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Aerosol particles in Southeast Asia are challenging to characterize due to their complex life cycle within the diverse topography and weather of the region. An emerging aerosol climatology was established based on AErosol RObotic NETwork (AERONET) data (December 2009 to October 2018) for clear-sky days in Metro Manila, the Philippines. Aerosol optical depth (AOD) values were highest from August to October, partly from fine urban aerosol particles, including soot, coinciding with the burning season in insular Southeast Asia when smoke is often transported to Metro Manila during the southwest monsoon. Clustering of AERONET volume size distributions (VSDs) resulted in five aerosol particle sources based on the position and magnitude of their peaks in the VSD and the contributions of specific particle species to AOD per cluster based on MERRA-2. The clustering showed that the majority of aerosol particles above Metro Manila were from a clean marine source (58 %), which could be related to AOD values there being relatively low compared to other cities in the region. The following are the other particle sources over Metro Manila: fine polluted sources (20 %), mixed-dust sources (12 %), urban and industrial sources (5 %), and cloud processing sources (5 %). Furthermore, MERRA-2 AOD data over Southeast Asia were analyzed using empirical orthogonal functions. Along with AOD fractional compositional contributions and wind regimes, four dominant aerosol particle air masses emerged: two sulfate air masses from East Asia, an organic carbon source from Indonesia, and a sulfate source from the Philippines. Knowing the local and regional aerosol particle air masses that impact Metro Manila is useful in identifying the sources while gaining insight into how aerosol particles are affected by long-range transport and their impact on regional weather. [ABSTRACT FROM AUTHOR]

The World Meteorological Organizations' International Cloud Atlas recognizes 10 basic cloud genera for classifying clouds. Many of these have been used for over 200 years and are based on cloud appearance and base altitude as seen from surface. Over the satellite era, several missions and programs provide public products that classify clouds into these cloud genera. Here, we provide the first comparison of three such satellite climatologies of cloud genera with surface observations. Specifically, we analyse 10 years (2007–2016) of CloudSat, 4 years (2007–2010) of joint CloudSat‐CALIPSO, 13 years (2000–2012) of ISCCP‐H, and 11 years (1998–2008) of the EECRA data between 50°N and 50°S for eight cloud genera. Averaged over this latitude range, the total cloud amounts for these datasets range from 0.56 to 0.65, with Cumulus (Cu) ranging from 0.06 to 0.14; Stratus (St) from 0.14 to 0.38; Altostratus (As) from 0.05 to 0.13; Altocumulus (Ac) from 0.07 to 0.17; Nimbostratus (Ns) from 0.03 to 0.06; Cirrus (Ci) from 0.1 to 0.19; and Deep‐convective (Dc) from 0.01 to 0.04. The largest disagreement among the sensors is observed for Dc cloud with the coefficient of variation of 44%. On the other hand, the cloud datasets show the best agreement for Ci cloud with the coefficient of variation of 24.1%. Regionally, however, the level of agreement and disagreement can vary drastically. For example, in Indian summer monsoon region (ISM 60°–90°E, 10°–30°N) Ci cloud shows a variation of 28%, whereas the Dc cloud shows 16% variation, which is the opposite of their near‐global feature. The observed discrepancies in cloud genera are discussed in terms of observing characteristics, including instrument, methods, and sampling. Greater effort is still required to reduce discrepancies among these datasets, and the assessment provided here can act as a guide for their use in climate studies. [ABSTRACT FROM AUTHOR]

The cloud drop effective radius (Re) of the drop size distribution derived from passive satellite sensors is a key variable used in climate research. Validation of these satellite products has often taken place under stratiform cloud conditions that favor the assumption of cloud horizontal homogeneity used by the retrieval techniques. However, many studies have noted concerns with respect to significant biases in retrieved Re arising from cloud heterogeneity, for example, in cumulus cloud fields. Here, we examine data collected during the 2019 "Cloud, Aerosol and Monsoon Processes Philippines Experiment" (CAMP 2 Ex), which, in part, targeted the objective of providing the first detailed evaluation of Re retrieved across multiple platforms and techniques in a cumulus and congestus cloud region. Our evaluation consists of cross-comparisons of Re between the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, the Research Scanning Polarimeter (RSP) onboard the NASA P-3 aircraft, and in situ measurements from both the NASA P-3 and Learjet aircraft that are all taken in close spatiotemporal proximity to the same cloud fields. A particular advantage of our approach lies in the capability of the RSP to retrieve Re using a bi-spectral MODIS approach and a polarimetric approach, which allows for the evaluation of bi-spectral and polarimetric Re retrievals from an airborne perspective using the same samples. Averaged over all P-3 flight segments examined here for warm clouds, the RSP polarimetric method, the in situ method, and the bias-adjusted MODIS method of Fu et al. (2019) show a comparable median (mean ± standard deviation) for the Re samples of 9.6 (10.2 ± 4.0) µm , 11.0 (13.6 ± 11.3) µm , and 10.4 (10.8 ± 3.8) µm , respectively. These values are far lower than the values of 15.1 (16.2 ± 5.5) µm and 17.2 (17.7 ± 5.7) µm from the bi-spectral retrievals of RSP and MODIS, respectively. Similar results are observed when Re is segregated by cloud-top height and in detailed case studies. The clouds sampled during CAMP 2 Ex consist of mostly small (mean transect length ∼ 1.4 km) and low clouds (mean cloud-top height ∼ 1 km), which had more numerous small clouds than the trade wind cumuli sampled in past field campaigns such as Rain in Shallow Cumulus over the Ocean (RICO) and the Indian Ocean Experiment (INDOEX). The overestimates of Re from the RSP bi-spectral technique compared with the polarimetric technique increased as cloud size and cloud optical depth decreased. Drizzle, cloud-top bumpiness, and solar zenith angle, however, are not closely correlated with the overestimate of bi-spectral Re. For shallow clouds that dominated the liquid cloud cover for the CAMP 2 Ex region and period, we show that 3-D radiative transfer and cloud heterogeneity, particularly for the optically thin and small clouds, appear to be the leading cause of the large positive biases in bi-spectral retrievals. Because this bias varies with the underlying structure of the cloud field, caution continues to be warranted in studies that use bi-spectral Re retrievals in cumulus cloud fields. [ABSTRACT FROM AUTHOR]

The spatial and temporal characteristics of fine particulate matter (PM2.5, particulate matter <2.5 μm in aerodynamic diameter) are increasingly being studied from satellite aerosol remote sensing data. However, cloud cover severely limits the coverage of satellite-driven PM2.5 models, and little research has been conducted on the association between cloud properties and PM2.5 levels. In this study, we analyzed the relationships between ground PM2.5 concentrations and two satellite-retrieved cloud parameters using data from the Southeastern Aerosol Research and Characterization (SEARCH) Network during 2000-2010. We found that both satellite-retrieved cloud fraction (CF) and cloud optical thickness (COT) are negatively associated with PM2.5 levels. PM2.5 speciation and meteorological analysis suggested that the main reason for these negative relationships might be the decreased secondary particle generation. Stratified analyses by season, land use type, and site location showed that seasonal impacts on this relationship are significant. These associations do not vary substantially between urban and rural sites or inland and coastal sites. The statistically significant negative associations of PM2.5 mass concentrations with CF and COT suggest that satellite-retrieved cloud parameters have the potential to serve as predictors to fill the data gap left by satellite aerosol optical depth in satellite-driven PM2.5 models. [ABSTRACT FROM AUTHOR]

This paper examines the effect of trade wind cumulus clouds on aerosol properties in the near-cloud environment using data from the Rain in Cumulus over the Ocean (RICO) campaign. Aerosol size distributions, particle concentrations, and optical properties are examined as a function of altitude and distance from cloud, at ambient relative humidity (RH) and adjusted to a constant RH to isolate effects of humidification from other processes. The cloud humidity halo extended about 1500-2000 m from the cloud edge, with no clear altitude dependence on horizontal extent over an altitude range of 600-1700 m. The combined effects of vertical transport of aerosol by clouds and cloud processing contributed to the modification of aerosol size distributions within the clouds' humidity halos, particularly close to the cloud boundaries. Backscatter at 532 nm, calculated from the aerosol properties, exhibited no distinguishable trend with altitude within 400 m of cloud edges, increased toward lower altitudes beyond 400 m, and decreased away from cloud boundaries at all altitudes. The mean aerosol diameter was found to rapidly decline from 0.8 to 0.4 μm from near the cloud boundary to the boundary of the humidity halo. Aerosol optical depth at 532 nm within the layer between 600 and 1700 m increased near exponentially from 0.02 to 0.2 toward the cloud boundaries within the humidity halo. These trends agreed qualitatively with past space-based lidar measurements of trade wind cloud margins, although quantitative differences were noted that likely arose from different sampling strategies and other factors. [ABSTRACT FROM AUTHOR]

Infrared channels on the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to infer cloud-top pressure (CTP), temperature, and effective cloud amount or emissivity. For low clouds, those with tops at pressures greater than 700 hPa, the infrared window 11-μm channel brightness temperature is used to determine the CTP and the corresponding cloud-top temperature by comparison with the temperature profile obtained from the NCEP Global Data Assimilation System meteorological analysis. In the presence of strong inversions which are common for marine stratus and stratocumulus, this leads to the identification of an erroneously high cloud-top height (CTH). This discrepancy is illustrated by comparing MODIS CTHs with those inferred from the geometric method used by the Multiangle Imaging SpectroRadiometer on the same satellite platform, and field observations. The error in CTH is typically about 2 km and depends on the shape of the actual temperature profile. It is shown that column water vapor above cloud retrieved from the MODIS solar infrared channels in the vicinity of the 0.94-μm water vapor absorption band can be used to flag the error and that the location of the true CTH could possibly be obtained using lapse rate formulations for cloud-topped boundary layers. [ABSTRACT FROM AUTHOR]

The Radiometric Camera-by-Camera Cloud Mask (RCCM) is archived at the NASA Langley Distributed Active Archive Center as one of the standard products from the Multi-angle Imaging SpectroRadiometer (MISR) mission. The RCCM algorithm applied over land surfaces uses an Automated Threshold Selection Algorithm (ATSA) to derive thresholds that are applied to a cloud masking test to determine whether a given image pixel is clear or contains cloud. In this article, we established a framework for the selection of ATSA and the cloud masking tests, which is not only suitable for the RCCM over land, but cloud detection for other satellite missions. Using this framework, we have undertaken the largest comparison of existing histogram-based ATSAs (16 in total) and applied them to four cloud masking tests that can be constructed from the MISR radiances, namely the red channel bidirectional reflectance function (BRF), the standard deviation (STDV) of the red channel BRF, the normalized difference vegetation index (NDVI), and a parameter D that is constructed by optimizing the information from NDVI and red channel BRF for cloud detection. The cloud masking tests and ATSAs are applied to 35 MISR scenes from six snow-free land surface types. To evaluate their performance, reference cloud masks are constructed for the 35 scenes using interactive, supervised learning, visualization software. Independent of the ATSA and as a single cloud masking test, D performed the best in terms of having the lowest misclassification rate using the best possible threshold, the highest bimodal rate in the shape of the histograms derived from the 35 scenes, and the least sensitivity to errors in the choice of threshold. Of the 16 ATSAs, the methods of Li and Lee [Li, C. H. , and Lee, C. K. , (1993). Minimum cross-entropy thresholding. Pattern Recognition, 26(4), 617-625. ] and Pal and Bhandari [Pal, N. R. , and Bhandari, D. , (1993). Image thresholding: some new techniques. Signal Processing, 33, 139–158. ] performed the best when applied to D, with essentially unbiased performance and a root mean square of 15% when compared to cloud masks using the best possible thresholds. It is recommended that increased performance of the RCCM-land algorithm can be had through an increase in the space–time sampling used to generate histograms of D and the addition of a STDV cloud masking test to improve the detection of small cumulus clouds. [Copyright &y& Elsevier]

The Mojave Desert of southern California is under constant pressure from anthropogenic influences on atmospheric pollutants and land management. Previously, the spatial, temporal, and source characteristics of aerosols over the Mojave Desert have been examined using in situ observations; however, in situ observations lack spatial coverage, and the only long-term measurements of aerosol optical depth (AOD) needed for aerosol impact studies on the local climate and biota is the AERONET site at Rogers Dry Lake. In this study, we provide the first moderately high spatial resolution (17. 6×17. 6 km2) study of AOD over the Mojave Desert of southern California using satellite data from the Multi-angle Imaging SpectroRadiometer (MISR) for the period of March 2000 to October 2005. We have demonstrated the seasonality of AOD over the entire Mojave Desert, and the spatial and temporal variability of AOD from the average AOD at national parks, military installations, and dry lakes and playas. Statistically significant differences from the Mojave Desert mean AOD can be attributed to proximity to urban sources (e. G. , Rogers Dry Lake is near the Los Angeles metropolitan area) and local sources (e. G. , mineral extraction at Bristol Dry Lake). The western Mojave, generally around Rogers Dry Lake and Harper Dry Lake exhibited the most sustained pattern of seasonally high aerosol optical depths throughout the year. As a result, the AERONET site at Rogers Dry Lake should not be used as a site representative of the aerosol conditions over the entire Mojave Desert of southern California. [Copyright &y& Elsevier]

Abstract: Four fractal dimension estimation algorithms are applied to three different simulated fractal functions over the entire range of fractal dimensions to demonstrate the effect of resolution, fractal type, and algorithm choice on the estimation of fractal dimension. Box counting, horizontal structuring element, variation, and power spectrum algorithms are reviewed and applied to 1-D Takagi, Weierstrass–Mandelbrot, and fractional Brownian motion curves to show that none of them perform consistently with each other or accurately when the correct fractal dimension is known. A better understanding of their limitations is achieved through a sliding window analysis, a procedure that calculates local slopes for linear curve fits. A synergistic approach involving knowledge of algorithm limitations is suggested to make progress toward more reliable estimates of fractal dimension. Implications for cloud studies are considered in lieu of these results. Scale breaks in clouds are investigated to see how well each estimator detects changes from one scaling regime to another. Results are presented to illustrate the importance of correcting errors in the fractal dimension estimation process when modeling cloud radiance fields. [Copyright &y& Elsevier]

Presents the first observational study on the potential of band-differenced angular signatures for cloud detection. use of radiative transfer simulations to propose that band-differenced angular signature may be able to produce good results in cloud detection.

Presents information on a study which derived a general equation for the total length of a geophysical parameter along a finite transect. Validation of the general equation; Hypothesis testing; Conclusion.

The inference of ice cloud properties from remote sensing data depends on the assumed forward ice particle model, as they are used in the radiative transfer simulations that are part of the retrieval process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (MC6) ice cloud property retrievals are produced in conjunction with a single-habit ice particle model with a fixed degree of ice particle surface roughness (the MC6 model). In this study, we examine the MC6 model and five other ice models with either smoother or rougher surface textures to determine an optimal model to reproduce the angular variation of the radiation field sampled by the Multi-angle Imaging Spectroradiometer (MISR) as a function of latitude. The spherical albedo difference (SAD) method is used to infer an optimal ice particle model. The method is applied to collocated MISR and MODIS data over ocean for clouds with temperatures ≤233 K during December solstice from 2012–2015. The range of solar zenith angles covered by the MISR cameras is broader at the solstices than at other times of the year, with fewer scattering angles associated with sun glint during the December solstice than the June solstice. The results suggest a latitudinal dependence in an optimal ice particle model, and an additional dependence on the solar zenith angle (SZA) at the time of the observations. The MC6 model is one of the most optimal models on the global scale. In further analysis, the results are filtered by a cloud heterogeneity index to investigate cloudy scenarios that are less susceptible to potential 3D effects. Compared to results for global data, the consistency between measurements and a given model can be distinguished in both the tropics and extra-tropics. The SAD analysis suggests that the optimal model for thick homogeneous clouds corresponds to more roughened ice particles in the tropics than in the extra-tropics. While the MC6 model is one of the models most consistent with the global data, it may not be the most optimal model for the tropics. [ABSTRACT FROM AUTHOR]

Characterizing 3-D structure of clouds is needed for a more complete understanding of the Earth's radiative and latent heat fluxes. Here we develop and explore a ray casting algorithm applied to data from the Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite, in order to reconstruct 3-D cloud volumes of observed clouds. The ray casting algorithm is first applied to geometrically simple synthetic clouds to show that, under the assumption of perfect, clear-conservative cloud masks, the reconstruction method yields overestimation in the volume whose magnitude depends on the cloud geometry and the resolution of the reconstruction grid relative to the image pixel resolution. The method is then applied to two hand-picked MISR scenes, fully accounting for MISR's viewing geometry for reconstructions over the Earth's ellipsoidal surface. The MISR Radiometric Camera-by-camera Cloud Mask (RCCM) at 1.1-km resolution and the custom cloud mask at 275-m resolution independently derived from MISR's red, green, and blue channels are used as input cloud masks. A wind correction method, termed cloud spreading, is applied to the cloud masks to offset potential cloud movements over short time intervals between the camera views of a scene. The MISR cloud-top height product is used as a constraint to reduce the overestimation at the cloud top. The results for the two selected scenes show that the wind correction using the cloud spreading method increases the reconstructed volume up to 4.7 times greater than without the wind correction, and that the reconstructed volume generated from the RCCM is up to 3.5 times greater than that from the higher-resolution custom cloud mask. Recommendations for improving the presented cloud volume reconstructions, as well as possible future passive remote sensing satellite missions, are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Southeast Asia (SEA) hosts one of the most complex aerosol systems in the world, with convoluted meteorological scales, sharp geographic and socioeconomic features, high biological productivity, mixtures of a wide range of atmospheric pollutants, and likely a significant susceptibility to global climate change. This physical complexity of SEA is coupled with one of the world''s most challenging environments for both in situ and remote sensing observation. The 7-Southeast Asian Studies (7SEAS) program was formed to facilitate interdisciplinary research into the integrated SEA aerosol environment via grass roots style collaboration. In support of the early 7SEAS program and the affiliated Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC4RS), this review was created to outline the network of connections linking aerosol particles in SEA with meteorology, climate and the total earth system. In this review, we focus on and repeatedly link back to our primary data source: satellite aerosol remote sensing and associated observability issues. We begin with a brief rationale for the program, outlining key aerosol impacts and, comparing their magnitudes to the relative uncertainty of observations. We then discuss aspects of SEA''s physical, socio-economic and biological geography relevant to meteorology and observability issues associated with clouds and precipitation. We show that not only does SEA pose significant observability challenges for aerosol particles, but for clouds and precipitation as well. With the fundamentals of the environment outlined, we explore SEA''s most studied aerosol issue: biomass burning. We summarize research on bulk aerosol properties for SEA, including a short synopsis of recent AERONET observations. We describe long range transport patterns. Finally, considerable attention is paid to satellite aerosol observability issues, with a face value comparison of common aerosol products in the region including passive and active aerosol products as well as fluxes. We show that satellite data products diverge greatly due to a host of known artifacts. These artifacts have important implications for how research is conducted, and care must be taken when using satellite products to study aerosol problems. The paper ends with a discussion of how the community can approach this complex and important environment. [Copyright &y& Elsevier]