates of surface PCs (and so surface reflectance) improve the inversions of fine-mode AOD, fine-mode fraction of AOD, Angstrom exponent, and AOD at all (440, 550, 550, 675 nm) wavelengths. At 440 nm, the linear fitting equation, the Pearson correlation coefficient (R2), and mean absolute error are improved respectively from y = 0.72x + 0.11, 0.53, and 0.05 (without update of PCs) to y = 1.055x + 0.01, 0.76, and 0.033. Future work is to prepare the algorithm for TEMPO that carries an enhanced version of GEO-TASO instrument.

Aerosol particles scatter and absorb solar radiation and affect the Earth's radiation budget. The aerosol particles are usually non-spherical in shape and inhomogeneous in chemical composition. For simplicity, these particles are approximated as homogeneous spheres/spheroids in radiative models and in retrieval algorithms of the ground and spaceborne observations. The lack of information on particle morphology (especially shape), chemical composition (that govern their spectral refractive indices) and most importantly internal structure (three dimensional spatial distribution of chemical species) lead to uncertainty in the numerical estimation of their optical and radiative properties. Here, we present a comprehensive assessment of the particles' volumetric composition. The particles were collected from Jaisalmer (arid environment) and Delhi (urban environment) of India and subjected to Focused Ion-Beam (FIB) coupled with Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscope (EDS). Based on analysis of #2 particles from Jaisalmer, particles were observed to be composed of Fe, Ca, C, Al, Cu and Mg rich shell with Si and O rich core as opposed to those of Delhi particles (no #3) which were observed to be with Cu and S rich core and Hg, Ag, C, S and N rich shell. Based on the homogeneous sphere/spheroid assumption, conventional SEM-EDS and FIB-SEM-EDS results, different particle model shapes [single species homogeneous sphere (SP1) and spheroid (SPH1); multiple species homogeneously mixed sphere (SP2) and spheroid (SPH2); and core-shell (CS)] were considered for simulating their respective optical properties; SSA (Single Scattering Albedo) and g (Asymmetry parameter). The effect of internal structure on SSA was found to be prominent in particles having low value of the imaginary part of refractive index (k). While the same was observed to be low (nearly negligible) for the particle with the high value of k. The particles rich in copper are found to have high light absorbing property which causes positive radiative forcing.

This investigation examines aerosol dynamics during major fine mode aerosol transboundary pollution events in South Korea primarily during the KORUS-AQ campaign from May 1 – June 10, 2016, particularly when cloud fraction was high and/or fog was present to quantify the change in aerosol characteristics due to near-cloud or fog interaction. We analyze the new AERONET Version 3 data that have significant changes to cloud screening algorithms, allowing many more fine-mode observations in the near vicinity of clouds or fog. Case studies for detailed investigation include May 25–26, 2016 when cloud fraction was high over much of the peninsula, associated with a weak frontal passage and advection of pollution from China. These cloud-influenced Chinese transport dates also had the highest aerosol optical depth (AOD), surface PM2.5 concentrations and fine mode particle sizes of the entire campaign. Another likewise cloud/high relative humidity (RH) case is June 9 and 10, 2016 when fog was present over the Yellow Sea that appears to have affected aerosol properties well downwind over the Korean peninsula. In comparison we also investigated aerosol properties on air stagnation days with very low cloud cover and relatively low RH (May 17 & 18, 2016), when local Korean emissions dominated. Aerosol volume size distributions show marked differences between the transport days (with high RH and cloud influences) and the local pollution stagnation days, with total column-integrated particle fine mode volume being an order of magnitude greater on the pollution transport dates. The PM2.5 over central Seoul were significantly greater than for coastal sites on the transboundary transport days yet not on stagnation days, suggesting additional particle formation from gaseous urban emissions in cloud/fog droplets and/or in the high RH humidified aerosol environment. Many days had KORUS-AQ research aircraft flights that provided observations of aerosol absorption, particle chemistry and vertical profiles of extinction. AERONET retrievals and aircraft in situ measurements both showed high single scattering albedo (weak absorption) on the cloudy or cloud influenced days, plus aircraft profile in situ measurements showed large AOD enhancements (versus dried aerosol) at ambient relative humidity (RH) on the pollution transport days, consistent with the significantly larger fine mode particle radii and weak absorption.

Since the first International Cooperative for Aerosol Prediction (ICAP) multi‐model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP‐MME over 2012–2017, with a focus on June 2016–May 2017. Evaluated with ground‐based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate‐resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP‐MME AOD consensus remains the overall top‐scoring and most consistent performer among all models in terms of root‐mean‐square error (RMSE), bias and correlation for total, fine‐ and coarse‐mode AODs as well as dust AOD; this is similar to the first ICAP‐MME study. Further, over the years, the performance of ICAP‐MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP‐MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP‐MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012–2017 suggests a general tendency for model improvements in fine‐mode AOD, especially over Asia. No significant improvement in coarse‐mode AOD is found overall for this time period., International Cooperative for Aerosol Predictions (ICAP) model 550 nm total AOD RMSE of the 72 h forecast versus corresponding mean AODs for AERONET sites listed in Table 2. Large black dots are ICAP multi‐model ensemble consensus means. Individual models are in small coloured dots. Validation of fine‐ and coarse‐modal AODs and dust AOD is also available in Figure 2.

The launch of the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Sumo-NPP satellite in 2011 ushered in a new era of using visible light and shortwave radiation at night to characterize aerosol and fire distributions from space. In order to exploit the full range of unprecedented observational capabilities of VIIRS, we have developed a nighttime shortwave radiative transfer model capability in the UNified and Linearized Radiative Transfer Model (UNL-VRTM). This capability is based on the use of additional source functions to treat illumination from the Moon, from fires, and from artificial lights. We have applied this model to address fundamental questions associated with the VIIRS sensing of aerosol and fire at night. Detailed description of model developments and validation (either directly with surface measurements of lunar spectra or indirectly through cross validation) are presented. Our analysis reveals that: (a) when convolution with the broad-range (500–900 nm) relative spectral response (RSR) function of the VIIRS Day-Night Band (DNB) is omitted, AOD retrieval from the DNB have uncertainties up to a factor of two in conditions with low or moderate AOD (

We present a comparison of 1064 nm aerosol optical depth (AOD) and aerosol extinction profiles from the Cloud-Aerosol Transport System (CATS) Level 2 aerosol product with collocated Aerosol Robotic Network (AERONET) AOD, Aqua and Terra Moderate Imaging Spectroradiometer (MODIS) Dark Target (AOD) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD and extinction data for the period of Feb. 2015–Oct. 2017. Upon quality assurance checks of CATS data, reasonable agreements are found between aerosol data from CATS and other sensors. Using quality assured CATS aerosol data, for the first time, variations in AODs and aerosol extinction profiles are evaluated at 00, 06, 12, and 18 UTC (and/or 0:00 am, 6:00 am, 12:00 pm and 6:00 pm local solar times) on both regional and global scales. This study suggests that marginal variations are found in AOD from a global mean perspective, with the maximum and minimum aerosol vertical profiles found at local noon and 6:00 pm local time respectively, for both the June–November and December–May seasons. Strong diurnal variations are found over North Africa and India for the December–May season, and over North Africa, Middle East, and India for the June–November season. In particular, over North Africa, during the June-November season, a diurnal peak in aerosol extinction profile of 20 % larger than daily mean is found at 6:00 am (early morning local time), which may possibly be associated with dust generation through the breaking down of low level jet during morning hours.

Using nighttime observations from Visible/Infrared Imager/Radiometer Suite (VIIRS) Day/Night band (DNB), the characteristics of artificial light sources are evaluated as functions of observation conditions and incremental improvements are documented on nighttime aerosol retrievals using VIIRS DNB data on a regional scale. We find that the standard deviation of instantaneous radiance for a given artificial light source is strongly dependent upon the satellite viewing angle, but is weakly dependent on lunar fraction and lunar angle. Retrieval of nighttime aerosol optical thickness (AOT) based on the novel use of these artificial light sources is demonstrated for three selected regions (United States, Middle East, and India) during 2015. Reasonable agreements are found between nighttime AOTs from VIIRS DNB and temporally adjacent daytime AOTs from AErosol RObotic NETwork (AERONET) as well as from coincident nighttime AOT retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), indicating the potential of this method to begin filling critical gaps in diurnal AOT information at both regional and global scales. Issues related to cloud, snow, and ice contamination during the winter season, as well as data loss due to the misclassification of thick aerosol plumes as clouds, must be addressed to make the algorithm operationally robust.

While stand alone satellite and model aerosol products see wide utilization, there is a significant need in numerous atmospheric and climate applications for a fused product on a regular grid. Aerosol data assimilation is an operational reality at numerous centers, and like meteorological reanalyses, aerosol reanalyses will see significant use in the near future. Here we present a standardized 2003–2013 global 1 × 1° and 6-hourly modal aerosol optical thickness (AOT) reanalysis product. This data set can be applied to basic and applied Earth system science studies of significant aerosol events, aerosol impacts on numerical weather prediction, and electro-optical propagation and sensor performance, among other uses. This paper describes the science of how to develop and score an aerosol reanalysis product. This reanalysis utilizes a modified Navy Aerosol Analysis and Prediction System (NAAPS) at its core and assimilates quality controlled retrievals of AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Multi-angle Imaging SpectroRadiometer (MISR) on Terra. The aerosol source functions, including dust and smoke, were regionally tuned to obtain the best match between the model fine- and coarse-mode AOTs and the Aerosol Robotic Network (AERONET) AOTs. Other model processes, including deposition, were tuned to minimize the AOT difference between the model and satellite AOT. Aerosol wet deposition in the tropics is driven with satellite-retrieved precipitation, rather than the model field. The final reanalyzed fine- and coarse-mode AOT at 550 nm is shown to have good agreement with AERONET observations, with global mean root mean square error around 0.1 for both fine- and coarse-mode AOTs. This paper includes a discussion of issues particular to aerosol reanalyses that make them distinct from standard meteorological reanalyses, considerations for extending such a reanalysis outside of the NASA A-Train era, and examples of how the aerosol reanalysis can be applied or fused with other model or remote sensing products. Finally, the reanalysis is evaluated in comparison with other available studies of aerosol trends, and the implications of this comparison are discussed.

We present a new approach to retrieve Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently does not conduct AOD retrieval in shallow waters that have visible sediments or sea-floor (i.e., Class 2 waters). Over the global coastal water regions in cloud-free conditions, coastal screening leads to ~20% unavailability of AOD retrievals. Here, we refine the MODIS DT algorithm by considering that water-leaving radiance at 2.1 μm to be negligible regardless of water turbidity, and therefore the 2.1 μm reflectance at the top of the atmosphere is sensitive to both change of fine-mode and coarse-mode AODs. By assuming that the aerosol single scattering properties over coastal turbid water are similar to those over the adjacent open-ocean pixels, the new algorithm can derive AOD over these shallow waters. The test algorithm yields ~18% more MODIS-AERONET collocated pairs for six AERONET stations in the coastal water regions. Furthermore, comparison of the new retrieval with these AERONET observations show that the new AOD retrievals have equivalent or better accuracy than those retrieved by the MODIS operational algorithm’s over coastal land and non-turbid coastal water product. Combining the new retrievals with the existing MODIS operational retrievals yields an overall improvement of AOD over those coastal water regions. Most importantly, this refinement extends the spatial and temporal coverage of MODIS AOD retrievals over the coastal regions where 60% of human population resides. This expanded coverage is crucial for better understanding of impact of anthropogenic aerosol particles on coastal air quality and climate.

As part of the Seven Southeast Asian Studies (7SEAS) program, an Aerosol Robotic Network (AERONET) sun photometer and a Micro-Pulse Lidar Network (MPLNET) instrument have been deployed at Singapore to study the regional aerosol environment of the Maritime Continent (MC). In addition, the Navy Aerosol Analysis and Prediction System (NAAPS) is used to model aerosol transport over the region. From 24 September 2009 to 31 March 2011, the relationships between ground-, satellite- and model-based aerosol optical depth (AOD) and particulate matter with aerodynamic equivalent diameters less than 2.5 μm (PM(2.5)) for air quality applications are investigated. When MPLNET-derived aerosol scale heights are applied to normalize AOD for comparison with surface PM(2.5) data, the empirical relationships are shown to improve with an increased 11 %, 10 % and 5 % in explained variances, for AERONET, MODIS and NAAPS respectively. The ratios of root mean square errors to standard deviations for the relationships also show corresponding improvements of 8 %, 6 % and 2 %. Aerosol scale heights are observed to be bimodal with a mode below and another above the strongly-capped/deep near-surface layer (SCD; 0 – 1.35 km). Aerosol extinctions within SCD are well-correlated with surface PM(2.5) concentrations, possibly due to strong vertical mixing in the region.

Seven and a half years (June 2006 to November 2013) of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol and cloud layer products are compared with collocated Ozone Monitoring Instrument (OMI) aerosol index (AI) data and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products in order to investigate variability in estimates of biannual and monthly above-cloud aerosol (ACA) events globally. The active- (CALIOP) and passive-based (OMI-MODIS) techniques have their advantages and caveats for ACA detection, and thus both are used to derive a thorough and robust comparison of daytime cloudy-sky ACA distribution and climatology. For the first time, baseline above-cloud aerosol optical depth (ACAOD) and AI thresholds are derived and examined (AI = 1.0, ACAOD = 0.015) for each sensor. Both OMI-MODIS and CALIOP-based daytime spatial distributions of ACA events show similar patterns during both study periods (December–May) and (June–November). Divergence exists in some regions, however, such as Southeast Asia during June through November, where daytime cloudy-sky ACA frequencies of up to 10 % are found from CALIOP yet are non-existent from the OMI-based method. Conversely, annual cloudy-sky ACA frequencies of 20–30 % are reported over northern Africa from the OMI-based method yet are largely undetected by the CALIOP-based method. Using a collocated OMI-MODIS-CALIOP data set, our study suggests that the cloudy-sky ACA frequency differences between the OMI-MODIS- and CALIOP-based methods are mostly due to differences in cloud detection capability between MODIS and CALIOP as well as QA flags used. An increasing interannual variability of ∼ 0.3–0.4 % per year (since 2009) in global monthly cloudy-sky ACA daytime frequency of occurrence is found using the OMI-MODIS-based method. Yet, CALIOP-based global daytime ACA frequencies exhibit a near-zero interannual variability. Further analysis suggests that the OMI-derived interannual variability in cloudy-sky ACA frequency may be affected by OMI row anomalies in later years. A few regions are found to have increasing slopes in interannual variability in cloudy-sky ACA frequency, including the Middle East and India. Regions with slightly negative slopes of the interannual variability in cloudy-sky ACA frequencies are found over South America and China, while remaining regions in the study show nearly zero change in ACA frequencies over time. The interannual variability in ACA frequency is not, however, statistically significant on both global and regional scales, given the relatively limited sample sizes. A longer data record of ACA events is needed in order to establish significant trends of ACA frequency regionally and globally.

An accurate estimation of biomass burning emissions is partially limited by the lack of knowledge of fire burning phase (smoldering vs. flaming). In recent years, several fire detection products have been developed to provide information of fire radiative power (FRP), location, size, and temperature of fire pixels, but no information regarding fire burning phase is retrieved. The Day-Night band (DNB) aboard Visible Infrared Imaging Radiometer Suite (VIIRS) is sensitive to visible light from flaming fires in nighttime scenes. In contrast, VIIRS 4 μm moderate resolution band #13 (M13), though capable of detecting fires at all phases, has no direct sensitivity for discerning fire phase. However, the hybrid usage of VIIRS DNB and M-bands data is hampered by their different scanning technology and spatial resolution. In this study, we present a novel method to rapidly and accurately resample DNB pixel radiances to the footprint of M-band pixels, accounting for onboard detector aggregation schemes and bowtie effect removals. The visible energy fraction (VEF) is subsequently introduced as an indicator of fire burning phase. VEF is calculated as the ratio of visible light power (VLP) to FRP for each fire pixel retrieved from the VIIRS 750 m active fire product. A global distribution of VEF values is quantitatively obtained, showing smaller VEF values in regions with mostly smoldering wildfires, such as peatland fires in Indonesia, larger VEF values in regions with flaming wildfires over grasslands and savannas in the sub-Sahelian region, and the largest VEF values associated with gas flaring in the Middle East. Mean VEF for different land cover types or regions is highly correlated with modified combustion efficiency (MCE). These results, together with a case study of the 2018 California Camp Fire, show that the VEF has the potential to be an indicator of fire combustion phase for each fire pixel, appropriate for estimating emission factors at the satellite pixel level.

Over forty scientists from six countries convened in Raleigh, NC on June 4-6 2012 to review the status and prospects of sea spray aerosol research. Participants were researchers from the oceanography and atmospheric science communities, including academia, private industry, and government agencies. The recommendations from the working groups are summarized in a science prioritization matrix that is meant to prioritize the research agenda and identify areas of investigation by the magnitude of their impact on proposed science questions. Str

The Maritime Continent (MC) is an exceedingly complex region from the perspective of both its meteorology and its aerosol characteristics. Convection in the MC is ubiquitous and assumes a wide variety of forms under the influence of an evolving large-scale dynamic and thermodynamic context. Understanding the interaction between convective systems and their environment, both individually and in the aggregate, requires knowledge of the dominant patterns of spatial and temporal variability. Ongoing cloud model ensemble studies require realistic perturbations to the atmospheric thermodynamic state to devise system sensitivities. Apart from modeling studies, evanescent signals in the tropical system are obscured by the underlying broad-scale meteorological variability, which if constrained could illuminate fine-scale physical processes. To this end, radiosonde observations from 2008 to 2016 are examined from three upper-air sounding sites within the MC for the purpose of exploring the dominant vertical temperature, humidity, and wind structures in the region. Principal component analysis is applied to the vertical atmospheric column to transform patterns present in radiosonde data into canonical thermodynamic and wind profiles for the MC. Both rotated and non-rotated principal components are considered, and the emerging structure functions reflect the fundamental vertical modes of short-term tropical variability. The results indicate that while there is tremendous spatial and temporal variability across the MC, the primary modes of vertical thermodynamic and wind variability in the region can be represented in a lower-dimensional subspace. In addition, the vertical structures are very similar among different sites around the region, though different structures may manifest more strongly at one location than another. The results indicate that, while different meteorology may be found in various parts of the MC at any given time, the processes themselves are remarkably consistent. The ability to represent this variability using a limited number of structure functions facilitates analysis of covariability between atmospheric structure and convective systems and also enables future systematic model-based ensemble analysis of cloud development, convection, and precipitation over the MC.

The largest 7 Southeast Asian Studies (7-SEAS) operations period within the Maritime Continent occurred in the 2012 August–September biomass burning season. Included where an enhanced deployment of Aerosol Robotic Network (AERONET) sun photometers, of multiple lidars, and of a Singapore supersite. Simultaneously, a ship was dispatched to the Palawan Archipelago and Sulu Sea of the Philippines for September 2012 to observe transported smoke and pollution as it entered the southwest monsoon monsoonal trough. Here we describe the nature of the overall 2012 southwest monsoon biomass burning season, but focus on the findings of the research cruise and the aerosol meteorology of this convectively active region. This 2012 cruise followed a 2 week cruise in 2011, and was in part consistent with the findings of that cruise for how smoke emission and transport relate to monsoonal flows, the propagation of the Madden Julian Oscillation (MJO), tropical cyclones, and covariance between smoke transport events and the atmosphere’s thermodynamic structure. Aerosol observations in the 2011 cruise also highlighted the importance of squall lines and cold pools as they propagate across the South China Sea, scavenging aerosol particles in their path. For 2012, the cruise experienced differing environments. The monsoonal flow direction was perturbed by easterly waves, leading at times to total flow reversal in the South China Sea. Two category 5 typhoons just east of the Philippines also modulated flow patterns and convection. Whereas in 2011 large synoptic scale aerosol events transported high concentrations of smoke into the Philippines over days, in 2012, measured aerosol events exhibited a much more short term variation, sometimes only over 3–12 hours. Navy Aerosol Analysis and Prediction System (NAAPS) simulations captured longer wavelength aerosol events quite well, but largely failed to capture the timing in high frequency phenomena. Also observed were nucleation events in cleaner and polluted conditions, as well as in urban plumes. Combined, observations indicate pockets of high particle counts are not uncommon in the region. Perhaps most interestingly, several cases of squall lines heralding major aerosol events were observed, as opposed to the previous observations in 2011 of these lines largely scavenging aerosol particles from the marine boundary layer. We hypothesize that these phenomena may originate from weakly forced convection ahead of polluted land breeze fronts caught in strong monsoonal flows. Ultimately, the research findings of the 2012 cruise nicely complement the narrative started by the 2011 research cruise, and point to the importance of small scale phenomena such as sea breezes and squall lines embedded in the large scale monsoonal flow patterns in dominating aerosol lifecycle and potentially effects. “Pure” biomass burning plumes are relatively rare and are usually mixed with significant amounts of anthropogenic pollution.

Model primary marine aerosol (mPMA) was produced by bubbling clean air through flowing natural seawater in a high-capacity generator deployed on ships in the eastern North Pacific and western North Atlantic Oceans. Physicochemical properties of seawater and mPMA were quantified to characterize factors that modulated production. Differences in surfactant organic matter (OM) and associated properties including surface tension sustained plumes with smaller bubble sizes, slower rise velocities, larger void fractions, and older surface ages in biologically productive relative to oligotrophic seawater. Production efficiencies for mPMA number (PEnum) and mass (PEmass) per unit air detrained from biologically productive seawater during daytime were greater and mass median diameters smaller than those in the same seawater at night and in oligotrophic seawater during day and night. PEmass decreased with increasing air detrainment rate suggesting that surface bubble rafts suppressed emission of jet droplets and associated mPMA mass. Relative to bubbles emitted at 60-cm depth, PEnum for bubbles emitted from 100-cm depth was approximately two times greater. mPMA OM enrichment factors (EFs) and mass fractions based on a coarse frit, fine frits, and a seawater jet exhibited similar size-dependent variability over a wide range in chlorophyll a concentrations. Results indicate that the physical production of PMA number and mass from the ocean surface varies systematically as interrelated functions of seawater type and, in biologically productive waters, time of day; bubble injection rate, depth, size, and surface age; and physical characteristics of the air-water interface whereas size-resolved OM EFs and mass fractions are relatively invariant.