Moderate Resolution Imaging Spectroradiometer Aerosol Research Articles

Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity

Abstract. While the use and data assimilation (DA) of operational Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace, MODIS is scheduled to sunset in the next year. For data continuity, focus has turned to the development of next-generation aerosol products and sensors such as those associated with the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and NOAA-20. Like MODIS algorithms, products from these sensors require their own set of extensive error characterization and correction exercises. This is particularly true in the context of monitoring significant aerosol events that tax an algorithm's ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar-orbiting satellite algorithms to monitor and characterize significant events, a level 3 (L3) product has been developed using a consistent aggregation methodology for 4 years of observations (2016–2019) that is referred to as the SSEC/NRL L3 product. Included in this product are the AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. These MODIS “baseline algorithms” are compared to NASA's recently released NASA Deep Blue algorithm for use with VIIRS. Using this new dataset, the relative performance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher-percentile aerosol optical depth (AOD) regions of the world by product identified those with the highest measured AODs and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range – in particular for higher AOD events. Most notable are differences in boreal biomass burning and Saharan dust. Significant percentile biases must be accounted for when data are used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions in lower boundary conditions and aerosol optical models.

Estimation of PM2.5 concentrations with high spatiotemporal resolution in Beijing using the ERA5 dataset and machine learning models

PM2.5 is the main component of most haze, and the presence of high concentrations of PM2.5 in the air for an extended time can cause serious effects on human health, so there is an urgent need for research work related to PM2.5. Traditional PM2.5 monitoring uses ground-based monitoring stations with low spatial resolution. Other studies have retrieved the Moderate Resolution Imaging Spectroradiometer aerosol optical depth product by the dark-target algorithm. However, the estimated PM2.5 concentration on the ground will produce missing values, which will lead to the reduction of spatial and temporal resolution. Based on this, this study proposes a machine learning algorithm to estimate PM2.5 by using the fifth generation reanalysis (ERA5) data set published by the European Center for Medium-range Weather Forecasts (ECMWF). In this study, two different methods of back propagation neural network (BPNN) and random forest (RF) were used to develop the models. Firstly, the meteorological parameters (precipitable water vapor, water vapor pressure and relative humidity, etc.) and pollution parameters (O3, CO, NO2, SO2, PM10, and PM2.5) were used to establish PM2.5 model in 2021. The results showed that the R2 and RMSE for BPNN and RF were 0.94/0.96 and 10.37/8.77 µg/m3, respectively. Then, due to the lack and the low spatial resolution of the pollution parameters, using only the ERA5 meteorological data with the high spatiotemporal resolution to develop the PM2.5 model in winter, the R2 of the RF model (0.93) was 0.05 higher and the RMSE (12.50 µg/m3) was 4.19 µg/m3 lower than that of the BPNN model, which indicates that it is feasible to develop the PM2.5 model using only meteorological parameters. Finally, using the RF model of the second stage and ERA5 meteorological data with a spatial resolution of 0.05° (obtained by cubic spline interpolation) to generate the hourly PM2.5 map of Beijing and compare it with China High Air Pollutants dataset, the R2 and RMSE of Beijing were 0.78 µg/m3 and 14.85 µg/m3, respectively. On this basis, it is found that the areas with high PM2.5 concentration are close to the areas with serious pollution in Hebei Province by analyzing the PM2.5 map of Beijing, and area transport and human activities are important sources of air pollution.

High‐Resolution Post‐Process Corrected Satellite AOD

AbstractPoor air quality poses a great threat to human health. Accurate high‐resolution satellite remote sensing of atmospheric aerosols would highly benefit satellite‐based air quality estimates. We have developed and validated a post‐process correction and downscaling approach for satellite remote sensing of aerosols. We use NASA's Moderate Resolution Imaging Spectroradiometer aerosol optical depth (AOD) over the Washington D.C.—Baltimore area during the Distributed Regional Aerosol Gridded Observation Networks campaign in 2011 to evaluate our approach. We derive and evaluate the AOD fields at high 250 m resolution. The results show that the post‐process correction approach is suitable for deriving downscaled, high‐resolution AOD estimates and significantly improves the accuracy of the AOD retrievals.

Characterization of dust activation and their prevailing transport over East Asia based on multi-satellite observations

Mineral dust from the deserts and arid areas in East Asia plays a critical role in regional climate and air quality. Based on integrated satellite observations and MERRA-2 reanalysis, we provide an observational insight into dust activities governing spatial patterns of dust aerosols over East Asia. With Moderate resolution Imaging Spectroradiometer Aerosol Optical Depth (AOD) and Ozone Monitoring Ultraviolet Aerosol Index (UVAI), fixed dust-active areas with distinct spatial-temporal differences are revealed. Besides the few dust storms mostly in spring, unnoticeable but frequent dust plumes are found to be the predominant sources of prevailing dust particles over East Asia. Compared with the active dust sources in Taklimakan Desert throughout the year, dust activities in Gobi deserts are at a much lower frequency. The elevated dust plumes are concentrated within 2–6 km over the deserts with large seasonal variations, and can be transported downstream to eastern China and Sichuan Basin within 3–5 days. Moreover, owing to the lack of enough strong winds, these dust plumes tend to be concentrated above the haze layers near surface in eastern China. The intense temperature inversion during winter and early spring can delay the mixing of dust-pollution. Despite a generally reasonable performance in dust AOD, MERRA-2 reanalysis exhibits some uncertainties in reproducing spatial locations of dust hotspots, dust concentration, and transport process. Our results emphasize significant role of the inapparent but prevalent dust plumes in air quality and in climate effects of East Asia.

Assimilating aerosol optical properties related to size and absorption from POLDER/PARASOL with an ensemble data assimilation system

Abstract. A data assimilation system for aerosol, based on an ensemble Kalman filter, has been developed for the ECHAM – Hamburg Aerosol Model (ECHAM-HAM) global aerosol model and applied to POLarization and Directionality of the Earth's Reflectances (POLDER)-derived observations of optical properties. The advantages of this assimilation system is that the ECHAM-HAM aerosol modal scheme carries both aerosol particle numbers and mass which are both used in the data assimilation system as state vectors, while POLDER retrievals in addition to aerosol optical depth (AOD) and the Ångström exponent (AE) also provide information related to aerosol absorption like aerosol absorption optical depth (AAOD) and single scattering albedo (SSA). The developed scheme can simultaneously assimilate combinations of multiple variables (e.g., AOD, AE, SSA) to optimally estimate mass mixing ratio and number mixing ratio of different aerosol species. We investigate the added value of assimilating AE, AAOD and SSA, in addition to the commonly used AOD, by conducting multiple experiments where different combinations of retrieved properties are assimilated. Results are evaluated with (independent) POLDER, Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target, MODIS Deep Blue and Aerosol Robotic Network (AERONET) observations. The experiment where POLDER AOD, AE and SSA are assimilated shows systematic improvement in mean error, mean absolute error and correlation for AOD, AE, AAOD and SSA compared to the experiment where only AOD is assimilated. The same experiment reduces the global ME against AERONET from 0.072 to 0.001 for AOD, from 0.273 to 0.009 for AE and from −0.012 to 0.002 for AAOD. Additionally, sensitivity experiments reveal the benefits of assimilating AE over AOD at a second wavelength or SSA over AAOD, possibly due to a simpler observation covariance matrix in the present data assimilation framework. We conclude that the currently available AE and SSA do positively impact data assimilation.

MODIS aerosol optical depth retrieval based on random forest approach

ABSTRACT Despite significant improvement in Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval, high-resolution–high-accuracy AOD retrieval remains a challenging task. This study utilizes machine learning for AOD retrieval of MODIS data. The global long-time-series data of AERONET sites and corresponding MODIS data in time and space were used as sample training data for aerosol retrieval via use of the random forest (RF) approach. The accuracy and stability of retrieval were ensured by processing AERONET site data, performing time–space matching between different data types, and determining related parameters in the RF model. MODIS data use bands 1–7 of the top-of-atmosphere reflectance (TOA) – extending from the visible to near-infrared radiation spectra – along with the corresponding observation geometry data, global land surface satellite (GLASS) albedo dataset, and normalized difference vegetation index (NDVI) data. The proposed method facilitates realizationthe of high-accuracy aerosol retrieval. Furthermore, significant enhancement in the efficiency of aerosol inversion is an added advantage.

Investigating Smoke Aerosol Emission Coefficients Using MODIS Active Fire and Aerosol Products: A Case Study in the CONUS and Indonesia

AbstractSmoke aerosols released from biomass burning greatly influence air quality, weather, and climate. The total particulate matter (TPM) of smoke aerosols has been demonstrated to be a linear function of fire radiative energy (FRE) during a period of biomass burning via a smoke aerosol emission coefficient (Ce). However, it remains challenging to quantify Ce appropriately through satellite observations. In this study, an innovative approach was put forward to explore Ce by establishing the relationships between FRE and TPM in two regions, the CONtiguous United States and Indonesia. Specifically, we identified 584 isolated fire clusters and smoke plumes in the CONtiguous United States and 248 in Indonesia using Moderate Resolution Imaging Spectroradiometer natural color images, and then calculated FRE from Moderate Resolution Imaging Spectroradiometer active fire product and TPM from Moderate Resolution Imaging Spectroradiometer aerosol optical depth product for each fire‐smoke matchup during Terra and Aqua overpasses. The relationships between TPM and FRE were constructed to determine Ce using an ordinary least squares regression. The results show that FRE and TPM are significantly correlated (r2 ≥ 0.63, p < 0.001) with the Ce varying across regions and fuel types. In the CONtiguous United States, forest Ce values are 21.3 and 34.1 g/MJ and savanna Ce values are 18.2 and 22.8 g/MJ for western and eastern regions, respectively; additionally, Ce is 20.9 g/MJ for grasslands and 5.0 g/MJ for shrublands. In Indonesia, Ce is 52.4 and 30.0 g/MJ for peatlands and forests, respectively. Overall, this study improves our understanding of Ce variations with fuel types and climate regions.

Aerosol Optical Depth from MODIS satellite data above the Pierre Auger Observatory

Aerosol optical depth can be retrieved from measurements performed by Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument. The MODIS satellite system includes two polar satellites, Terra and Aqua. Each of them flies over the Pierre Auger Observatory once a day, providing two measurements of aerosols per day and covering the whole area of the Observatory. MODIS aerosol data products have been generated by three dedicated algorithms over bright and dark land and over ocean surface. We choose the Deep Blue algorithm data to investigate the distribution of aerosols over the Observatory, as this algorithm is the most appropriate one for semi-arid land of the Pierre Auger Observatory. This data algorithm allows us to obtain aerosol optical depth values for the investigated region, and to build cloud-free aerosol maps with a horizontal resolution 0.1°×0.1°. Since a suffcient number of measurements was obtained only for Loma Amarilla and Coihueco fluorescence detector (FD) sites of the Pierre Auger Observatory, a more detailed analysis of aerosol distributions is provided for these sites. Aerosols over these FD sites are generally distributed in a similar way each year, but some anomalies are also observed. These anomalies in aerosol distributions appear mainly due to some transient events, such as volcanic ash clouds, fires etc. We conclude that the Deep Blue MODIS algorithm provides more realistic aerosol optical depth values than other available algorithms.

MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison

Recently, the newest Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products were available with various refinements and improvements made to both the radiation calibration and Dark Target (DT) and Deep Blue (DB) algorithms. A combined DT and DB dataset (DTB) was also added based on piecewise fixed thresholds using the Normalized Difference Vegetation Index (NDVI) for taking advantage of one's merits. This study provides a cross-comparison and evaluation of these Terra MODIS aerosol products with reference to the enhanced ground-based AOD measurements by the Aerosol Robotic Network (AERONET) Level 3 Version 2.0 data at 384 ground stations. Their absolute and relative performance are evaluated in the period of 2013–2017 among the products, as well as between the current (C6.1) and previous (C6) releases. In general, the C6.1 aerosol products are found to be superior over the C6 products for three datasets from all scales, but the differences and improvements are rather non-uniform that varies with region. Overall, the DB AOD products show the best performance in most regions at about half of the sites, especially in Europe and North America. Meanwhile, besides bright surfaces (i.e., deserts and arid/semi-arid areas), DB products match more closely with the AERONET AODs than that of DT over medium or densely vegetated areas. The dependences of retrieval errors illustrate that the performance of three datasets deteriorates as surface reflectance, elevation and aerosol loading increase. However, the DB algorithm remains relatively more stable and less affected by changes in atmospheric and surface conditions. While the merged product using NDVI has some improvements over individual ones in general, worse performance is also shown in many cases. A more optimal method is thus wanting.

Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates

In 2016, three Nigerian cities were listed amongst the World's most polluted in terms of particulate matter (PM). Acknowledging Nigeria's limited resources for outdoor air pollution monitoring, this study attempts to investigate the effects on atmospheric aerosol optical depth and ground PM on GPS derived-precipitable water vapour estimates. The study utilized available GPS-derived precipitable water vapour (GPSPWV), the moderate resolution imaging spectroradiometer aerosol optical depth (MODISAOD) and the ground level particulate matter of less than 10 μm (GPM) datasets for December 2015–November 2016. All the datasets were tested for normality. To evaluate the atmospheric aerosol properties, the MODISPWV estimates were pre-validated using the GPSPWV measurements. The results revealed GPSPWV-MODISPWV agreement (R = 0.964; RMSE = 3.810 mm). The GPSPWV-MODISAOD analysis showed relationship (R ≤ −0.636; RMSE ≤ 0.563) for the atmospheric aerosol experiment, while the collocating GPSPWV-GPM seasonal analysis also revealed significant correlation (R < −0.660). The correlation of combined seasonal datasets for the GPSPWV-MODISAOD and GPSPWV-GPM relationships showed high negative correlation values of 0.79 and 0.68 respectively. The findings of this study is in agreement with similar related studies, as well as serve as position accuracy for future related studies.

A new approach developed to study variability in North African dust transport routes over the Atlantic during 2001–2015

AbstractWe investigated the variability in the North African dust transport routes over the Atlantic (NAD routes) by extracting the dust transport central axis using Moderate Resolution Imaging Spectroradiometer aerosol data for 2001–2015. The results showed that the NAD routes can be classified into two regimes, a southern route centered at the southernmost position of 6.08 ± 1.12°N during November to March and a northern route centered at the northernmost position of 18.21 ± 1.04°N during April to October. In the southern route, large intervariation was correlated with the Intertropical Convergence Zone (ITCZ) and North Atlantic Oscillation (NAO), but the ITCZ and NAO jointly explained only 38% of the variation. In the northern route, the ITCZ alone explained 67% of the intervariation. The extracted trends during 2001–2015 exhibited a northward shift of 1.68° for the southern route and of 0.52° for the northern route. The causes for the shift were also examined.

Observed Covariations of Aerosol Optical Depth and Cloud Cover in Extratropical Cyclones

AbstractUsing NASA Moderate Resolution Imaging Spectroradiometer aerosol optical depth and total cloud cover retrievals, CloudSat‐CALIPSO cloud profiles, and a database of extratropical cyclones and frontal boundary locations, relationships between changes in aerosol optical depth and cloud cover in extratropical cyclones occurring over Northern Hemisphere oceans are examined. A reanalysis data set is used to constrain column water vapor and ascent strength in the cyclones in an attempt to distinguish their impact on cloud cover from the effect of changes in aerosol loading. The results suggest that high aerosol optical depth cyclones exhibit larger middle‐ and high‐level cloud cover than their low aerosol optical depth counterparts. However, the opposite behavior is found for low‐level cloud cover. These relationships are found to depend on the large‐scale environment, in particular the column water vapor and vertical motion. Despite the inability of the observations to provide a causal physical link between aerosol load and cloud cover, these results can help to constrain and evaluate model simulations.

Exploiting TERRA-AQUA MODIS Relationship in the Reflective Solar Bands for Aerosol Retrieval

Satellite remote sensing has been providing aerosol data with ever-increasing accuracy, representative of the MODerate-resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and Deep Blue (DB) aerosol retrievals. These retrievals are generally performed over spectrally dark objects and therefore may struggle over bright surfaces. This study proposed an analytical TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. For the relationship development, the bidirectional reflectance distribution function (BRDF) effects were adjusted using reflectance ratios in the MODIS 2.13 μm band and the path radiance was approximated as an analytical function of aerosol optical thickness (AOT) and scattering phase function. Comparisons with MODIS observation data, MODIS AOT data, and sun photometer measurements demonstrate the validity of the proposed relationship for aerosol retrieval. The synergetic TERRA-AQUA MODIS retrievals are highly correlated with the ground measured AOT at TERRA MODIS overpass time (R2 = 0.617; RMSE = 0.043) and AQUA overpass time (R2 = 0.737; RMSE = 0.036). Compared to our retrievals, both the MODIS DT and DB retrievals are subject to severe underestimation. Sensitivity analyses reveal that the proposed method may perform better over non-vegetated than vegetated surfaces, which can offer a complement to MODIS operational algorithms. In an analytical form, the proposed method also has advantages in computational efficiency, and therefore can be employed for fine-scale (relative to operational 10 km MODIS product) MODIS aerosol retrieval. Overall, this study provides insight into aerosol retrievals and other applications regarding TERRA-AQUA MODIS data.

Aerosol radiative effects under clear skies over Europe and their changes in the period of 2001–2012

ABSTRACTIn the study, the clear‐sky aerosol radiative effects have been quantified at 52 European sites using empirical and radiative transfer modelling approaches. Furthermore, the trends of the aerosol radiative effects are also determined for the period of 2001–2012. The aerosol radiative effects are simulated using the Mesoscale Atmospheric Global Irradiance Code radiation code with aerosol and water vapour climatology. The annual mean of the surface aerosol radiative effects in clear‐sky situations over Europe is −7.1 ± 2.9 W m−2. The trends of the aerosol radiative effects are derived assigning radiative effects to monthly Moderate Resolution Imaging Spectroradiometer aerosol optical depth time series applying linear fitting. In the indirect approach, the clear‐sky situations are selected from daily surface solar radiation observations from the Word Radiation Data Centre, and the trends of the aerosol radiative effects are delimitated in these clear‐sky radiation time series. The two approaches give good fit, based on the direct approach the annual trend of the aerosol radiative effects on clear‐sky solar surface radiation is −4.41 W m−2 per decade for the period of 2001–2013, while in the case of the indirect approach, this trend is found to be −4.46 W m−2 per decade. The seasonal trends of the aerosol radiative effects vary between −7.57 and −0.17 W m−2 per decade indicating stronger decreases in summertime. The results are presented for four sub‐regions of Europe detecting negative changes in all cases with a stronger decrease in the central, north‐eastern and southern part of the continent. The trends of the aerosol radiative effect have the same magnitude as the trends detected in clear‐sky solar surface radiation, and are an order of magnitude higher than the radiative effects of water vapour (0.20 W m−2 per decade).

All-Sky Direct Radiative Effects of Urban Aerosols in Beijing and Shanghai, China

AbstractAerosol particles can directly alter the radiation balance by scattering and absorbing incident solar radiation, thus decreasing the amount of light reaching the surface and increasing the fraction of diffuse radiation—the so-called ‘aerosol direct radiative effect’. Using the Moderate Resolution Imaging Spectroradiometer aerosol products, the aerosol direct radiative effects under all-sky conditions in Beijing and Shanghai in 2007 were explored in this study. The total shortwave radiation was calculated using the Fu-Liou radiative transfer model, with the influence of clouds taken into account through sunshine-duration data, and the diffuse radiation was calculated with radiation decomposition models. Good correlation between measured and calculated total radiation was obtained at both cities, with an R greater than 0.9, and thus this calculation method was adopted to derive aerosol direct radiative effects. The presence of aerosols caused the mean total and diffuse solar radiation reaching the s...

Statistical analysis of aerosols over the Gangetic–Himalayan region using ARIMA model based on long-term MODIS observations

Long-term observations and modeling of aerosol loading over the Indo-Gangetic plains (IGP), the Indian desert region and Himalayan slopes are analyzed in the present study. The Box–Jenkins popular ARIMA (AutoRegressive Integrated Moving Average) model was applied to simulate the monthly-mean Terra MODIS (MODerate Resolution Imaging Spectroradiometer) Aerosol Optical Depth (AOD550 nm) over eight sites in the region covering a period of about 13years (March 2000–May 2012). The autocorrelation structure has been analyzed indicating a deterministic pattern in the time series that it regains its structure every 24month period. The ARIMA models namely ARIMA(2,0,12), ARIMA(1,0,6), ARIMA(3,0,0), ARIMA(2,0,13) ARIMA(0,0,12), ARIMA(2,0,2), ARIMA(1,0,12) and ARIMA(0,0,1) have been developed as the most suitable for simulating and forecasting the monthly-mean AOD over the eight selected locations. The Stationary R-squared, R-squared, Root Mean Square Error (RMSE) and Normalized BIC (Bayesian Information Criterion) are used to test the validity and applicability of the developed ARIMA models revealing adequate accuracy in the model performance. The values of Hurst Exponent, Fractal Dimension and Predictability Index for AODs are about 0.5, 1.5 and 0, respectively, suggesting that the AODs in all sites follow the Brownian time-series motion (true random walk). High AOD values (>0.7) are observed over the industrialized and densely-populated IGP sites associated with low ones over the foothills/slopes of the Himalayas. The trends in AOD during the ~13-year period differentiates depending on season and site. During post-monsoon and winter accumulation of aerosols and increasing trends are shown over IGP sites, which are neutralized in pre-monsoon and become slightly negative in monsoon. The AOD over the Himalayan sites does not exhibit any significant trend and seems to be practically unaffected by the aerosol built-up over IGP.

Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand

This study explored the use of satellite data to monitor carbon monoxide (CO) and particulate matter (PM) in Northern Thailand during the dry season when forest fires are known to be an important cause of air pollution. Satellite data, including Measurement of Pollution in the Troposphere (MOPITT) CO, Moderate Resolution Imaging Spectroradiometer aerosol optical depth (MODIS AOD), and MODIS fire hotspots, were analyzed with air pollution data measured at nine automatic air quality monitoring stations in the study area for February-April months of 2008-2010. The correlation analysis showed that daily CO and PM with size below 10 μm (PM10) were associated with the forest fire hotspot counts, especially in the rural areas with the maximum correlation coefficient (R) of 0.59 for CO and 0.65 for PM10. The correlations between MODIS AOD and PM10, between MOPITT CO and CO, and between MODIS AOD and MOPITT CO were also analyzed, confirming the association between these variables. Two forest fire episodes were selected, and the dispersion of pollution plumes was studied using the MOPITT CO total column and MODIS AOD data, together with the surface wind vectors. The results showed consistency between the plume dispersion, locations of dense hotspots, ground monitoring data, and prevalent winds. The satellite data were shown to be useful in monitoring the regional transport of forest fire plumes.

The effect of smoke emission amount on changes in cloud properties and precipitation: A case study of Canadian boreal wildfires of 2007

AbstractWe investigate the influence of wildfire smoke aerosols on cloud microphysics and precipitation using a coupled aerosol‐cloud microphysics‐meteorology model WRF‐Chem‐SMOKE. The Wildfire Automated Biomass Burning Algorithm products are used to compute “online” hourly size‐ and composition‐resolved smoke emission fluxes during Canadian boreal wildfires in the summer of 2007. Comparisons with Moderate Resolution Imaging Spectroradiometer aerosol optical depth, Ozone Monitoring Instrument aerosol index, and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation vertical feature mask demonstrate that WRF‐Chem‐SMOKE captures both the horizontal and vertical spatial distribution of smoke. However, estimated smoke emissions result in much lower aerosol optical depth values than those of observations (by about tenfold). Modeling experiments with varying amounts of smoke emissions of 5 to 10 times as high as the original load reveal that low smoke load favors the collision‐coalescence process at a certain stage, leading to either positive or negative changes in the cloud water path (CWP) relative to smoke‐free conditions. For high smoke emissions, changes in CWP are positive, as large as 0.5 kg/m2. A domain‐integrated increase in CWP is proportional to smoke loading. By contrast, both positive and negative changes in the rain water path (RWP) and the snow water path (SWP) are found. While domain‐integrated changes in RWP are negative, those in SWP go from negative to positive under a high smoke load. Higher smoke loadings suppress precipitation initially, because of smoke‐induced reduction of the collision‐coalescence and riming processes, but ultimately cause an invigoration of precipitation. We found that precipitation is highly sensitive to 3‐D smoke fields and varies in a nonlinear manner with smoke loads.

On the differences of ultraviolet and visible irradiance calculations in the Mediterranean basin due to model- and satellite-derived climatologies of aerosol optical properties

The variability of aerosol optical properties over the Mediterranean basin, in the ultraviolet (UV) and visible (VIS) spectral regions, is examined, using ground-based data from eight stations of the AErosol RObotic NETwork (AERONET), climatological values from the AeroCom (Aerosol Comparisons between Observations and Models) project and data from the MODerate resolution Imaging Spectroradiometer (MODIS) on-board the Terra and Aqua satellites. The aerosol optical properties from the three datasets are used as input data in a radiative transfer model, in order to estimate the differences in irradiance at the ground under cloud-free skies. The MODIS aerosol optical depth climatology shows better agreement with AERONET data. The highest difference in the monthly average values is equal to 0.09 at 550 nm, while the differences between the AERONET and the AeroCom climatologies reach 0.25 and 0.15 in the UV and VIS wavelengths, respectively. As a result, the AERONET modelled VIS and UV irradiances are closer to MODIS, with the absolute differences in average values reaching 15 (6%) and 2 W m–2 (6%), respectively, while absolute differences with AeroCom irradiances can reach up to 30 (12%) and 4.5 W m–2 (12%). The differences are higher in FORTH Crete, Nes Ziona, Sede Boker and Blida, which are influenced by desert dust aerosols. The separate effect on irradiances due to differences in aerosol optical depth and single scattering albedo from the three datasets has been also examined; according to results, the effect of aerosol optical depth is dominant. At this stage, the use of MODIS aerosol climatology would be recommended for calculations of UV and VIS irradiance in the Mediterranean basin. However, the AeroCom climatology, with some further improvements in the dust cycle, could be a valuable tool in future studies in this area, due to the provision of natural and manmade aerosol optical properties with higher spectral resolution.

Emission and transport of cesium‐137 from boreal biomass burning in the summer of 2010

While atmospheric concentrations of cesium‐137 (137Cs) have decreased since the nuclear testing era, resuspension of 137Cs during biomass burning provides an ongoing emission source. The summer of 2010 was an intense biomass burning season in western Russia, with high levels of particulate matter impacting air quality and visibility. A radionuclide monitoring station in western Russia shows enhanced airborne 137Cs concentrations during the wildfire period. Since 137Cs binds to aerosols, satellite observations of aerosols and fire occurrences can provide a global‐scale context for 137Cs emissions and transport during biomass burning events. We demonstrate that high values of the Moderate Resolution Imaging Spectroradiometer aerosol optical depth coincide with detections of 137Cs, and use the relationship between 137Cs and aerosols to model 137Cs based on organic carbon emissions and transport with the Goddard Earth Observing System, version 5 model. The model's boreal biomass burning tracer explains approximately half of the daily variability in detected 137Cs concentrations at a monitoring station in western Russia. Constraining the model with the station observations, we calculate 137Cs emissions of 1.5 × 1012 Bq from biomass burning north of 40° in July and August 2010. The emissions and subsequent deposition lead to a small northward redistribution of 137Cs.