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The current study primarily sought to select the best index for elaborating on dust activities, analyze the temporal and spatial changes of the index’s trend, and examine the relationship of the index with environmental factors in Iranian dry regions. To this end, the study examined the data collected on dust concentration, dust storm index, the number of dusty days (NDD), the pollution caused by dust storms, and the frequency of all dust events over a period of 18 years (2001-2018) using the MODIS-aerosols optical depth (AOD) product. Moreover, Pearson's correlation coefficient was used to analyze the correlation between the indices and AOD data sets. On the other hand, the trend of the best index annual changes in twenty-eight Iranian urban areas was analyzed using the Mann-Kendall method. Also, the most important environmental factors controlling dust activities in high-risk areas were identified using the random forest model. The results of the study indicated a strong correlation between NDD and AOD in Iranian dry regions (r= 0.7; p-value= 0.001). It was also found that the trend of NDD’s annual changes significantly increased in Torbat Heydarieh, Nehbandan, and Anar (Z>+1.96). However, the trend significantly decreased in Chabahar and Iranshahr (Z>│-1.96│. Generally, the results indicated an insignificant decreasing trend of annual NDD changes across the entire Iranian arid regions from 2001 to 2018 (Z= -0.45). on the other hand, the random forest model suggested that air pressure and wind speed exerted the greatest influence on dust activities that occurred in Iran’s high-risk area throughout the study period. Therefore, it could be argued that the findings of this study can help better monitor dust events and reduce their environmental risks in Iranian dry areas. [ABSTRACT FROM AUTHOR]

As complex climatic events, dust storms could be managed by considering their nature and attributes. Therefore, this study sought to investigate interactions between the aerosol optical depth index and climatic land surface characteristics using data mining and zoning techniques in dust-prone regions of the Yazd province, Iran. To this end, the required data was collected from several climatic products of the University of Idaho and Modis Sensor for the 2000–2017 period using Google Earth Engine. Moreover, the image of the maximum dust was processed using AOD Modis and ENVI 5.1 software. Then, the underlying correlation between the variables was identified through various data mining techniques. In addition, the ROC curve was used for cross-validation, and different metrics were applied to assess the model, including Square Root of Error, Absolute Normalized Error, Classification Error, Absolute Error, and Crucial Class Fraction Ratio. Finally, the best data mining approach was used to determine the location and zoning of dust-prone regions. The findings of the study indicated that the decision tree outperformed the Bayesian theory with 89.53% accuracy and that it performed better than the nearest neighbor with an accuracy of 61.3% and 81.31%, respectively. As for the validation of the models, the decision tree methods, nearest neighbor search, and Bayesian network theory showed 74.21%, 64.39%, and 55.42% values, respectively. Moreover, in regions with crucial harvest and dust ranges, wind speed and soil surface moisture were found to have the most significant role. On the other hand, the zoning of dustprone regions revealed that 888,067067 Km2 of areas were covered with the highest concentrations of dust, most of which were located in the central and eastern parts of Yazd province, with the AOD values being 0.465, 0.309, 0.162, and 0.065 for the ranges of 0-0.036, 0.036-0.072, 0.072-0.107, and 0.107-0.3, respectively. [ABSTRACT FROM AUTHOR]

The observation and quantification of mineral dust fluxes from high-latitude sources remains difficult due to a known paucity of year-round in situ observations and known limitations of satellite remote sensing data (e.g., cloud cover and dust detection). Here we explore the chronology of dust emissions at a known and instrumented high latitude dust source: Lhυave;'ààn MÃn (Kluane Lake) in Yukon, Canada. At this location we combine ground instrumentation, space-based remote sensing platforms, ground-based AERONET data, and oblique camera images to (i) investigate the daily to annual chronology of dust emissions recorded by these instrumental and remote sensing methods (at timescales ranging from minutes to years), and (ii) use data intercomparisons to comment on the principal factors that control the detection of dust in each case. Dust emissions were observed using oblique time-lapse (RC) cameras installed at Lhυave;'ààn MÃn for up to 23 hours a day. These were used as a baseline for analysis of aerosol retrievals from in situ metrological data, AERONET, and co-incident MODIS MAIAC. Use of high-cadence remote camera (RC) data collected during dust events allowed us to optimise the use of combination of date quality (DQ) 1 (aerosol optical depth - AOD) and DQ2 (single scattering albedo and Angstöm exponent) to best represent AOD dust retrievals from AERONET. Nevertheless, when compared with time series of RC data, optimised AERONET data only manage an overall 26 % detection rate for events (sub day) but 100 % detection rate for dust event days (DED) when dust was within the field of view. Here, in this instance, RC and remote sensing data were able to suggest that the low event detection rate was attributed to fundamental variations in dust advection trajectory, dust plume height, and inherent restrictions in sun angle at high latitudes. Working with a time series of optimised AOD data (covering 2018/2019), we were able to investigate the gross impacts of DQ choice on DED detection at the month/year scale. Relative to ground observations, AERONET's DQ2.0 cloud screening algorithm may remove as much as 97 % of known dust events (3 % detection). Finally, when undertaking an AOD comparison for DED and non-DED retrievals, we find that cloud screening of MODIS/AERONET lead to a combined low sample of co-incident dust events, and weak correlations between retrievals. Our results quantify and explain the extent of under-representation of dust in both ground and space remote sensing method; a factor which impacts on the effective calibration and validation of global climate and dust models. [ABSTRACT FROM AUTHOR]

In this paper, a bias-correction of clear-sky models in prediction of the diffuse solar irradiance is presented. High quality ground date from two networks is used for model input and validation: aerosol optical depth from AERONET and diffuse solar irradiance from BSRN. Four established clear-sky models are tested: REST2, Yang, PS and MAC2. Two types of bias-corrections are developed: i) a correction for multiple aerosol scattering (MSC), accounting for extinction at high turbidity, and ii) a general regression on the model errors as a function of Sun elevation angle (h) and turbidity (ß) correction (MOS). While the impact of MSC proves marginal, the MOS correction shows good results. Two versions of MOS are developed, one on the global dataset and a site-specific adaptation. Both versions bring significant improvements over the original implementation of the models, while the site-specific version reduces the errors with a few additional percentage points. The aggregate nRMSE of the REST2 model is reduced from 17.8% (no correction) to 11.3% (global MOS) and 9.4% (site-specific MOS). [ABSTRACT FROM AUTHOR]

Land-atmosphere interactions govern the process of dust emission and transport. An accurate depiction of these physical processes within numerical weather prediction (NWP) models allows for better estimating the spatial and temporal distribution of the dust burden and the characterisation of source and recipient areas. In the presented study, the ECMWF-IFS (European Centre for Medium-Range Weather Forecast - Integrated Forecasting System) outputs are used to simulate two-month long periods in the spring and autumn of 2020, focusing on a case study in October. The ECMWF-IFS outputs are produced with and without assimilation of Aeolus quality-assured Rayleigh-clear and Mie-cloudy Horizontal Line of Sight (HLOS) wind profiles. The experiments have been performed over the broader Eastern Mediterranean and Middle East (EMME) region that is frequently subjected to dust transport, as it encompasses some of the most active erodible dust sources. Aerosol and dust-related model outputs (extinction coefficient, optical depth and concentrations) are qualitatively and quantitatively evaluated against ground- and satellite-based observations. Ground-based columnar and vertically resolved aerosol optical properties are acquired through AERONET sun photometers and PollyXT lidar, while near-surface concentrations are taken from EMEP. Satellite-derived vertical dust and columnar aerosol optical properties are acquired through LIVAS and MIDAS, respectively. Overall, in cases of either high or low aerosol loadings, the model predictive skill is improved when WRF simulations are initialised with IFS meteorological fields in which Aeolus wind profiles have been assimilated. The improvement varies in space and time, with the most significant impact observed for the autumn months in the study region. Comparison with observation datasets saw a remarkable improvement in columnar aerosol optical depths, vertically resolved dust mass concentrations and near-surface particulate concentrations in the assimilated run against the control run. Reductions of model biases, either positive or negative, and an increase in the correlation between simulated and observed values were achieved. [ABSTRACT FROM AUTHOR]

Based on the strict radiative transfer algorithm, a new method is developed to derive the Precipitable Water Vapor (PWV) and Aerosol optical depth (AOD) from the ground-based measurements of direct sun irradiance. The attenuated direct irradiance from 300 nm to 1700 nm with FWHM of 6.5 nm are measured by a pair of grating spectroradiometers MS711 and MS712, located at the Institute of Atmospheric Physics, Chinese Academy of Sciences (39.98° N, 116.38° E), from June 2020 to March 2021. Compared to that of regular sun photometers such as CIMEL and POM, a strong water vapor absorption band near 1370 nm is introduced to derive PWV for the relatively dry atmosphere. The PWV and AOD inversion results obtained by EKO are compared with the synchronous data of CIMEL, and the two are highly consistent. The correlation coefficient, mean bias and standard deviation of PWVEKO and PWVCIMEL are 0.999, -0.027 cm (-3.57 %) and 0.054 cm (3.93 %) respectively, and the relative deviations of the differences between the two are slightly larger for drier air (PWV<5 mm) and lower solar elevation angle. The correlation coefficients of AODEKO and AODCIMEL at 380, 440, 500, 675, 870, 1020 nm are greater than 0.99, and the relative deviations are between -13.59 % and 9.37 %. [ABSTRACT FROM AUTHOR]

This work serves as the second of a two‐part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multisatellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS‐Chem, WRF‐Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over nonrural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and nonrural areas, referred to as "extended ground truth" or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias‐corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two‐part study develops a multimodel and multi‐AOD bias‐correction technique that has the potential to improve PM2.5 forecasts in both rural and nonrural areas in near real time, and be readily implemented at state levels. Plain Language Summary: The U.S. Environmental Protection Agency's AirNow program reports current or forecasted air quality to the general public in the form of Air Quality Index (AQI). The forecasted AQI is made available by local and state air quality agencies across more than 500 cities across the U.S. However, since surface observations of particulate matter (PM) are primarily located in the urban areas, observation‐based AQI in the rural areas is limited, and either the current or the forecasted AQI from AirNow has large uncertainties that are difficult to assess, especially during the fire season. Satellite observation with large spatial coverage provides a promising opportunity to fill in the gaps in areas where observations are spare. Building upon our previous work, here we develop a statistical technique to improve surface PM forecasts in the rural areas of continental U.S. through the use of satellite observations of aerosols, surface observations, and air quality forecasting models. Assessment with the data from Interagency Monitoring of Protected Visual Environments (IMPROVE) network shows the promise of our technique. The technique is designed with the consideration of the forecast in near real time, and is efficient with minimal requirement of computing. Key Points: Ensemble‐based AOD from different sensors and algorithms shows better performance than individual ensemble memberA multimodel and multi‐AOD ensemble bias correction via Kalman filter improves PM2.5 forecasts in rural areas lack of ground observationsAn ensemble framework for producing the single best PM2.5 forecast for next day in near real time via bias correction is established [ABSTRACT FROM AUTHOR]

Aerosols are tiny particles (liquid or solid) suspended in the atmosphere. They play a significant role in climate dynamics directly or indirectly. Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) are significant parameters to study the concentration and size or type of aerosol over an area, respectively. In this article, we utilized three years of AOD and AE parameters derived from moderate resolution imaging spectroradiometer (MODIS) satellite during the period January, 2013 to December, 2015 over Ethiopia. In order to study the spatiotemporal pattern of aerosols, we choose three areas (Debretabour, Gojjam and Addis Ababa) over Ethiopian highlands, which are representative of nonindustrial, agricultural and industrial areas respectively. Further we compare continental aerosols with marine aerosols from Djibouti. Our results clearly depicts the aerosol distribution over Ethiopia is highly variable spatially and temporally. The results indicates that the urban and biomass aerosols are dominate over Addis Ababa, and Gojjam respectively, whereas dust and biomass aerosols are present over Debretabour, while Djibouti is loaded by sea spray aerosols. The seasonal variability of AOD is found to be maximum during the kiremt (summer) and minimum during bega (winter) over all areas (continental and marine). [ABSTRACT FROM AUTHOR]

In this article, we study the effect of various environmental factors on intra-atomic frequency comb (I-AFC) based quantum memory. The effect of the environment is incorporated as random fluctuations and non-uniformity in the parameters such as comb spacing and the optical depth, of the frequency comb. We found that the I-AFC is viable for photon storage even for very large fluctuations in the parameters of the frequency comb, which makes I-AFC a robust platform for photon storage. Furthermore, we show that the non-uniform frequency combs without any fluctuations in the comb parameters can also yield efficient quantum memory. Since the intra-atomic frequency combs found in natural atomic systems are often non-uniform, our results suggest that a large class of these systems can be used for I-AFC based efficient quantum memory. [ABSTRACT FROM AUTHOR]

The combination of Rydberg atoms and electromagnetically induced transparency (EIT) has been extensively studied in the strong-interaction regime. Here we proposed utilizing an EIT medium with a high optical depth (OD) and a Rydberg state of low principal quantum number to create a many-body system of Rydberg polaritons in the weak-interaction regime. The phase shift and attenuation induced by the dipole–dipole interaction (DDI) were still significant, and can be viewed as the consequences of elastic and inelastic collisions among Rydberg polaritons. We further observed that the width of the transverse momentum distribution of Rydberg polaritons at the exit of the system became notably smaller as compared with that at the entrance. The observation demonstrates the cooling effect in this system. The μs-long interaction time due to the high-OD EIT medium plus the μm2-size collision cross section due to the DDI suggests a feasible platform of polariton Bose–Einstein condensation. Rydberg-state atoms are characterized by significant dipole–dipole interaction making them useful for quantum information applications. The authors present experimental results on a high-optical-depth electromagnetically-induced-transparency medium of weakly interacting Rydberg atoms as a way of realizing Bose–Einstein condensates of polaritons. [ABSTRACT FROM AUTHOR]

Two gamma‐ray glows were observed by a high‐altitude NASA ER‐2 aircraft flying at 20 km altitude over a thunderstorm in Colorado, USA. The flux of the first glow rapidly intensified and then abruptly decreased within a few tens of milliseconds. On a timescale of seconds, the flux decrease occurred simultaneously with a hybrid intra‐cloud/cloud‐to‐ground lightning discharge beneath the aircraft. However, a more detailed analysis of the discharge dynamics indicated that the discharge activity was unusually calm during the actual period of the flux decrease. The lightning was observed with on‐board antennas, optical sensor, and ground‐based lightning mapping and location networks. Its closest activity was 12 km away from the aircraft, below and slightly ahead the course. The gamma‐ray flux reduction happened roughly in the middle of the lightning development process. The glow spectral analysis for the periods of a weak and strong flux enhancement has been done. The spectra were found to be background‐like and similar to each other. Key Points: A rapid reduction of a gamma‐ray glow flux was observed from an aircraft at 20 km altitudeThe reduction happened synchronously with a lightning discharge beneath the aircraft and lasted for a few tens of millisecondsSpectral analysis of the weak and the strong glow fluxes do not suggest their different production mechanism [ABSTRACT FROM AUTHOR]

Cloud‐top heights (CTH) from the Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra constitute our longest‐running single‐platform CTH record from a stable orbit. Here, we provide the first evaluation of the Terra Level 2 CTH record against collocated International Space Station Cloud‐Aerosol Transport System (CATS) lidar observations between 50ºN and 50ºS. Bias and precision of Terra CTH relative to CATS is shown to be strongly tied to cloud horizontal and vertical heterogeneity and altitude. For single‐layered, unbroken, optically thick clouds observed over all altitudes, the uncertainties in MODIS and MISR CTH are −540 ± 690 m and −280 ± 370 m, respectively. The uncertainties are generally smaller for lower altitude clouds and larger for optically thin clouds. For multi‐layered clouds, errors are summarized herein using both absolute CTH and CATS‐layer‐altitude proximity to Terra CTH. We show that MISR detects the lower cloud in a two‐layered system, provided top‐layer optical depth <∼0.3, but MISR low‐cloud CTH errors are unaltered by the presence of thin cirrus. Systematic and random errors are propagated to explain inter‐sensor disagreements, as well as to provide the first estimate of the MISR stereo‐opacity bias. For MISR, altitude‐dependent wind‐retrieval bias (−90 to −110 m) and stereo‐opacity bias (−60 to −260 m) and for MODIS, CO2‐slicing bias due to geometrically thick cirrus leads to overall negative CTH bias. MISR's precision is largely driven by precision in retrieved wind‐speed (3.7 m s−1), whereas MODIS precision is driven by forward‐modeling uncertainty. Plain Language Summary: Cloud‐top height (CTH) is an essential climate variable that impacts the Earth's energy budget and hydrological cycle. We are greatly interested in CTHs for their possible application in detecting signatures of forced climate change in the more than two‐decade long (2000–present) CTH record from NASA's enduring mission, Terra. Since Terra has offered longevity and orbital stability, the remaining criterion for a successful climate dataset is an in‐depth understanding and quantification of uncertainty in the data. To ascertain the uncertainty of CTH retrievals from two Terra instruments, namely MISR and MODIS, we compare a subset of their observations against a lidar called CATS that operated from the International Space Station from 2015 to 2017. We determined that both MISR and MODIS have provided us with robust CTHs, with MISR being about twice as accurate and precise as MODIS. Each instrument demonstrates strengths and weaknesses depending on the types of clouds being observed. We note that the MISR error budget is self‐contained and that we were able to close the error budget. This study has also provided needed CTH error characteristics that can help inform future satellite architecture for observing CTH. Key Points: We present the first semi‐global (50°N–50°S) comparison of Terra cloud‐top heights with coincident samples from a space‐based lidarUsing lidar as truth, Terra cloud‐top height bias and precision are summarized as a function of cloud geometrical and optical propertiesWith the first measurement of stereo‐opacity bias (−60 to −260 m, depending on cloud type), MISR cloud height error‐budget is closed [ABSTRACT FROM AUTHOR]

Aerosol characteristics and aerosol–cloud interactions remain uncertain in remote marine regions. We use over a decade of data (2000–2012) from the NASA AErosol RObotic NETwork, aerosol and wet deposition samples, satellite remote sensors, and models to examine aerosol and cloud droplet number characteristics at a representative open ocean site (Bermuda) over the Western North Atlantic Ocean (WNAO). Annual mean values were as follows: aerosol optical depth (AOD) = 0.12, Ångström Exponent (440/870 nm) = 0.95, fine mode fraction = 0.51, asymmetry factor = 0.72 (440 nm) and 0.68 (1020 nm), and Aqua‐MODIS cloud droplet number concentrations = 51.3 cm−3. The winter season (December–February) was characterized by high sea salt optical thickness and the highest aerosol extinction in the lowest 2 km. Extensive precipitation over the WNAO in winter helps contribute to the low FMFs in winter (∼0.40–0.50) even though air trajectories often originate over North America. Spring and summer had more pronounced influence from sulfate, dust, organic carbon, and black carbon. Volume size distributions were bimodal with a dominant coarse mode (effective radii: 1.85–2.09 µm) and less pronounced fine mode (0.14–0.16 µm), with variability in the coarse mode likely due to different characteristic sizes for transported dust (smaller) versus regional sea salt (larger). Extreme pollution events highlight the sensitivity of this site to long‐range transport of urban emissions, dust, and smoke. Differing annual cycles are identified between AOD and cloud droplet number concentrations, motivating a deeper look into aerosol–cloud interactions at this site. Key Points: While anthropogenic influence is lowest in winter, cloud drop number concentrations are close to peak values along with high sea salt levelsBimodal volume size distributions with a dominant coarse mode peak that shifts to smaller sizes in spring/summer due in part to dustExtreme pollution events show the sensitivity of remote marine sites like Bermuda to a continental outflow of urban emissions, dust, and smoke [ABSTRACT FROM AUTHOR]

The record of downwelling solar irradiance and other surface radiation budget components for the U.S. has been extended through 2019 using SURFRAD Network data. Brightening of surface solar irradiance of +7.36 Wm−2/decade occurred from 1996 through 2012. In 2013, surface solar radiation sharply decreased to the long‐term mean (representing 1996–2019) and remained near that level through 2017. Successive decreases in 2018 and 2019 yielded a dimming trend of −3.90 Wm−2/decade after 2012, but with a high uncertainty owing to the observed variability and brief period covered. Individually, all stations but Penn State showed brightening trends consistent with the network average, and surface solar irradiance decreased at all stations after 2012. Total surface net radiation showed similar tendencies but the reversal from increasing to decreasing was more gradual because of the response of surface net longwave to the changing solar input. Aerosol optical depth decreased continuously throughout the tenure of the network but accounted for only 3% of the variability of surface solar irradiance, while cloud fraction explained 62%. The mean cloud fraction was 2.4% greater during the dimming period than the brightening period but showed no trends due to high interannual variability. However, annual anomalies of direct‐normal solar radiation, which relate to sun duration and clouds, generally increased to 2012 and then decreased thereafter. Collectively, these results indicate that changing cloud cover was the primary source of brightening and dimming over the U.S. from 1996 to 2019. Plain Language Summary: Since the 1980s, it has been known that solar radiation at the surface of the Earth goes through increases (brightening) for two‐to‐three decades followed by decreases (dimming) over similar periods. These cyclic patterns are not caused by variations in the sun's emission but rather by changes in cloud cover and dust in the atmosphere. Brightening and dimming occur all over the globe. Dimming was documented in the U.S. from the 1950s to about the mid‐1980s. In the mid‐to‐late 1980s, solar radiation at the surface reversed course and increased for more than 20 years. Here, we show that this most recent brightening period in the U.S. ended in 2012. Surface solar radiation decreased over the U.S. after 2013, signaling the possible beginning of a new dimming period. We determined that systematic changes in cloud cover were mostly responsible for these trends and that atmospheric dust played only a minor role. Knowledge of dimming and brightening is useful for research in weather, climate, agriculture, renewable energy, and any other process that responds to systematic changes of solar energy at the surface. Key Points: The brightening/dimming record of surface solar radiation over the U.S. has been updated through 2019Brightening over the U.S. that began in the 1980s seems to have ended in 2012Surface brightening and dimming trends in the U.S. since the 1990s have been attributed primarily to changes in cloud cover [ABSTRACT FROM AUTHOR]

Aerosol climatology during typical haze dominating period over South and Southeast Asia was explored using several Earth‐Observing A‐Train satellite products retrieved in between 2010 and 2020. Comparatively high aerosol optical depth (AOD) with dominance of fine and UV‐absorbing aerosols are noted across the Indo‐Gangetic plain, South Asia (IGP; AOD: 0.58; UVAI: 0.74) against weak UV‐absorbing fine aerosols over Southeast Asia (SEA; AOD: 0.26; UVAI: 0.07). Among inland IGP sites, decadal mean AOD resembles in Lahore (0.72 ± 0.45), Delhi (0.81 ± 0.46), Kanpur (0.84 ± 0.42), and Varanasi (0.78 ± 0.45); all exhibiting bimodal AOD distribution with a first peak in early November followed by a second in early January. In contrast, except mainland site Chiang Mai, all SEA maritime cities resemble in having typical September‐October AOD peak, with the presence of fine and UV‐neutral aerosols. Urban hotspots across IGP and SEA (except Dhaka, Chiang Mai) denote a spatially consistent minor increasing trend in AOD (0.2–1.8 × 10−2 year−1) while increase in UVAI is more prominent over upper IGP. Dust aerosols dominate only in Karachi (46%) against strong UV‐absorbing smoke aerosols over rest of the IGP (71–91%), and UV‐neutral smoke aerosols across SEA (84–92%). Vertical stratification of aerosol types is noted across IGP as in lower atmosphere (<4 km) polluted dust/urban aerosols remain abundant, with gradual decrease in dust aerosols from upper to lower IGP and consequent increase in smoke aerosols. At upper atmosphere (>4 km), however, dust aerosols clearly dominate. Over SEA, smoke are the most abundant aerosols across the atmospheric column followed by polluted dust. No evidence of intracontinental transport of aerosols from IGP to Southeast Asia or vice versa is, however, noted. Plain Language Summary: Aerosol climatology during typical haze dominating period over two global aerosol hotspots: South and Southeast Asia was explored using several satellite‐based aerosol optical properties retrieved using Earth Observing A‐Train satellites (Aqua, Aura, CALIPSO). Comparatively high aerosol optical depth with dominance of fine and absorbing aerosol are noted across the Indo‐Gangetic plain, South Asia against weak absorbing fine particles over Southeast Asia. Presence of strong absorbing aerosol over Indo‐Gangetic plain may have greater implications on regional climate. We note absorbing dust aerosols only over Karachi against strong absorbing smoke aerosols over Lahore, Delhi, Kanpur, Varanasi, and Dhaka, and UV‐neutral smoke aerosols over Singapore, Penang, and Kuala Lumpur. Vertical stratification of aerosol types is also noted across IGP as in lower atmosphere (<4km) polluted dust/ polluted urban aerosol remains abundant, with gradual decrease in dust aerosols from upper to lower IGP and consequent increase in smoke aerosols. No evidence in intracontinental transport of smoke aerosols is found, as biomass burning emissions over upper IGP, Kalimantan, and Sumatra primarily modulating climatology of regional aerosols. Key Points: Smoke aerosols are more UV‐absorbing across South Asia compared to Southeast AsiaVertical stratification of aerosols is prominent over IGP as urban aerosols and smoke dominate at low altitude while mineral dust at high altitudeSmoke and urban aerosols are abundant across the atmospheric column over SEA [ABSTRACT FROM AUTHOR]

The early months of 2020 showed record-breaking levels of aerosol optical depth (AOD) over the Southern Hemisphere (SH). Apart from the tropics, monthly AOD values over most of the SH exceeded the average by more than three standard deviations. This anomalous AOD is attributed to a combination of the intensity and location of the Australian bushfires. The fires took place south enough, where the tropopause altitude is relatively low, within the mid-latitude cyclone belt. This location allowed for deep convection over and downwind of the fires, which transported the smoke to the stratosphere, where its lifetime is an order of magnitude longer than it would have been in the lower atmosphere. The lower bound of the stratospheric smoke mass in January 2020 was ~2.1 ± 1 teragrams, which lead to cooling by more than 1.0 ± 0.6 watts per square meter over cloud-free oceanic areas. [ABSTRACT FROM AUTHOR]

This study reports a very high-resolution (400 m grid-spacing) operational air quality forecasting system developed to alert residents of Delhi and the National Capital Region (NCR) about forthcoming acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real-time aerosol observations from in situ and space-borne platform in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to produce a 72-h forecast daily in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM2.5 observations improves the initial condition for surface PM2.5 by about 45 µg/m3 (about 50%).The accuracy of the forecast degrades slightly with lead time as mean bias increase from + 2.5 µg/m3 on the first day to − 17 µg/m3 on the third day of forecast. Our forecast is found to be very skillful both for PM2.5 concentration and unhealthy/ very unhealthy air quality index categories, and has been helping the decision-makers in Delhi make informed decisions. [ABSTRACT FROM AUTHOR]

Surface PM2.5 concentrations are required for exposure assessment studies. Remotely sensed Aerosol Optical Depth (AOD) has been used to derive PM2.5 where ground data is unavailable. However, two key challenges in estimating surface PM2.5 from AOD using statistical models are (i) Satellite data gaps, and (ii) spatio-temporal variability in AOD-PM2.5 relationships. In this study, we estimated spatially continuous (0.03° × 0.03°) daily surface PM2.5 concentrations using MAIAC AOD over Madhya Pradesh (MP), central India for 2018 and 2019, and validated our results against surface measurements. Daily MAIAC AOD gaps were filled using MERRA-2 AOD. Imputed AOD together with MERRA-2 meteorology and land use information were then used to develop a linear mixed effect (LME) model. Finally, a geographically weighted regression was developed using the LME output to capture spatial variability in AOD-PM2.5 relationship. Final Cross-Validation (CV) correlation coefficient, r2, between modelled and observed PM2.5 varied from 0.359 to 0.689 while the Root Mean Squared Error (RMSE) varied from 15.83 to 35.85 µg m−3, over the entire study region during the study period. Strong seasonality was observed with winter seasons (2018 and 2019) PM2.5 concentration (mean value 82.54 µg m−3) being the highest and monsoon seasons being the lowest (mean value of 32.10 µg m−3). Our results show that MP had a mean PM2.5 concentration of 58.19 µg m−3 and 56.32 µg m−3 for 2018 and 2019, respectively, which likely caused total premature deaths of 0.106 million (0.086, 0.128) at the 95% confidence interval including 0.056 million (0.045, 0.067) deaths due to Ischemic Heart Disease (IHD), 0.037 million (0.031, 0.045) due to strokes, 0.012 million (0.009, 0.014) due to Chronic Obstructive Pulmonary Disease (COPD), and 1.2 thousand (1.0, 1.5) due to lung cancer (LNC) during this period. [ABSTRACT FROM AUTHOR]

By using multiple satellite measurements, the changes of the aerosol optical depth (AOD) and nitrogen dioxide (NO2) over South Korea were investigated from January to March 2020 to evaluate the COVID-19 effect on the regional air quality. The NO2 decrease in South Korea was found but not significant, which indicates the effects of spontaneous social distancing under the maintenance of ordinary life. The AODs in 2020 were normally high in January, but they became lower starting from February. Since the atmosphere over Eastern Asia was unusually stagnant in January and February 2020, the AOD decrease in February 2020 clearly reveals the positive effect of the COVID-19. Considering the insignificant NO2 decrease in South Korea and the relatively long lifetime of aerosols, the AOD decrease in South Korea may be more attributed to the improvement of the air quality in neighboring countries. In March, regional atmosphere became well mixed and ventilated over South Korea, contributing to large enhancement of air quality. While the social activity was reduced after the COVID-19 outbreak, the regional meteorology should be also examined significantly to avoid the biased evaluation of the social impact on the change of the regional air quality. [ABSTRACT FROM AUTHOR]

The Epoch of Reionization (EoR) depends on the complex astrophysics governing the birth and evolution of the first galaxies and structures in the intergalactic medium. EoR models rely on cosmic microwave background (CMB) observations, and in particular the large-scale E-mode polarization power spectra (EE PS), to help constrain their highly uncertain parameters. However, rather than directly forward-modelling the EE PS, most EoR models are constrained using a summary statistic – the Thompson scattering optical depth, τe. Compressing CMB observations to τe requires adopting a basis set for the EoR history. The common choice is the unphysical, redshift-symmetric hyperbolic tangent (tan h) function, which differs in shape from physical EoR models based on hierarchical structure formation. Combining public EoR and CMB codes, 21 cmfast and class , here we quantify how inference using the τe summary statistic impacts the resulting constraints on galaxy properties and EoR histories. Using the last Planck 2018 data release, we show that the marginalized constraints on the EoR history are more sensitive to the choice of the basis set (tan h versus physical model) than to the CMB likelihood statistic (τe versus PS). For example, EoR histories implied by the growth of structure show a small tail of partial reionization extending to higher redshifts. However, biases in inference using τe are negligible for the Planck 2018 data. Using EoR constraints from high-redshift observations including the quasar dark fraction, galaxy UV luminosity functions, and CMB EE PS, our physical model recovers |$\tau _\mathrm{ e} = 0.0569_{-0.0066}^{+0.0081}$|⁠. [ABSTRACT FROM AUTHOR]

We present a validation study of direct normal irradiance (DNI) estimates from HelioMont with ground-based measurements from two European sites for the year 2015. The HelioMont algorithm infers irradiance with data from the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument as the primary source of information on clouds, and data from models or reanalysis for other influential input parameters. The validation sites are the Plataforma Solar de Almería (PSA), a solar power research facility in Southern Spain characterized by arid conditions and the Swiss Baseline Surface Radiation Network (BSRN) site of Payerne, characterized by a much more frequent cloud coverage. Our analysis shows the importance of separately evaluating the quality of (1) the clear-sky irradiance computation and (2) the determination of the cloud effect. We also specifically investigate the cloud modification factor (CMF) using a validation CMF derived from ground-based data, giving us more insight into event-by-event agreement between HelioMont estimates and measured irradiances. The clear-sky HelioMont DNI uncertainty is mainly influenced by the aerosol optical depth (AOD) input data. Using the original AOD input (a 2008 climatology based on data from the Aerosol Comparisons between Observations and Models project) leads to large negative biases of 115 W m−2 to 145 W m−2. Using AOD from the Copernicus Atmosphere Monitoring Service (CAMS) allows reducing these biases to 15 W m−2 to 25 W m−2 (2% to 3%) with a dispersion of ±12% to ±15%, which is the HelioMont clear-sky DNI expanded uncertainty when using CAMS AOD. Using ground-measured AOD reduces this uncertainty to ±5% to ±6.5%, which is probably the limit of what is achievable with HelioMont. For all-sky comparisons, mean biases were between about −5 W m−2 and 55 W m−2 (depending on AOD input and station), while the root-mean-square deviation (RMSD) was between about 175 W m−2 and 195 W m−2. Our validation method yielded correlation between HelioMont and validation CMF between 0.79 and 0.92 (Pearson's correlation coefficient r), while RMSD was between 0.18 and 0.24. The computation of the cloud effect is the part of HelioMont that is the main source of uncertainty. Systematic errors were identified (underestimation of the number of near-zero DNI and overestimation of the number of clear-sky cases) and solving them may lead to substantial improvement. [ABSTRACT FROM AUTHOR]

We present results from spectroscopic observations of AT 2018hyz, a transient discovered by the All-Sky Automated Survey for Supernova survey at an absolute magnitude of MV ∼ −20.2 mag, in the nucleus of a quiescent galaxy with strong Balmer absorption lines. AT 2018hyz shows a blue spectral continuum and broad emission lines, consistent with previous TDE candidates. High cadence follow-up spectra show broad Balmer lines and He  i in early spectra, with He  ii making an appearance after ∼70–100 d. The Balmer lines evolve from a smooth broad profile, through a boxy, asymmetric double-peaked phase consistent with accretion disc emission, and back to smooth at late times. The Balmer lines are unlike typical active galactic nucleus in that they show a flat Balmer decrement (H α /H β ∼ 1.5), suggesting the lines are collisionally excited rather than being produced via photoionization. The flat Balmer decrement together with the complex profiles suggests that the emission lines originate in a disc chromosphere, analogous to those seen in cataclysmic variables. The low optical depth of material due to a possible partial disruption may be what allows us to observe these double-peaked, collisionally excited lines. The late appearance of He  ii may be due to an expanding photosphere or outflow, or late-time shocks in debris collisions. [ABSTRACT FROM AUTHOR]

Over the past decades, scientific visualization became a fundamental aspect of modern scientific data analysis. Across all data-intensive research fields, ranging from structural biology to cosmology, data sizes increase rapidly. Dealing with the growing large-scale data is one of the top research challenges of this century. For the visual exploratory data analysis, interactivity, a view-dependent visibility optimization and frame coherence are indispensable. In this work, we extend the recent decoupled opacity optimization framework to enable a navigation without occlusion of important features through large geometric data. By expressing the accumulation of importance and optical depth in Fourier basis, the computation, evaluation and rendering of optimized transparent geometry become not only order-independent, but also operate within a fixed memory bound. We study the quality of our Fourier approximation in terms of accuracy, memory requirements and efficiency for both the opacity computation, as well as the order-independent compositing. We apply the method to different point, line and surface data sets originating from various research fields, including meteorology, health science, astrophysics and organic chemistry. [ABSTRACT FROM AUTHOR]

Combing the color transmission image and the Beer–Lambert law shows a great application prospect in quantifying each material in multilayer specimen. Here, a novel, low-cost, and efficient optical algorithm is proposed to predict the thickness of each color material in a multilayer specimen from the color transmission image based on the Beer–Lambert Law. In this work, a normal scanner is employed to achieve the color transmission image of the monochrome transparent films. RGB values represent the transmitted intensity. A linear relationship between the optical depth and physical thickness is observed under different monochromatic lights. It is supposed that for a multilayer transparent film which consisted of different monochrome transparent films, the optical depth is related to the physical thickness of each monochrome transparent component. Therefore, an estimating equation is proposed to predict the thickness of each color material in the multilayer specimen. According to the result, the standard deviation of predicted thickness and practical thickness of each color film in the multilayer specimen is 0.93%. Fairly good agreement and high accuracy are obtained between the practical and predicted values, and the validity of this method is confirmed. [ABSTRACT FROM AUTHOR]

The Mars Science Laboratory rover (Curiosity) has monitored the line‐of‐sight extinction within Gale Crater since mission sol 100 (16 November 2012, MY 31 Ls 208°) with Navcam and since mission sol 1,187 (8 December 2015, MY 33 Ls 79°) with the color imager, Mastcam. This work reports 1,375 sols (>2 MYs) of line‐of‐sight extinction monitoring within Gale Crater with Mastcam and updates the Navcam line‐of‐sight extinction record to sol 2,556 (15 October 2019, MY 35 Ls 93°). A cyclical pattern with one or more peaks in the dusty season (Ls∼180–360°) and a trough in the less‐dusty season (Ls∼0–180°) was observed with both imagers and the results from Mastcam red and Navcam agree well. Green and blue filter Mastcam data show generally lower extinction than in the red filter data. Extinction as a function of azimuth and elevation angle were investigated and the extinction as a function of azimuth was generally found to be smooth and thus the dust well‐mixed horizontally. The extinction as a function of elevation shows increased dust loading at lower elevations during dusty seasons, indicative of dust lifting from the base of the crater. Plain Language Summary: The Curiosity rover has been taking images of the northern rim of Gale Crater, Mars, since 100 Mars days after landing (16 November 2012) with its single‐color Navigational camera and since 1,187 Mars days after landing (8 December 2015) with the Mastcam full‐color camera. Using these images, we can infer how much dust there is between the rover and the edge of the crater. Over the course of the mission, the results from both cameras agree well, showing an increase in dust during the Southern Hemisphere summer and a drop in dust during the Southern Hemisphere winter. Looking at the mixing of dust inside Gale Crater, it appears that the dust is well‐mixed horizontally, but there is evidence of more dust closer to the crater floor in some seasons, which could mean that dust is being lifted off the crater floor. Key Points: 1,375 sols of Mastcam‐derived line‐of‐sight extinction within Gale Crater are presented along with updates to the Navcam extinction recordA cyclical pattern with one or more peaks in the dusty season and a trough in the less‐dusty season was observed with both camerasExtinction suggests increased dust loading at lower elevations during dusty seasons, indicative of lifting or external introduction of dust [ABSTRACT FROM AUTHOR]

Climate change and land use are altering the landscape of the U.S. Great Plains, producing increases in windblown dust. These increases are investigated by combining coarse mode aerosol observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor in addition to the Aerosol Robotic Network (AERONET) and Interagency Monitoring of Protected Visual Environments (IMPROVE) aerosol monitoring networks. Increasing trends of up to 5%/year in MODIS aerosol optical depth for dust observations are observed throughout the Great Plains (2000–2018). Cropland coverage has increased 5–10% over the majority of the Great Plains (2008–2018), and positive monthly trends in IMPROVE (1988–2018) and AERONET (1995–2018) coarse mode 90th percentile observations coincide with planting and harvesting seasons of predominant crops. Presently, results suggest increased dust due to agricultural expansion is negatively influencing human health and visibility in the Great Plains. Furthermore, results foreshadow a future where desertification becomes an increasing risk in the Great Plains. Plain Language Summary: Throughout the U.S. Great Plains, satellite data combined with surface networks have shown a significant increase in airborne dust over the last two decades. This airborne dust is negatively influencing human health and visibility and coincides with increases in agricultural production. Key Points: Dust loading throughout the U.S. Great Plains has increased over the last two decadesPositive correlations between cropland expansion and dust trends downwind suggest that agriculture contributed to these increasesIn the event of enhanced drought due to climate change, results support an increased risk of desertification from agricultural development [ABSTRACT FROM AUTHOR]

While the dynamical origin of the variability observed in Galactic black hole systems, such as quasi-periodic oscillations (QPOs), is still a matter of debate, insight into the radiative components responsible for such behaviour can be obtained by studying their energy-dependent temporal behaviour. In particular, one needs to ascertain which variations of the parameters of the best-fitting time-averaged spectral components reproduce the observed energy-dependent fractional rms and time-lags. However, to obtain meaningful interpretation, the standard spectral component parameters have to be recast to physically relevant ones. Then, the energy-dependent temporal variations that their fluctuations will cause, needs to be predicted and compared with observations. In this work, we describe a generic method to do this and apply the technique to the ∼3–4 Hz QPOs observed in the black hole system GRS 1915+105 as observed by AstroSat where the time-averaged spectra can be represented by emission from a truncated disc and hot thermal Comptonizing coronae in the inner regions. We find that the QPOs and their harmonic can be explained in terms of correlated local accretion rate variations in the disc, the truncated disc radius, the optical depth and the heating rate of the coronae with time-delays between them. We highlight the potential of such techniques to unravel the radiative process responsible for variability using high-quality spectral and temporal data from AstroSat and NICER. [ABSTRACT FROM AUTHOR]

Galactic microlensing datasets now comprise in excess of 104 events and, with the advent of next-generation microlensing surveys that may be undertaken with facilities such as the Rubin Observatory (formerly LSST) and Roman Space Telescope (formerly WFIRST), this number will increase significantly. So too will the fraction of events with measurable higher order information, such as finite-source effects and lens–source relative proper motion. Analysing such data requires a more sophisticated Galactic microlens modelling approach. We present a new second-generation Manchester–Besançon Microlensing Simulator (MaBμlS-2), which uses a version of the Besançon population synthesis Galactic model that provides good agreement with stellar kinematics observed by the Hubble Space Telescope (HST) towards the bulge. MaBμlS-2 provides high-fidelity signal-to-noise limited maps of the microlensing optical depth, rate and average time-scale towards a 400 deg2 region of the Galactic bulge in several optical to near-infrared pass-bands. The maps take full account of the unresolved stellar background, as well as limb-darkened source profiles. Comparing MaBμlS-2 with the efficiency-corrected OGLE-IV 8000 event sample shows a much improved agreement over the previous version of MaBμlS and succeeds in matching even small-scale structural features in the OGLE-IV event rate map. However, evidence remains for a small underprediction of the event rate per source and overprediction of the time-scale. MaBμlS-2 is available online (www.mabuls.net , Specht & Kerins) to provide on-the-fly maps for user-supplied cuts in survey magnitude, event time-scale and relative proper motion. [ABSTRACT FROM AUTHOR]

A validation study is conducted regarding aerosol optical size property retrievals from only measurements of the direct Sun beam (without the aid of diffuse radiation). The study focuses on testing with real data the new GRASP-AOD application which uses only spectral optical depth measurements to retrieve the total column aerosol size distributions, assumed as bimodal log-normal. In addition, a set of secondary integral parameters of aerosol size distribution and optical properties are provided: effective radius, total volume concentration and fine mode fraction of aerosol optical depth. The GRASP-AOD code is applied to almost three million observations acquired during twenty years (1997-2016) at thirty AERONET (Aerosol Robotic Network) sites. These validation sites have been selected based on known availability of an extensive data record, significant aerosol load variability along the year, wide worldwide coverage and divers aerosol types and source regions. The output parameters are compared to those coming from the operational AERONET retrievals. The retrieved fine mode fractions at 500 nm (τf(500)) obtained by GRASP-AOD application are compared to those retrieved by the Spectral Deconvolution Algorithm and by AERONET aerosol retrieval algorithm. The size distribution properties obtained by GRASP-AOD are compared to their equivalent values from the AERONET aerosol retrieval algorithm. The analysis showed the convincing capacity of GRASP-AOD approach to successfully discriminate between fine and coarse mode extinction to robustly retrieve τf(500). The comparisons of 2 million results of τf(500) retrieval by GRASP-AOD and SDA showed high correlation with a root-mean-square-error of 0.015. Also, the analysis showed that the τf(500) values computed by AERONET aerosol retrieval algorithm agree slightly better with GRASP-AOD (RMSE = 0.018, from 148526 comparisons) than with SDA (RMSE = 0.022, from 127203 comparisons). The comparisons of the size distribution retrieval showed the agreement for fine mode median radius between GRASP-AOD and AERONET aerosol retrieval algorithm results with RMSE of 0.032 μm (or 18.7 % in relative terms) for the situations when τ(440) > 0.2 that occurs for more than eighty thousand pairs of the study. For the cases where fine mode is dominant (i.e. α < 1.2), the RMSE is only of 0.023 μm (or 13.9 % in relative terms). Major limitations in the retrieval were found for the retrieval of the coarse mode details. For example, the analysis revealed that GRASP-AOD retrieval is not sensitive to the small oscillations of the coarse mode volume median radius for different aerosol types observed in different locations. Nonetheless GRASP-AOD retrieval provides reasonable agreement with AERONET aerosol retrieval algorithm for overall properties of coarse mode with with RMSE = 0.500 μm (RMSRE = 20 %) when τ(440) > 0.2. The values of effective radius and total volume concentration computed from GRASP-AOD retrieval have been compared to those estimated by AERONET aerosol retrieval algorithm. The RMSE values of the correlations were of 30 % for the effective radius and 25 % for the total volume concentration when τ(440) > 0.2. Finally, the study discusses the importance of employing the assumption of bimodal log-normal size distribution. It also evaluates the potential of using ancillary data, in particular aureole measurements, for improving the characterization of the aerosol coarse mode properties. [ABSTRACT FROM AUTHOR]

Current remote sensing products are mainly generated from polar-orbiting satellite data using parameter-specific algorithms. These products lack physical consistency and cannot accurately characterize intra-day variations of parameters, such as the fraction of absorbed photosynthetically active radiation (FAPAR) and surface albedo. In this study, a multi-parameter consistent retrieval method is proposed to simultaneously retrieve aerosol optical depth (AOD), leaf area index (LAI), photosynthetically active radiation (PAR), FAPAR, surface albedo, and incident shortwave radiation (ISR) from top of atmosphere (TOA) reflectance data acquired by the Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite-R series (GOES-R). The retrieved parameter values were evaluated through comparisons with corresponding Moderate Resolution Imaging Spectroradiometer (MODIS), the second version of the Geoland2 (GEOV2), and GOES-R products and ground measurements over five surface radiation budget network (SURFRAD) sites with different vegetation types. The results demonstrate that the retrieved AOD, PAR, ISR, and surface albedo values have consistent intra-day variations with the ground measurements, and the retrieved parameter values achieve good performance against the ground measurements for all the five SURFRAD sites. The root mean square errors of the retrieved AOD, shortwave albedo, ISR, and PAR values against the ground measurements are 0.071, 0.032, 50.943 W m–2, and 27.975 W m–2, respectively. [ABSTRACT FROM AUTHOR]

Vegetation optical depth (VOD) retrieved from microwave radiometry correlates with the total amount of water in vegetation, based on theoretical and empirical evidence. Because the total amount of water in vegetation varies with relative water content (as well as with biomass), this correlation further suggests a possible relationship between VOD and plant water potential, a quantity that drives plant hydraulic behavior. Previous studies have found evidence for that relationship on the scale of satellite pixels tens of kilometers across, but these comparisons suffer from significant scaling error. Here we used small-scale remote sensing to test the link between remotely sensed VOD and plant water potential. We placed an L-band radiometer on a tower above the canopy looking down at red oak forest stand during the 2019 growing season in central Massachusetts, United States. We measured stem xylem and leaf water potentials of trees within the stand, and retrieved VOD with a single-channel algorithm based on continuous radiometer measurements and measured soil moisture. VOD exhibited a diurnal cycle similar to that of leaf and stem water potential, with a peak at approximately 5 AM. VOD was also positively correlated with both the measured dielectric constant and water potentials of stem xylem over the growing season. The presence of moisture on the leaves did not affect the observed relationship between VOD and stem water potential. We used our observed VOD-water potential relationship to estimate stand-level values for a radiative transfer parameter and a plant hydraulic parameter, which compared well with the published literature. Our findings support the use of VOD for plant hydraulic studies in temperate forests. [ABSTRACT FROM AUTHOR]

To establish a new model for estimating ground-level PM2.5 concentration over Beijing, China, the relationship between aerosol optical depth (AOD) and ground-level PM2.5 concentration was derived and analysed firstly. Boundary layer height (BLH) and relative humidity (RH) were shown to be two major factors influencing the relationship between AOD and ground-level PM2.5 concentration. Thus, they are used to correct MODIS AOD to enhance the correlation between MODIS AOD and PM2.5. When using corrected MODIS AOD for modelling, the correlation between MODIS AOD and PM2.5 was improved significantly. Then, normalized difference vegetation index (NDVI), surface temperature (ST) and surface wind speed (SPD) were introduced as auxiliary variables to further improve the performance of the corrected regression model. The seasonal and annual average distribution of PM2.5 concentration over Beijing from 2014 to 2016 were mapped for intuitively analysing. Those can be regarded as important references for monitoring the ground-level PM2.5 concentration distribution. It is obviously that the PM2.5 concentration distribution over Beijing revealed the trend of "southeast high and northwest low", and showed a significant decrease in annual average PM2.5 concentration from 2014 to 2016. [ABSTRACT FROM AUTHOR]

Investigations on the chronic health effects of fine particulate matter (PM2.5) exposure in China are limited due to the lack of long-term exposure data. Using satellite-driven models to generate spatiotemporally resolved PM2.5 levels, we aimed to estimate high-resolution, long-term PM2.5 and associated mortality burden in China. The multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) at 1-km resolution was employed as a primary predictor to estimate PM2.5 concentrations. Imputation techniques were adopted to fill in the missing AOD retrievals and provide accurate long-term AOD aggregations. Monthly PM2.5 concentrations in China from 2000 to 2016 were estimated using machine-learning approaches and used to analyze spatiotemporal trends of adult mortality attributable to PM2.5 exposure. Mean coverage of AOD increased from 56 to 100% over the 17-y period, with the accuracy of long-term averages enhanced after gap filling. Machine-learning models performed well with a random cross-validation R² of 0.93 at the monthly level. For the time period outside the model training window, prediction R² values were estimated to be 0.67 and 0.80 at the monthly and annual levels. Across the adult population in China, long-term PM2.5 exposures accounted for a total number of 30.8 (95% confidence interval [CI]: 28.6, 33.2) million premature deaths over the 17-y period, with an annual burden ranging from 1.5 (95% CI: 1.3, 1.6) to 2.2 (95% CI: 2.1, 2.4) million. Our satellite-based techniques provide reliable long-term PM2.5 estimates at a high spatial resolution, enhancing the assessment of adverse health effects and disease burden in China. [ABSTRACT FROM AUTHOR]

The Dark‐target (DT) aerosol algorithm retrieves spectral Aerosol Optical Depth (AOD) and other aerosol properties from Moderate‐resolution Imaging Spectrometer (MODIS) reflectance observations. Over the ocean, the DT algorithm is known to contain scattering‐angle‐dependent biases in its retrievals of AOD, Angstrom Exponent (AE), and Fine Mode Fraction (FMF) for dust aerosols. Following a two‐step strategy to improve the DT retrieval of dust over ocean, for which the first step is to identify dusty pixels (reported in "Part 1"), in this "Part 2," we report on construction of a new dust model lookup table (LUT) and the strategy for applying it within the existing DT algorithm. In particular, we evaluate different characterizations of dust optical properties from a variety of frameworks and databases, and compare them with the current DT retrieval assumptions. Substituting the standard operational LUT with a spheroid dust model with identified dusty pixels shows significant improvement when compared with collocated AERONET‐identified dusty pixels. Specifically, the application of the new dust model to dusty pixels reduces their AOD bias from 0.06 to 0.02 while improving the fraction of retrievals within expected error from 64% to 82%. At the same time, the overall bias in AE is reduced from 0.13 to 0.06, and the scattering‐angle‐dependent AE bias is largely eliminated. In testing on two full months of data (April and July), the new retrieval will reduce the monthly mean AOD by up to 0.1 and 0.2 in the north Atlantic and Arabian seas, respectively. The average AE and FMF are also reduced in these dust heavy regions. Key Points: MODIS Dark Target Ocean aerosol retrieval algorithm has a long‐standing retrieval bias for dust aerosolThis study implements a nonspherical dust aerosol model to better represent dust optical propertiesThe new algorithm reduces biases in AOD, AE, and FMF in dusty pixels and causes an overall reduction in AOD in major dust regions over ocean [ABSTRACT FROM AUTHOR]

To prepare for implementation of a new aerosol retrieval specifically designed for dust aerosol over ocean in the operational Dark‐Target (DT) algorithms for the Moderate‐resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors, we focus on the challenge of detecting dust. We first survey the literature on existing dust detection algorithms and then develop an innovative algorithm that combines near‐UV (deep blue), visible, and thermal infrared (TIR) wavelength spectral tests. The new detection algorithm is applied to Terra and Aqua MODIS granules and compared with other dust detection possibilities from existing MODIS products. Quantitative evaluation of the new dust detection algorithm is conducted using both a collocated AERONET‐MODIS data set and collocated Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)‐MODIS data set. From comparison with both AERONET and CALIOP measurements, we estimate the new dust detection algorithm detects about 30% of weakly dusty pixels and more than 80% of heavily dusty pixels, with false detections in the range of 1–2%. The very low false detection rate is particularly noteworthy in comparison with existing literature. Compared with the dust flag currently available as part of the MODIS cloud mask product (MOD35/MYD35), and dust classification based on commonly used thresholds with aerosol optical depth (AOD) and Angstrom exponent (AE), the new dust detection algorithm finds more dusty pixels and fewer false detections. Plain Language Summary: The Dark‐Target (DT) aerosol retrieval is applied to measurements from the Moderate‐resolution Imaging Spectrometer (MODIS) on the Terra and Aqua satellites to retrieve spectral aerosol optical depth (AOD) over land and ocean. The algorithm generally provides high‐quality retrievals within specified error bar. However, the DT‐Ocean algorithm tends to provide biased retrievals of AOD, Angstrom exponent (AE), and fine mode fraction (FMF) for scenes containing dust aerosol of African or Asian origin. These biases are scattering angle dependent, which suggests errors in the assumed optical properties and phase function from the spherical dust models used. Therefore, we aim to improve the DT retrieval of dust over ocean with a two‐step strategy. Here in Part 1, we describe Step 1 in which we develop an innovative dust detection algorithm that combines deep‐blue, visible, shortwave infrared, and thermal infrared wavelength spectral tests that are based on a survey of existing dust detection algorithms. Step 2 is described in Part 2, where we develop new nonspherical dust models and apply it to identified heavy dust pixels. Combing dust detection and nonspherical dust model has led to significant improvements in retrieved AOD, AE, and FMF in dust regions. Key Points: A dust detection algorithm is developed for the MODIS Dark‐Target aerosol retrieval algorithm as a prestep of choosing a dust modelThe algorithm uses spectral dust detection tests from deep‐blue, visible, near‐infrared, and infrared channelsThe algorithm compares well with dust detection from collocated ground Sun photometers and space lidar measurements [ABSTRACT FROM AUTHOR]

To help satellite retrieval of aerosols and studies of their radiative effects, we demonstrate that daytime aerosol optical depth over low-level clouds is similar to that in neighboring clear skies at the same heights. Based on recent airborne lidar and sun photometer observations above the southeast Atlantic, the mean aerosol optical depth (AOD) difference at 532 nm is between 0 and -0.01 , when comparing the cloudy and clear sides, each up to 20 km wide, of cloud edges. The difference is not statistically significant according to a paired t test. Systematic differences in the wavelength dependence of AOD and in situ single scattering albedo are also minuscule. These results hold regardless of the vertical distance between cloud top and aerosol layer bottom. AOD aggregated over ∼2 ∘ grid boxes for each of September 2016, August 2017 and October 2018 also shows little correlation with the presence of low-level clouds. We posit that a satellite retrieval artifact is entirely responsible for a previous finding of generally smaller AOD over clouds (Chung et al., 2016), at least for the region and time of our study. Our results also suggest that the same values can be assumed for the intensive properties of free-tropospheric biomass-burning aerosol regardless of whether clouds are present below. [ABSTRACT FROM AUTHOR]

The aim of this work is to study structure and gas kinematics in the photodissociation regions (PDRs) around the compact H  ii regions S235 A and S235 C. We observe the [C  ii ], [13C  ii ], and [O  i ] line emission, using SOFIA/upGREAT, and complement them by data of HCO+ and CO. We use the [13C  ii ] line to measure the optical depth of the [C  ii ] emission, and find that the [C  ii ] line profiles are influenced by self-absorption, while the [13C  ii ] line remains unaffected by these effects. Hence, for dense PDRs, [13C  ii ] emission is a better tracer of gas kinematics. The optical depth of the [C  ii ] line is up to 10 in S235 A. We find an expanding motion of the [C  ii ]-emitting layer of the PDRs into the front molecular layer in both regions. Comparison of the gas and dust columns shows that gas components visible neither in the [C  ii ] nor in low- J CO lines may contribute to the total column across S235 A. We test whether the observed properties of the PDRs match the predictions of spherical models of expanding H  ii region + PDR + molecular cloud. Integrated intensities of the [13C  ii ], [C  ii ], and [O  i ] lines are well represented by the model, but the models do not reproduce the double-peaked [C  ii ] line profiles due to an insufficient column density of C+. The model predicts that the [O  i ] line could be a more reliable tracer of gas kinematics, but the foreground self-absorbing material does not allow using it in the considered regions. [ABSTRACT FROM AUTHOR]

We propose a new cloud screening method for sun photometry that is designed to effectively filter thin clouds. Our method is based on a k-nearest neighbour algorithm instead of scanning timeseries of aerosol optical depth. Using ten years of data from a precision filter radiometer in Innsbruck, we compare our new method and the currently employed screening technique. We exemplify the performance of the two routines in different cloud conditions. While both algorithms agree on the classification of a datapoint as clear or cloudy in a majority of the cases, the new routine is found to be more effective in flagging thin clouds. We conclude that this simple method can serve as a valid alternative for cloud detection, and discuss the generalizability to other observation sites. [ABSTRACT FROM AUTHOR]

We perform Lyα radiative transfer calculations to reproduce the Lyα properties of star-forming galaxies at high redshifts. We model a galaxy as a halo in which the density distributions of Lyα sources and H i plus dust medium are described with exponential functions. We also consider an outflow of the medium that represents a momentum-driven wind in a gravitational potential well. We successfully reproduce both the spectra and the surface brightness profiles of eight star-forming galaxies at z = 3–6 observed with the Multi-Unit Spectroscopic Explorer using this outflowing-halo model with Lyα scattering. The best-fit model parameters (i.e., the outflowing velocity and optical depth) for these galaxies are consistent with those in other studies. We examine the impacts of individual model parameters and input spectrum on the emerging spectrum and surface brightness profile. Further investigations of the correlations among observables (i.e., the spatial extent of Lyα halos and Lyα spectral features) and model parameters, and of spatially resolved spectra are presented as well. We demonstrate that the combination of spectrum and surface brightness profile provides strong constraints on model parameters and thus on the spatial/kinematic distributions of the medium. [ABSTRACT FROM AUTHOR]

The Stratospheric Aerosol and Gas Experiment (SAGE) III has been operating on the International Space Station (ISS) since mid 2017. Nitrogen dioxide (NO2) number density profiles are routinely retrieved from SAGE III/ISS solar occultation measurements in the middle atmosphere. Although NO2 density varies throughout the day due to photochemistry, the standard SAGE NO2 retrieval algorithm neglects these variations along the instrument's line of sight by assuming that the number density has a constant gradient within a given vertical layer of the atmosphere. This assumption will result in a retrieval bias for a species like NO2 that changes rapidly across the terminator. In this work we account for diurnal variations in retrievals of NO2 from the SAGE III/ISS measurements, and determine the impact of this algorithm improvement on the resulting NO2 number densities. The diurnal correction is applied by first undoing the SAGE III/ISS retrieval using publicly available SAGE III/ISS products to obtain an optical depth profile. The retrieval is then performed with a new matrix that applies photochemical scale factors for each point along the line of sight according to the changing solar zenith angle. In general NO2 that is retrieved by accounting for these diurnal variations is more than 10 % lower than the standard algorithm below 30 km. This effect is greatest in winter at high latitudes, and generally greater for sunrise occultations than sunset. Comparisons with coincident profiles from the Optical Spectrograph and InfraRed Imager System (OSIRIS) show that NO2 from SAGE III/ISS is generally biased high, however the agreement improves by up to 20 % in the mid stratosphere when diurnal variations are accounted for in the retrieval. We conclude that diurnal variations along the SAGE III/ISS line of sight are an important term to consider for NO2 analyses at altitudes below 30 km. [ABSTRACT FROM AUTHOR]

The Yonsei AErosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 minutes AHI or 1 hour GOCI data at 6 km x 6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), and bias correction based on normalized difference vegetation indexes; and (2) estimation of the fused product using ensemble-mean and maximum-likelihood estimation methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error, median bias error than the retrieved result for each product. [ABSTRACT FROM AUTHOR]

An optically thin cirrus cloud was profiled concurrently with nadir‐pointing 1,064 nm lidars on 11 August 2017 over eastern Texas, including NASA's airborne Cloud Physics Lidar (CPL) and space‐borne Clouds and Aerosol Transport System (CATS) instruments. Despite resolving fewer (37% vs. 94%) and denser (i.e., more emissive) clouds (average cloud optical depth of 0.10 vs. 0.03, respectively), CATS data render a near‐equal estimate of the top‐of‐atmosphere (TOA) net cloud radiative forcing (CRF) versus CPL. The sample‐relative TOA net CRF solved from CPL is 1.39 W/m2, which becomes 1.32 W/m2 after normalizing by occurrence frequency. Since CATS overestimates extinction for this case, the sample‐relative TOA net forcing is ~3.0 W/m2 larger than CPL, with the absolute value reduced to within 0.3 W/m2 of CPL due its underestimation of cloud occurrence. We discuss the ramifications of thin cirrus cloud detectability from satellite and its impact on attempts at TOA CRF closure. Key Points: Coincident nadir 1,064 nm lidar measurements of optically thin cirrus clouds were collected from aboard a high‐altitude aircraft and the International Space StationThe relative top‐of‐the‐atmosphere (TOA) net cloud radiative forcing (CRF) solved from the two data sets reveal sensitivities to the algorithms used to derived cloud propertiesThe absolute TOA net CRF solved from the two data sets are consistent despite limited cloud detection capabilities in the ISS product [ABSTRACT FROM AUTHOR]

An interesting feature of the tropical cirrus clouds is its descending nature, which is not well characterized over the tropics due to the scarcity of continuous observations over a single location. In order to quantify the morphological characteristics of the descending cirrus, data from systematic ground-based Lidar observations of cirrus clouds over a tropical site Gadanki (13.5° N, 79.2° E) is analyzed during 2000–2015. Time series and altitude structures of different descending cirrus layers are discussed. A total of 105 cases of descending cirrus (about 9%) are observed against 10 (about 1%) cases of ascending cirrus. The mean displacements of the base and top of descending cirrus clouds are 1.26 ± 1.04 km and 1.21 ± 0.99 km, respectively. About 94% of the descending cirrus have their descent speeds of < 0.8 m s−1. As 75% of descending cirrus has Cloud Optical Depth (COD) between 0.1 and 0.9, it is obvious that most of the observed descending cirrus clouds are classified as either thick or thin clouds. Surprisingly, none of the cases showed that descending cirrus are of a sub-visible type. The present exploratory study on descending nature of cirrus clouds would be useful in understanding the process of cirrus sedimentation, which plays a major role in regulating the moisture content in Tropical Tropopause Layer (TTL) as well as on the Earth's radiation budget. [ABSTRACT FROM AUTHOR]

We present an analysis of the evolution of the Lyman-series forest into the epoch of reionization using cosmological radiative transfer simulations in a scenario where reionization ends late. We explore models with different mid-points of reionization and gas temperatures. We find that once the simulations have been calibrated to match the mean flux of the observed Lyman- α (Ly  α) forest at 4 < z < 6, they also naturally reproduce the distribution of effective optical depths of the Lyman- β (Ly  β) forest in this redshift range. We note that the tail of the largest optical depths that is most challenging to match corresponds to the long absorption trough of ULAS J0148+0600, which we have previously shown to be rare in our simulations. We consider the evolution of the Lyman-series forest out to higher redshifts, and show that future observations of the Ly  β forest at z > 6 will discriminate between different reionization histories. The evolution of the Ly  α and Ly  γ forests are less promising as a tool for pushing studies of reionization to higher redshifts due to the stronger saturation and foreground contamination, respectively. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory 2 (OCO-2) carries a hyperspectral A-band sensor that can obtain information about cloud geometric thickness (H). The OCO2CLD-LIDAR-AUX product retrieved H with the aid of collocated CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar data to identify suitable clouds and provide a priori cloud top pressure (Ptop). This collocation is no longer possible, since CALIPSO's coordination flying with OCO-2 has ended, so here we introduce a new cloud flagging and a priori assignment using only OCO-2 data, restricted to ocean footprints where solar zenith angle <45∘. Firstly, a multi-layer perceptron network was trained to identify liquid clouds over the ocean with sufficient optical depth (τ>1) for a valid retrieval, and agreement with MODIS–CALIPSO (Moderate Resolution Imaging Spectroradiometer) is 90.0 %. Secondly, we developed a lookup table to simultaneously retrieve cloud τ , effective radius (re) and Ptop from A-band and CO2 band radiances, with the intention that these will act as the a priori state estimate in a future retrieval. Median Ptop difference vs. CALIPSO is 12 hPa with an inter-decile range of [-11,87]hPa , substantially better than the MODIS–CALIPSO range of [-83,81]hPa. The MODIS–OCO-2 τ difference is 0.8[-3.8,6.9] , and re is -0.3[-2.8,2.1]µm. The τ difference is due to optically thick and horizontally heterogeneous cloud scenes. As well as an improved passive Ptop retrieval, this a priori information will allow for a purely OCO-2-based Bayesian retrieval of cloud droplet number concentration (Nd). Finally, our cloud flagging procedure may also be useful for future partial-column above-cloud CO2 abundance retrievals. [ABSTRACT FROM AUTHOR]

Simulating global and regional climate at high resolution is essential to study the effects of climate change and capture extreme events affecting human populations. To achieve this goal, the scalability of climate models and efficiency of individual model components are both important. Radiative transfer is among the most computationally expensive components in a typical climate model. Here we attempt to model this component using a neural network. We aim to study the feasibility of replacing an explicit, physics-based computation of longwave radiative transfer by a neural network emulator and assessing the resultant performance gains. We compare multiple neural-network architectures, including a convolutional neural network, and our results suggest that the performance loss from the use of conventional convolutional networks is not offset by gains in accuracy. We train the networks with and without noise added to the input profiles and find that adding noise improves the ability of the networks to generalise beyond the training set. Prediction of radiative heating rates using our neural network models achieve up to 370 × speedup on a GTX 1080 GPU setup and 11 × speedup on a Xeon CPU setup compared to the a state-of-the-art radiative transfer library running on the same Xeon CPU. Furthermore, our neural network models yield less than 0.1 K d -1 mean squared error across all pressure levels. Upon introducing this component into a single-column model, we find that the time evolution of the temperature and humidity profiles is physically reasonable, though the model is conservative in its prediction of heating rates in regions where the optical depth changes quickly. Differences exist in the equilibrium climate simulated when using the neural network, which are attributed to small systematic errors that accumulate over time. Thus, we find that the accuracy of the neural network in the "offline" mode does not reflect its performance when coupled with other components. [ABSTRACT FROM AUTHOR]

The integration of satellite-derived aerosol optical depth (AOD) and station-measured PM 2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) provides a promising approach for the monitoring of PM 2.5. Previous models have generally only considered either the spatiotemporal heterogeneities of the AOD-PM 2.5 relationship or the nonlinear relationship between AOD and PM 2.5. In this paper, to simultaneously allow for the nonlinearity and spatiotemporal heterogeneities of the AOD-PM 2.5 relationship, the geographically and temporally weighted neural network (GTWNN) model is proposed for the satellite-based estimation of ground-level PM 2.5. The GTWNN model represents the nonlinear AOD-PM 2.5 relationship via a generalized regression neural network, and is separately established for individual locations and times, to address the spatiotemporal heterogeneities of the AOD-PM 2.5 relationship. Meanwhile, a spatiotemporal weighting scheme is incorporated in the GTWNN model to capture the local relations of samples for the training of the AOD-PM 2.5 relationship. By the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD product, meteorological data, and MODIS normalized difference vegetation index (NDVI) data as input, the GTWNN model was verified using ground station PM 2.5 measurements from China in 2015. The GTWNN model achieved sample-based cross-validation (CV) and site-based CV R2 values of 0.80 and 0.79, respectively, and it outperformed the geographically and temporally weighted regression model (CV R2: 0.75 and 0.73) and the daily geographically weighted regression model (CV R2: 0.72 and 0.72). The proposed model implements the combination of geographical law and a neural network, and will be of great use for remote sensing retrieval of environmental variables. [ABSTRACT FROM AUTHOR]