AERONET Observations Research Articles

Evaluation and analysis of long-term MODIS MAIAC aerosol products in China

NASA has released the latest Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) Collection 6 (C6) and Collection 6.1 (C6.1) aerosol optical depth (AOD) products with 1 km spatial resolution. This study validated and compared C6 and C6.1 MAIAC AOD products with AERONET observations in terms of accuracy and stability, and analyzed the spatiotemporal characteristics of AOD at different scales in China. The results show that the overall accuracy of MAIAC products is good, with correlation coefficient (R) > 0.9, mean bias (BIAS) < 0.015, and the fraction within the expected error (EE) > 68 %. However, after the algorithm update, the accuracy of Terra MAIAC aerosol products C6.1 has significantly decreased. The accuracy of the products varies with the region. The accuracy of C6.1 in North China, Central East China, and West China, is comparable to or even exceeds that of C6, but performs poorly in South China. In addition, the stability of the updated C6.1 MAIAC aerosol products has not seen significantly improvement. The metrics of no product can all meet the stability goals of the Global Climate Observing System (GCOS, 0.02 per decade) in China. C6.1 improves the retrieval frequency in many regions and temporarily solves the problem of AOD discontinuity at the boundaries of different aerosol models in C6, but there are some fixed climatological AOD blocks (AOD = 0.014) in the eastern Tibetan Plateau region. Both C6 and C6.1 can capture similar annual variation characteristics of AOD to those observed at the AERONET sites. The study provides possible references for improving the MAIAC algorithm and building long-term stable aerosol records.

Investigation of Dust‐Induced Direct Radiative Forcing Over the Arabian Peninsula Based on High‐Resolution WRF‐Chem Simulations

AbstractThis study investigates the impact of dust on radiation over the Arabian Peninsula (AP) during the reported high, low, and normal dust seasons (March–August) of 2012, 2014, and 2015, respectively. Simulations were performed using the Weather Research and Forecasting model coupled to a Chemistry module (WRF‐Chem). The simulated seasonal horizontal and vertical dust concentrations, and their interannual distinctions, match well with those from two ground‐based AERONET observations, and measurements from MODIS and CALIOP satellites. The maximum dust concentrations over the dust‐source regions in the southern AP reach vertically upto 700 hPa during the high dust season, but only upto 900–950 hPa during the low/normal dust seasons. Stronger incoming low‐level winds along the southern Red Sea and those from Iraq bring in higher‐than‐normal dust during the high dust summers. We conducted a sensitivity experiment by switching‐off the dust module to assess the radiative perturbations due to dust. The results suggest that active dust‐module improved the fidelity of simulated radiation fluxes distributions at the surface and top of the atmosphere vis‐à‐vis Clouds and the Earth's Radiant Energy System (CERES) measurements. Dust results in a 26 Wm−2 short‐wave (SW) radiative forcing in the tropospheric‐column over the AP. The SW radiative forcing increases by another 6–8 Wm−2 during the high dust season due to the increased number of extreme dust days, which also amplifies atmospheric heating. During extreme dust days, the heating rate exhibits a dipolar structure, with cooling over the Iraq region and warming of 40%–60% over the southern‐AP.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

An improved approach to determine aerosol properties from all-sky camera imagery: Sensitivity to the partially cloud scenes

We present a new approach to determine aerosol properties from radiometrically calibrated images provided by an all-sky camera. It is designed to be used regardless of the sky conditions. However, we especially focus on partially cloudy scenes, which is the main novelty of this work. Our methodology is based on using a small sector of the image that contains the principal plane of the Sun. The RGB principal plane radiances are associated to the aerosol optical depth (AOD) and Angstrom exponent (AE) AERONET observations through a Gaussian Process Regression (GPR) machine learning (ML) model. We identify the cloudy points within our working sector and the principal plane signal for the RGB radiances is averaged and smoothed. Then, we use the Pérez model to synthesize the principal plane signal in the cloudy spots. Finally, 2-year dataset has been used to test the method considering different atmospheric conditions related to the presence of clouds and aerosols, according to their amount and type. In addition, we have developed a method to evaluate the quality of predictions based on the standard deviation of the GPR. This quality assurance method may be fine-tuned according to the desired accuracy based on the application for which it is intended. Our AOD and AE predictions show an excellent overall agreement with AERONET measurements that substantially improves when our quality assurance method is applied. In that case, we obtain a high degree of correlation (R2 ¿ 0.97) and an overall MAE lower than the nominal uncertainty of AERONET measurements (0.006 and 0.05 for AOD and AE, respectively). Moreover, more than 83% and 77% of the predictions fall within the nominal uncertainty associated with AERONET measurements for AOD and AE, respectively. A comprehensive sensitivity analysis of the factors affecting the performance of the proposed methodology confirms that our method is stable and not very sensitive to external and methodological factors, especially when we apply quality assurance criteria. All this supports that our methodology is a reliable alternative to retrieve the optical properties of aerosols independently of the cloud conditions. Our results may contribute to the operational use of all-sky cameras, which may be an interesting complement regarding the study of aerosol-cloud interactions in partially cloud scenarios.

Variability of Aerosol Optical Depth and Altitude for Key Aerosol Types over Southern West Africa via CALIPSO/CALIOP Observations

The atmosphere of southern West Africa (SWA) is one of the world’s most aerosol-laden regions. This study investigated the seasonal variability of aerosol optical thickness using photometric and CALIPSO/CALIOP observations. The mean daily aerosol optical thickness (AOD) at 550 nm over the 11 AERONET stations in SWA ranged from 0.35 in the Sahel area to 0.49 in the Guinea coast area and a maximum of 0.53 observed in the Savanna area. The AERONET and CALIOP observations reveal a seasonal variability of AOD that is comparable across all sub-regions studied. The seasonal variation of the AOD spectral dependency was related to the change in the occurrence of the CALIOP-derived aerosol types, and in particular to the contribution of dust to the AOD. We also observed a north–south gradient of 2 km in the mean extinction height of the dust layer, being at 4 km and 6 km high in the Sahel and Guinea areas, respectively. This gradient is attributed to the northward migration of the monsoon flow.

VIIRS Version 2 Deep Blue Aerosol Products

AbstractNASA's Deep Blue aerosol project has developed global aerosol data records using consistent retrieval algorithms applied to various satellite sensors. The primary components of these data records are derived from the series of Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar‐orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration or NOAA‐20+ satellites as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), among others. These instruments provide over 23 years of measurements with similar radiometric characteristics for aerosol retrievals. The algorithms used for the initial Version 1 SNPP VIIRS data set were based on the MODIS Collection 6.1 Deep Blue algorithm over land and Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water. For VIIRS Version 2 data reprocessing, major updates have been made to the algorithm suite, including better accounting for effects of surface pressure, improved determination of surface reflectance, and the inclusion of fine‐mode aerosol optical models to better represent anthropogenic aerosols over land. Cross‐calibration gain factors are derived for the NOAA‐20 VIIRS measurements to be consistent with the SNPP VIIRS, which allows the use of a unified algorithm package for both instruments. Comparisons against AERONET observations indicate that the Version 2 AOD data from SNPP VIIRS are significantly better than the Version 1 counterpart over land and slightly degraded over water in exchange for better spatial coverage. The AOD data from SNPP and NOAA‐20 VIIRS are comparable, indicating that cross‐calibration enables the creation of consistent aerosol data records using the series of VIIRS sensors.

Evaluation of WRF-Chem-simulated meteorology and aerosols over northern India during the severe pollution episode of 2016

Abstract. We use a state-of-the-art regional chemistry transport model (WRF-Chem v4.2.1) to simulate particulate air pollution over northern India during September–November 2016. This period includes a severe air pollution episode marked by exceedingly high levels of hourly PM2.5 (particulate matter having an aerodynamic diameter ≤ 2.5 µm) during 30 October to 7 November, particularly over the wider Indo-Gangetic Plain (IGP). We provide a comprehensive evaluation of simulated seasonal meteorology (nudged by ERA5 reanalysis products) and aerosol chemistry (PM2.5 and its black carbon (BC) component) using a range of ground-based, satellite and reanalysis products, with a focus on the November 2016 haze episode. We find the daily and diurnal features in simulated surface temperature show the best agreement followed by relative humidity, with the largest discrepancies being an overestimate of night-time wind speeds (up to 1.5 m s−1) confirmed by both ground and radiosonde observations. Upper-air meteorology comparisons with radiosonde observations show excellent model skill in reproducing the vertical temperature gradient (r&gt;0.9). We evaluate modelled PM2.5 at 20 observation sites across the IGP including eight in Delhi and compare simulated aerosol optical depth (AOD) with data from four AERONET sites. We also compare our model aerosol results with MERRA-2 reanalysis aerosol fields and MODIS satellite AOD. We find that the model captures many features of the observed aerosol distributions but tends to overestimate PM2.5 during September (by a factor of 2) due to too much dust, and underestimate peak PM2.5 during the severe episode. Delhi experiences some of the highest daily mean PM2.5 concentrations within the study region, with dominant components nitrate (∼25 %), dust (∼25 %), secondary organic aerosols (∼20 %) and ammonium (∼10 %). Modelled PM2.5 and BC spatially correlate well with MERRA-2 products across the whole domain. High AOD at 550nm across the IGP is also well predicted by the model relative to MODIS satellite (r≥0.8) and ground-based AERONET observations (r≥0.7), except during September. Overall, the model realistically captures the seasonal and spatial variations of meteorology and ambient pollution over northern India. However, the observed underestimations in pollutant concentrations likely come from a combination of underestimated emissions, too much night-time dispersion, and some missing or poorly represented aerosol chemistry processes. Nevertheless, we find the model is sufficiently accurate to be a useful tool for exploring the sources and processes that control PM2.5 levels during severe pollution episodes.

Validation of MODIS AOD retrievals in West Africa: a comparison with AERONET observations

Validation of MODIS AOD retrievals in West Africa: a comparison with AERONET observations

Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

Enhanced Light Absorption and Radiative Forcing by Black Carbon Agglomerates

The climate models of the Intergovernmental Panel on Climate Change list black carbon (BC) as an important contributor to global warming based on its radiative forcing (RF) impact. Examining closely these models, it becomes apparent that they might underpredict significantly the direct RF for BC, largely due to their assumed spherical BC morphology. Specifically, the light absorption and direct RF of BC agglomerates are enhanced by light scattering between their constituent primary particles as determined by the Rayleigh–Debye–Gans theory interfaced with discrete dipole approximation and recent relations for the refractive index and lensing effect. The light absorption of BC is enhanced by about 20% by the multiple light scattering between BC primary particles regardless of the compactness of their agglomerates. The resulting light absorption agrees very well with the observed absorption aerosol optical depth of BC. ECHAM-HAM simulations accounting for the realistic BC morphology and its coatings reveal high direct RF = 3–5 W/m2 in East, South Asia, sub-Sahara, western Africa, and the Arabian peninsula. These results are in agreement with satellite and AERONET observations of RF and indicate a regional climate warming contribution by 0.75–1.25 °C, solely due to BC emissions.

A Pre-Operational System Based on the Assimilation of MODIS Aerosol Optical Depth in the MOCAGE Chemical Transport Model

In this study we present a pre-operational forecasting assimilation system of different types of aerosols. This system has been developed within the chemistry-transport model of Météo-France, MOCAGE, and uses the assimilation of the Aerosol Optical Depth (AOD) from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard both Terra and Aqua. It is based on the AOD assimilation system within the MOCAGE model. It operates on a daily basis with a global configuration of 1∘×1∘ (longitude × latitude). The motivation of such a development is the capability to predict and anticipate extreme events and their impacts on the air quality and the aviation safety in the case of a huge volcanic eruption. The validation of the pre-operational system outputs has been done in terms of AOD compared against the global AERONET observations within two complete years (January 2018–December 2019). The comparison between both datasets shows that the correlation between the MODIS assimilated outputs and AERONET over the whole period of study is 0.77, whereas the biases and the RMSE (Root Mean Square Error) are 0.006 and 0.135, respectively. The ability of the pre-operational system to predict extreme events in near real time such as the desert dust transport and the propagation of the biomass burning was tested and evaluated. We particularly presented and documented the desert dust outbreak which occurred over Greece in late March 2018 as well as the wildfire event which happened on Australia between July 2019 and February 2020. We only presented these two events, but globally the assimilation chain has shown that it is capable of predicting desert dust events and biomass burning aerosols which happen all over the globe.

Observationally constrained representation of brown carbon emissions from wildfires in a chemical transport model

Representing brown carbon absorption, photobleaching, and secondary organic aerosol formation in wildfire emissions results in improved agreement between WRF-Chem simulations and AERONET observations.

Validation, Analysis, and Comparison of Misr V23 Aerosol Optical Depth Products with Modis and Aeronet Observations

Validation, Analysis, and Comparison of Misr V23 Aerosol Optical Depth Products with Modis and Aeronet Observations

Evaluation of Nine Operational Models in Forecasting Different Types of Synoptic Dust Events in the Middle East

This study investigates four types of synoptic dust events in the Middle East region, including cyclonic, pre-frontal, post-frontal and Shamal dust storms. For each of these types, three intense and pervasive dust events are analyzed from a synoptic meteorological and numerical simulation perspective. The performance of 9 operational dust models in forecasting these dust events in the Middle East is qualitatively and quantitatively evaluated against Terra-MODIS observations and AERONET measurements during the dust events. The comparison of model AOD outputs with Terra-MODIS retrievals reveals that despite the significant discrepancies, all models have a relatively acceptable performance in forecasting the AOD patterns in the Middle East. The models enable to represent the high AODs along the dust plumes, although they underestimate them, especially for cyclonic dust storms. In general, the outputs of the NASA-GEOS and DREAM8-MACC models present greater similarity with the satellite and AERONET observations in most of the cases, also exhibiting the highest correlation coefficient, although it is difficult to introduce a single model as the best for all cases. Model AOD predictions over the AERONET stations showed that DREAM8-MACC exhibited the highest R2 of 0.78, followed by NASA_GEOS model (R2 = 0.74), which both initially use MODIS data assimilation. Although the outputs of all models correspond to valid time more than 24 h after the initial time, the effect of data assimilation on increasing the accuracy is important. The different dust emission schemes, soil and vegetation mapping, initial and boundary meteorological conditions and spatial resolution between the models, are the main factors influencing the differences in forecasting the dust AODs in the Middle East.

Tropospheric and stratospheric wildfire smoke profiling with lidar: mass, surface area, CCN, and INP retrieval

Abstract. We present retrievals of tropospheric and stratospheric height profiles of particle mass, volume, surface area, and number concentrations in the case of wildfire smoke layers as well as estimates of smoke-related cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations from backscatter lidar measurements on the ground and in space. Conversion factors used to convert the optical measurements into microphysical properties play a central role in the data analysis, in addition to estimates of the smoke extinction-to-backscatter ratios required to obtain smoke extinction coefficients. The set of needed conversion parameters for wildfire smoke is derived from AERONET observations of major smoke events, e.g., in western Canada in August 2017, California in September 2020, and southeastern Australia in January–February 2020 as well as from AERONET long-term observations of smoke in the Amazon region, southern Africa, and Southeast Asia. The new smoke analysis scheme is applied to CALIPSO observations of tropospheric smoke plumes over the United States in September 2020 and to ground-based lidar observation in Punta Arenas, in southern Chile, in aged Australian smoke layers in the stratosphere in January 2020. These case studies show the potential of spaceborne and ground-based lidars to document large-scale and long-lasting wildfire smoke events in detail and thus to provide valuable information for climate, cloud, and air chemistry modeling efforts performed to investigate the role of wildfire smoke in the atmospheric system.

A correlation-based inversion method for aerosol property (CIMAP) retrieval from AERONET measurements

We developed a principal-component based inversion approach – CIMAP, to retrieve aerosol properties from AERONET measurements of aerosol optical depths (AODs) and sky radiances. Capitalizing on the correlation in aerosol properties, CIMAP reduces the aerosol retrieval parameter space and saves retrieval time by more than 80% from adopting 7-8 principal components in the retrieval process, compared to retrievals in regular parameter space. As algorithm test, CIMAP is implemented to retrieve 60 sets of AERONET observations in several selected areas in southern California, southern Africa, and north China around Beijing, and the results are compared to the AERONET measurement and operational retrieval products. The mean absolute differences are found to be 0.004, 0.019, 0.032, 0.039, 0.003 and 0.044µm for AOD, single scattering albedo, Ångström exponent, real and imaginary parts of refractive index and effective radius of aerosols, respectively. These differences are within or close to AERONET measurement or retrieval uncertainties.

Characterization of temporal and spatial variability of aerosols from ground-based climatology: towards evaluation of satellite mission requirements

The high variability of atmospheric aerosol in space and time poses significant challenges for aerosol observation and simulation, as well as for the design of aerosol monitoring systems. Multi-Angular Polarimeters (MAP) have been identified to provide highly accurate data for characterizing in detail columnar properties of atmospheric aerosol. Obtaining such multi-angular observations at high spatial resolution is very challenging, and even more so from satellite observations. At present, the most advanced MAP instruments are intended to provide observations at the spatial resolution of about 2 km to 4 km. The practical understanding of aerosol loading and type variability at fine to moderate spatial scales is still limited. In this paper, we provide insight on the spatial variability of ambient aerosol by combining the full archive of AERONET observations with ancillary wind speeds from the Modern-Era Retrospective Analysis for Research Application, version 2 (MERRA-2) reanalysis dataset. First, the temporal variability of aerosol observations at the smallest AERONET time scale of 15-30 minutes was used to estimate maximum temporal variability of the aerosol loading (aerosol optical depth - AOD), size (Ångström exponent - AE) and absorption (single scattering albedo - SSA) over a selection of 30 typical AERONET sites. In the subsequent step, the derived aerosol temporal variability for AOD, AE and SSA are converted to maximum spatial variability using the mean wind speed from MERRA-2. In the final step, the mean aerosol variability difference was analyzed at spatial scales of 2 km and 4 km, which are the spatial scales considered for the MAP instrument to be deployed as part of the Copernicus Anthropogenic CO2 Monitoring (CO2M) mission. The mean aerosol parameters obtained at these spatial scales showed very small differences: only 0.004 for AOD (440 nm), 0.004 for AE (440/870), and 0.0005 for SSA (440 nm). The analysis of maximum spatial variation of aerosol concentrations showed some non-negligible spikes, up to ∼0.2 for AOD (440 nm) at spatial scales of 4 km. However, those high fluctuations correspond to highly polluted urban sites (i.e. Beijing and Mexico City), and the maximum AOD changes per km remain at ∼6% with respect to the total AOD. The maximum spatial variability for AE and SSA also showed no significant deviations at 4 km (<0.2 for AE; <0.03 for SSA). Therefore, we conclude that using a 4 km spatial resolution for MAP sensors is sufficient for capturing the main features of aerosol variability that is required for the CO2M mission.

A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products

Due to a limited number of monitoring stations in Western Africa, the impact of mineral dust on PM10 surface concentrations is still poorly known. We propose a new method to retrieve PM10 dust surface concentrations from sun photometer aerosol optical depth (AOD) and CALIPSO/CALIOP Level 2 aerosol layer products. The method is based on a multi linear regression model that is trained using co-located PM10, AERONET and CALIOP observations at 3 different locations in the Sahel. In addition to the sun photometer AOD, the regression model uses the CALIOP-derived base and top altitude of the lowermost dust layer, its AOD, the columnar total and columnar dust AOD. Due to the low revisit period of the CALIPSO satellite, the monthly mean annual cycles of the parameters are used as predictor variables rather than instantaneous observations. The regression model improves the correlation coefficient between monthly mean PM10 and AOD from 0.15 (AERONET AOD only) to 0.75 (AERONET AOD and CALIOP parameters). The respective high and low PM10 concentration during the winter dry season and summer season are well produced. Days with surface PM10 above 100 μg/m3 are better identified when using the CALIOP parameters in the multi linear regression model. The number of true positives (actual and predicted concentrations above the threshold) is increased and leads to an improvement in the classification sensitivity (recall) by a factor 1.8. Our methodology can be extrapolated to the whole Sahel area provided that satellite derived AOD maps are used in order to create a new dataset on population exposure to dust events in this area.

Investigation of the relationship between the fine mode fraction and Ångström exponent: Cases in Korea

Based on long-term AERONET observations in the Korean peninsula, we evaluate how well the Ångström exponent (AE) can describe the regional aerosol size well in comparison to the fine mode fraction (FMF). Despite close relationships between AE and FMF previously reported, we found that AE is not always linearly correlated to the FMF in the Korean peninsula, particularly during the summertime. Seasonal patterns of volume size distribution indicate that the volume median and peak radius of the fine mode are obviously enhanced in the summer. Since the ambient conditions in Korean summer are highly humid, cloudy, and stagnant, the size of regional fine mode aerosol can increase significantly due to hygroscopic growth and/or cloud processing in this highly polluted atmosphere. In contrast to the AE, the AE ratio (AE at longer wavelengths divided by AE at shorter wavelengths; AER) correlates better with the FMF variation in summer, but a little worse in the spring. This suggests that the combined consideration of AE and AER may provide a better characterization relative to FMF. Thus, we try to estimate the FMF based on the multiple linear regression method using both AE and AER. As expected, the estimated FMF shows remarkably high linear correlations with the AERONET FMF without any seasonal bias. These findings imply that the FMF in South Korea can be simply obtained if just the AOT measurement at multiple wavelengths (in the range from ultraviolet to near-infrared channels) are available.

Variability in lidar-derived particle properties over West Africa due to changes in absorption: towards an understanding

Abstract. Measurements performed in western Africa (Senegal) during the SHADOW field campaign are analyzed to show that spectral dependence of the imaginary part of the complex refractive index (CRI) of dust can be revealed by lidar-measured particle parameters. Observations in April 2015 provide good opportunity for such study, because, due to high optical depth of the dust, exceeding 0.5, the extinction coefficient could be derived from lidar measurements with high accuracy and the contribution of other aerosol types, such as biomass burning, was negligible. For instance, in the second half of April 2015, AERONET observations demonstrated a temporal decrease in the imaginary part of the CRI at 440 nm from approximately 0.0045 to 0.0025. This decrease is in line with a change in the relationship between the lidar ratios (the extinction-to-backscattering ratio) at 355 and 532 nm (S355 and S532). For instance in the first half of April, S355∕S532 is as high as 1.5 and the backscatter Ångström exponent, Aβ, is as low as −0.75, while after 15 April S355/S532=1.0 and Aβ is close to zero. The aerosol depolarization ratio δ532 for the whole of April exceeded 30 % in the height range considered, implying that no other aerosol, except dust, occurred. The performed modeling confirmed that the observed S355∕S532 and Aβ values match the spectrally dependent imaginary part of the refractive index as can be expected for mineral dust containing iron oxides. The second phase of the SHADOW campaign was focused on evaluation of the lidar ratio of smoke and estimates of its dependence on relative humidity (RH). For five studied smoke episodes the lidar ratio increases from 44±5 to 66±7 sr at 532 nm and from 62±6 to 80±8 sr at 355 nm, when RH varied from 25 % to 85 %. Performed numerical simulations demonstrate that observed ratio S355∕S532, exceeding 1.0 in the smoke plumes, can indicate an increase in the imaginary part of the smoke particles in the ultraviolet (UV) range.