MISR Global Aerosol Product Assessment by Comparison with AERONET

A statistical approach is used to assess the quality of the MISR Version 22 (V22) aerosol products. Aerosol Optical Depth (AOD) retrieval results are improved relative to the early post- launch values reported by Kahn et al. [2005a], varying with particle type category. Overall, about 70% to 75% of MISR AOD retrievals fall within 0.05 or 20% AOD of the paired validation data, and about 50% to 55% are within 0.03 or 10% AOD, except at sites where dust, or mixed dust and smoke, are commonly found. Retrieved particle microphysical properties amount to categorical values, such as three groupings in size: "small," "medium," and "large." For particle size, ground-based AERONET sun photometer Angstrom Exponents are used to assess statistically the corresponding MISR values, which are interpreted in terms of retrieved size categories. Coincident Single-Scattering Albedo (SSA) and fraction AOD spherical data are too limited for statistical validation. V22 distinguishes two or three size bins, depending on aerosol type, and about two bins in SSA (absorbing vs. non-absorbing), as well as spherical vs. non-spherical particles, under good retrieval conditions. Particle type sensitivity varies considerably with conditions, and is diminished for mid-visible AOD below about 0.15 or 0.2. Based on these results, specific algorithm upgrades are proposed, and are being investigated by the MISR team for possible implementation in future versions of the product

Reducing the Uncertainties in Direct Aerosol Radiative Forcing

Airborne particles, which include desert and soil dust, wildfire smoke, sea salt, volcanic ash, black carbon, natural and anthropogenic sulfate, nitrate, and organic aerosol, affect Earth's climate, in part by reflecting and absorbing sunlight. This paper reviews current status, and evaluates future prospects for reducing the uncertainty aerosols contribute to the energy budget of Earth, which at present represents a leading factor limiting the quality of climate predictions. Information from satellites is critical for this work, because they provide frequent, global coverage of the diverse and variable atmospheric aerosol load. Both aerosol amount and type must be determined. Satellites are very close to measuring aerosol amount at the level-of-accuracy needed, but aerosol type, especially how bright the airborne particles are, cannot be constrained adequately by current techniques. However, satellite instruments can map out aerosol air mass type, which is a qualitative classification rather than a quantitative measurement, and targeted suborbital measurements can provide the required particle property detail. So combining satellite and suborbital measurements, and then using this combination to constrain climate models, will produce a major advance in climate prediction

A STUDY OF REMOTELY SENSED AEROSOL PROPERTIES FROM GROUND-BASED SUN AND SKY SCANNING RADIOMETERS

Aerosol particles impact human health by degrading air quality and affect climate by heating or cooling the atmosphere. The Indo-Gangetic Plain (IGP) of Northern India, one of the most populous regions in the world, produces and is impacted by a variety of aerosols including pollution, smoke, dust, and mixtures of them. The NASA Aerosol Robotic Network (AERONET) mesoscale distribution of Sun and sky-pointing instruments in India was established to measure aerosol characteristics at sites across the IGP and around Kanpur, India, a large urban and industrial center in the IGP, during the 2008 pre-monsoon (April-June). This study focused on detecting spatial and temporal variability of aerosols, validating satellite retrievals, and classifying the dominant aerosol mixing states and origins. The Kanpur region typically experiences high aerosol loading due to pollution and smoke during the winter and high aerosol loading due to the addition of dust to the pollution and smoke mixture during the pre-monsoon. Aerosol emissions in Kanpur likely contribute up to 20% of the aerosol loading during the pre-monsoon over the IGP. Aerosol absorption also increases significantly downwind of Kanpur indicating the possibility of the black carbon emissions from aerosol sources such as coal-fired power plants and brick kilns. Aerosol retrievals from satellite show a high bias when compared to the mesoscale distributed instruments around Kanpur during the pre-monsoon with few high quality retrievals due to imperfect aerosol type and land surface characteristic assumptions. Aerosol type classification using the aerosol absorption, size, and shape properties can identify dominant aerosol mixing states of absorbing dust and black carbon particles. Using 19 long-term AERONET sites near various aerosol source regions (Dust, Mixed, Urban/Industrial, and Biomass Burning), aerosol absorption property statistics are expanded upon and show significant differences when compared to previous work. The sensitivity of absorption properties is evaluated and quantified with respect to aerosol retrieval uncertainty. Using clustering analysis, aerosol absorption and size relationships provide a simple method to classify aerosol mixing states and origins and potentially improve aerosol retrievals from ground-based and satellite-based instrumentation

Global and regional trends of Aerosol Optical Thickness derived using satellite- and ground-based observations

Atmospheric aerosol plays a critical role for human health, air quality, long range transport of pollution, and the Earth s radiative balance, thereby influencing global climate change. To test our scientific understanding and provide an evidence base for policymakers, long-term temporal changes of local, regional, and global aerosols are needed. Remote sensing from satellite borne and ground based observations offers unique opportunities to provide such data. However, only a few studies have discussed the limitations, associated with unrepresentative sampling originating from large/persistent cloud disturbance and limited/different sampling (limited orbital periods and different sampling times) in the trend analysis. Using a linear weighted model, the long-term trends of global AOTs from various polar orbiting satellites and ground observations: MODIS (aboard Terra), MISR (Terra), SeaWiFS (OrbView-2), MODIS (Aqua), and AERONET have been analyzed. In this manner, the present study attempts to minimize the influence of unrepresentative sampling in the trend analysis. Throughout terrestrial and marine regions, temporal increase of cloud-free AOTs were dominat over the globe (GL), northern (NH), and southern hemisphere (SH) (up to 0.00348±0.00185 for GL, 0.00514±0.00272 for NH, and 0.00232±0.00124 per year for SH). Generally, consistently in all observations, the weighted trends over Eastern US and OECD Europe showed a strong decreasing AOT (up to -0.00376±0.00174 for Eastern US and -0.00530±0.00304 per year for OECD Europe) attributed to the recent environmental legislation and resulting regulation of emissions. A significant increase was observed over Saharan/Arabian deserts, South, and East Asia (up to 0.00618±0.00326, 0.01452±0.00615, and 0.01939±0.00986 per year, respectively). These in part dramatic increases are caused by the enhanced amount of aerosol transported/emitted from industrialization, urbanization, deforestation, desertification, and climate change. Overall large/persistent cloud disturbance all year round and the limited/different sampling of polar orbiting satellites represent a challenge, which has been addressed successfully in this study for the accurate determination of aerosol amount and its trends

Sensor capability and atmospheric correction in ocean colour remote sensing

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges

GLOBAL SCALE AEROSOL PROPERTIES: IMPLICATIONS FOR SURFACE SHORTWAVE RADIATION BUDGET

Aerosols are known to affect the shortwave radiation budget of the Earth-atmosphere system. Using truncated Empirical Orthogonal Functions (EOF) fitting, we derive monthly mean aerosol optical depth (AOD) at 0.55 μm using information from: the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model; the MODerate resolution Imaging Spectro-radiometer (MODIS); and the AErosol RObotic NETwork (AERONET). The single scattering albedo, the asymmetry parameter and the normalized extinction coefficient over the solar spectrum are estimated from GOCART data, MODIS Ångström exponent and AERONET almucantar retrievals. The University of Maryland (UMD) Global Energy and Water Cycle Experiment (GEWEX) shortwave Surface Radiation Budget (SRB) model is updated to allow the treatment of complex aerosol properties. The modified model is implemented with the International Satellite Cloud Climatology Project (ISCCP) D1 for a one year period. From the evaluation of the improvements against ground measurements we find that the bias in retrieved AOD at 0.55 μm is reduced from 0.20 to 0.05. The overall bias in the estimated surface SW fluxes is reduced by about 7 Wm-2 for the total irradiance and 11 and 4 Wm-2 for the direct and diffuse parts, respectively. The new version of the UMD SRB model has now the capability to address the issue of aerosol direct radiative effects. Annually averaged global clear-sky direct radiative aerosol forcing is estimated to be -1.31 Wm-2 at the top of atmosphere and -2.71 Wm-2 at the surface. This indicates that the effect of aerosols on the SW energy absorption is comparable with their effect on the reflection at the TOA. At regional scales, aerosol effects can be much larger. In a case study preformed at a sub-Sahel site in Africa, the depletion of the daily surface irradiance can be as large as 120 Wm-2. Compared with other methods used to estimate aerosol direct effects, the advantage of our scheme is that it preserves closure with TOA satellite measurements. With anticipated progresses in aerosol research and satellite observations, the UMD SRB model has the potential to address aerosol radiative effects in a realistic and coherent way

Development Of A Deep Neural Network Based Method For Quality Control Of Modis Maiac Aerosol Data For Aerosol Modeling Applications

Quality-assured satellite aerosol data have been shown to improve aerosol analysis and forecasts in Chemical Transport Models. However, biases present in the satellite-based aerosol data can also introduce non-negligible uncertainties in the downstream aerosol forecasts and impact model forecast accuracy. Therefore, in this study we evaluated uncertainties in Moderate Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol products and developed a deep neural network (DNN) based method for quality control of Terra and Aqua MODIS MAIAC Aerosol Optical Depth (AOD) data using the version 3 level 2 AErosol RObotic NETwork (AERONET) data as the ground truth. This method is done using 14 years of Aqua MODIS (2002-2016) and 16 years of Terra MODIS (2000-2016) MAIAC data which are collocated with the AERONET observations. The resulting trained network, which is tested on one year of Aqua/Terra data, can detect and significantly reduce noisy retrieval in MAIAC AOD data resulting in an approximate 31%/27% reduction in Root-Mean-Square-Error in Aqua/Terra MODIS MAIAC AOD with an associated 14%/16% data loss. A sensitivity study performed in this effort suggests that reducing the number of output categories and hidden layers can significantly improve performance of the deep neural network in this case. This study suggests that DNN can be used as an effective method for quality control of satellite based AOD data for potential modeling applications

Aerosol Absorption: Progress Towards Global and Regional Constraints

Some aerosols absorb solar radiation, altering cloud properties, atmospheric stability and circulation dynamics, and the water cycle. Here we review recent progress towards global and regional constraints on aerosol absorption from observations and modeling, considering physical properties and combined approaches crucial for understanding the total (natural and anthropogenic) influences of aerosols on the climate. We emphasize developments in black carbon absorption alteration due to coating and ageing, brown carbon characterization, dust composition, absorbing aerosol above cloud, source modeling and size distributions, and validation of high-resolution modeling against a range of observations. Both observations and modeling of total aerosol absorption, absorbing aerosol optical depths and single scattering albedo, as well as the vertical distribution of atmospheric absorption, still suffer from uncertainties and unknowns significant for climate applications. We offer a roadmap of developments needed to bring the field substantially forward

Improving Our Understanding of Atmospheric Aerosols and Their Climate Effects: Implications for Satellite Retrievals and GCM Simulations

This dissertation is a collection of studies focusing on improving our understanding of atmospheric aerosols using both observational data and model simulations. EOF analysis of Aerosol Index (AI) product from Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) reveals global distribution of absorbing aerosols, with major sources lying in Sahara deserts, the Sahel region, South America and South Africa. Analysis of aerosol Single Scattering Albedo (SSA) data from AErosol RObotic NETwork (AERONET) further indicate trends in SSA over a number of globally distributed stations, which might be associated with changes in aerosol composition and thus their optical properties. More importantly, the changes in SSA alter the radiative forcing of aerosols. They may also potentially impact satellite retrievals of aerosol properties as generally a constant SSA is assumed in the retrieval algorithms. In order to assess satellite retrieved aerosol optical properties, collocated pixel level Aerosol Optical Depth (AOD) and Ångström Exponent (AE) data from MODerate resolution Imaging Spectroradiometer (MODIS) are compared with AERONET measurements over 10 stations representing typical aerosol regimes. The results show that while MODIS AOD well agrees with AERONET in both the magnitude and seasonal variability for all stations, comparatively large discrepancies are found in the AE, especially for over land. Further investigation reveals that the dependence of the AE on AOD for MODIS data are quite different from AERONET data, which suggest problems in the aerosol models used in MODIS retrieval. MODIS ocean data are generally reliable. Focusing on ocean data, a strong correlation between the AE and ENSO index has been found, and the roles of relevant physical mechanisms are discussed. While the exact cause of the correlation is still unclear, the results indicate aerosol properties can be influenced by major climate modes such as ENSO. The sensitivity of aerosol Direct Radiative Forcing (DRF) to perturbations of major aerosol parameters are tested using the GISS GCM. Among the three perturbed parameters, AOD, SSA and asymmetry parameter g, DRF appears to be most sensitive to SSA. Moreover, changing aerosol dry sizes result in larger fluctuation in DRF than the previous three parameters. Based on the sensitivity studies, an optimal fitting technique based on AERONET data is developed to better constrain aerosol dry size parameterization in the GCM. Model results for AOD and SSA are also improved by adjusting the size and applying "uncertainty parameters". The fitting results indicate an overall underestimate in GCM aerosol loading. In particular, aerosol absorption has been underestimated by approximately a factor of 2. The low bias might be attributed to insufficient aerosol mass loading, lack of internal mixing of black carbon with other species, etc. After incorporating the optimized sizes and uncertainty parameters into the GCM, estimated global mean DRF is significantly larger than the original aerosol field. Regionally the changes in DRF are more diverse due to the relative fraction of absorbing and non-absorbing aerosols. The method still has limitations. Further improvements are required including examining the fine/coarse aerosol fraction, better identifying the absorbing species, and using advanced observations with global coverage