Multi-angle Imaging SpectroRadiometer Data Research Articles

Difference between forward- and backward-looking bands of GOSAT-2 CAI-2 cloud discrimination used with Terra MISR data

ABSTRACTGreenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2017. GOSAT-2 will be equipped with two Earth-observing instruments: the thermal and near-infrared sensor for carbon observation Fourier transform spectrometer 2 (TANSO-FTS-2) and TANSO-cloud and aerosol imager 2 (CAI-2). The FTS-2 data will be used to determine atmospheric concentrations of greenhouse gases, such as CO2 (carbon dioxide), CH4 (methane) and CO (carbon monoxide). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands for observing the optical properties of aerosols and clouds, and for monitoring the status of urban air pollution and transboundary air pollution over oceans. An important role of CAI-2 is to perform cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA) will be used for cloud discrimination with CAI-2. The Multi-angle Imaging Spectroradiometer (MISR) aboard the Terra spacecraft provides radiometrically and geometrically calibrated images for spectral bands at nine widely spaced angles. In this study, we examined the difference between forward and backward cloud discrimination by using CLAUDIA with Terra MISR data. The results were as follows: (1) in land areas and polar regions, cloud discrimination results obtained with either band could be used; and (2) in sea areas, cloud discrimination results that include no-sun-glint regions should be used.

The regional distribution characteristics of aerosol optical depth over the Tibetan Plateau

Abstract. The Tibetan Plateau (TP) is representative of typical clean atmospheric conditions. Aerosol optical depth (AOD) retrieved by the Multi-angle Imaging SpectroRadiometer (MISR) is higher over Qaidam Basin than the rest of the TP throughout the year. Different monthly variation patterns of AOD are observed over the southern and northern TP, whereby the aerosol load is usually higher in the northern TP than in the southern part. The aerosol load over the northern part increases from April to June, peaking in May. The maximum concentration of aerosols over the southern TP occurs in July. Aerosols appear to be more easily transported to the main body of the TP across the northern edge rather than the southern edge. This is maybe partly because the altitude is lower at the northern edge than that of the Himalayas located along the southern edge of the TP. Three-dimensional distributions of dust, polluted dust, polluted continental aerosol and smoke are also investigated, based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. Dust is found to be the most prominent aerosol type on the TP, and other types of aerosols affect the atmospheric environment slightly. A dividing line of higher dust occurrence in the northern TP and lower dust occurrence in the southern TP can be observed clearly at an altitude of 6–8 km above sea level, especially in spring and summer. This demarcation appears around 33–35° N in the middle of the plateau, and it is possibly associated with the high-altitude terrain in the same geographic location. Comparisons of CALIPSO and MISR data show that the vertical dust occurrences are consistent with the spatial patterns of AOD. The different seasonal variation patterns between the northern and southern TP are primarily driven by atmospheric circulation, and are also related to the emission characteristics over the surrounding regions.

Bias in MODIS cloud drop effective radius for oceanic water clouds as deduced from optical thickness variability across scattering angles

AbstractThe Moderate Resolution Imaging Spectroradiometer (MODIS) represents our most advanced system for long‐term, global observations of cloud optical depth (τ) and cloud drop effective radius (Re). Here we show that the both zonal means of (1) MODIS‐retrieved τ and Re and (2) τ retrieved from the Multi‐angle Imaging Spectroradiometer (MISR) using MODIS‐retrieved Re as input exhibit a large, local negative bias in the rainbow scattering direction relative to adjacent scattering angles for oceanic water clouds. Using radiative transfer simulations, we demonstrate that these local biases are explained by overestimates in MODIS‐retrieved Re. While 3‐D factors on the angular variability of τ are evident, we conclude that a Re bias is itself an important contributor to this variability. With 3‐D factors present, we show that upper and lower bound estimates on the biases in the three MODIS Re products, namely Re1.6, Re2.1, and Re3.7 retrieved from 1.6, 2.1, and 3.7 µm channels, respectively, are possible from a combination of MISR and MODIS data. The midpoints of the upper and lower bounds in zonal mean biases are estimated to be 3–11 µm for Re2.1 and Re1.6 and 2–7 µm for Re3.7, depending on latitude, with smaller biases at latitudes where more stratiform clouds contribute to the total low cloud cover. Removing the bias from the zonal means reveals that Re1.6, Re2.1, and Re3.7 are more similar with each other compared to their original values. The change in zonal mean of Re as a function of latitude is also very different between original and bias‐corrected products. These differences change the interpretation of the vertical and horizontal distribution of Re from the original MODIS product.

Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data

This study provides an intercomparison of aerosol optical depth (AOD) retrievals from satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), Ozone Monitoring Instrument (OMI), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) instrumentation over Karachi, Lahore, Jaipur, and Kanpur between 2007 and 2013, with validation against AOD observations from the ground-based Aerosol Robotic Network (AERONET). Both MODIS Deep Blue (MODISDB) and MODIS Standard (MODISSTD) products were compared with the AERONET products.The MODISSTD–AERONET comparisons revealed a high degree of correlation for the four investigated sites at Karachi, Lahore, Jaipur, and Kanpur, the MODISDB–AERONET comparisons revealed even better correlations, and the MISR–AERONET comparisons also indicated strong correlations, as did the OMI–AERONET comparisons, while the CALIPSO–AERONET comparisons revealed only poor correlations due to the limited number of data points available.We also computed figures for root mean square error (RMSE), mean absolute error (MAE) and root mean bias (RMB). Using AERONET data to validate MODISSTD, MODISDB, MISR, OMI, and CALIPSO data revealed that MODISSTD data was more accurate over vegetated locations than over un-vegetated locations, while MISR data was more accurate over areas close to the ocean than over other areas. The MISR instrument performed better than the other instruments over Karachi and Kanpur, while the MODISSTD AOD retrievals were better than those from the other instruments over Lahore and Jaipur. We also computed the expected error bounds (EEBs) for both MODIS retrievals and found that MODISSTD consistently outperformed MODISDB in all of the investigated areas. High AOD values were observed by the MODISSTD, MODISDB, MISR, and OMI instruments during the summer months (April–August); these ranged from 0.32 to 0.78, possibly due to human activity and biomass burning. In contrast, high AOD values were observed by the CALIPSO instrument between September and December, due to high concentrations of smoke and soot aerosols. The variable monthly AOD figures obtained with different sensors indicate overestimation by MODISSTD, MODISDB, OMI, and CALIPSO instruments over Karachi, Lahore, Jaipur and Kanpur, relative to the AERONET data, but underestimation by the MISR instrument.

Intercomparison of clumping index estimates from POLDER, MODIS, and MISR satellite data over reference sites

Clumping index is the measure of foliage grouping relative to a random distribution of leaves in space. It is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. The Normalized Difference between Hotspot and Darkspot (NDHD) index has been previously used to retrieve global clumping index maps from POLarization and Directionality of the Earth’s Reflectances (POLDER) data at ∼6km resolution and the Bidirectional Reflectance Distribution Function (BRDF) product from Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution. Most recently the algorithm was also applied with Multi-angle Imaging SpectroRadiometer (MISR) data at 275m resolution over selected areas. In this study for the first time we characterized and compared the three products over a set of sites representing diverse biomes and different canopy structures. The products were also directly validated with both in-situ vertical profiles and available seasonal trajectories of clumping index over several sites. We demonstrated that the vertical distribution of foliage and especially the effect of understory need to be taken into account while validating foliage clumping products from remote sensing products with values measured in the field. Satellite measurements responded to the structural effects near the top of canopies, while ground measurements may be biased by the lower vegetation layers. Additionally, caution should be taken regarding the misclassification in land cover maps as their errors can propagate into the foliage clumping maps. Our results indicate that MODIS data and MISR data, with 275m in particular, can provide good quality clumping index estimates at spatial scales pertinent for modeling local carbon and energy fluxes.

Spectral anisotropy of subtropical deciduous forest using MISR and MODIS data acquired under large seasonal variation in solar zenith angle

Recent studies in Amazonian tropical evergreen forests using the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) have highlighted the importance of considering the view-illumination geometry in satellite data analysis. However, contrary to the observed for evergreen forests, bidirectional effects have not been evaluated in Brazilian subtropical deciduous forests. In this study, we used MISR data to characterize the reflectance and vegetation index anisotropies in subtropical deciduous forest from south Brazil under large seasonal solar zenith angle (SZA) variation and decreasing leaf area index (LAI) from the summer to winter. MODIS data were used to observe seasonal changes in the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Topographic effects on their determination were inspected by dividing data from the summer to winter and projecting results over a digital elevation model (DEM). By using the PROSAIL, we investigated the relative contribution of LAI and SZA to vegetation indices (VI) of deciduous forest. We also simulated and compared the MISR NDVI and EVI response of subtropical deciduous and tropical evergreen forests as a function of the large seasonal SZA amplitude of 33°. Results showed that the MODIS-MISR NDVI and EVI presented higher values in the summer and lower ones in the winter with decreasing LAI and increasing SZA or greater amounts of canopy shadows viewed by the sensors. In the winter, NDVI reduced local topographic effects due to the red-near infrared (NIR) band normalization. However, the contrary was observed for the three-band EVI that enhanced local variations in shaded and sunlit surfaces due to its strong dependence on the NIR band response. The reflectance anisotropy of the MISR bands increased from the summer to winter and was stronger in the backscattering direction at large view zenith angles (VZA). EVI was much more anisotropic than NDVI and the anisotropy increased from the summer to winter. It also increased from the forward scatter to the backscattering direction with the predominance of sunlit canopy components viewed by MISR, especially at large VZA. Modeling PROSAIL results confirmed the stronger anisotropy of EVI than NDVI for the subtropical deciduous and tropical evergreen forests. PROSAIL showed that LAI and SZA are coupled factors to decrease seasonally the VIs of deciduous forest with the first one having greater importance than the latter. However, PROSAIL seasonal variations in VIs were much smaller than those observed with MODIS data probably because the effects of shadows in heterogeneous canopy structures or/and cast by emergent trees and from local topography were not modeled.

Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud mask products

Abstract. Using Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-based cloud screening methods, the impacts of cloud contamination on the Terra Multi-angle Imaging Spectroradiometer (MISR) aerosol optical depth (AOD) product are evaluated. Based on one year of collocated MISR and MODIS data, this study suggests that cloud contamination exists in both over-water and over-land MISR AOD data, with heavier cloud contamination occurring over the high latitude southern hemispheric oceans. On average globally, this study shows that thin cirrus cloud contamination introduces a possible ~ 0.01 high bias for the over-water MISR AOD retrievals. Over the mid- to high-latitude oceans and Southeast Asia, this number increases to 0.015–0.02. However, biases much larger than this mean value are found in individual retrievals, especially in retrievals that are near cloud edges. This study suggests that cloud-clearing methods using observations from MISR alone, which has only visible and near-infrared channels, may not be sufficient for all scenarios. Measurements from MODIS can be applied to assist cloud-clearing of the MISR aerosol retrievals. Cloud screening algorithms based on multi-sensor approaches are feasible and should be considered for current and future satellite aerosol studies.

Application of spectral analysis techniques in the intercomparison of aerosol data: Part III. Using combined PCA to compare spatiotemporal variability of MODIS, MISR, and OMI aerosol optical depth

AbstractSatellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space‐time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.

Trend analysis of the aerosol optical depth from fusion of MISR and MODIS retrievals over China

Atmospheric aerosol plays an important role in the climate change though direct and indirect processes. In order to evaluate the effects of aerosols on climate, it is necessary to have a research on their spatial and temporal distributions. Satellite aerosol remote sensing is a developing technology that may provide good temporal sampling and superior spatial coverage to study aerosols. The Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) have provided aerosol observations since 2000, with large coverage and high accuracy. However, due to the complex surface, cloud contamination, and aerosol models used in the retrieving process, the uncertainties still exist in current satellite aerosol products. There are several observed differences in comparing the MISR and MODIS AOD data with the AERONET AOD. Combing multiple sensors could reduce uncertainties and improve observational accuracy. The validation results reveal that a better agreement between fusion AOD and AERONET AOD. The results confirm that the fusion AOD values are more accurate than single sensor. We have researched the trend analysis of the aerosol properties over China based on nine-year (2002-2010) fusion data. Compared with trend analysis in Jingjintang and Yangtze River Delta, the accuracy has increased by 5% and 3%, respectively. It is obvious that the increasing trend of the AOD occurred in Yangtze River Delta, where human activities may be the main source of the increasing AOD.

Forest Biomass Mapping of Northeastern China Using GLAS and MODIS Data

In this study, several major issues associated with forest biomass mapping have been investigated using an integrated dataset, and a preliminary forest biomass map of northeastern China is presented. Three biomass regression models, stepwise regression (SR), partial least-squares regression (PLSR), and support vector regression (SVR), were developed based on field biomass data, Geoscience Laser Altimeter System (GLAS) data, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The biomass estimates using the SVR model were the most reasonable. The accuracy of the biomass predictions was improved through a combination of bootstrapping and the SVR method. The rich temporal information in MODIS data and the multiple-angle information in Multi-angle Imaging Spectro Radiometer (MISR) data were also explored for forest biomass mapping. Results indicated that a MODIS time series data alone, without MISR data, was capable of mapping forest biomass. A forest biomass map was generated using the optimal biomass regression model and the MODIS time series data. Finally, an uncertainty analysis of the biomass map was carried out and a comparison with published results using other methods was made.

Aerosol optical depths over oceans: A view from MISR retrievals and collocated MAN and AERONET in situ observations

AbstractIn this study, aerosol optical depths over oceans are analyzed from satellite and surface perspectives. Multiangle Imaging SpectroRadiometer (MISR) aerosol retrievals are investigated and validated primarily against Maritime Aerosol Network (MAN) observations. Furthermore, AErosol RObotic NETwork (AERONET) data from 19 island and coastal sites is incorporated in this study. The 270 MISR/MAN comparison points scattered across all oceans were identified. MISR on average overestimates aerosol optical depths (AODs) by 0.04 as compared to MAN; the correlation coefficient and root‐mean‐square error are 0.95 and 0.06, respectively. A new screening procedure based on retrieval region characterization is proposed, which is capable of substantially reducing MISR retrieval biases. Over 1000 additional MISR/AERONET comparison points are added to the analysis to confirm the validity of the method. The bias reduction is effective within all AOD ranges. Setting a clear flag fraction threshold to 0.6 reduces the bias to below 0.02, which is close to a typical ground‐based measurement uncertainty. Twelve years of MISR data are analyzed with the new screening procedure. The average over ocean AOD is reduced by 0.03, from 0.15 to 0.12. The largest AOD decrease is observed in high latitudes of both hemispheres, regions with climatologically high cloud cover. It is postulated that the screening procedure eliminates spurious retrieval errors associated with cloud contamination and cloud adjacency effects. The proposed filtering method can be used for validating aerosol and chemical transport models.

Comparison of GEOS‐Chem aerosol optical depth with AERONET and MISR data over the contiguous United States

Aerosol optical properties simulated by the global 3‐D tropospheric chemistry and transport model Goddard Earth Observing System (GEOS)‐Chem (GC) from 2008 to 2010 over the contiguous United States were evaluated with ground observations from Aerosol Robotic Network (AERONET) sites and aerosol products reported by the Multiangle Imaging Spectroradiometer (MISR). Overall, the correlation coefficient (r) and regression slope between AERONET and GC2° × 2.5° (2° latitude × 2.5° longitude) daily total column aerosol optical depth (AOD) were 0.6 and 0.51, respectively. After using the nested GC0.5° × 0.667° model to control for spatial variability, removing several outliers, and averaging over a monthly timescale, the agreement was significantly improved to an r of 0.84 and a slope of 0.75. Seasonal, hourly, and geographical statistics for GC0.5° × 0.667° and AERONET AODs show a similar data range and variation, with higher mean values in the summer, the evening, and in the eastern U.S. Smaller correlation coefficients are seen in the summer and winter, in the evening, and in the western U.S. To investigate the optical properties of major GC tracers, MISR level 2 aerosol products were used to calculate inorganic aerosol, dust, and absorbing non‐dust AOD. Both GC and MISR suggest that on average, inorganic aerosol has the highest AOD (GC: 0.071, MISR: 0.089) nationally, followed by absorbing non‐dust species (GC: 0.025, MISR: 0.041), and dust (GC: 0.013, MISR: 0.014). The large discrepancies in our intercomparison are due to GC underestimation of inorganic aerosol levels during all four seasons in the western U.S. and dust during summer in the eastern U.S., along with overestimation of summertime‐absorbing non‐dust species over the northwestern U.S. These uncertainties are attributed to underestimation of inorganic aerosol emissions in more polluted western regions, the transport of Sahara dust in the summer, misuse of the fire files, MISR retrieval uncertainties in the surface, and choice of aerosol models.

Investigation of cosmic ray–cloud connections using MISR

Numerous empirical studies have analyzed International Satellite Cloud Climatology Project data and reached contradictory conclusions regarding the influence of solar‐modulated galactic cosmic rays on cloud fraction and cloud properties. The Multiangle Imaging Spectroradiometer (MISR) instrument on the Terra satellite has been in continuous operation for 13 years and thus provides an independent (and previously unutilized) cloud data set to investigate purported solar‐cloud links. Furthermore, unlike many previous solar‐climate studies that report cloud fraction MISR measures albedo, which has clearer climatological relevance. Our long‐term analysis of MISR data finds no statistically significant correlations between cosmic rays and global albedo or globally averaged cloud height, and no evidence for any regional or lagged correlations. Moreover, epoch superposition analysis of Forbush decreases reveals no detectable albedo response to cosmic ray decreases, thereby placing an upper limit on the possible influence of cosmic ray variations on global albedo of 0.0029 per 5% decrease. The implications for recent global warming are discussed.

Aerosol airmass type mapping over the Urban Mexico City region from space-based multi-angle imaging

Abstract. Using Multi-angle Imaging SpectroRadiometer (MISR) and sub-orbital measurements from the 2006 INTEX-B/MILAGRO field campaign, in this study we explore MISR's ability to map different aerosol air mass types over the Mexico City metropolitan area. The aerosol air mass distinctions are based on shape, size and single scattering albedo retrievals from the MISR Research Aerosol Retrieval algorithm. In this region, the research algorithm identifies dust-dominated aerosol mixtures based on non-spherical particle shape, whereas spherical biomass burning and urban pollution particles are distinguished by particle size. Two distinct aerosol air mass types based on retrieved particle microphysical properties, and four spatially distributed aerosol air masses, are identified in the MISR data on 6 March 2006. The aerosol air mass type identification results are supported by coincident, airborne high-spectral-resolution lidar (HSRL) measurements. Aerosol optical depth (AOD) gradients are also consistent between the MISR and sub-orbital measurements, but particles having single-scattering albedo of ≈0.7 at 558 nm must be included in the retrieval algorithm to produce good absolute AOD comparisons over pollution-dominated aerosol air masses. The MISR standard V22 AOD product, at 17.6 km resolution, captures the observed AOD gradients qualitatively, but retrievals at this coarse spatial scale and with limited spherical absorbing particle options underestimate AOD and do not retrieve particle properties adequately over this complex urban region. However, we demonstrate how AOD and aerosol type mapping can be accomplished with MISR data over complex urban regions, provided the retrieval is performed at sufficiently high spatial resolution, and with a rich enough set of aerosol components and mixtures.

Retrieving vegetation clumping index from Multi-angle Imaging SpectroRadiometer (MISR) data at 275 m resolution

Clumping index, the measure of foliage grouping relative to a random distribution of leaves in space, is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. In this study, we retrieve the clumping index using the original 275m resolution data of the Multi-angle Imaging SpectroRadiometer (MISR) instrument over a set of sites representing diverse biomes and different canopy structures. Also for the first time, the MISR derived clumping index values are directly validated with both in-situ vertical profiles and seasonal trajectories of clumping index. Our results illustrate that MISR data with 275m allow clumping index estimates at much more pertinent scales (both spatial and temporal) than previous maps from Polarization and Directionality of Earth Reflectances (POLDER) and Moderate Resolution Imaging Spectroradiometer (MODIS) for modeling local carbon and energy fluxes.

Improvements to MISR stereo motion vectors

[1] This study evaluated the effects of recent major modifications to the Multiangle Imaging SpectroRadiometer (MISR) wind retrieval algorithm by repeating an earlier yearlong comparison with Meteosat-9 cloud motion vectors. Algorithm upgrades included an area-based stereo matcher, enhanced quality control, georegistration corrections to focal plane distortions, and increased retrieval resolution. The new winds had better quality at all levels, yielding a particularly large increase in coverage at middle and high levels. The upgrades had an overall neutral impact on the E-W wind comparison statistics, which were already quite good in the preceding MISR data set. The comparison statistics for the more error-prone N-S wind, however, improved significantly on all scales—global, zonal, and regional—and throughout the entire atmosphere. Both the negative N-S wind bias and root mean square difference decreased, and correlation increased substantially, with middle and high levels, and tropics and subtropics experiencing the largest improvements. Subpixel georegistration corrections reduced cross-swath variations in N-S wind and height by half. As the net effect of these improvements, the error characteristics of the previously more uncertain N-S wind component became comparable to those of the E-W wind component at low and middle levels. Despite the substantial reductions in N-S wind errors, which are highly correlated with stereo height errors, the MISR – Meteosat-9 height comparison did not generally improve, strongly suggesting Meteosat-9 height assignment errors as the primary driver of discrepancy.

Eyjafjallajökull volcano plume particle-type characterization from space-based multi-angle imaging

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) Research Aerosol algorithm makes it possible to study individual aerosol plumes in considerable detail. From the MISR data for two optically thick, near-source plumes of the spring 2010 Eyjafjallajökull volcano eruption, we map aerosol optical depth (AOD) gradients and changing aerosol particle types with this algorithm; several days downwind, we identify the occurrence of volcanic ash particles and retrieve AOD, demonstrating the extent and the limits of ash detection and mapping capability with the multi-angle, multi-spectral imaging data. Retrieved volcanic plume AOD and particle microphysical properties are distinct from background values near-source, as well as for over-water cases several days downwind. The results also provide some indication that as they evolve, plume particles brighten, and average particle size decreases. Such detailed mapping offers context for suborbital plume observations having much more limited sampling. The MISR Standard aerosol product identified similar trends in plume properties as the Research algorithm, though with much smaller differences compared to background, and it does not resolve plume structure. Better optical analogs of non-spherical volcanic ash, and coincident suborbital data to validate the satellite retrieval results, are the factors most important for further advancing the remote sensing of volcanic ash plumes from space.

A stochastic model for predicting aerosol optical depth over the north Indian region

A simplistic model to forecast aerosol optical depth (AOD) over north India is presented in this study. The forecasts are generated by integrating the available high-resolution AOD data using time series modelling techniques. The forecasts are done using the autoregressive integrated moving average (ARIMA) method. It is found that the modelled values show good fit with the multiangle imaging spectroradiometer data during the years 2000–2010. This long-term statistical dependence shows that AOD over the north Indian region exhibits a long memory. The forecasts for the next 12 months were done at a 95% level of confidence. Our analysis confirms that using time series models prediction of AOD is possible, particularly during the summer months when the region is dominated by dust aerosols. The results obtained using the chosen ARIMA model suggest that this model proposes a simple and efficient method for determining the future values of AOD compared to more complex deterministic models.

Seasonal variability of atmospheric aerosol over the North Indian region during 2005–2009

The Indo-Gangetic basin (IGB) extends 2000km in length along NW–SE and has 400km width, in the north the basin is bounded by towering Himalaya. High aerosol optical depth (AOD) is observed over the IGB throughout the year. The Himalaya restricts the transport of aerosols across Tibet and China. We have used ground based Kanpur and Gandhi College Aerosol Robotic Network (AERONET) stations and Multiangle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra level-3 AOD products for the years 2005–2009 to study the variability of aerosol over the Indo-Gangetic (IG) plains. An increase in both satellite-derived as well as ground observed aerosol loading during 2005–2009 has been found over major cities located in the IG plains. The correlation coefficients between AERONET and MISR data are found to be 0.70, 0.36 0.82, in contrast the correlation coefficients between AERONET and MODIS 0.49, 0.68, and 0.43, respectively during summer, winter and monsoon seasons. The AOD estimation using MISR is found to be close to AERONET data during summer and monsoon seasons, in contrast MODIS estimation is better during winter season.

MISR observations of Etna volcanic plumes

In the last twelve years, Mt. Etna, located in eastern Sicily, has produced a great number of explosive eruptions. Volcanic plumes have risen to several km above sea level and created problems for aviation and the communities living near the volcano. A reduction of hazards may be accomplished using remote sensing techniques to evaluate important features of volcanic plumes. Since 2000, the Multiangle Imaging SpectroRadiometer (MISR) on board NASA's Terra spacecraft has been extensively used to study aerosol dispersal and to extract the three‐dimensional structure of plumes coming from anthropogenic or natural sources, including volcanoes. In the present work, MISR data from several explosive events occurring at Etna are analyzed using a program named MINX (MISR INteractive eXplorer). MINX uses stereo matching techniques to evaluate the height of the volcanic aerosol with a precision of a few hundred meters, and extracts aerosol properties from the MISR Standard products. We analyzed twenty volcanic plumes produced during the 2000, 2001, 2002–03, 2006 and 2008 Etna eruptions, finding that volcanic aerosol dispersal and column height obtained by this analysis is in good agreement with ground‐based observations. MISR aerosol type retrievals: (1) clearly distinguish volcanic plumes that are sulphate and/or water vapor dominated from ash‐dominated ones; (2) detect even low concentrations of volcanic ash in the atmosphere; (3) demonstrate that sulphate and/or water vapor dominated plumes consist of smaller‐sized particles compared to ash plumes. This work highlights the potential of MISR to detect important volcanic plume characteristics that can be used to constrain the eruption source parameters in volcanic ash dispersion models. Further, the possibility of discriminating sulphate and/or water vapor dominated plumes from ash‐dominated ones is important to better understand the atmospheric impact of these plumes.