Improving GERB scene identification using SEVIRI: Infrared dust detection strategy

The combination of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Geostationary Earth Radiation Budget (GERB) instruments on Meteosat-8 provides a powerful new tool for detecting aerosols and estimating their radiative effect at high temporal and spatial resolution. However, at present no specific aerosol treatment is performed in the GERB processing chain, severely limiting the use of the data for aerosol studies. A particular problem relates to the misidentification of Saharan dust outbreaks as cloud which can bias the shortwave and longwave fluxes. In this paper an algorithm is developed which employs multiple-linear regression, using information from selected thermal infrared SEVIRI channels, to detect dust aerosol over ocean and provide an estimate of the optical depth at 0.55 μm (τ₀₅₅). To test the performance of the algorithm, it has been applied to a number of dust events observed by SEVIRI during March and June 2004. The results are compared to co-located MODIS observations taken from the Terra and Aqua platforms, and ground based observations from the Cape Verde AERONET site. In terms of detection capability, employing the algorithm results in a notable improvement in the routine GERB scene identification. Locations identified by MODIS as being likely to be dust contaminated were originally classified as cloud in over 99.5% of the cases studied. With the application of the detection algorithm approximately 60-70% of these points are identified as dusty depending on the dust model employed. The algorithm is also capable of detecting dust in regions and at times which would be excluded when using shortwave observations, due for example to the presence of sun-glint, or through the night. We further investigate whether the algorithm is capable of generating useful information concerning the aerosol loading. Comparisons with co-located retrievals from the SEVIRI 0.6 μm solar reflectance band observations show a level of agreement consistent with that expected from the simulations, with rms differences of between 0.5 and 0.8, and a mean bias ranging from − 0.5 to 0.3 dependent on the dust representation employed in the algorithm. Temporally resolved comparisons with observations from the Capo Verde AERONET site through the months of March and June reinforce these findings, but also indicate that the algorithm is capable of discerning the diurnal pattern in aerosol loading. The algorithm has now been incorporated within the routine GERB processing in detection mode, and will be used to provide an experimental aerosol product for assessment by the scientific community.     

Comparing MODIS and AERONET aerosol optical depth over China

The newest daily and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depths (AOD or τ) dataset over land, C005, retrieved using the second-generation operational algorithm, were evaluated using a ground-based Aerosol Robotic Network (AERONET) dataset from 13 sites over China. The dataset covers the period 2003–2006. Daily MODIS C005 AODs over China were found to have a positive bias with a relationship of τ_MODIS?=?0.135?+?1.022τ_AERONET, for which the offset is larger than reported global validation results. However, the relationship τ_MODIS?=?0.021?+?0.929τ_AERONET showed that monthly MODIS C005 AODs were an overestimation for small AOD and underestimation for high AOD. Both daily and monthly MODIS AOD retrievals showed poor performance in extreme aerosol conditions, e.g. under dust events or heavy urban/industrial haze. Nevertheless, both daily and monthly MODIS C005 AOD datasets can be used for investigation of aerosol spatial distribution and temporal variation over China.     

Intercomparison of CALIOP,MODIS, and AERONET aerosol optical depth over China during the past decade

ABSTRACT

Aerosol optical depth (AOD) data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were intercompared and validated against ground-based measurements from Aerosol Robotic Network (AERONET) as well as space-based Moderate Resolution Imaging Spectroradiometer (MODIS) over China during June 2006 to December 2015. This article aims to evaluate CALIOP daytime AOD using MODIS and AERONET AODs. Comparing the AOD between CALIOP and AERONET in different regions over China using quality control flags to screen the AOD data, we find that CALIOP AOD is generally lower than AERONET AOD especially at optical depths over 0.4 likely due to differences in the cloud screening algorithms and general retrieval uncertainty. Comparison between CALIOP AOD and MODIS AOD results show that the overall spatio-temporal distribution of CALIOP AOD and MODIS AOD is basically consistent. As for the spatial distribution, both data sets show several high-value regions and low-value regions in China. CALIOP is systematically lower than MODIS over China, especially over high AOD value regions for all seasons. As for the temporal variation, both data sets show a significant seasonal variation: AOD is largest in spring, then less in summer, and smallest in winter and autumn. A long-term linear trend analysis based on the domain averaged monthly mean CALIOP and MODIS AOD shows agreement among CALIOP and MODIS for the trends over the 10-year period in four regions examined. The trends in AOD derived from CALIOP and MODIS indicate a decline in aerosol loading in China since 2006. It is found from frequency comparison that CALIOP and MODIS AOD generally exhibit a degree of correlation over China. Statistical frequency analysis shows that CALIOP AOD frequency distribution shows a higher peak than MODIS AOD when AOD < 0.4. For the most part, mean MODIS AOD is higher than mean CALIOP AOD. Evaluation of CALIOP AOD retrievals provides the prospect for application of CALIOP data. The intercomparison suggests that CALIOP has systematically underestimated daytime AOD retrievals, especially deteriorating with increasing AOD, and therefore, CALIOP daytime AOD retrievals should be treated with some degree of caution when the AOD is over 0.4.     

An evaluation of satellite aerosol products against sunphotometer measurements

Because atmospheric aerosols scatter sunlight back to space, reflectance measurements from spaceborne radiometers can be used to estimate the aerosol load and its optical properties. Several aerosol products have been generated in a systematic way, and are available for further studies. In this paper, we evaluate the accuracy of such aerosol products derived from the measurements of POLDER, MODIS, MERIS, SEVIRI and CALIOP, through a statistical comparison with Aerosol Optical Depth (AOD) measurements from the AERONET sunphotometer network. Although this method is commonly used, this study is, to our knowledge, among the most extensive of its type since it compares the performance of the products from 5 different sensors using up to five years of data for each of them at global scale. The choice of these satellite aerosol datasets was based on their availability at the ICARE Data and Service Centre (www.icare.univ-lille1.fr).We distinguish between retrievals over land and ocean and between estimates of total and fine mode AOD. Over the oceans, POLDER and MODIS retrievals are of similar quality, with RMS difference lower than 0.1 and a correlation with AERONET of around 0.9. The POLDER estimates suffer from a small positive bias for clean atmospheres, which weakens its statistics. The other aerosol products are of lesser quality, although the SEVIRI products may be of interest for some applications that require a high temporal resolution. The MERIS product shows a very high bias. Over land, only the MODIS product offers a reliable estimate of the total AOD. On the other hand, the polarization-based retrieval using POLDER data allows a better fine mode estimate than that from MODIS. These results suggest the need for a product combining POLDER and MODIS products over land.The paper also analyses how the statistics change with the spatial and temporal thresholds that are used. Spatio-temporal averaging improves the statistics only slightly, which indicates that random errors are not dominant in the error budget. The paper includes various statistical indicators at global scale, and detailed results at individual ground stations can be obtained on request from the authors.     

Spatial distributions and temporal variations of atmospheric aerosols and the affecting factors: a case study for a region in central China

Using Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data, temporal variations and the spatial distribution of aerosol optical depth (AOD or τ) over the Hubei Province in China were investigated from 2003 to 2008. self-organizing maps (SOMs) and linear models were further used to analyse the relationships between AODs and elevation, normalized difference vegetation index (NDVI) and population density. The results were as follows: high AOD values were observed in south-central areas with lower elevations, lower NDVI and larger population densities, whereas low AOD values were observed in the western, northeastern and southeastern areas. The highest AOD values were observed in spring; summer was characterized by lower AOD values, but also the largest ratio of fine particles; in autumn, the coverage of AOD was only smaller than spring with most being fine particles; in winter, coarse particles were dominant when AOD values were the lowest. The AOD monthly average rose substantially in the winter–spring season and dropped sharply in the spring–winter season. Based on these data, both SOMs and linear models show that AOD distribution is influenced by the complex interactions that occur among various elements. The annual AODs are negatively related to ln(elevation) and NDVI and positively related to ln(population density). The ln(elevation) factor affects aerosol distribution more than do the other two factors. Compared to fine-particle aerosols, the selected three factors have a greater impact on the coarse particles.     

Cloud screening in IRS-P4 OCM satellite data: potential of spatial coherence method in the absence of thermal channel information

Cloud screening of satellite data for the remote sensing of atmospheric aerosols, ocean sediments, chlorophyll, and phytoplankton in the marine environment is a major problem in the absence of information from thermal channel. This is particularly the case with the data from some of the highly potential satellite sensors such as the Ocean Colour Monitor (OCM—on board the Indian Remote Sensing Satellite, IRS-P4) and the SeaWiFS. Two main tests conventionally used for cloud screening of data from such satellite sensors are the threshold method applied to visible and near-IR bands and the visible to near-IR channel ratio method. These methods do not have the potential to eliminate the pixels with small cloud fractions, leading to overestimation of the aerosol optical depth (AOD) derived from satellite data, and might also identify the pixels with high values of AOD as cloudy. The purpose of this paper is to study the potential of Spatial Coherence Test (SCT) applied to the data from the near-IR bands for cloud screening of satellite data over the oceanic environment. We use here the data from IRS-P4 OCM. Though more computationally intensive, the SCT does not suffer from the serious limitations of the threshold and channel ratio methods and is found to be superior in identifying the clear sky pixels that are not affected by clouds. Although the SCT applied to near-IR channel data may be overestimating the number of cloud affected pixels, it neither leads to overestimation of AOD nor identifies the pixels with high AOD values as cloudy.     

The effect of aerosol vertical profiles on satellite-estimated surface particle sulfate concentrations

The aerosol vertical distribution is an important factor in determining the relationship between satellite retrieved aerosol optical depth (AOD) and ground-level fine particle pollution concentrations. We evaluate how aerosol profiles measured by ground-based lidar and simulated by models can help improve the association between AOD retrieved by the Multi-angle Imaging Spectroradiometer (MISR) and fine particle sulfate (SO4) concentrations using matched data at two lidar sites. At the Goddard Space Flight Center (GSFC) site, both lidar and model aerosol profiles marginally improve the association between SO4 concentrations and MISR fractional AODs, as the correlation coefficient between cross-validation (CV) and observed SO4 concentrations changes from 0.87 for the no-scaling model to 0.88 for models scaled with aerosol vertical profiles. At the GSFC site, a large amount of urban aerosols resides in the well-mixed boundary layer so the column fractional AODs are already excellent indicators of ground-level particle pollution. In contrast, at the Atmospheric Radiation Measurement Program (ARM) site with relatively low aerosol loadings, scaling substantially improves model performance. The correlation coefficient between CV and observed SO4 concentrations is increased from 0.58 for the no-scaling model to 0.76 in the GEOS-Chem scaling model, and the model bias is reduced from 17% to 9%. In summary, despite the inaccuracy due to the coarse horizontal resolution and the challenges of simulating turbulent mixing in the boundary layer, GEOS-Chem simulated aerosol profiles can still improve methods for estimating surface aerosol (SO4) mass from satellite-based AODs, particularly in rural areas where aerosols in the free troposphere and any long-range transport of aerosols can significantly contribute to the column AOD.     

Spatial characteristics of the difference between MISR and MODIS aerosol optical depth retrievals over mainland Southeast Asia

The difference between aerosol optical depths (AODs) retrieved from the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) is examined over mainland Southeast Asia from a spatial perspective. Though ideally the difference between these measurement methods should be small and randomly distributed over space, our analysis suggests that this difference has a strong negative relationship with MODIS AODs and tend to be spatially clustered. We quantify the spatial dependence in MISR/MODIS AOD differences and explore the extent to which the spatial patterns in these differences can be explained by variables that reflect the influence of physical environment and human activities. While these variables show a strong relationship with MISR/MODIS AOD differences, the results also suggest that further research is needed to fully understand the spatial dependence in these differences.     

Physical and chemical properties of surface and column aerosols at a rural New England site during MODIS overpass

Surface-based measurements of aerosol optical depth at a rural site in southern New Hampshire (43.11°N, 70.95°W) are compared to retrievals of the same parameter by the Moderate Resolution Imaging Spectrometer (MODIS) during April-August, 2001. Hourly averages of aerosol optical depth (AOD) were derived using a multi-filter rotating shadowband radiometer (MFRSR) at the time of NASA's Terra satellite overpass. The MODIS Level 2 aerosol product at a wavelength of 550 nm was directly compared to the MFRSR interpolated AOD at 550 nm. We were able to compare the two AOD measurement platforms on 46 days (out of a possible 128 days) and observed a good agreement between the two methods (R=0.81; slope=0.95±0.10). However, there were 11 days during this study period when MODIS measured AOD at the site, but the MFRSR did not due to excessive cloud cover. There were also 7 days when clear skies prevailed at the site during the time of MODIS overpass, but there was no AOD retrieved by MODIS. Surface measurements of fine particle (PM_2.5) mass, chemical composition, and optical properties were also performed during summer 2001. A good correlation (R=0.87) between fine particle mass and AOD measured by the MFRSR was observed. A comparison between fine particle light extinction at the surface and MFRSR AOD (at the same wavelength) also showed good agreement (R=0.80). Aerosol chemical analysis revealed that ammonium sulfate was the main aerosol component during times of very high turbidity, while organic carbon dominated during times of below-average turbidity.     

PM10 remote sensing from geostationary SEVIRI and polar-orbiting MODIS sensors over the complex terrain of the European Alpine region

The subject of this study is to investigate the capability of spaceborne remote sensing data to predict ground concentrations of PM₁₀ over the European Alpine region using satellite derived Aerosol Optical Depth (AOD) from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the polar-orbiting MODerate resolution Imaging Spectroradiometer (MODIS). The spatial and temporal resolutions of these aerosol products (10 km and 2 measurements per day for MODIS, ∼ 25 km and observation intervals of 15 min for SEVIRI) permit an evaluation of PM estimation from space at different spatial and temporal scales. Different empirical linear relationships between coincident AOD and PM₁₀ observations are evaluated at 13 ground-based PM measurement sites, with the assumption that aerosols are vertically homogeneously distributed below the planetary Boundary Layer Height (BLH). The BLH and Relative Humidity (RH) variability are assessed, as well as their impact on the parameterization. The BLH has a strong influence on the correlation of daily and hourly time series, whilst RH effects are less clear and smaller in magnitude. Despite its lower spatial resolution and AOD accuracy, SEVIRI shows higher correlations than MODIS (r_SEV∼ 0.7, r_MOD∼ 0.6) with regard to daily averaged PM₁₀. Advantages from MODIS arise only at hourly time scales in mountainous locations but lower correlations were found for both sensors at this time scale (r∼ 0.45). Moreover, the fraction of days in 2008 with at least one satellite observation was 27% for SEVIRI and 17% for MODIS. These results suggest that the frequency of observations plays an important role in PM monitoring, while higher spatial resolution does not generally improve the PM estimation. Ground-based Sun Photometer (SP) measurements are used to validate the satellite-based AOD in the study region and to discuss the impact of aerosols' micro-physical properties in the empirical models. A lower error limit of 30 to 60% in the PM₁₀ assessment from space is estimated in the study area as a result of AOD uncertainties, variability of aerosols properties and the heterogeneity of ground measurement sites. It is concluded that SEVIRI has a similar capacity to map PM as sensors on board polar-orbiting platforms, with the advantage of a higher number of observations. However, the accuracy represents a serious limitation to the applicability of satellites for ground PM mapping, especially in mountainous areas.     

Validation of MODIS derived aerosol optical depth over the Yangtze River Delta in China

MODIS derived aerosol optical depths (AODs) at 550 nm are compared with sunphotometer CE318 measurements at 7 sites located at Yangtze River Delta (YRD) in China from July to October, 2007. The evaluation result indicates that MODIS AODs (Collection 5, C005) are in good agreement with those from CE318 in dense vegetation regions, but show more differences in those regions with complex underlying surface (such as at lake water and urban surface sites). Reasons for these differences are discussed after removing cases with significant errors caused by validation scheme. The final validation result shows that MODIS AODs are in good agreement with CE318 with a correlation coefficient of 0.85 and RMS of 0.15. 90% of MODIS cases fall in the range of Δτ = ± 0.05 ± 0.20τ, indicating MODIS aerosol retrieval algorithm, aerosol models and surface reflectance estimate are generally suitably reasonable for aerosol retrieval in YRD. However, MODIS AODs show a systemic errors with fitted line of y = 0.75x + 0.13, indicating underestimation of AOD when aerosol loadings are high. Aerosol models and surface reflectance estimations are dominant sources of MODIS aerosol retrieval errors.     

Effect of major dust storm on optical,physical, and radiative properties of aerosols over coastal and urban environments in Western India

The present study deals with a major summertime dust storm event that occurred over the Arabian Desert, Saudi Arabia (SA), and the United Arab Emirates (UAE) and spread to Pakistan, the Arabian Sea, and Western India (in particular over Alibaug and Pune), using multi-satellite- and ground-based measurements. Analysis of the aerosol parameters retrieved over Alibaug and Pune from the ground-based MICROTOPS-II Sunphotometer, seven Aeronet Robotic Network (AERONET) stations, including Pune, and satellite (Moderate-resolution Imaging Spectroradiometer (MODIS)) and Ozone Monitoring Instrument ((OMI-Aura)) measurements shows significant changes on dusty days as compared with non-dusty days prior to and after the dust storm event. A large increase in aerosol optical depths (AODs) and decrease in Angstrom exponent (AE, α_440-870 nm), showing the presence of a larger fraction of coarser particles comparable to those of other intense dust outbreak episodes worldwide, have been found at all these sites during the dust storm event. Higher observed AODs are considered to be the combined effect of desert dust and burning biomass/biofuel-induced aerosols. AERONET-retrieved mean aerosol volume size distributions (AVSDs) on non-dusty and dusty days are bimodal in nature at the stations of Pune, Kanpur, Jaipur, Karachi, and Lahore, while at the stations of KAUST_Campus (SA) and Mezaira (UAE), AVSDs are monomodal. This indicates that at the first five stations, the aerosol system is a complex mixture of fine- and coarse-mode aerosols with coarse mode exerting a strong influence on the system. Dust-induced turbid conditions triggered significant extinction of 30–40%, in shortwave (SW) global solar irradiance, resulting in an increase of 57% and 74% in aerosol direct radiative forcing (ADRF) at Alibaug and Pune, respectively, causing perturbation in the radiation budget. The space-borne Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-retrieved aerosol vertical profiles reveal the presence of polluted dust (a mixture of fine- and coarse-mode aerosols), corroborating well with the aerosol behaviour captured by the MICROTOPS/AERONET measurements and MODIS retrievals over the study areas. The Dust Regional Atmospheric Model (BSC-DREAM8b) predictions are also found to be consistent with satellite retrievals, implying the ability of the model to monitor dust transport over the study region.     

A new method for improving the retrieved aerosol fine-mode fraction from MODIS over ocean

A new method for improving the retrieved aerosol fine-mode fraction (₅₅₀) based on the current Moderate Resolution Imaging Spectroradiometer (MODIS) ocean algorithm is proposed. In the current MODIS ocean algorithm, the top of the atmosphere (TOA) apparent reflectance needs calculation from lookup tables (LUTs). The weighting parameters used in the calculation show an obvious spectral dependence, which is not taken into account in the current algorithm. The main measure taken in this study is to consider the spectral dependence of the weighting parameters. The MODIS aerosol products and the Aerosol Robotic Network (AERONET) data of Hong Kong Hok Tsui, Midway Island, Martha’s Vineyard Coastal Observatory (MVCO) and COVE, Virginia, where aerosols exhibit different loading and size distribution, are used to test the new method. The results show that the new method improves the retrieved fine-mode fraction, which is underestimated in anthropogenic-dominated aerosol conditions and overestimated in the sea salt-dominated aerosol conditions by the current algorithm. The correlation of the retrieved fine-mode fraction between the new method and AERONET is much higher (correlation coefficient, r?=?0.92) than that between the current MODIS and AERONET (r?=?0.80). The retrieved aerosol optical depth (AOD) is also improved. More AODs retrieved from the new method lie within the expected error bars.     

Effects of implementing satellite observed aerosols into a mesoscale atmosphere model

This study investigated the performance of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU-NCAR) Mesoscale Model (MM5) in calculating the aerosol forcing on cloud cover, incoming surface solar radiation, and near-surface air temperature via the implementation of aerosol optical depth in the shortwave radiation parameterization. MM5 simulations with and without aerosol data are performed in the periods of 6–7 August 2003 and 19–21 September 2003 during which strong aerosol forcing was observed with Moderate Resolution Imaging Spectroradiometer (MODIS) data in the mid-Atlantic region. Both periods clearly showed that aerosols had a direct negative effect on surface solar radiation through aerosol scattering. For example, every 0.1 change in MODIS aerosol optical thickness (AOT) results in 44 and 59?W?m^?2 decreases in surface solar radiation for the first and second periods, respectively. A magnitude of 0.1 increment in MODIS AOT reduces air temperature 0.36 and 0.56?K for the first and second periods, respectively. Comparisons with satellite-derived surface solar radiation retrievals showed that aerosol implementation in MM5 consistently showed better incoming surface solar radiation than that of the non-aerosol case. This helps to reduce uncertainties related to the radiation–cloud–aerosol interaction in numerical weather modelling systems.     

Aerosol characteristics over Delhi national capital region: a satellite view

Multi-sensor aerosol data sets are analysed to examine the aerosol characteristics over the Delhi national capital region. Both the Multiple-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) capture the seasonal cycle of aerosol optical depth (AOD) as observed by ground-based measurements. However, AOD from MISR shows a low bias relative to AOD from MODIS, which increases linearly at high AOD conditions. A large difference (by >25 W m^–2 per unit AOD) in the top-of-atmosphere direct radiative forcing efficiency derived from MODIS and MISR-retrieved AOD is observed during the winter and pre-monsoon seasons relative to the other seasons. The ubiquitous presence of dust (as indicated by non-spherical particle fraction to AOD and linear depolarization ratio values) is observed throughout the year. The aerosol layer is mostly confined to within 2 km of surface in the winter and post-monsoon seasons, while it expands beyond 6 km in the pre-monsoon and monsoon seasons. Columnar AOD is found to be highly sensitive to aerosol vertical distribution. The applicability of multi-sensor data sets and climatic implications are discussed.     

Characterization and transport of aerosols over the Bay of Bengal during the winter monsoon: a comparative study using in-situ and satellite measurements

Natural processes, such as dust storms and sea salt spray, and anthropogenic activities, such as the burning of fossil fuels and biomass, introduce aerosols into the atmosphere. Their concentration, geographic distribution and particle size promote significant climatic consequences. Aerosol transport processes, from landmasses to oceans, are scarcely understood because of inadequate in-situ observations. This study reports the results of spectral aerosol optical depth (AOD) measurements using a five-channel (380, 440, 500, 675 and 870 nm) handheld MICROTOPS Sun-photometer used during a sea-truth data collection campaign conducted in the central Bay of Bengal (BOB) during the northeastern monsoon period (10 November to 13 December 2007). For the entire cruise period, the mean values of the daily average of the AODs at 500 nm and 870 nm were 0.39 ± 0.065 and 0.22 ± 0.047, respectively, the mean value of the Angstrom exponent (α) was 1.23 ± 0.2 and the turbidity parameter (β) was 0.183 ± 0.044. A smaller α value together with a larger β value suggests the presence of an abundance of smaller aerosol particles near the coast. An air mass back-trajectory analysis was undertaken to identify the potential source regions of the aerosols. Analysis of the results demonstrated the effect of the aerosol transport and source regions on the spectral behaviour of the AODs. In-situ measured AOD (550 nm) and α (550 nm, 865 nm) values were further compared with Moderate Resolution Imaging Spectroradiometer (MODIS)-derived parameters. The in-situ and MODIS-derived AOD values were found to be in good agreement, with a coefficient of determination (R ²) of 0.78 and a standard error of 0.05, while the R ² for α was 0.68 with a standard error of 0.14.     

An automated day-time cloud detection technique applied to MSG-SEVIRI data over Western Europe

Since February 2003, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the first Meteosat Second Generation (MSG) satellite has provided radiance data in 12 spectral bands for a full Earth hemisphere every 15 minutes. This high frame rate renders it an excellent tool for studies of atmospheric transport of pollutants, aerosol and clouds. TNO (Netherlands Organisation for Applied Scientific Research) is currently developing an algorithm for the retrieval of aerosol properties from MSG-SEVIRI observations over cloud-free scenes. This requires rigorous cloud screening for which a fast and stand-alone algorithm is developed. The detection technique described in this paper, which is based on the ATSR-2 (Along Track Scanning Radiometer 2) cloud screening algorithm, can be easily implemented, and satisfactorily identifies clouds. The study presented here focuses on Western Europe for the year 2006. Cloud detection results are compared to the KNMI/MF (Royal Netherlands Meteorological Institute/Meteo-France) and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection algorithms. According to the statistics, the results obtained with our algorithm show good agreement (>80%) with these data sets.     

Satellite and surface-based remote sensing of Saharan dust aerosols

The spatial and temporal characteristics of dust aerosols and their properties are assessed from satellite and ground-based sensors. The spatial distribution of total column aerosol optical depth at 550 nm (AOD) from the Moderate Resolution Imaging SpectroRadiometer (MODIS) coupled with top of atmosphere Clouds and the Earth's Radiant Energy System (CERES) shortwave fluxes are examined from the Terra satellite over the Atlantic Ocean. These data are then compared with AOD from two Aerosol Robotic Network (AERONET) ground-based sun photometer measurement sites for nearly six years (2000-2005). These two sites include Capo Verde (CV) (16°N, 24°W) near the Saharan dust source region and La Paguera (LP) (18°N, 67°W) that is downwind of the dust source regions. The AOD is two to three times higher during spring and summer months over CV when compared to LP and the surrounding regions. For a unit AOD value, the instantaneous TOA shortwave direct radiative effect (DRE) defined as the change in shortwave flux between clear and aerosol skies for CV and LP are − 53 and − 68 Wm^− 2 respectively. DRE for LP is likely more negative due to fall out of larger particles during transport from CV to LP. However, separating the CERES-derived DRE by MODIS aerosol effective radii was difficult. Satellite and ground-based dust aerosol data sets continue to be useful to understand dust processes related to the surface and the atmosphere.     

Impact of MODIS-derived cloud cover on aerosol optical depth measured with a sun photometer: a case study from Nanjing,China

Sun photometers have been used increasingly to monitor the atmospheric environment by measuring indicators such as aerosol optical depth (AOD). However, ground-measured AOD results are subject to the presence of clouds in the air. When cloud cover is not extensive, it is still possible to use sun photometry to determine AOD, even though accuracy is reduced by cloud contamination. This research aims to detect cloud cover from Moderate Resolution Imaging Spectroradiometer (MODIS) data and then assess its impact on in situ-measured AOD. Normalized difference cloud index (NDCI) and linear spectral unmixing (LSU) were used to detect cloud cover from MODIS data. AOD at the time of acquisition of MODIS data was measured on the ground by sun photometry within 20 min of satellite overpasses (10 min before and 10 min after). Correlation analysis of NDCI- and LSU-derived cloud cover with in situ-measured AOD data demonstrates that LSU has a higher correlation coefficient with AOD than with NDCI. At 550 nm, a unit of cloud cover (e.g. 1%) raises ground-observed AOD by 0.0157. The findings of this study can be used to modify ground-derived AOD results to improve their reliability.     

Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

A regional chemical transport model assimilated with daily mean satellite and ground-based aerosol optical depth (AOD) observations is used to produce three-dimensional distributions of aerosols throughout Europe for the year 2005. In this paper, the AOD measurements of the Ozone Monitoring Instrument (OMI) are assimilated with Polyphemus model. In order to overcome missing satellite data, a methodology for preprocessing AOD based on neural network (NN) is proposed. The aerosol forecasts involve two-phase process assimilation and then a feedback correction process. During the assimilation phase, the total column AOD is estimated from the model aerosol fields. The main contribution is to adjust model state to improve the agreement between the simulated AOD and satellite retrievals of AOD. The results show that the assimilation of AOD observations significantly improves the forecast for total mass. The errors on aerosol chemical composition are reduced and are sometimes vanished by the assimilation procedure and NN preprocessing, which shows a big contribution to the assimilation process.