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Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models. [ABSTRACT FROM AUTHOR]

The effect of fine particulates matter (PM2.5) as the main source of air pollution on human health and the environment is one of the major problems in Tehran. Due to the limited number of ground-level monitoring stations, PM2.5 concentrations are not widely monitored in Tehran. This present study aimed to estimate ground-based PM2.5 concentrations with Merra-2 aerosol components and compare them with Merra-2 PM2.5. In the first, ground-based PM2.5 concentrations from 23 stations of the Air Quality Control Company and Merra-2 PM2.5 components concentrations, Merra-2 PM2.5 concentrations, and aerosol optical thickness from the satellite reanalysis model were recorded over Tehran from 2012 to 2021. Then, we have analyzed the quantified impacts of PM2.5 components (including organic carbon, black carbon, sulfate, sea salt, and dust), meteorological variables (including temperature, wind speed, relative humidity, precipitation, boundary layer height, and cloud cover), and aerosol optical thickness on ground-based and Merra-2 PM2.5 concentrations. Our analysis shows that the Merra-2 PM2.5 concentrations were recorded much lower than the ground-based PM2.5. Also, the annual trend of PM2.5 components decreased except for dust. A strong linear correlation is obtained between Merra-2 PM2.5 concentrations and dust. Boundary layer height among the meteorological variables influencing PM2.5 plays a major role in PM2.5 concentrations. There is a relatively good correlation between ground-based and Merra-2 PM2.5 with aerosol optical thickness. Black carbon has the most significant anti-correlation with boundary layer height. Also, black carbon and relative humidity have a very good correlation. Finally, we validate the estimation of ground-based PM2.5 concentrations with aerosol components using a nonlinear multivariate power regression model ( R 2 = 0.970 , RMSE = 5.942 ). Overall, this study underlines the impact of meteorological variables and anthropogenic emissions on ground-based PM2.5 growth. [ABSTRACT FROM AUTHOR]

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.

The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) Aerosol Optical Thickness (AOT) dataset is a consistent and comprehensive dataset combining observations from various satellite instruments and other sources with a numerical model, supporting climate studies, atmospheric modeling, air quality monitoring, and environmental research. Due to the uneven and sparse distribution of the Aerosol Robotic Network (AERONET) in China, the validation for the MERRA-2 AOT dataset over China is inadequate. The construction of the National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) is helpful to compensate for MERRA-2 AOT dataset validation over China. The validation results show that the accuracy of the MERRA-2 AOT goes down along with the aerosol loading in the atmosphere increase. In general, when the AOT is less than 1.0, the slope can reach 0.712 with R2 = 0.584. The percentage of data pairs that fall within the GCOS minimum requirement is less than 60%. Research also shows that MERRA-2 has a lower simulation quality of AOT at high altitudes than at low altitudes in China. Additionally, MERRA-2's AOT simulation quality varies by season. Simulated quality is worst in spring, improving in subsequent seasons. During the winter season, simulations are of the highest quality. [ABSTRACT FROM AUTHOR]

The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) Aerosol Optical Thickness (AOT) dataset is a consistent and comprehensive dataset combining observations from various satellite instruments and other sources with a numerical model, supporting climate studies, atmospheric modeling, air quality monitoring, and environmental research. Due to the uneven and sparse distribution of the Aerosol Robotic Network (AERONET) in China, the validation for the MERRA-2 AOT dataset over China is inadequate. The construction of the National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) is helpful to compensate for MERRA-2 AOT dataset validation over China. The validation results show that the accuracy of the MERRA-2 AOT goes down along with the aerosol loading in the atmosphere increase. In general, when the AOT is less than 1.0, the slope can reach 0.712 with R2 = 0.584. The percentage of data pairs that fall within the GCOS minimum requirement is less than 60%. Research also shows that MERRA-2 has a lower simulation quality of AOT at high altitudes than at low altitudes in China. Additionally, MERRA-2’s AOT simulation quality varies by season. Simulated quality is worst in spring, improving in subsequent seasons. During the winter season, simulations are of the highest quality.

Long-term satellite climate data records of aerosol and cloud along with meteorological reanalysis data have been used to study the aerosol effects on deep convective clouds (DCCs) over the global oceans from a climatology perspective. Our focus is on identifying sensitive aerosol regimes and active geolocations of the aerosol effects on DCCs by using statistical analyses on long-term averaged aerosol and cloud variables. We found the aerosol effect tends to manifest relatively easily on the long-term mean values of observed cloud microphysical variables (e.g., cloud particle size and ice water amount) compared to observed cloud macrophysical variables (e.g., cloud cover and cloud top height). An increase of aerosol loading tends to increase DCC particle size and ice water amount in the tropical convergence zones but decrease them in the subtropical subsidence regions. The aerosol effect on the cloud microphysical variables is also likely to manifest over the northwestern Pacific Ocean and central and eastern subtropical Pacific Ocean. The aerosol effect manifested on the microphysical cloud variables may also propagate to cloud cover but weakly to cloud top height since the latter is more susceptible to the influence of cloud dynamical and thermodynamic processes. Our results, based on the long-term averaged operational satellite observation, are valuable for the evaluation and improvement of aerosol-cloud interactions in global climate models. [ABSTRACT FROM AUTHOR]

Thailand, especially in the northern region, often encounters the problem of having PM10 exceeding the normal standard level, which could do harm to people’s health. Mostly, such problem is caused by the burning of forest area and open area; this is clearly seen during January–April of every year. Also, the problem as mentioned is caused by the meteorological conditions and the terrains in the northern region that make it easy for PM10 to be accumulated. The aim of this study was to analyze the patterns of relationship between PM10 measured from the ground monitoring station and AOT data received from MODIS sensor onboard of Terra satellite in Phrae Province located in the northern region of Thailand. The method performed was by analyzing the correlation between PM10 data obtained from the ground monitoring station and the AOT data received from the MODIS sensor onboard of Terra satellite during January–April 2018. It was found from the study that the change of the intensity of PM10 and AOT in the climate was highly related; it appeared that the correlation coefficient (r) in January–April was 0.92, 0.91, 0.91 and 0.92, respectively. This research pointed out that during February– –April, the areas of Phrae Province had the level of PM10 that affected health. Besides, from the method in this research, it revealed AOT data received from MODIS sensor onboard of Terra satellite could be applied in order to follow up, monitor, and notify the spatial changes of PM10 efficiently.

The column-averaged, dry air mole fractions of CO2 and CH4 (XCO2 and XCH4, respectively) were retrieved from short-wavelength infrared (SWIR) spectra observed by the Greenhouse gases Observing SATellite (GOSAT). Continuous measurements of SWIR spectra have been made via GOSAT since 2009, but there has been insufficient investigation of the effects of cirrus clouds and aerosols on the observations. In this work, we investigated the influences of aerosols and cirrus clouds on the differences between GOSAT observations and Total Carbon Column Observing Network (TCCON) data for XCO2 and XCH4 (ΔXCO2 and ΔXCH4) at three sites: Tsukuba and Saga in Japan, and Lauder in New Zealand. We used aerosol optical thickness (AOT), Angstrom exponents (AEs), and single scattering albedo (SSA), all obtained from sky radiometer observations, as well as vertical profiles of aerosols and thin cirrus clouds from lidar observations. Matchups were performed within ±0.1° latitude/longitude rectangular areas of each TCCON site, and within 30 min of the GOSAT overpass time. The results show a negative slope between ΔXCO2 and AOT at 500 nm determined from sky radiometer data at Tsukuba and Saga. The GOSAT XCO2 values tended to be lowered in the presence of cirrus clouds and dense boundary-layer aerosols. Moreover, a significant negative ΔXCO2 was observed at times of large AOTs that resulted from dust-like events. At Lauder, ΔXCO2 was negatively correlated with the AOT at 500 nm, although the AOT at this site was generally small. The mean ± standard deviation for ΔXCO2 and ΔXCH4 at Lauder are −0.80 ± 1.83 (ppm) and −5.27 ± 10.79 (ppb) with correlation coefficients r between GOSAT and TCCON of 0.94 and 0.83, respectively. Both ΔXCO2 and ΔXCH4 were significantly and negatively correlated with the AOT during Sep-Oct-Nov. In addition, stratospheric aerosols caused large negative biases of ΔXCO2 and ΔXCH4 at Lauder despite the small stratospheric aerosol optical depth at that site. [ABSTRACT FROM AUTHOR]

Purpose: The work is aimed at searching, analyzing and comparison of weekly cycles in the behavior of optical characteristics of atmospheric aerosols, namely, the aerosol optical thickness in the regions of Ukraine with different degrees of anthropogenic load on the environment. Design/methodology/approach: To search for the seven-day variations, the data were used from the worldwide aerosol monitoring network AERONET, whose activity is based on the Cimel CE-318 automated unified solar photometer robot (France). An array of annual measurements of the aerosol optical thickness in the two spectral channels – 440 and 870 nm (hereinafter AOT440 and AOT870), in 2014 was analyzed. The monitoring data of two Ukrainian stations were compared. The first is located in Kyiv, the second – in the village of Martove, Pechenihy district, Kharkiv region. Since the data of the solar photometer are discrete, the method of superposing epochs was chosen for their statistical processing. Findings: As a result of calculations of the AOT440 and AOT870 parameters, the presence of seven-day variations in these parameters in both regions was established. In order to verify the similarity of the “weekend effect” in variations in the concentration of aerosols in the atmosphere over the city of Kyiv and the village of Martove, the maximum values of the mutual correlation coefficients of the parameters under study were determined. It is established that the correlation coefficient for the two series of the AOT440 parameter average values, calculated according to the two stations data, is approximately 0.7, and of the AOT870 reaches 0.9. These results suggest that the manifestations of the “weekend effect” in variations in the concentration of aerosols both over an ecologically clean area (Martove village) and over the industrially developed city of Kyiv are almost identical. Conclusions: The presence of seven-day variations in the parameters of atmospheric aerosols AOT440 and AOT870 at two observation points in Ukraine has been established, the maximum average values of the parameters recorded are observed on Friday. The analysis showed the high degree of correlation of the results of observations at two stations. Given the varying degree of anthropogenic load of the urbanized region and the recreational zone of Ukraine in which the stations are located, and the high similarity of the seven-day cycles of the parameters AOT440 and AOT870 recorded in 2014, it can be argued that the previous studies findings on the global nature of human influence on the environment were confirmed.

Thailand, especially in the northern region, often encounters the problem of having PM10 exceeding the normal standard level, which could do harm to people’s health. Mostly, such problem is caused by the burning of forest area and open area; this is clearly seen during January–April of every year. Also, the problem as mentioned is caused by the meteorological conditions and the terrains in the northern region that make it easy for PM10 to be accumulated. The aim of this study was to analyze the patterns of relationship between PM10 measured from the ground monitoring station and AOT data received from MODIS sensor onboard of Terra satellite in Phrae Province located in the northern region of Thailand. The method performed was by analyzing the correlation between PM10 data obtained from the ground monitoring station and the AOT data received from the MODIS sensor onboard of Terra satellite during January–April 2018. It was found from the study that the change of the intensity of PM10 and AOT in the climate was highly related; it appeared that the correlation coefficient (r) in January–April was 0.92, 0.91, 0.91 and 0.92, respectively. This research pointed out that during February– –April, the areas of Phrae Province had the level of PM10 that affected health. Besides, from the method in this research, it revealed AOT data received from MODIS sensor onboard of Terra satellite could be applied in order to follow up, monitor, and notify the spatial changes of PM10 efficiently. [ABSTRACT FROM AUTHOR]

In order to make the concentrating solar power (CSP) more competitive, an accurate prediction of the solar radiation incident on CSP tower plant receivers is necessary. The extinction in the heliostat-receiver pathway plays an important role in the optical loss of the solar field and therefore, in the performance of the plant. In order to correctly operate these kind of plants and to select new potential emplacements, it is necessary to know the on-site levels of extinction. A methodology was developed in another published work, called Extinction AOT method (EAM), with the purpose of finding out the levels of extinction present at a location using AERONET AOT as input data. However, there is no AERONET AOT data available at any potential site of interest for setting up a solar tower plant, so, alternative approaches were necessary. This paper proposes to determine levels of extinction at any location using AOT satellite data instead of AERONET data. These results have been compared with results obtained applying the EAM with AERONET data and using real extinction measurements obtained with the CIEMAT extinction measurement system. The extinction obtained at PSA has been 5% in all cases considering the error rates. • Extinction AOT method provides atmospheric extinction levels at places of interest. • Extinction AOT method has been validated with real measurements with CIEMAT system. • The use of AOT satellite data in the Extinction AOT method has been validated. • Satellite data can be used to obtain accurate extinction levels from any part of the world. [ABSTRACT FROM AUTHOR]

In this paper, we report first-time observations of total column ozone (TCO), aerosol optical thickness (AOT) and precipitable water content (PWC) at five different wavelengths using compact, handheld, multichannel sun photometer (Microtops II ozonometer) at Atigre village (16.74°N latitude, 74.37°E longitude, 604 ms altitude above sea level). The three optical filters at wavelengths 305 nm, 312 nm and 320 nm are used to detect the TCO, while two optical filters 936 nm and 1020 nm are used to detect the PWC and AOT, respectively. We have been collecting data from September 1, 2017, and 122 days are selected from September 1, 2017, to December 31, 2017, to present as first observations of TCO, AOD and PWC. The mean values of TCO, AOT and PWC are found to be 261.2 (± 9.22) DU, 0.25 (± 0.04) and 1.98 (± 0.79) cm, respectively, during this period. The TCO measurements taken by the Microtops II ozonometer are compared with the ozone monitoring instrument satellite data over Atigre. We found that PWC and AOT values are negatively correlated (− 0.54) during monsoon season, while they show positive correlation (0.63) during other seasons with 90% significance level. The diurnal variation of TCO, PWC and AOT differ in monsoon and the post-monsoon season. [ABSTRACT FROM AUTHOR]

The Global Change Observation Mission-Climate (GCOM-C) is currently the only satellite sensor providing aerosol optical thickness (AOT) in the ultraviolet (UV) region during the morning overpass time. The observations in the UV region are important to detect the presence of absorbing aerosols in the atmosphere. The recently available GCOM-C dataset of AOT at 380 nm for January to September 2019 were evaluated using ground-based SKYNET sky radiometer measurements at Chiba, Japan (35.62° N, 140.10° E) and Phimai, central Thailand (15.18° N, 102.56° E), representing urban and rural sites, respectively. AOT retrieved from sky radiometer observations in Chiba and Phimai was compared with coincident AERONET and multi-axis differential optical absorption spectroscopy (MAX-DOAS) AOT values, respectively. Under clear sky conditions, the datasets showed good agreement. The sky radiometer and GCOM-C AOT values showed a positive correlation (R) of ~0.73 for both sites, and agreement between the datasets was mostly within ±0.2 (the number of coincident points at both sites was less than 50 for the coincidence criterion of ≤30 km). At Chiba, greater differences in the AOT values were primarily related to cloud screening in the datasets. The mean bias error (MBE) (GCOM-C – sky radiometer) for the Chiba site was −0.02 for a coincidence criterion of ≤10 km. For a similar coincidence criterion, the MBE values were higher for observations at the Phimai site. This difference was potentially related to the strong influence of biomass burning during the dry season (Jan–Apr). The diurnal variations in AOT, inferred from the combination of GCOM-C and ozone monitoring instrument (OMI) observations, showed good agreement with the sky radiometer data, despite the differences in the absolute AOT values. Over Phimai, the AOT diurnal variations from the satellite and sky radiometer observations were different, likely due to the large differences in the AOT values during the dry season.

Satellite remote sensing offers an effective approach to estimate indicators of air quality on a large scale. It is critically significant for air quality monitoring in areas experiencing rapid urbanization and consequently severe air pollution, like the Pearl River Delta (PRD) in China. This paper starts with examining ground observations of particulate matter (PM) and the relationship between PM10 (particles smaller than 10 μm) and aerosol optical thickness (AOT) by analyzing observations on the sampling sites in the PRD. A linear regression (R2 = 0.51) is carried out using MODIS-derived 500 m-resolution AOT and PM10 concentration from monitoring stations. Data of atmospheric boundary layer (ABL) height and relative humidity are used to make vertical and humidity corrections on AOT. Results after correction show higher correlations (R2 = 0.55) between extinction coefficient and PM10. However, coarse spatial resolution of meteorological data affects the smoothness of retrieved maps, which suggests high-resolution and accurate meteorological data are critical to increase retrieval accuracy of PM. Finally, the model provides the spatial distribution maps of instantaneous and yearly average PM10 over the PRD. It is proved that observed PM10 is more relevant to yearly mean AOT than instantaneous values.

Seasonal haze pollution is a big issue in northern part of Thailand for last decade. It normally happens in dry season during February to May annually. Chiang Mai is one of effected city and become economics and health concerns for local people seriously. Thus, haze pollution monitoring remains a challenge. PM10 value is the key indicator of air quality. Many publications have been reported the relationship between PM10 value and the aerosol optical thickness (AOT) which can be analyzed by MODIS Aerosol Product. This study examined the temporal and spatial distribution of PM10 base on a long-term monitoring data (2007-2016) using Terra-MODIS MOD04_3K product with 3 km resolution. The images were analyzed and calculated AOT and PM10 using existing model. The results were converted to air quality index according to standard of Thailand Pollution Control Department as good, moderate, unhealthy, very unhealthy, and hazardous. The result showed that high PM10 values (> 120 µg/m3) which unhealthy for sensitive groups and very unhealthy levels were exposed in February, March, and April for some areas. The highest PM10 value was found in February and has been continuously decreasing in March, April and May, respectively. These results are useful for haze resilience against impact in the near future. [ABSTRACT FROM AUTHOR]

Analysis of long-term variations of aerosol optical thickness (AOT) is essential to understand the climate change and earth radiation budget. A simplified method was developed in this study to derive the AOT using more than thirty-year surface visibility and aerosol vertical distribution from LiDAR climatology of vertical aerosol structure for space-based LiDAR simulation studies data. Moderate resolution imaging spectroradiometer (MODIS) TERRA and AQUA daily measurements were used to evaluate the performance of the inferred AOT. Results show that the correlations of monthly AOT between MODIS measurements and inferred data are higher than 0.59 and the annual AOT values have stronger correlations (r > 0.82) than monthly data. The inferred AOT can capture the general spatial distribution similar to satellite images with long-term scale. The correlation of seasonal variation between two datasets is also higher than 0.77. The root mean squared error of inferred AOT relative to MODIS AOT is about 0.22 and 0.11 for monthly and yearly data, respectively. Analysis using singular value decomposition shows that the inferred AOT is consistent with MODIS measurements during 2002–2014 spatially and temporally. The average annual AOT in over 200 stations in China between 1980 and 2014 shows generally upward trends. Results from this paper can be used for further analysis of the aerosol effects on climate change in a regional scale. [ABSTRACT FROM PUBLISHER]

Aerosol optical thickness (AOT), temperature and precipitable water vapour (PWV) were measured using MICROTOPS II Sunphotometer during the month of January 2011 and January 2014 for their analysis in the winter season over South Karanpura Coalfield region, Jharkhand, India. Patterns of AOT concentration (at five different wavelengths 340, 500, 870, 936 and 1020 nm) were measured along with water vapour and temperature, which were then spatially analyzed with reference to land use/land cover (LU/LC) of the region. Spatial distribution of AOT indicated higher concentration over industrial area (1.923–3.333 at 340 nm) and construction sites (> 2.955 at 340 nm) and lower concentrations (< 0.511 at 340 nm) over planned residential areas. To determine the aerosol size distribution, Ǻngstrӧm parameters ( α , β ) were calculated at wavelengths 340–870 nm (2011 and 2014) which ranged between 0.422 and 1.286 and 0.472 to 2.593 in January 2011 and 2014 respectively for ‘ α ’, whereas ‘ β ’ value lies between 0.127 and 1.379 in January 2011 and 0.031 to 1.923 in January 2014. The study indicates the presence of different particle sizes of aerosol associated with industries and coal mining activities which contribute significantly to increased aerosol concentration in the study area. [ABSTRACT FROM AUTHOR]

An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical thickness (AOT) and columnar water vapor (CWV). This paper describes problems associated with this process and recommends an improved strategy for processing remote sensing data, collected from both visible to near-infrared and shortwave infrared modules, to retrieve accurate AOT, CWV, and surface reflectance values. This strategy includes a workflow for radiometric and spatial cross-calibration and a method to retrieve atmospheric parameters and surface reflectance based on a radiative transfer function. This method was tested using data collected with the Compact Airborne Spectrographic Imager (CASI) and SWIR Airborne Spectrographic Imager (SASI) from a site in Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. The results showed that retrieving AOT from the remote sensing data required establishing empirical relationships between 465.6 nm/659 nm and 2105 nm, augmented by ground-based reflectance validation data, and minimizing the merit function based on AOT@550 nm optimization. The paper also extends the second-order difference algorithm (SODA) method using Powell’s methods to optimize CWV retrieval. The resulting CWV image has fewer residual surface features compared with the standard methods. The derived remote sensing surface reflectance correlated significantly with the ground spectra of comparable vegetation, cement road and soil targets. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This study provides a good reference for surface reflectance inversion that lacks synchronized atmospheric parameters. [ABSTRACT FROM AUTHOR]

Persistent cloud cover is an important obstacle to studying the ground surface of tropical rain forest areas using high-resolution optical data, such as those obtained with Landsat satellites. The identification and masking of the cloud-affected parts of such images is a necessary preprocessing step, but it easily leads to impractical fragmentation of the informative image area. Pixel-based multitemporal image compositing solves the fragmentation problem, but depends on a predefined compositing period length and compositing criterion. Here, we evaluate the radiometric consistency of Landsat TM/ETM+ composite images over undisturbed Amazonian forests and test to what degree it varies with the number of available multitemporal observations per pixel and the compositing criterion. Five compositing criteria were tested: maximum NDVI, median red, median near-infrared, multidimensional medoid, and minimum aerosol optical thickness. Each was applied to datasets consisting of 3–30 observations per pixel. Compositing quality was assessed both visually and with quantitative measures using the overlap area of neighboring WRS-2 scenes. We found that the medoid approach generated the most radiometrically consistent composite images. Composite image quality increased monotonically with the number of observations, but with diminishing returns. Satisfactory results were generally obtained with 10–15 observations per pixel. [ABSTRACT FROM PUBLISHER]

A simple graphical method is presented to classify aerosol types based on a combination of aerosol optical thickness (AOT) and aerosol relative optical thickness (AROT). Six aerosol types, including maritime (MA), desert dust (DD), continental (CO), sub-continental (SC), urban industry (UI) and biomass burning (BB), are discriminated in a two dimensional space of AOT 440 and AROT 1020/440 . Numerical calculations are performed using MIE theory based on a multi log-normal particle size distribution, and the AROT ranges for each aerosol type are determined. More than 5 years of daily observations from 8 representative aerosol sites are applied to the method to confirm spatial applicability. Finally, 3 individual cases are analyzed according to their specific aerosol status. The outcomes indicate that the new graphical method coordinates well with regional characteristics and is also able to distinguish aerosol variations in individual situations. This technique demonstrates a novel way to estimate different aerosol types and provide information on radiative forcing calculations and satellite data corrections. [ABSTRACT FROM AUTHOR]

The Model of Atmospheric Transport and Chemistry (MATCH) developed by the US National Center for Atmospheric Research (NCAR) was used to calculate the aerosol optical thickness (AOT) over China in 2006, with emission source data of the Intercontinental Chemical Transport Experiment Phase B (INTEX-B) and NCEP/NCAR reanalysis data as inputs. The simulation results of AOT were then validated with observational data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Chinese Sun Hazemeter Network (CSHNET), Aerosol Robotics Network (AERONET), and China Aerosol Remote Sensing Network (CARSNET) at more than 30 stations over China. The comparison results indicated that the high values of AOT in the areas such as the Sichuan basin and East and South China and the low values of AOT over the Tibetan Plateau and Northwest and Northeast China were reasonably simulated by the MATCH. This model tended to underestimate the AOT values in high-aerosol-loading areas but overestimate the AOT values in less polluted areas because there are still large uncertainties in the expression of emission sources, the description of the optical properties of aerosols, the treatment of cloud and precipitation, and the selection of grid resolution. The modeling results were consistent with the CSHNET, CARSNET, AERONET, and MODIS data in most parts of China, and the correlation coefficient of the monthly mean AOT between the model and the observation was 0.79 with CSHNET data at 23 stations, 0.51 with MODIS data, and 0.88 with data at 3 CARSNET stations and 2 other stations. All of them passed the significance test with α < 0.0001. The results demonstrated that the MATCH has the ability to simulate the characteristics of the AOT distribution and its seasonal variation over China. [ABSTRACT FROM AUTHOR]

Abstract: Although aerosols have great impacts on Surface Downward Shortwave Flux (SDSF), the relationship between aerosol loading and SDSF in global models has not yet been adequately investigated. In this study, we attempt to investigate the effects of aerosol optical thickness (AOT) and single scattering albedo (SSA) on SDSF through an integrative analysis of modeling and observation. At first, we compared the results obtained by a global aerosol model, SPRINTARS, with in-situ measurements, AERONET and BSRN. And then we estimated the impacts of AOT and SSA on SDSF through an offline radiative transfer model, Rstar. Through this study, we found that the difference in SDSF between SPRINTARS and BSRN is much larger over heavy aerosol regions than those over regions. Using the Rstar radiative transfer model, we demonstrated that the AOT difference usually has the strongest impact on the SDSF difference and the SSA difference has a moderate impact over heavy aerosol loading regions, whereas the effect of water vapor can be ignored. Finally, we generated a contour plot to demonstrate the relationships between AOT-SSA-SDSF. For example, at low AOT (e.g., 0.15), the 20 W m−2 changes in SDSF are required to make more than 0.2 changes of SSA, whereas at high AOT (e.g., 0.85), the same changes in SDSF are required to have only 0.05 changes of SSA. [Copyright &y& Elsevier]

Abstract: Trans-Pacific Asian dust transport has been well documented, but little is known about dust invasion to the South China Sea (SCS). This study presents the first detailed characterization of dust aerosols transported to the northern SCS. On 21 March 2010, a strong Asian dust storm affected large areas from the Gobi deserts to the West Pacific, including Taiwan and Hong Kong, and was also observed by a comprehensive set of instruments at Dongsha Island, a small island (about 2 km2, 20°42′52" N, 116°43′51" E) in the northern SCS. Aerosol measurements including particle mass concentrations, size distribution, optical properties, hygroscopicity, and vertical profiles help depict the evolution of this dust event. Our results indicate that the dust particles were mixed with anthropogenic and marine aerosols, and transported within 250 m above ground level. The long-range transport of Asian dust to the northern SCS could significantly impact the ecosystems in the region. [Copyright &y& Elsevier]

Abstract: We present a method for retrieving atmospheric particulate matter (PM10) from sun-sky photometer measurements (AOT). As PM10 is a “surface parameter” and AOT is an “integrated parameters”, we first determined whether a “functional relationship” linking these two quantities exists. Since these two parameters strongly depend on atmospheric structures and meteorological variables, we classified the meteorological situations in terms of weather types by using a neuronal classifier (Self organizing Map). For each weather type, we found that a relationship between AOT and PM10 can be established. We applied this approach to the Lille region (France) for the summer 2007 and then extended to a five summer period (summers of the years 2003–2007) in order to increase the statistical confidence of the PM10 retrieval from AOT measurements. The good performances of the method led us to envisage the possibility of deriving the PM10 from satellite observations. [Copyright &y& Elsevier]

dust deflation module was developed and coupled with the air quality modeling system RAMS-CMAQ to simultaneously treat all the major tropospheric aerosols (i.e., organic and black carbons, sulfate, nitrate, ammonia, soil dust, and sea salt). Then the coupled system was applied to East Asia to simulate Asian dust aerosol generation, transport and dry/wet removal processes during 14-25 March 2002 when two strong dust storms occurred consecutively. To evaluate model performance and to analyze the observed features of dust aerosols over the East Asian region, model results were compared to concentrations of suspended particulate matter of 10 µm or less (PM; 1-h intervals) at four remote Japanese stations and daily air pollution index (API) values for PM at four large Chinese cities. The modeled values were generally in good agreement with observed data, and the model reasonably reproduced two dust storm outbreaks and generally predicted the dust onset and cessation times at each observation site. In addition, hourly averaged values of aerosol optical thickness (AOT) were calculated and compared with observations at four Aerosol Robotic Network (AERONET) stations to assess the model's capability of estimating dust aerosol column burden. Analysis shows that modeled and observed AOT values were generally comparable and that the contribution of dust aerosols to AOT was significant only with regard to their source regions and their transport paths. [ABSTRACT FROM AUTHOR]

Data merging with interpolation is a method of combining near-coincident satellite observations to provide complete global or regional maps for comparison with models and ground station observations. We investigate various methods and limitations of data merging (or data fusion), with and without interpolation, as a first step toward merging data sets archived in the National Aeronautics and Space Administration Goddard Earth Sciences Data and Information Services Center and made public through the Goddard Interactive Online Visualization and ANalysis Infrastructure (Giovanni) data portals. As a prototype for the data-merging algorithm, this paper uses daily global observations of aerosol optical thickness (AOT), as measured by the MODerate resolution Imaging Spectroradiometer onboard the Terra and Aqua satellites. The goal is to develop a very fast and accurate online method of data merging for implementation into Giovanni. We demonstrate three different methods for pure merging (without interpolation): simple arithmetic averaging (SAA), maximum likelihood estimate (MLE), and weighting by pixel counts. All three methods are roughly comparable, with the MLE (SAA) being slightly preferable when validating with respect to the AOT standard deviations (AOT means). To evaluate the merged product, we introduce two confidence functions, which characterize the percentage of the merged AOT pixels as a function of the relative deviation of the merged AOT from the initial Terra and Aqua AOTs. Eight combinations of merging–interpolation are applied to scenes with regular and irregular data gap patterns. Our results show that the merging–interpolation procedure can produce complete spatial fields with acceptable errors. [ABSTRACT FROM AUTHOR]

Far from land-based sources, long-term statistics of aerosol optical thickness (AOT) over the oceans is expected to have a negligible diurnal signal. Therefore, comparing large-scale regional and global mean AOT derived independently from Terra and Aqua Moderate Resolution Imaging Spectroradiometer data will provide a measure of the precision of the overall product. Three years of overlapping records of quality and pixel-weighted level-3 statistics were analyzed. These data show that the instruments' measurement of the long-term global annual mean agree to within 1% or 0.0014-in optical thickness with an rms difference of 0.004, which is calculated from global monthly means. Two-thirds of the regions have long-term annual regional means that agree to within 2.5%. The highest regional difference is 5%, which is found over the southern circumpolar ocean. Other aerosol parameters such as particle size will show larger differences. Datasets that are not quality and pixel weighted may also show larger differences [ABSTRACT FROM PUBLISHER]

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001–2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 km × 30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r = 0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product. [ABSTRACT FROM AUTHOR]

The Global Change Observation Mission-Climate (GCOM-C) is currently the only satellite sensor providing aerosol optical thickness (AOT) in the ultraviolet (UV) region during the morning overpass time. The observations in the UV region are important to detect the presence of absorbing aerosols in the atmosphere. The recently available GCOM-C dataset of AOT at 380 nm for January to September 2019 were evaluated using ground-based SKYNET sky radiometer measurements at Chiba, Japan (35.62° N, 140.10° E) and Phimai, central Thailand (15.18° N, 102.56° E), representing urban and rural sites, respectively. AOT retrieved from sky radiometer observations in Chiba and Phimai was compared with coincident AERONET and multi-axis differential optical absorption spectroscopy (MAX-DOAS) AOT values, respectively. Under clear sky conditions, the datasets showed good agreement. The sky radiometer and GCOM-C AOT values showed a positive correlation (R) of ~0.73 for both sites, and agreement between the datasets was mostly within ±0.2 (the number of coincident points at both sites was less than 50 for the coincidence criterion of ≤30 km). At Chiba, greater differences in the AOT values were primarily related to cloud screening in the datasets. The mean bias error (MBE) (GCOM-C – sky radiometer) for the Chiba site was −0.02 for a coincidence criterion of ≤10 km. For a similar coincidence criterion, the MBE values were higher for observations at the Phimai site. This difference was potentially related to the strong influence of biomass burning during the dry season (Jan–Apr). The diurnal variations in AOT, inferred from the combination of GCOM-C and ozone monitoring instrument (OMI) observations, showed good agreement with the sky radiometer data, despite the differences in the absolute AOT values. Over Phimai, the AOT diurnal variations from the satellite and sky radiometer observations were different, likely due to the large differences in the AOT values during the dry season. [ABSTRACT FROM AUTHOR]