A23B-0279
Analysis of MODIS and MISR Pixel-Level Aerosol Products
Satellite observations are an indispensable source of information about aerosol characteristics and aerosol effects on climate. The accuracy of aerosol retrievals from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging SpectroRadiometer (MISR) radiances has previously been estimated based on limited comparisons with ground-based observations. In this study we analyze the entire ~8 years of the MODIS-Terra and MISR pixel-level aerosol optical thickness (AOT) and Angstrom exponent (AE) globally as well as over selected AERONET sites in order to get a better understanding of the previously found significant disagreements between the two satellite instruments. Comparisons with ground-truth aerosol data allow us to evaluate the accuracy estimates of individual retrievals, their global applicability and their relationship with the accuracy of long-term averages of aerosol properties.
A23B-0280
Trans-Pacific Transport Events of Asian Dust and Pollution: Changes in Transport Pathways and a Global Model Simulation.
In May 2003, both MODIS aerosol optical depth (AOD) and carbon monoxide (CO) measurements from MOPITT and AIRS show significant trans-Pacific transport to North America. We apply the global chemical transport model, GEOS-Chem, to analyze the main features of the long-range transport events. Enhancements of CO over the tropical Pacific are much broader than MODIS AOD enhancements. We find that a substantial fraction of the CO enhancements is due to boreal fire emissions in April. Biomass burning CO was recirculated into the subtropical high pressure systems and lingered for a much longer period than aerosols transported at higher latitudes. AOD enhancements are mainly due to a combination of dust, sulfate, and organic and elemental carbons. Fire contributions, although not as significant as in CO, are pronounced. Dust contribution dominates the AOD enhancements in early May. MODIS observations indicate a bias in model simulated dust AOD distributions; the altitude of dust transport appears to be too high. Sensitivities of dust transport to emission algorithms and transport processes are explored.
A23B-0281
Aerosol Particle Property Comparisons Between MISR and AERONET Retrieved Values
As a further step in validating the NASA Earth Observing System Terra satellite's Multi-angle Imaging SpectroRadiometer (MISR) aerosol products, an extensive comparison of particle micro-physical properties has been made against the Aerosol Robotic Network (AERONET). Angstrom exponent, single scattering albedo, and size distribution characteristic values and variance envelopes for individual sites and aggregates are compared, stratified by expected aerosol air mass type, optical depth magnitude and season. Specific examples illustrating strengths and weaknesses of this approach will be shown. More than seven years of data from about 52 geographically diverse sites having good long-term measurement records are first stratified by expected aerosol air mass types: maritime, biomass burning, desert dust, urban pollution, continental and mixed dust+smoke aerosols. Having observations in at least three of the four seasons is an additional constraint on the selection of sites. The number of actual coincident measurements is limited by requiring AERONET direct sun aerosol optical thickness (AOT) data be obtained from a two-hour window centered on the MISR overpass time, and AERONET sky scans, which provide particle micro-physical properties and are taken only once an hour, obtained from a four-hour window also centered on the overpass. 3605 coincident observations are included in the data set. Both AERONET sun and sky data are averaged over the measurements obtained within these windows and are then interpolated to the MISR wavelengths to facilitate comparison. All AERONET measurements are Level 1.5, Version 2 data. A previous, systematic comparison of MISR and AERONET AOT data [Kahn, Gaitley et al., JGR 110, 2005] was used to suggest improvements to the MISR Standard Aerosol retrieval algorithms. The MISR aerosol products have been almost completely reprocessed with the upgraded algorithms. This new, uniformly processed database, that is used in the current study, which is aimed at further refining the MISR aerosol products, especially the particle property results. This work is performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration and at the NASA Goddard Space Flight Center.
A23B-0282
Estimation of top-altitude of Asian Dust from Satellite Observations of Backscattered UV Radiation
Asian dust (Hwangsa in Korean), which is a typical example of mineral dust aerosol, frequently occurs in the desert and loess plateau in northern China and Mongolia during the spring season (Park and Lee 2004). In particular, they mainly originate from the arid area above 1500m from sea level (Wang et al. 2000) and some of them affect to North Pacific Ocean and North America (Husar et al. 2001). In addition, UV-absorbing aerosols such as mineral dust have a strong altitude dependence in the near UV region that is a low surface reflectivity and nearly constant over land and water ( Herman et al. 1997; Torres et al. 1998). Thus, in this study, we concentrate on estimation of top-altitude for UV-absorbing aerosol like a mineral dust by using weather charts and radiative transfer model (RTM) and satellite data. To estimate the top-altitude of Asian dust using multiple satellite data, in the first, we investigate the source regions of Asian dust based on results of HYSPLIT backward trajectory for the period from Jan 2001 to May 2008 and analyze qualitative synoptic weather patterns associated with the long-range transport. Next, assuming that vertical profile of Asian dust is similar to Gaussian distribution from surface to maximum altitude, we select a sensitive wavelength for Asian dust from RTM test. Then, we evaluate the top-altitude of Asian dust estimated from the satellite data, MODIS, OMI, CALIOP and Rstar5b model. Rstar5b inputs are generated by MODIS-AOD and OMI geometry information, which is located in minimum distance from CALIOP data pixel. At last, we compare altitude calculated from Rstar5b with that retrieved from CALIOP using radiance of simulated Rstar5b and measured OMI. This simultaneous approach of multi-satellite platform and RTM is expected to contribute to the comprehension of the mechanism as well as the estimation of the altitude for Asian dust.
A23B-0283
Improved aerosol characterization from combined OMI-MODIS retrieval
The Ozone Monitoring Instrument (OMI) aboard EOS-Aura and the MODerate resolution Imaging Spectroradiometer (MODIS) on board EOS-Aqua fly in formation as part of the A-Train. Both instruments retrieve aerosol properties operationally. OMI retrieves aerosol optical depth (AOD) and aerosol absorption, but must assume aerosol layer height. The MODIS aerosol retrieval is not sensitive to aerosol layer height and with its smaller pixel size is less affected by sub-pixel clouds. However, MODIS cannot retrieve aerosol absorption with any accuracy. Here, we demonstrate an approach that uses MODIS-retrieved AOD to constrain the OMI retrieval, freeing OMI from making an a priori estimate of aerosol height and allowing a more direct retrieval of absorption. To predict UV optical depths using MODIS data we rely on the spectral curvature of the MODIS-retrieved visible and near IR aerosol spectral optical depths (AODs). Applying the joint retrieval over the north tropical Atlantic shows good agreement between OMI and MODIS-adjusted AODs in the UV, which implies that the aerosol height assumed in the OMI-only algorithm is probably correct. In contrast, over the Arabian Sea, MODIS-adjusted AOD deviated from the OMI-only retrieval, and combined OMI-MODIS retrievals substantially improved information on aerosol layer height (based on validation against air-borne lidar measurements). This implies an improvement to the aerosol absorption retrieval, but lack of UV absorption measurements prevents a true validation. Our study demonstrates the potential of multi- satellite analysis of A-train data to improve the accuracy of retrieved aerosol products and suggests that a combined OMI-MODIS-CALIPSO retrieval has large potential to further improve assessments of aerosol absorption.
A23B-0284
Comparisons of Aerosol Type Derived from the CALIPSO Level 2 Feature Mask and GEOS-5
A-train sensors such as MODIS, MISR, and CALIPSO are used to determine aerosol properties, and in the process a means of estimating aerosol type (e.g. smoke vs. dust). Correct classification of aerosol type is important for climate assessment, air quality applications, and for comparisons and analysis with aerosol transport models. The Aerosols-Clouds-Ecosystems (ACE) satellite mission proposed in the NRC Decadal Survey describes a next generation aerosol and cloud suite similar to the current A-train, including a lidar. The future ACE lidar must be able to determine aerosol type effectively in conjunction with modeling activities to achieve ACE objectives. Here we examine the current capabilities of CALIPSO and the NASA Goddard Earth Observing System general circulation model and data assimilation system (GEOS-5), to place future ACE needs in context. The CALIPSO level 2 feature mask includes vertical profiles of aerosol layers classified by type. GEOS-5 provides global 3D aerosol mass for sulfate, sea salt, dust, and black and organic carbon. A GEOS aerosol scene classification algorithm has been developed to provide estimates of aerosol mixtures and extinction profiles along the CALIPSO orbit track. In previous work, initial comparisons between GEOS-5 derived aerosol mixtures and CALIPSO derived aerosol types were presented for July 2007. In general, the results showed that model and lidar derived aerosol types did not agree well in the boundary layer. Agreement was poor over Europe, where CALIPSO indicated the presence of dust and pollution mixtures yet GEOS-5 was dominated by pollution with little dust. Over the ocean in the tropics, the model appeared to contain less sea salt than detected by CALIPSO, yet at high latitudes the situation was reserved. Agreement between CALIPSO and GEOS-5 aerosol types improved above the boundary layer, primarily in dust and smoke dominated regions. At higher altitudes (> 5 km), the model contained aerosol layers not detected by CALIPSO. Here we investigate potential causes of poor agreement in the previous study. CALIPSO derived aerosol types are separated into day and night to assess the impact of undetected layers by the lidar during noisier daytime data. In addition, a sensitivity study was performed to determine if the GEOS-5 scene classification algorithm is generating layers with negligible optical depth (model noise), particularly at high altitude. Finally, we will discuss sources of the poor agreement in the boundary layer over Europe.
A23B-0285
Determination of Aerosol Single Scattering Albedo Using Coincident MISR and AERONET Data
The determination of aerosol single scattering albedo via remote sensing can be an extremely difficult problem if the data are obtained from only a single sensor, observing at a single height or altitude. Unless additional information is available, e.g., surface directional reflectance and aerosol particle size and amount, the retrieval problem is generally underconstrained, resulting in a range of possible solutions. We investigate the use of AERONET sunphotometer data in combination with coincident MISR data to provide the necessary constraints in retrieving aerosol single scattering albedo at an AERONET site. The sun photometer contributes information on aerosol amount and particle size from measurements of almucanter and principal plane sky radiance at ground level. MISR contributes multi-directional surface reflectance and top-of-atmosphere (TOA) radiance at nine distinct view angles. Together, these data sets provide the necessary boundary conditions (top and bottom of atmosphere) along with sufficient aerosol information to allow a meaningful retrieval of aerosol single scattering albedo. At a given AERONET site, data from two MISR overpasses are required in the analysis; the first overpass observes the site when the aerosol amount is small and the second overpass, near in time to the first, when the aerosol amount is substantial. The first data set allows an accurate retrieval of the surface directional reflectance at and around the AERONET site. Assuming no significant surface reflectance changes during the time between the two MISR overpasses, these directional reflectance data are then used as the surface boundary condition when analyzing the MISR and AERONET data from the second overpass. The requirement that the aerosol load be heavy during the second overpass translates into a greater sensitivity to the single scattering albedo which allows its retrieval in all four MISR spectral bands (446, 552, 672, and 866 nm). This poster presents some results from a pilot study in which the analysis as described above was used on data obtained at a few select AERONET sites where significant aerosol amounts can occur, either from regional biomass burning or dust storms. It is expected that the results of this and a more extensive study will be used to improve the current capability of the MISR aerosol retrieval algorithm. This work was performed at the Jet Propulsion Laboratory, California Institute of Technology under contract with the National Aeronautics and Space Administration.
A23B-0286
A Geostatistical Data Fusion Technique for Merging MISR and MODIS Aerosol Optical Thickness (AOT) Retrievals With AERONET AOT Measurements
The Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System's Terra satellite have been measuring aerosol optical thickness (AOT) since early 2000. These remote-sensing platforms complement the extensive ground-based AErosol RObotic NETwork (AERONET) in better understanding the role of aerosols in climate and atmospheric chemistry. To date, however, there have been only very limited attempts to exploit the complementary multiangle (MISR) and multispectral (MODIS) capabilities of these sensors in an integrated analysis. Current analyses are limited by incomplete spatial coverage in areas with cloud interference, or in areas where retrievals are not possible. The ground-based network, on the other hand, is sparse, and hence fails to capture the spatial variability of the AOT distribution. The joint assimilation of MISR and MODIS AOT information has immense potential given the limitations of the individual data records. Using Level 2.0 AERONET, MISR and MODIS AOT data for the contiguous US, we describe a geostatistical data fusion technique based on universal kriging that can take advantage of the spatial autocorrelation of the AOT distribution, while making optimal use of all available datasets. In this approach, the MISR and MODIS data inform the large-scale spatial patterns that exists in the AOT distribution, and which are not captured by the sparse AERONET measurements. The residuals of AERONET AOT from MISR and MODIS AOT define deviations from this spatial trend, and these deviations are assumed to be spatially correlated. This approach not only provides more accurate estimates of AOT, but also rigorous estimates of uncertainty and key information about how variability observed in the AERONET data can be represented using MISR and MODIS. Results show that MISR has a higher signal to noise ratio relative to MODIS, and exhibits a stronger correlation with AERONET data. Concurrent use of MISR and MODIS can explain up to 80% of the variability observed in the AERONET data. Application to several test regions over the US in 2001 indicates that this approach can successfully assimilate information from multiple sensors, and provide reasonably accurate estimates of the spatial distribution of AOT.
A23B-0287
Multi-sensor aerosol retrievals using joint inversion of AERONET and satellite observations: concept and applications.
It has been recognized recently that the future progress in aerosol knowledge should be associated with the integration of data from an increasing number of aerosol information sources, including diverse satellite and ground based instruments, in situ measurements, transport model forecasts, etc. Such aerosol information integration should not be limited to straightforward data accumulation and inter-comparison of different aerosol products, but rather rely on simultaneous and interactive processing of all available data, resulting in additional enhancement of the joint product. The concept of joint inversion is to invert the observations from different sensors combined into one data set with the weights which are proportional to the accuracy of individual observations of each type. Combining data from coincident ground-based and satellite observations into single retrieval gives an example of multi-instrument synergy. Up- and down- looking sensors observing the same atmospheric column provide complimentary aerosol information in terms of the complimentary ranges of scattering angles. Furthermore due to the different sensitivity of ground-based and space-born observations to surface reflectance separation between aerosol and surface signals becomes possible. It has been demonstrated that the joint inversion of AERONET measurements with observations from multi-wavelength and/or multi- angle sensors such as MODIS, MISR and POLDER allows simultaneous retrieval of both aerosol and surface properties with minimal assumptions. Recently our joint inversion efforts have been extended to collocated AERONET/lidar observations. As a result vertical variability of aerosol concentration is retrieved in addition to standard aerosol parameters provided by AERONET operational algorithm. The inversion algorithm does not require a priori knowledge of lidar ratio (which is required in lidar retrievals alone). In addition lidar observation can help to improve aerosol retrievals by providing additional constraints on aerosol phase function. The retrieval algorithm was developed and applied to joint AERONET/CALIOP collocated measurements collected during CALIPSO and Twilight Zone 2007 (CATZ) and TIGERZ (2008) field campaigns. The examples of retrieval results are presented and compared to CALIOP operational retrievals.
A23B-0288
Comparison of Geographic Distribution and Seasonal Variation of UV Absorbing Aerosols between GISS GCM Simulations and TOMS Aerosol Index Data
Since absorbing aerosols play key roles in global climate change, it is important that absorbing aerosols be accurately addressed in aerosol models and GCMs. In this study, we compared the geographic distribution and seasonal variation of absorbing aerosols between GISS GCM and TOMS Aerosol Index product via the construction of an Aerosol Index simulator to operate within the GCM radiation model. In addition to aerosol optical depth and single scattering albedo, the GCM simulated AI is sensitive to the height of aerosols, surface albedo and solar zenith angle. The simulated AI and TOMS AI show qualitative agreement, especially over dust regions of Persian Gulf and Sahara and biomass burning regions over South America. However, dust transport off Northwest Africa coast and smoke transport from South Africa are poorly characterized in the GCM simulation. The seasonal cycles of the GCM AI over representative regions generally agree with those of TOMS AI, but are biased low in amplitude in most areas and have a phase shift over South Africa. Negative AI produced by scattering aerosols is included in the GCM simulations, but is typically excluded in the TOMS AI data product. Further inspection of each aerosol component reveals that dust and sulfate aerosols may not be properly distributed over some regions. Biomass burning simulation may need to be adjusted in the chemical transport model. And organic carbon and dust aerosols appear to need several kinds of Mie scattering parameters to be distinguished within the GCM aerosol climatology.
A23B-0289
Integrated Approach for Characterizing Aerosols and Their Impacts on Climate
Tropospheric aerosols have been recognized as an important, although the most highly uncertain, atmospheric constituent affecting the global climate system, with large uncertainties related to aerosols involving clouds, albedo and physical processes, in general. During the past decade, assessments of aerosol-climate interactions highlighted the associated uncertainties, the sources of which include: an incomplete observational record; inadequate understanding of the complex physical and chemical processes involved; inadequate knowledge of aerosol source strengths; insufficient understanding of the relative importance of various mechanisms of aerosol-radiation and aerosol-cloud interactions; and the difficulty in representing processes that take place on submicron to sub-kilometer scales within climate models whose grid cells vary in size, and can measure from tens to hundreds of kilometers in size. Several satellite missions and numerous ground-based instrumentation systems and networks (e.g.: EARLINET, AERONET), with variable measurement approaches have been established to deduce the variability of critical atmospheric aerosol properties. At the same time, most aerosol investigators advocate that retrievals of combined data from multiple instruments rather than products of separate processing, would improve aerosol characterization accuracy. The full potential of such a multi-sensor retrieval approach remains still untapped but is expected to be the norm in the future. In particular, vertical distributions of various atmospheric parameters obtained from e.g. AIRS on Aqua, other sensors such as MISR on Terra, aerosol information such as total attenuated backscatter from CALIPSO, will have to be combined with two dimensional information e.g. MODIS on both Terra and Aqua as well as with ground observations and field experiments to provide the full potential needed. Here, we present the need for the development and application of an integrated approach that merges multiple observational methods, inversion techniques, and modeling capabilities, into a systematic framework, implementing enhanced retrievals of aerosol properties, including their vertical distribution, as derived from the synergetic use of multi-sensor satellite and state-of-the-art ground-based data, advanced inversion, thermodynamic, dust, chemical and regional air quality modeling. In particular, the methodology focuses on crucial optical and physical properties as well as chemical composition of aerosols, in order to acquire a complete and accurate aerosol type characterization on regional scales. The methodology we advocate can be applied on state-of-the-art ground-based measurements that are performed by the use of inversion schemes and satellite sensor synergy aiming for a comprehensive retrieval of aerosol optical and physical properties. Thermodynamic and chemical modeling could then build on the physical retrievals to derive the aerosol chemical composition. The methodology to be discussed can serve to reduce biases in satellite observations and facilitate interpretation of the columnar and vertical profiling measurements in combination with surface data. This will result in a significant improvement in the use of existing and future satellite data. Through the approach presented here, future satellite and ground-based measurement needs can be better defined. Moreover, optical-physical-chemical aerosol datasets and methodologies employed will be extremely valuable for the aerosol modeling community.
A23B-0290
Intercomparisons of MODIS-Aqua Deep Blue Aerosol Products With Ground-Based and Other Existing Satellite Measurements Over Bright Surfaces
Satellite aerosol retrievals over bright surfaces, such as deserts, have been limited. The MODIS Deep Blue aerosol retrieval algorithm expands the capacity of the MODIS instrument to such areas of the globe, thus completing the dataset. The latest version of the Deep Blue algorithm, contained in MODIS-Aqua Collection 5.1 Level 2 and Level 3 aerosol products, has just become available. Deep Blue Collection 5.1 improves upon previous versions with substantive updates to surface bidirectional reflectance distribution function (BRDF) characterization and cloud-screening techniques. Also, in contrast to previous versions, quality assurance (QA) determination is now robust. Here we use AERONET data to validate this latest version for the available MODIS-Aqua time series (July 2002 - present). Results are given for a range of spatial and temporal scales. Comparisons with other existing satellite measurements are also shown, and possible differences between the sensors are discussed. Finally, seasonal and interannual variability of aerosol source regions, such as the Sahara Desert and China, are characterized. Appropriate use and potential applications of this emerging dataset, particularly for constraining aerosol transport models and reducing the uncertainty in climate forcing due to tropospheric aerosols, will also be addressed.
A23B-0291
Validating dust layer heights and infrared optical depths from AIRS data
NASA's A-Train suite of polar orbiting satellites provide daily global coverage of dust related events. From this, dust aerosol optical depths are routinely retrieved using instruments that mainly utilize the ultraviolet and visible portions of the spectrum. However, the potential contributions of the hyperspectral infrared Atmospheric Infrared Sounder (AIRS) on board the Aqua platform are usually un-noticed. In this talk we use data from AIRS, coupled with a fast infrared scattering radiative transfer model to retrieve infrared optical depths and dust layer heights, day or night, over both ocean and land. We focus on five days of coverage of a late February 2007 Saharan dust storm which advected from Morocco, over the Sahara to Egypt before moving over the Eastern Mediterranean. The height retrievals are compared to CALIOP lidar data. Daytime OD retrievals are compared to those from PARASOL, MODIS, CALIOP and OMI, and nearby ground based AERONET observation stations, while night time results are compared against CALIOP retrievals. The retrieved thermal infrared optical depths and heights are in excellent agreement with these instruments. AIRS data also provides information to estimate the dust radiative forcing in the longwave.
A23B-0292
Characterizing error distributions for MISR and MODIS optical depth data
The Multi-angle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's EOS satellites collect massive, long term data records on aerosol amounts and particle properties. MISR and MODIS have different but complementary sampling characteristics. In order to realize maximum scientific benefit from these data, the nature of their error distributions must be quantified and understood so that discrepancies between them can be rectified and their information combined in the most beneficial way. By 'error' we mean all sources of discrepancies between the true value of the quantity of interest and the measured value, including instrument measurement errors, artifacts of retrieval algorithms, and differential spatial and temporal sampling characteristics. Previously in [Paradise et al., Fall AGU 2007: A12A-05] we presented a unified, global analysis and comparison of MISR and MODIS measurement biases and variances over lives of the missions. We used AErosol RObotic NETwork (AERONET) data as ground truth and evaluated MISR and MODIS optical depth distributions relative to AERONET using simple linear regression. However, AERONET data are themselves instrumental measurements subject to sources of uncertainty. In this talk, we discuss results from an improved analysis of MISR and MODIS error distributions that uses errors-in-variables regression, accounting for uncertainties in both the dependent and independent variables. We demonstrate on optical depth data, but the method is generally applicable to other aerosol properties as well.
A23B-0293
Global error maps of aerosol optical properties: an error propagation analysis
Among the numerous atmospheric constituents, aerosols play a unique role on climate, due to their
scattering and absorbing capabilities, visibility degradation and their effect on incoming and outgoing
radiation. The most important optical properties are the aerosol optical depth (AOD), the asymmetry
parameter (g) and the single scattering albedo (SSA). Uncertainties in aerosol microphysics in global models,
which in turn affect their optical properties, propagate to uncertainties on the effect of aerosols on climate.
This study aims to estimate the uncertainty of AOD, g and SSA attributable to the aerosol representation in
models, namely mixing state, aerosol size and aerosol associated water. As a reference, the monthly mean
output of the general circulation model LMDz-INCA from the international comparison exercise AEROCOM B
was used. For the optical properties calculations, aerosols were considered either externally mixed,
homogeneously internally mixed or coated spheres. The radius was allowed to vary by 20% (with 2%
intervals) and the aerosol water content by 50% (with 5% intervals) with respect to the reference model
output. All of these possible combinations were assumed to be equally likely and the optical properties were
calculated for each one of them. A probability density function (PDF) was constructed at each model grid
point for AOD, g and SSA. From this PDF, the 1 sigma and 2 sigma uncertainties of the AOD, g and SSA
were calculated and are available as global maps for each month. For the range of the cases studied, we
derive a maximum 2 sigma uncertainty range in AOD of 70%, while for g and SSA the maxima reach 18%
and 28% respectively. The mixing state was calculated to be important, with the aerosol absorption and SSA
being the most affected properties when absorbing aerosols are present.
http://www.atmos-
chem-phys-discuss.net/8/16027/2008/
A23B-0294
Scattering Aerosol Index From Satellite Observations – A new Tool in the Aerosol Retrieval Toolbox for the Quantification of Aerosols in Presence and Absence of Clouds
Aerosol particles have an important influence on the transfer of solar radiation through the atmosphere. The "direct" effects of aerosols on the distribution of radiative energy in the atmosphere are relatively well understood; the "indirect" effects, meaning those caused by interactions between aerosols and clouds, need more investigation. The study of the interaction between clouds and aerosols by satellite remote sensing is impeded by the fact that for most methods it is not possible to retrieve aerosol information in pixels that contain clouds. This is not the case for the UV Aerosol Index (UVAI), a method that is based on the spectral contrast of a scene at two wavelengths in the UV spectral range. The UVAI can be determined in the presence of clouds and is also insensitive to surface type. For over a decade now, UV-absorbing aerosols have been studied using the Absorbing Aerosol Index (AAI). We have recently introduced its counterpart, the Scattering Aerosol Index (SAI), with which scattering aerosols, such as sulphate particles, can be monitored. By radiative transfer modeling we have shown that clouds contribute to both AAI and SAI. To overcome this problem, we have developed a two-step cloud-correction scheme to remove both the cloud contribution to UVAI and to compensate for cloud shielding effects. We will present our recent UV Aerosol Index results from the SCIAMACHY satellite instrument and introduce our cloud-correction schemes. We will also show results from radiative transfer modelling of aerosols and clouds using our Monte Carlo radiative transfer model McArtim.
A23B-0295
Comparison of Aerosol Type Classification From Satellite With CALIOP and AERONET Sunphotometer Measurements
Aerosol type classification from satellite remote sensing has been one of the important problems recently in an effort to better quantify the effect of different aerosol type to climate changes and to better retrieve aerosol optical properties. Based on improved nadir-viewing satellite sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI), and retrieval algorithms, many studies have shown the possibility of aerosol type classification from satellite-based remote sensing. Despite the importance of validation of retrieved results from satellite, it was difficult to find out the ¡®true value' due to limitation of aerosol type measurements to validate retrieved aerosol types. In this study, types of aerosol from proposed aerosol remote sensing algorithms from satellite [cf. Kim et al., 2007; Lee et al., 2007] are compared with those of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Aerosol Robotic Network (AERONET) sunphotometer measurements. CALIOP measurements provide valuable opportunity to validate satellite algorithm with respect to aerosol layer height, and accurate measurements of aerosol optical properties from abundant AERONET stations provide proxy of ¡®true value'. The aerosol types from three different methods show reasonable consistency, especially, in optically thick and higher layer cases. Although the analysis in this study are limited, satellite observations showed feasibility in understanding the global distribution and characteristics of aerosol type by using well-known, state-of-the art MODIS and OMI, and possibly other relevant satellites.
A23B-0296
An Evaluation of Aerosol Spatial Scales in A-Train Observations and Transport Model Simulations: Does ACE Need a Multi-Beam Lidar?
Ground-based, airborne, and space-based observations of tropospheric aerosols suggest horizontal homogeneity on spatial scales of about 50 to 200 km, whereas the observation swath for a polar orbiting radiometer (e.g., MODIS) is about 2000 km. The Aerosols-Clouds-Ecosystem (ACE) satellite mission proposed in the recent NRC Decadal Survey couples lidar, radiometer, and polarimeter on a single satellite platform, mimicking some key capabilities of the current A-Train constellation (i.e., CALIPSO, MODIS, PARASOL) on one spacecraft. There is some question as to whether a single-beam lidar like the current CALIPSO instrument provides sufficient information about the distributions of aerosols to represent the entire swath observed by the polarimeter/radiometer package. We address this question by "flying" a hypothetical multi-beam lidar (MBL) along the A-Train track to investigate the spatial correlation of columnar aerosol optical thickness from MODIS retrievals sampled at the MBL beam spacing. A similar analysis is conducted using simulated aerosol observations from the NASA GEOS-5 transport model.
A23B-0297 TI: MISR (the Multi-angle Imaging SpectroRadiometer)and MODIS (the MODerate resolution Imaging Spectrometer) are two primary sources of satellite remote sensing information about aerosols in the Earth atmosphere. Since 2000 both instruments have been in orbit on the Terra platform, collecting data on optical depth and particle properties using different retrieval algorithms, optical techniques, and sampling characteristics. These difference are actually an advantage because of the instruments' complementary strengths. In principle this allows better estimates of aerosol properties to be made by fusing the MISR and MODIS retrieved data in a mathematically rigorous manner that properly treats uncertainties in the measurements and in the underlying field. In this talk, we demonstrate and discuss a hierarchical spatial statistical model for deriving optimal estimates of aerosol optical depth (aod) and their associated uncertainties, from MISR and MODIS data. We also address issues of scientific interpretation and computational considerations.
A23B-0298
Retrieval of aerosol properties from polarization measurements
The Global Ozone Monitoring Experiment-2 (GOME-2) has been launched in October 2006. In addition to measuring the intensity in the UV, Visible and Near Infrared at medium to high spectral resolution, GOME-2 also measures the polarization of light in 15 spectral bands. The polarization measurements provide valuable information on aerosol microphysical- and optical properties. We present a retrieval algorithm for the retrieval of aerosol total amount, size distribution, refractive index, and the corresponding optical properties. The retrieval is based on online vector radiative transfer calculations and an analytical inversion approach. Hence, it is not restricted to a number of standard aerosol models. We will show first results for aerosol retrieval from GOME-2 and discuss the accuracy of the polarization measurements.