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By accounting for surface-based light source emissions and top-of-atmosphere (TOA) downward lunar fluxes, we adapted the spherical harmonics discrete ordinate method (SHDOM) 3-dimensional (3-D) radiative transfer model (RTM) to simulate nighttime 3-D TOA radiances as observed from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi-NPP satellite platform. Used previously for daytime 3-D applications, these new SHDOM enhancements allow for the study of the impacts of various observing conditions and aerosol properties on simulated VIIRS-DNB TOA radiances. Observations over Dakar, Senegal, selected for its bright city lights and a large range of aerosol optical depth (AOD), were investigated for potential applications and opportunities for using observed radiances containing VIIRS-DNB “bright pixels” from artificial light sources to conduct aerosol retrievals. We found that using the standard deviation (SD) of such bright pixels provided a more stable quantity for nighttime AOD retrievals than direct retrievals from TOA radiances. Further, both the mean TOA radiance and SD of TOA radiances over artificial sources are significantly impacted by satellite viewing angles. Light domes, the enhanced radiances adjacent to artificial light sources, are strong functions of aerosol properties and especially aerosol vertical distribution, which may be further utilized for retrieving aerosol layer height in future studies. Through inter-comparison with both day- and nighttime Aerosol Robotic Network (AERONET) data, the feasibility of retrieving nighttime AODs using 3-D RTM SHDOM over artificial light sources was demonstrated. Our study shows strong potential for using artificial light sources for nighttime AOD retrievals, while also highlighting larger uncertainties in quantifying surface light source emissions. This study underscores the need for surface light emission source characterizations as a key boundary condition, which is a complex task that requires enhanced input data and further research. We demonstrate how quality-controlled nighttime light data from the NASA's Black Marble product suite could serve as a primary input into estimations of surface light source emissions for nighttime aerosol retrievals.

The sub-micron (SM) aerosol optical depth (AOD) is an optical separation based on the fraction of particles below a specified cutoff radius of the particle size distribution (PSD) at a given particle radius. It is fundamentally different from spectrally separated FM (fine-mode) AOD. We present a simple (AOD-normalized) SM fraction versus FM fraction (SMF vs. FMF) linear equation that explains the well-recognized empirical result of SMF generally being greater than the FMF. The AERONET inversion (AERinv) products (combined inputs of spectral AOD and sky radiance) and the spectral deconvolution algorithm (SDA) products (input of AOD spectra) enable, respectively, an empirical SMF vs. FMF comparison at similar (columnar) remote sensing scales across a variety of aerosol types.SMF (AERinv-derived) vs. FMF (SDA-derived) behavior is primarily dependent on the relative truncated portion (epsilon(c)) of the coarse-mode (CM) AOD associated with the cutoff portion of the CM PSD and, to a second order, the cutoff FM PSD and FM AOD (epsilon(f)). The SMF vs. FMF equation largely explains the SMF vs. FMF behavior of the AERinv vs. SDA products as a function of PSD cutoff radius ( "inflection point ") across an ensemble of AERONET sites and aerosol types (urban-industrial, biomass burning, dust, maritime and a mixed class of Arctic aerosols). The overarching dynamic was that the linear SMF vs. FMF relation pivots clockwise about the approximate (SMF, FMF) singularity of (1, 1) in a "linearly inverse " fashion (slope and intercept of approximately 1-epsilon(c) and epsilon(c)) with increasing cutoff radius. SMF vs. FMF slopes and intercepts derived from AERinv and SDA retrievals confirmed the general domination of epsilon(c) over epsilon f in controlling that dynamic. A more general conclusion is the apparent confirmation that the optical impact of truncating modal (whole) PSD features can be detected by an SMF vs. FMF analysis., GSFC/NASA AERONET project, CANDAC (the Canadian Network for the Detection of Atmospheric Change) through the PAHA (Probing the Atmosphere of the High Arctic), Natural Sciences and Engineering Research Council of Canada (NSERC), CSA's ESS-DA program CSA's FAST program

Source functions for mechanically driven coarse-mode sea spray and dust aerosol particles span orders of magnitude owing to a combination of physical sensitivity in the system and large measurement uncertainty. Outside special idealized settings (such as wind tunnels), aerosol particle fluxes are largely inferred from a host of methods, including local eddy correlation, gradient methods, and dry deposition methods. In all of these methods, it is difficult to relate point measurements from towers, ships, or aircraft to a general representative flux of aerosol particles. This difficulty is from the particles' inhomogeneous distribution due to multiple spatiotemporal scales of an evolving marine environment. We hypothesize that the current representation of a point in situ measurement of sea spray or dust particles is a likely contributor to the unrealistic range of flux and concentration outcomes in the literature. This paper aims to help the interpretation of field data: we conduct a series of high-resolution, cloud-free large eddy simulations (LESs) with Lagrangian particles to better understand the temporal evolution and volumetric variability of coarse- to giant-mode marine aerosol particles and their relationship to turbulent transport. The study begins by describing the Lagrangian LES model framework and simulates flux measurements that were made using numerical analogs to field practices such as the eddy covariance method. Using these methods, turbulent flux sampling is quantified based on key features such as coherent structures within the marine atmospheric boundary layer (MABL) and aerosol particle size. We show that for an unstable atmospheric stability, the MABL exhibits large coherent eddy structures, and as a consequence, the flux measurement outcome becomes strongly tied to spatial length scales and relative sampling of crosswise and streamwise sampling. For example, through the use of ogive curves, a given sampling duration of a fixed numerical sampling instrument is found to capture 80 % of the aerosol flux given a sampling rate of zf/w∗∼ 0.2, whereas a spanwise moving instrument results in a 95 % capture. These coherent structures and other canonical features contribute to the lack of convergence to the true aerosol vertical flux at any height. As expected, sampling all of the flow features results in a statistically robust flux signal. Analysis of a neutral boundary layer configuration results in a lower predictive range due to weak or no vertical roll structures compared to the unstable boundary layer setting. Finally, we take the results of each approach and compare their surface flux variability: a baseline metric used in regional and global aerosol models.

We present an Arctic aerosol optical depth (AOD) climatology and trend analysis for 2003–2019 spring and summertime periods derived from a combination of multi-agency aerosol reanalyses, remote-sensing retrievals, and ground observations. This includes the U.S. Navy Aerosol Analysis and Prediction System ReAnalysis version 1 (NAAPS-RA v1), the NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA). Spaceborne remote-sensing retrievals of AOD are considered from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Ground-based data include sun photometer data from AErosol RObotic NETwork (AERONET) sites and oceanic Maritime Aerosol Network (MAN) measurements. Aerosol reanalysis AODs and spaceborne retrievals show consistent climatological spatial patterns and trends for both spring and summer seasons over the lower Arctic (60–70∘ N). Consistent AOD trends are also found for the high Arctic (north of 70∘ N) from reanalyses. The aerosol reanalyses yield more consistent AOD results than climate models, can be verified well with AERONET, and corroborate complementary climatological and trend analysis. Speciated AODs are more variable than total AOD among the three reanalyses and a little more so for March–May (MAM) than for June–August (JJA). Black carbon (BC) AOD in the Arctic comes predominantly from biomass burning (BB) sources in both MAM and JJA, and BB overwhelms anthropogenic sources in JJA for the study period. AOD exhibits a multi-year negative MAM trend and a positive JJA trend in the Arctic during 2003–2019, due to an overall decrease in sulfate/anthropogenic pollution and a significant JJA increase in BB smoke. Interannual Arctic AOD variability is significantly large, driven by fine-mode and, specifically, BB smoke, with both smoke contribution and interannual variation larger in JJA than in MAM. It is recommended that climate models should account for BB emissions and BB interannual variabilities and trends in Arctic climate change studies.

This work focuses on total organic carbon (TOC) and contributing species in cloud water over Southeast Asia using a rare airborne dataset collected during NASA's Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP 2 Ex), in which a wide variety of maritime clouds were studied, including cumulus congestus, altocumulus, altostratus, and cumulus. Knowledge of TOC masses and their contributing species is needed for improved modeling of cloud processing of organics and to understand how aerosols and gases impact and are impacted by clouds. This work relies on 159 samples collected with an axial cyclone cloud-water collector at altitudes of 0.2–6.8 km that had sufficient volume for both TOC and speciated organic composition analysis. Species included monocarboxylic acids (glycolate, acetate, formate, and pyruvate), dicarboxylic acids (glutarate, adipate, succinate, maleate, and oxalate), methanesulfonic acid (MSA), and dimethylamine (DMA). TOC values range between 0.018 and 13.66 ppm C with a mean of 0.902 ppm C. The highest TOC values are observed below 2 km with a general reduction aloft. An exception is samples impacted by biomass burning for which TOC remains enhanced at altitudes as high as 6.5 km (7.048 ppm C). Estimated total organic matter derived from TOC contributes a mean of 30.7 % to total measured mass (inorganics + organics). Speciated organics contribute (on a carbon mass basis) an average of 30.0 % to TOC in the study region and account for an average of 10.3 % to total measured mass. The order of the average contribution of species to TOC, in decreasing contribution of carbon mass, is as follows ( ± 1 standard deviation): acetate (14.7 ± 20.5 %), formate (5.4 ± 9.3 %), oxalate (2.8 ± 4.3 %), DMA (1.7 ± 6.3 %), succinate (1.6 ± 2.4 %), pyruvate (1.3 ± 4.5 %), glycolate (1.3 ± 3.7 %), adipate (1.0 ± 3.6 %), MSA (0.1 ± 0.1 %), glutarate (0.1 ± 0.2 %), and maleate ( 0.1 ± 0.1 %). Approximately 70 % of TOC remains unaccounted for, highlighting the complex nature of organics in the study region; in samples collected in biomass burning plumes, up to 95.6 % of TOC mass is unaccounted for based on the species detected. Consistent with other regions, monocarboxylic acids dominate the speciated organic mass ( ∼ 75 %) and are about 4 times more abundant than dicarboxylic acids. Samples are categorized into four cases based on back-trajectory history, revealing source-independent similarity between the bulk contributions of monocarboxylic and dicarboxylic acids to TOC (16.03 %–23.66 % and 3.70 %–8.75 %, respectively). Furthermore, acetate, formate, succinate, glutarate, pyruvate, oxalate, and MSA are especially enhanced during biomass burning periods, which is attributed to peat emissions transported from Sumatra and Borneo. Lastly, dust (Ca 2+ ) and sea salt ( Na + / Cl - ) tracers exhibit strong correlations with speciated organics, supporting how coarse aerosol surfaces interact with these water-soluble organics.

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.

There is need for information on the general crop physiology and nitrogen (N) fertiliser responses of beetroot in New Zealand. We investigated the responses to N of beetroot grown in a silt loam ne...

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.

In New Zealand horticulture there is a widely-held opinion that target soil test P (Olsen P) values increase with soil anion storage capacity (ASC) – an idea that arose in the 1986 Fertiliser Recom...

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.

Alcohol consumption level and alcohol use disorder (AUD) diagnosis are moderately heritable traits. We conduct genome-wide association studies of these traits using longitudinal Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) scores and AUD diagnoses in a multi-ancestry Million Veteran Program sample (N = 274,424). We identify 18 genome-wide significant loci: 5 associated with both traits, 8 associated with AUDIT-C only, and 5 associated with AUD diagnosis only. Polygenic Risk Scores (PRS) for both traits are associated with alcohol-related disorders in two independent samples. Although a significant genetic correlation reflects the overlap between the traits, genetic correlations for 188 non-alcohol-related traits differ significantly for the two traits, as do the phenotypes associated with the traits’ PRS. Cell type group partitioning heritability enrichment analyses also differentiate the two traits. We conclude that, although heavy drinking is a key risk factor for AUD, it is not a sufficient cause of the disorder., The genetic underpinnings of alcohol use disorder and consumption are incompletely understood. Here, the authors perform GWAS for Alcohol Use Disorder (AUD) Identification Test-Consumption scores and AUD diagnosis from electronic health records of 274,424 individuals and identify a total of 18 associated loci.

A persistent weakness of root crop models has been reliance on empirical descriptions of dry matter partitioning and leaf area growth. This paper presents an alternative, mechanistic, approach. Thi...

When unaccounted for in numerical weather prediction (NWP) models, heavy aerosol events can cause significant unrealized biases in forecasted meteorological parameters such as surface temperature. To improve near-surface forecasting accuracies during heavy aerosol loadings, we demonstrate the feasibility of incorporating aerosol fields from a global chemical transport model as initial and boundary conditions into a higher resolution NWP model with aerosol-meteorological coupling. This concept is tested for a major biomass burning smoke event over the Northern Great Plains region of the United States that occurred during summer of 2015. Aerosol analyses from the global Navy Aerosol Analysis and Prediction System (NAAPS) are used as initial and boundary conditions for Weather Research and Forecasting with Chemistry (WRF-Chem) simulations. Through incorporating more realistic aerosol direct effects into the WRF-Chem simulations, errors in WRF-Chem simulated surface downward shortwave radiative fluxes and near-surface temperature are reduced compared with surface-based observations. This study confirms the ability to decrease biases induced by the aerosol direct effect for regional NWP forecasts during high-impact aerosol episodes through the incorporation of analyses and forecasts from a global aerosol transport model.

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.