Optical Depth Retrievals Research Articles

The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

Long-term MAX-DOAS network observations of NO<sub>2</sub> in Russia and Asia (MADRAS) during the period 2007–2012: instrumentation, elucidation of climatology, and comparisons with OMI satellite observations and global model simulations

Abstract. We conducted long-term network observations using standardized Multi-Axis Differential optical absorption spectroscopy (MAX-DOAS) instruments in Russia and ASia (MADRAS) from 2007 onwards and made the first synthetic data analysis. At seven locations (Cape Hedo, Fukue and Yokosuka in Japan, Hefei in China, Gwangju in Korea, and Tomsk and Zvenigorod in Russia) with different levels of pollution, we obtained 80 927 retrievals of tropospheric NO2 vertical column density (TropoNO2VCD) and aerosol optical depth (AOD). In the technique, the optimal estimation of the TropoNO2VCD and its profile was performed using aerosol information derived from O4 absorbances simultaneously observed at 460–490 nm. This large data set was used to analyze NO2 climatology systematically, including temporal variations from the seasonal to the diurnal scale. The results were compared with Ozone Monitoring Instrument (OMI) satellite observations and global model simulations. Two NO2 retrievals of OMI satellite data (NASA ver. 2.1 and Dutch OMI NO2 (DOMINO) ver. 2.0) generally showed close correlations with those derived from MAX-DOAS observations, but had low biases of up to ~50%. The bias was distinct when NO2 was abundantly present near the surface and when the AOD was high, suggesting a possibility of incomplete accounting of NO2 near the surface under relatively high aerosol conditions for the satellite observations. Except for constant biases, the satellite observations showed nearly perfect seasonal agreement with MAX-DOAS observations, suggesting that the analysis of seasonal features of the satellite data were robust. Weekend reduction in the TropoNO2VCD found at Yokosuka and Gwangju was absent at Hefei, implying that the major sources had different weekly variation patterns. While the TropoNO2VCD generally decreased during the midday hours, it increased exceptionally at urban/suburban locations (Yokosuka, Gwangju, and Hefei) during winter. A global chemical transport model, MIROC-ESM-CHEM (Model for Interdisciplinary Research on Climate–Earth System Model–Chemistry), was validated for the first time with respect to background NO2 column densities during summer at Cape Hedo and Fukue in the clean marine atmosphere.

Retrievals of aerosol optical depth and total column ozone from Ultraviolet Multifilter Rotating Shadowband Radiometer measurements based on an optimal estimation technique

A Bayesian optimal estimation (OE) retrieval technique was used to retreive aerosol optical depth (AOD), aerosol single scattering albedo (SSA), and an asymmetry factor (g) at seven ultraviolet wavelengths, along with total column ozone (TOC), from the measurements of the UltraViolet Multifilter Rotating Shadowband Radiometer (UV-MFRSR) deployed at the Southern Great Plains (SGP) site during March through November in 2009. The OE technique specifies appropriate error covariance matrices and optimizes a forward model (Tropospheric ultraviolet radiative transfer model, TUV), and thus provides a supplemental method for use across the network of the Department of Agriculture UV-B Monitoring and Research Program (USDA UVMRP) for the retrieval of aerosol properties and TOC with reasonable accuracy in the UV spectral range under various atmospheric conditions. In order to assess the accuracy of the OE technique, we compared the AOD retreivals from this method with those from Beer’s Law and the AErosol RObotic Network (AERONET) AOD product. We also examine the OE retrieved TOC in comparison with the TOC from the U.S. Department of Agriculture UV-B Monitoring and Research Program (USDA UVMRP) and the Ozone Monitoring Instrument (OMI) satellite data. The scatterplots of the estimated AOD from the OE method agree well with those derived from Beer’s law and the collocated AERONETAOD product, showing high values of correlation coefficients, generally 0.98 and 0.99, and large slopes, ranging from 0.95 to 1.0, as well as small offsets, less than 0.02 especially at 368 nm. The comparison of TOC retrievals also indicates the promising accuracy of the OE method in that the standard deviations of the difference between the OE derived TOC and other TOC products are about 5 to 6 Dobson Units (DU). Validation of the OE retrievals on these selected dates suggested that the OE technique has its merits and can serve as a supplemental tool in further analyzing UVMRP data.

Aerosol microphysical retrievals from precision filter radiometer direct solar radiation measurements and comparison with AERONET

Abstract. Synchronized sun-photometric measurements from the AERONET-CIMEL (AErosol RObotic NETwork) and GAW-PFR (Global Atmospheric Watch–Precision Filter Radiometer) aerosol networks are used to compare retrievals of the aerosol optical depth (AOD), effective radius, and volume concentration during a high-temporal-resolution measurement campaign at the Athens site in the Mediterranean Basin from 14 to 22 July 2009. During this period, direct-sun AOD retrievals from both instruments exhibited small differences in the range 0.01–0.02. The AODs measured with CIMEL and PFR instruments were inverted to retrieve particle microphysical properties using the linear estimation (LE) technique. For low aerosol loads (AOD < 0.2), measurements of the effective radius by the PFR were found to be −20% to +30% different from CIMEL values for both direct-sun data and inversion data. At higher loads (AOD > 0.4), measurements of the effective radius by the PFR are consistently 20 % lower than CIMEL for both direct-sun and inversion data. Volume concentrations at low aerosol loads from the PFR are up to 80% higher than the CIMEL for direct-sun data but are up to 20% lower when derived from inversion data under these same conditions. At higher loads, the percentage difference in volume concentrations from the PFR and CIMEL is systematically negative, with inversion data predicting differences 30% lower than those obtained from direct-sun data. An assessment of the effect of errors in the AOD retrieval on the estimation of PFR bulk parameters was performed and demonstrates that it is possible to estimate the particle volume concentration and effective radius with an uncertainty < 65% when AOD < 0.2 and when input errors are as high as 10%.

High temporal resolution aerosol retrieval using Geostationary Ocean Color Imager: application and initial validation

The Geostationary Ocean Color Imager (GOCI) provides multispectral imagery of the East Asia region hourly from 9:00 to 16:00 local time (GMT+9) and collects multispectral imagery at eight spectral channels (412, 443, 490, 555, 660, 680, 745, and 865 nm) with a spatial resolution of 500 m. Thus, this technology brings significant advantages to high temporal resolution environmental monitoring. We present the retrieval of aerosol optical depth (AOD) in northern China based on GOCI data. Cross-calibration was performed against Moderate Resolution Imaging Spectrometer (MODIS) data in order to correct the land calibration bias of the GOCI sensor. AOD retrievals were then accomplished using a look-up table (LUT) strategy with assumptions of a quickly varying aerosol and a slowly varying surface with time. The AOD retrieval algorithm calculates AOD by minimizing the surface reflectance variations of a series of observations in a short period of time, such as several days. The monitoring of hourly AOD variations was implemented, and the retrieved AOD agreed well with AErosol RObotic NETwork (AERONET) ground-based measurements with a good R2 of approximately 0.74 at validation sites at the cities of Beijing and Xianghe, although intercept bias may be high in specific cases. The comparisons with MODIS products also show a good agreement in AOD spatial distribution. This work suggests that GOCI imagery can provide high temporal resolution monitoring of atmospheric aerosols over land, which is of great interest in climate change studies and environmental monitoring.

A novel method for estimating shortwave direct radiative effect of above-cloud aerosols using CALIOP and MODIS data

Abstract. This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. It addresses the overlap of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure while also accounting for subgrid-scale variations of aerosols. The method is computationally efficient because of its use of grid-level cloud and aerosol statistics, instead of pixel-level products, and a precomputed look-up table based on radiative transfer calculations. We verify that for smoke and polluted dust over the southeastern Atlantic Ocean the method yields a seasonal mean instantaneous (approximately 13:30 local time) shortwave DRE of above-cloud aerosol (ACA) that generally agrees with a more rigorous pixel-level computation within 4%. We also estimate the impact of potential CALIOP aerosol optical depth (AOD) retrieval bias of ACA on DRE. We find that the regional and seasonal mean instantaneous DRE of ACA over southeastern Atlantic Ocean would increase, from the original value of 6.4 W m−2 based on operational CALIOP AOD to 9.6 W m−2 if CALIOP AOD retrievals are biased low by a factor of 1.5 (Meyer et al., 2013) and further to 30.9 W m−2 if CALIOP AOD retrievals are biased low by a factor of 5 as suggested in Jethva et al. (2014). In contrast, the instantaneous ACA radiative forcing efficiency (RFE) remains relatively invariant in all cases at about 53 W m−2 AOD−1, suggesting a near-linear relation between the instantaneous RFE and AOD. We also compute the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global oceans based on 4 years of CALIOP and MODIS data. We find that given an above-cloud aerosol type the optical depth of the underlying clouds plays a larger role than above-cloud AOD in the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol. While we demonstrate our method using CALIOP and MODIS data, it can also be extended to other satellite data sets.

Evaluating the impact of aerosol particles above cloud on cloud optical depth retrievals from MODIS

AbstractUsing two different operational Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) cloud optical depth (COD) retrievals (0.86 versus 1.6 µm), we evaluate the impact of above‐cloud smoke aerosol particles on near‐IR (0.86 µm) COD retrievals. Aerosol Index (AI) from the collocated Ozone Monitoring Instrument (OMI) are used to identify above‐cloud aerosol particle loading over the southern Atlantic Ocean, including both smoke and dust from the African subcontinent. Collocated Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation data constrain cloud phase and provide contextual above‐cloud aerosol optical depth. The frequency of occurrence of above‐cloud aerosol events is depicted on a global scale for the spring and summer seasons from OMI and Cloud Aerosol Lidar with Orthogonal Polarization. Seasonal frequencies for smoke‐over‐cloud off the southwestern Africa coastline reach 20–50% in boreal summer. We find a corresponding low COD bias of 10–20% for standard MODIS COD retrievals when averaged OMI AI are larger than 1. No such bias is found over the Saharan dust outflow region off northern Africa, since both MODIS 0.86 and 1.6 µm channels are vulnerable to radiance attenuation due to dust particles. A similar result is found for a smaller domain, in the Gulf of Tonkin region, from smoke advection over marine stratocumulus clouds and outflow into the northern South China Sea in spring. This study shows the necessity of accounting for the above‐cloud aerosol events for future studies using standard MODIS cloud products in biomass burning outflow regions, through the use of collocated OMI AI and supplementary MODIS 1.6 µm COD products.

Opposite effects of absorbing aerosols on the retrievals of cloud optical depth from spaceborne and ground‐based measurements

AbstractAbsorbing aerosols above or within cloud layers have drawn much attention in recent years due to substantially enhanced absorption of solar radiation that may affect reflection at the top of the atmosphere. The retrieval of cloud properties is usually conducted without any regard to aerosols. This study illustrates that retrievals of cloud optical depth (τc) from spaceborne and ground‐based sensors are both affected by such aerosols and lead to opposite biases. A ground‐based retrieval algorithm is developed for the simultaneous retrieval of τc and cloud droplet effective radius using spectral irradiance measurements from a multifilter rotating spectroradiometer and liquid water path (LWP) data from a microwave radiometer deployed in China. The algorithm is applied to data acquired from 17 May 2008 to 12 May 2009 at a heavily polluted site in the heart of the Yangtze delta region in China. The ground‐based retrieval of cloud droplet effective radius increases with increasing LWP. Moderate Resolution Imaging Spectroradiometer retrievals tend to overestimate (underestimate) LWP when cloud LWP is less (greater) than about 200 g/m2. Model tests show strong sensitivities to the retrieval of τc from ground and spaceborne sensors under varying absorption, loading, and vertical distribution conditions. For absorbing aerosol mixed with cloud, τc tends to be underestimated from space, but overestimated from the ground, leading to very poor agreement between ground‐based and Moderate Resolution Imaging Spectroradiometer retrievals. Their differences increase with increasing τc. This finding suggests that in a turbid atmosphere with absorbing aerosols, the aerosol effect should be considered, or it would mislead any validation using satellite and ground‐based retrievals.

A numerical testbed for remote sensing of aerosols, and its demonstration for evaluating retrieval synergy from a geostationary satellite constellation of GEO-CAPE and GOES-R

We present a numerical testbed for remote sensing of aerosols, together with a demonstration for evaluating retrieval synergy from a geostationary satellite constellation. The testbed combines inverse (optimal-estimation) software with a forward model containing linearized code for computing particle scattering (for both spherical and non-spherical particles), a kernel-based (land and ocean) surface bi-directional reflectance facility, and a linearized radiative transfer model for polarized radiance. Calculation of gas absorption spectra uses the HITRAN (HIgh-resolution TRANsmission molecular absorption) database of spectroscopic line parameters and other trace species cross-sections. The outputs of the testbed include not only the Stokes 4-vector elements and their sensitivities (Jacobians) with respect to the aerosol single scattering and physical parameters (such as size and shape parameters, refractive index, and plume height), but also DFS (Degree of Freedom for Signal) values for retrieval of these parameters. This testbed can be used as a tool to provide an objective assessment of aerosol information content that can be retrieved for any constellation of (planned or real) satellite sensors and for any combination of algorithm design factors (in terms of wavelengths, viewing angles, radiance and/or polarization to be measured or used). We summarize the components of the testbed, including the derivation and validation of analytical formulae for Jacobian calculations. Benchmark calculations from the forward model are documented. In the context of NASA׳s Decadal Survey Mission GEO-CAPE (GEOstationary Coastal and Air Pollution Events), we demonstrate the use of the testbed to conduct a feasibility study of using polarization measurements in and around the O2A band for the retrieval of aerosol height information from space, as well as an to assess potential improvement in the retrieval of aerosol fine and coarse mode aerosol optical depth (AOD) through the synergic use of two future geostationary satellites, GOES-R (Geostationary Operational Environmental Satellite R-series) and TEMPO (Tropospheric Emissions: Monitoring of Pollution). Strong synergy between GEOS-R and TEMPO are found especially in their characterization of surface bi-directional reflectance, and thereby, can potentially improve the AOD retrieval to the accuracy required by GEO-CAPE.

Effective cloud optical depth for overcast conditions determined with a UV radiometers

Using a sky camera, episodes characterized by overcast low clouds in the Valencia region of Spain have been selected for analysis. One year of cloud optical depth data have been produced by inverting UV erythemal irradiance measured with a UVB-1 radiometer from Yankee Environmental Systems using the LibRadtran radiative transfer model. Measurement uncertainties, aerosol single scattering albedo and cloud properties are, in order of decreasing importance, the most important factors influencing the accuracy of cloud optical depth retrieval when using UV erythemal radiometers. Statistics for cloud optical depth show a highly skewed frequency distribution best described by a gamma distribution with highest frequency between optical depths of 10 and 15 and optical depths exceeding 100 at the extreme high end.

Assessment of aerosol models to AOD retrieval from HJ1 Satellites

The Chinese environmental satellites HJ1 A and B can play a significant role in the aerosol retrieval due to their high spatial and temporal resolution. The current Aerosol Optical Depth (AOD) retrieval methods from HJ1-CCD are almost based on the LUT (Look-Up Table), by selecting the best fitting result to determine the AOD. However, aerosol model selection has an important impact on the retrieval results when creating the lookup table; inappropriate choice of aerosol model will significantly affect the accuracy and applicability of the method. This paper determined the local aerosol physical properties (such as complex refractive index, and size distribution) based on the observational data, thus we defined the aerosol type and retrieved the AOD of the local aerosol. Furthermore we compared the results retrieved from the measurement aerosol model with those retrieved from the inherent aerosol model in the radiative transfer model and then evaluate its effect on the aerosol type.

Retrieval of aerosol optical depth over Bohai rim region by exploiting multifunctional transport satellite and MODIS data

Atmospheric aerosol particles have a significant impact on global climate change. For the purpose of estimating the effect of aerosols on climate, information on the spatial and temporal distribution of the aerosols is necessary. In this study, we developed an algorithm to retrieve hourly aerosol optical depth (AOD) by using the Moderate Resolution Imaging Spectroradiometer (MODIS) and the data from the multifunctional transport satellite (MTSAT) visible channel over Bohai rim region. First, the MODIS data was used to identify aerosol module, and then the MTSAT data was used to calculate hourly AOD. The results show that the AOD derived from AERONET observations agrees well with the MTSAT-AOD products with spatio-temporal scale, as well as spectral scale. Therefore, the algorithm developed in this study has potential for monitoring AOD hourly.

Preliminary results of the aerosol optical depth retrieval in Johor, Malaysia

Monitoring of atmospheric aerosols over the urban area is important as tremendous amounts of pollutants are released by industrial activities and heavy traffic flow. Air quality monitoring by satellite observation provides better spatial coverage, however, detailed aerosol properties retrieval remains a challenge. This is due to the limitation of aerosol retrieval algorithm on high reflectance (bright surface) areas. The aim of this study is to retrieve aerosol optical depth over urban areas of Iskandar Malaysia; the main southern development zone in Johor state, using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m resolution data. One of the important steps is the aerosol optical depth retrieval is to characterise different types of aerosols in the study area. This information will be used to construct a Look Up Table containing the simulated aerosol reflectance and corresponding aerosol optical depth. Thus, in this study we have characterised different aerosol types in the study area using Aerosol Robotic Network (AERONET) data. These data were processed using cluster analysis and the preliminary results show that the area is consisting of coastal urban (65%), polluted urban (27.5%), dust particles (6%) and heavy pollution (1.5%) aerosols.

Comparison of MODIS 3 km and 10 km resolution aerosol optical depth retrievals over land with airborne sunphotometer measurements during ARCTAS summer 2008

Abstract. Airborne sunphotometer measurements acquired by the NASA Ames Airborne Tracking Sunphotometer (AATS-14) aboard the NASA P-3 research aircraft are used to evaluate dark-target over-land retrievals of extinction aerosol optical depth (AOD) from spatially and temporally near-coincident measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) during the summer 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. The new MODIS Collection 6 aerosol data set includes retrievals of AOD at both 10 km × 10 km and 3 km × 3 km (at nadir) resolution. In this paper we compare MODIS and AATS AOD at 553 nm in 58 10 km and 134 3 km retrieval grid cells. These AOD values were derived from data collected over Canada on four days during short time segments of five (four Aqua and one Terra) satellite overpasses of the P-3 during low-altitude P-3 flight tracks. Three of the five MODIS–AATS coincidence events were dominated by smoke: one included a P-3 transect of a well-defined smoke plume in clear sky, but two were confounded by the presence of scattered clouds above smoke. The clouds limited the number of MODIS retrievals available for comparison, and led to MODIS AOD retrievals that underestimated the corresponding AATS values. This happened because the MODIS aerosol cloud mask selectively removed 0.5 km pixels containing smoke and clouds before the aerosol retrieval. The other two coincidences (one Terra and one Aqua) occurred during one P-3 flight on the same day and in the same general area, in an atmosphere characterized by a relatively low AOD (< 0.3), spatially homogeneous regional haze from smoke outflow with no distinguishable plume. For the ensemble data set for MODIS AOD retrievals with the highest-quality flag, MODIS AOD agrees with AATS AOD within the expected MODIS over-land AOD uncertainty in 60% of the retrieval grid cells at 10 km resolution and 69% at 3 km resolution. These values improve to 65 % and 74%, respectively, when the cloud-affected case with the strongest plume is excluded. We find that the standard MODIS dark-target over-land retrieval algorithm fails to retrieve AOD for thick smoke, not only in cloud-contaminated regions but also in clear sky. We attribute this to deselection, by the cloud and/or bright surface masks, of 0.5 km resolution pixels that contain smoke.

Revisiting haboobs in the southwestern United States: An observational case study of the 5 July 2011 Phoenix dust storm

Convectively-driven dust storms (or haboobs) are common phenomena in the southwestern United States. However, studies about haboobs in this region are limited. Here, we investigate the state and fate of a massive haboob that hit Phoenix, Arizona on 5 July 2011 using satellite, radar, and ground-based observations. This haboob was a result of strong outflow boundaries (with peak wind gusts of 29 m s−1) from storms that were initiated in the southeast of Tucson. In particular, we find three major outflow systems (based on radar data) that were generated by forward propagating storms, ultimately merging near Phoenix. This resulted in peak hourly PM10 and PM2.5 concentrations of 1974 μg m−3 and 907 μg m−3 at US EPA stations near Phoenix. The high PM concentration is consistent in space and time with the dust wall movement based on our analysis of radar data on hydrometeor classification. Enhanced aerosol loadings over metropolitan Phoenix were also observed on 6 July from NASA Terra/Aqua MODIS aerosol optical depth (AOD) retrievals (AOD > 0.8). We infer from CALIOP vertical feature masks and HYSPLIT back trajectories that remnants of the haboob were transported to northwest of Phoenix on 6 July at 2–4 km above ground level. Ratios of PM2.5 to PM10 from IMPROVE stations also imply low-level transport to the east of Phoenix on 8 July. Finally, we find that this haboob, which had local and regional impacts, is atypical of other dust events in this region. We note from this analysis that extreme events such as this haboob require an integrated air quality observing system to provide a more comprehensive assessment of these events.

Improving Satellite-Based Convective Cloud Growth Monitoring with Visible Optical Depth Retrievals

AbstractThe use of geostationary satellites for monitoring the development of deep convective clouds has been recently well documented. One such approach, the University of Wisconsin Cloud-Top Cooling Rate (CTC) algorithm, utilizes frequent Geostationary Operational Environmental Satellite (GOES) observations to diagnose the vigor of developing convective clouds through monitoring cooling rates of infrared window brightness temperature imagery. The CTC algorithm was modified to include GOES visible optical depth retrievals for the purpose of identifying growing convective clouds in regions of thin cirrus clouds. An automated objective skill analysis of the two CTC versions (with and without the GOES visible optical depth) versus a variety of Next Generation Weather Radar (NEXRAD) fields was performed using a cloud-object tracking system developed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies. The skill analysis was performed in a manner consistent with a recent study employing the same cloud-object tracking system. The analysis indicates that the inclusion of GOES visible optical depth retrievals in the CTC algorithm increases probability of detection and critical success index scores for all NEXRAD fields studied and slightly decreases false alarm ratios for most NEXRAD thresholds. In addition to better identifying vertically growing storms in regions of thin cirrus clouds, the analysis further demonstrates that the strongest cooling rates associated with developing convection are more reliably detected with the inclusion of visible optical depth and that storms that achieve intense reflectivity and large radar-estimated hail exhibit strong cloud-top cooling rates in much higher proportions than they do without the inclusion of visible optical depth.

Quantitative evaluation of uncertainties in satellite retrieval of dust-like aerosols induced by spherical assumption

It has been frequently pointed out the necessity to consider non-sphericity of dust-like aerosols in modeling their optical properties. However, most existing aerosol retrieval algorithms still utilize the Mie theory in defining aerosol models and creating look up tables (LUTs) for simplicity. Neglecting non-sphericity can severely influence aerosol optical depth (AOD, τ) retrieval in case of dust-like aerosols—largely due to the difference of derived phase functions under spherical and non-spherical assumptions—whereas this uncertainty has rarely been quantitatively studied. This paper aims at a better understanding of uncertainties on AOD retrieval caused by aerosol non-sphericity. From a dust aerosol model, we adopt different methods to simulate and compare aerosol optical properties on the basis of spherical and non-spherical assumptions respectively. Consequently we generate two LUTs under varieties of observing conditions, aerosol loadings, and surface brightness using radiative transfer (RT) code. From the obtained LUTs we thoroughly evaluate and analyze the differences of TOA reflectance (ΔρTOA) and AOD retrieval errors (Δτ) induced by straightforwardly utilizing the Mie theory in dust-like aerosol retrieval. Errors may be positive or negative, depending on the specific geometry. For low aerosol loading (τ~0.25) and black surface, |ΔρTOA| could be greater than 0.06, with maximum |Δτ| of ~0.12. Moreover, this error can increase more than ten times and become even irregular as aerosol loading gets higher and surface gets brighter. Mean Δτ statistics show that this uncertainty significantly influences derived aerosol climatology as well. Therefore we conclude that the neglect of non-sphericity introduces substantial errors on RT simulation and AOD retrieval, and a representative aspheric aerosol model other than Mie calculation is recommended for inversion algorithms related with dust-like non-spherical aerosols.

Satellite Retrievals of Aerosol Optical Depth over a Subtropical Urban Area: The Role of Stratification and Surface Reflectance

ABSTRACTWe explore the relationship between satellite retrievals of aerosol optical depth (AOD) and surface aerosol mass concentrations over a subtropical urban area, namely, Santiago, Chile (33.5°S, 70.6°W, 500 m.a.s.l.). We compare 11 years of AOD from the MODerate resolution Imaging Spectroradiometer (MODIS) with in situ particulate matter mass concentrations (PM). MODIS AOD reaches its maximum in summer and minimum in winter, the opposite of the annual cycle of surface PM. To improve our understanding of the relevant governing processes, we use a simple model that estimates the boundary layer (BL) AOD based on measured PM, relative humidity and BL height (BLH) as well as best estimates of aerosol composition, size distribution, and optical properties. Model results indicate that a weak annual AOD cycle is due to the opposite annual cycles in BLH and PM, which is largely supported by the Aerosol Robotic NETwork (AERONET) data collected in 2001 and 2002 in Santiago. We identify a possible bias linked to the operational estimate of surface reflectance that may lead to a spurious summer maximum in MODIS AOD over Santiago. This misfit in surface reflectance appears to affect not only Santiago but also a significant area of the semi-arid Southern South America. Sensitivity experiments with the simple model indicate an underestimate of simulated AOD as compared to AERONET data. This underestimate points to the possible role of residual aerosol layers in the AOD measured at the surface (not included in the simple model). Cirrus clouds appear not to play a significant role in explaining the MODIS AOD seasonality. The need for improved characterizations of aerosol properties and their temporal and spatial distribution in cities such as Santiago is emphasized.

Population Exposure to Ambient PM<sub>2.5</sub> at the Subdistrict Level in China

Fine particle pollution is a major public health concern in China. This paper assesses China's potential population exposure to PM2.5 at the subdistrict level, the smallest census unit with public information. Using both ground-based measurements and satellite-based Aerosol Optical Depth retrievals, we estimate subdistrict-level daily PM2.5 concentrations by means of the block cokriging approach. Our results reveal that China's population-weighted annual average PM2.5 concentration during April 08, 2013 and April 07, 2014 is nearly 7 times the annual average level suggested by the World Health Organization (WHO). About 1,322 million people, or 98.6% of China's total population, are exposed to PM2.5 at levels above WHO's daily guideline for longer than half a year. We also simulate the effects of China's most recent action plan on air pollution control. The simulation results demonstrate that the population exposed to PM2.5 above China's daily standard for longer than half a year will be reduced by 85% if the plan can achieve its target by 2017. Nevertheless, PM2.5 will be still at a harmful level to public health, which calls for additional effort in pollution control.

High resolution aerosol data from MODIS satellite for urban air quality studies

AbstractThe Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM2.5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R2 =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM2.5 ground concentrations. Finally, we studied the relationship between PM2.5 and AOD at the intra-urban scale (≤10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM2.5 relationship does not depend on relative humidity and air temperatures below ~7 °C. The correlation improves for temperatures above 7–16 °C. We found no dependence on the boundary layer height except when the former was in the range 250–500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM2.5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM2.5 mass concentrations are highly correlated with the actual observations (out-of-sample R2 of 0.86). Therefore, adjustment for the daily variability in the AOD-PM2.5 relationship provides a means for obtaining spatially-resolved PM2.5 concentrations.