Multi-angle Imaging SpectroRadiometer Observations Research Articles

Evolving Particles in the 2022 Hunga Tonga—Hunga Ha'apai Volcano Eruption Plume

AbstractThe Multi‐angle Imaging SpectroRadiometer (MISR) aboard NASA's Terra satellite observed the Hunga Tonga—Hunga Ha'apai (HTHH) 15 January eruption plume on eight occasions between 15 and 23 January 2022. From the MISR multi‐angle, multi‐spectral imagery we retrieve aerosol plume height geometrically, along with plume‐level motion vectors, and derive radiometrically constraints on particle effective size, shape, and light‐absorption properties. Parts of two downwind aerosol layers were observed in different places and times, one concentrated in the upper troposphere (11–18 km ASL), and a mid‐stratosphere layer ∼23–30+ km ASL. After the initial day (1/15), the retrievals identified only spherical, non‐light‐absorbing particles, typical of volcanic sulfate/water particles. The near‐tropopause plume particles show constant, medium‐small (several tenths of a micron) effective size over 4 days. The mid‐stratosphere particles were consistently smaller, but retrieved effective particle size increased between 1/17 and 1/23, though they might have decreased slightly on 1/22. As a vast amount of water was also injected into the stratosphere by this eruption, models predicted relatively rapid sulfate particle growth from the modest amounts of SO2 gas injected by the eruption to high altitudes along with the water (Zhu et al., 2022, https://doi.org/10.5194/acp‐22‐10267‐2022). MISR observations up to 10 days after the eruption are consistent with these model predictions. The possible decrease in stratospheric particle size after initial growth was likely caused by evaporation, as the plume mixed with drier, ambient air. Particles in the lower‐elevation plume observed on 1/15 were larger than all the downwind aerosols and contained significant non‐spherical (likely ash) particles.

Improved estimates of smoke exposure during Australia fire seasons: importance of quantifying plume injection heights

Abstract. Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM2.5) from wildfire smoke in key cities over northern and southeastern Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) together with assimilated PBL heights from NASA Modern-Era Retrospective Analysis for Research and Application (MERRA) version 2. For the second method, we develop a novel approach based on the Multi-angle Imaging SpectroRadiometer (MISR) observations and a random forest, machine learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We apply the resulting plume injection fractions quantified by the two methods to smoke PM2.5 concentrations simulated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in target cities. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM2.5, especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. However, using climatological injection profiles cannot capture well the spatiotemporal variability in plume injection fractions, resulting in a 63 % underestimation of daily fire emission fluxes injected above the PBL in comparison with those fluxes derived from MISR injection fractions. Our random forest model successfully reproduces the daily injected fire emission fluxes against MISR observations (R2=0.88, normalized mean bias = 10 %) and predicts that 27 % and 45 % of total fire emissions rise above the PBL in northern and southeastern Australia, respectively, from 2009 to 2020. Using the plume behavior predicted by the random forest method also leads to better model agreement with observed surface PM2.5 in several key cities near the wildfire source regions, with smoke PM2.5 accounting for 5 %–52 % of total PM2.5 during fire seasons from 2009 to 2020.

Analysis of simultaneous aerosol and ocean glint retrieval using multi-angle observations

Abstract. Since early 2000, NASA's Multi-angle Imaging SpectroRadiometer (MISR) instrument has been performing remote sensing retrievals of aerosol optical properties from the polar-orbiting Terra spacecraft. A noteworthy aspect of MISR observations over the ocean is that, for much of the Earth, some of the multi-angle views have contributions from solar reflection by the ocean surface (glint, or glitter), while others do not. Aerosol retrieval algorithms often discard these glint-influenced observations because they can overwhelm the signal and are difficult to predict without knowledge of the (wind-speed-driven) ocean surface roughness. However, theoretical studies have shown that multi-angle observations of a location at geometries with and without reflected sun glint can be a rich source of information, sufficient to support simultaneous retrieval of both the aerosol state and the wind speed at the ocean surface. We are in the early stages of creating such an algorithm. In this paper, we describe our assessment of the appropriate level of parameterization for simultaneous aerosol and ocean surface property retrievals using sun glint. For this purpose, we use generalized nonlinear retrieval analysis (GENRA), an information content assessment (ICA) technique employing Bayesian inference, and simulations from the Ahmad–Fraser iterative radiative transfer code. We find that four parameters are suitable: aerosol optical depth (τ), particle size distribution (expressed as the fine mode fraction f of small particles in a bimodal size distribution), surface wind speed (w), and relative humidity (r, to define the aerosol water content and complex refractive index). None of these parameters define ocean optical properties, as we found that the aerosol state could be retrieved with the nine MISR near-infrared views alone, where the ocean body is strongly absorbing in the open ocean. We also found that retrieval capability varies with observation geometry and that as τ increases so does the ability to determine aerosol intensive optical properties (r and f, while it decreases for w). Increases in w decrease the ability to determine the true value of that parameter but have minimal impact on retrieval of aerosol properties. We explored the benefit of excluding the two most extreme MISR view angles for which radiative transfer with the plane-parallel approximation is less certain, but we found no advantage in doing so. Finally, the impact of treating wind speed as a scalar parameter, rather than as a two-parameter directional wind, was tested. While the simpler scalar model does contribute to overall aerosol uncertainty, it is not sufficiently large to justify the addition of another dimension to parameter space. An algorithm designed upon these principles is in development. It will be used to perform an atmospheric correction with MISR for coincident ocean color (OC) observations by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, also on the NASA Terra spacecraft. Unlike MISR, MODIS is a single-view-angle instrument, but it has a more complete set of spectral channels ideal for determination of optical ocean properties. The atmospheric correction of MODIS OC data can therefore benefit from MISR aerosol retrievals. Furthermore, higher-spatial-resolution data from coincident MISR observations may also improve glint screening.

The 2018 fire season in North America as seen by TROPOMI: aerosol layer height intercomparisons and evaluation of model-derived plume heights

Abstract. Before the launch of the TROPOspheric Monitoring Instrument (TROPOMI), only two other satellite instruments were able to observe aerosol plume heights globally, the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The TROPOMI aerosol layer height is a potential game changer, since it has daily global coverage, and the aerosol layer height retrieval is available in near real time. The aerosol layer height can be useful for aviation and air quality alerts, as well as for improving air quality forecasting related to wildfires. Here, TROPOMI's aerosol layer height product is evaluated with MISR and CALIOP observations for wildfire plumes in North America for the 2018 fire season (June to August). Further, observing system simulation experiments were performed to interpret the fundamental differences between the different products. The results show that MISR and TROPOMI are, in theory, very close for aerosol profiles with single plumes. For more complex profiles with multiple plumes, however, different plume heights are retrieved; the MISR plume height represents the top layer, and the plume height retrieved with TROPOMI tends to have an average altitude of several plume layers. The comparison between TROPOMI and MISR plume heights shows that, on average, the TROPOMI aerosol layer heights are lower, by approximately 600 m, compared to MISR, which is likely due to the different measurement techniques. From the comparison to CALIOP, our results show that the TROPOMI aerosol layer height is more accurate over dark surfaces, for thicker plumes, and plumes between approximately 1 and 4.5 km. MISR and TROPOMI are further used to evaluate the plume height of Environment and Climate Change Canada's operational forecasting system FireWork with fire plume injection height estimates from the Canadian Forest Fire Emissions Prediction System (CFFEPS). The modelled plume heights are similar compared to the satellite observations but tend to be slightly higher with average differences of 270–580 and 60–320 m compared to TROPOMI and MISR, respectively.

Six global biomass burning emission datasets: intercomparison and application in one global aerosol model

Abstract. Aerosols from biomass burning (BB) emissions are poorly constrained in global and regional models, resulting in a high level of uncertainty in understanding their impacts. In this study, we compared six BB aerosol emission datasets for 2008 globally as well as in 14 regions. The six BB emission datasets are (1) GFED3.1 (Global Fire Emissions Database version 3.1), (2) GFED4s (GFED version 4 with small fires), (3) FINN1.5 (FIre INventory from NCAR version 1.5), (4) GFAS1.2 (Global Fire Assimilation System version 1.2), (5) FEER1.0 (Fire Energetics and Emissions Research version 1.0), and (6) QFED2.4 (Quick Fire Emissions Dataset version 2.4). The global total emission amounts from these six BB emission datasets differed by a factor of 3.8, ranging from 13.76 to 51.93 Tg for organic carbon and from 1.65 to 5.54 Tg for black carbon. In most of the regions, QFED2.4 and FEER1.0, which are based on satellite observations of fire radiative power (FRP) and constrained by aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), yielded higher BB aerosol emissions than the rest by a factor of 2–4. By comparison, the BB aerosol emissions estimated from GFED4s and GFED3.1, which are based on satellite burned-area data, without AOD constraints, were at the low end of the range. In order to examine the sensitivity of model-simulated AOD to the different BB emission datasets, we ingested these six BB emission datasets separately into the same global model, the NASA Goddard Earth Observing System (GEOS) model, and compared the simulated AOD with observed AOD from the AErosol RObotic NETwork (AERONET) and the Multiangle Imaging SpectroRadiometer (MISR) in the 14 regions during 2008. In Southern Hemisphere Africa (SHAF) and South America (SHSA), where aerosols tend to be clearly dominated by smoke in September, the simulated AOD values were underestimated in almost all experiments compared to MISR, except for the QFED2.4 run in SHSA. The model-simulated AOD values based on FEER1.0 and QFED2.4 were the closest to the corresponding AERONET data, being, respectively, about 73 % and 100 % of the AERONET observed AOD at Alta Floresta in SHSA and about 49 % and 46 % at Mongu in SHAF. The simulated AOD based on the other four BB emission datasets accounted for only ∼50 % of the AERONET AOD at Alta Floresta and ∼20 % at Mongu. Overall, during the biomass burning peak seasons, at most of the selected AERONET sites in each region, the AOD values simulated with QFED2.4 were the highest and closest to AERONET and MISR observations, followed closely by FEER1.0. However, the QFED2.4 run tends to overestimate AOD in the region of SHSA, and the QFED2.4 BB emission dataset is tuned with the GEOS model. In contrast, the FEER1.0 BB emission dataset is derived in a more model-independent fashion and is more physically based since its emission coefficients are independently derived at each grid box. Therefore, we recommend the FEER1.0 BB emission dataset for aerosol-focused hindcast experiments in the two biomass-burning-dominated regions in the Southern Hemisphere, SHAF, and SHSA (as well as in other regions but with lower confidence). The differences between these six BB emission datasets are attributable to the approaches and input data used to derive BB emissions, such as whether AOD from satellite observations is used as a constraint, whether the approaches to parameterize the fire activities are based on burned area, FRP, or active fire count, and which set of emission factors is chosen.

Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China

Recently, NASA's Multiangle Imaging SpectroRadiometer (MISR) team released the Version 23 (V23) 4.4-km Aerosol Optical Depth (AOD) product, which has better spatial resolution than V22 at 17.6 km. However, its quality has not been validated in China. Here, V23 products for different spatiotemporal domains are obtained for validation against Aerosol Robotic NETwork (AERONET) AOD measurements for 29 sites from 2008 to 2017. Based on the national daily mean, V23 AOD yields a correlation coefficient (R) of 0.902 with AERONET; 59.45% of retrievals fall within the expected error (=EE; ±0.05 or ±0.2×AOD). A mean error (ME) of −0.0605 with 24.11% of retrievals falling below the EE indicates that MISR data are still underestimated at high AODs. The sample numbers and accuracies of spatially averaged 17.6-km and 50-km data are greatly improved relative to V22. The seasonal mean of V23 retrievals = EE in fall and winter are highest, followed by those in summer and spring; the validation results at 17.6 km are generally better than those at 50 km. By region, V23 retrievals = EE in the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions are 68.87%, 61.43%, and 73.13% respectively, while that of Taiwan is below 60% (55.75%). No V22 records exist in PRD, and V23 products over other regions in all seasons perform well; V22 retrievals in summer are also recommended. Compared with Terra/MODIS 3-km AOD in 2016, the V23 product has a slightly higher R value (0.925) with AERONET than MODIS (0.909). The MISR AOD bias is lower, and MODIS AOD is overestimated relative to the ground truth; both present consistent seasonality characteristics (spring > winter > summer > autumn), with the maximum in March and minimum in August. To investigate the spatiotemporal characteristics over long-term AOD, MISR V23 4.4-km AOD can be used in combination with other observation data.

Cloud Heterogeneity in the Marine Midlatitudes: Dependence on Large‐Scale Meteorology and Implications for General Circulation Models

AbstractWe examine the sensitivity of cloud heterogeneity to large‐scale meteorology in the marine midlatitudes using satellite observations from the Multiangle Imaging Spectroradiometer and Moderate Resolution Imaging Spectroradiometer instruments aboard the Terra satellite and nudged simulations from the UK Met Office's Global Atmosphere 7.0 (GA7) for the year 2007. Using Multiangle Imaging Spectroradiometer observations, we quantify several sources of observational uncertainty due to cloud heterogeneity such as finite‐resolution cloud fraction biases and subpixel heterogeneity. With a simple measure of cloud geometry, we show that these sources of observational uncertainty are maximized in post–cold‐frontal conditions, where scattered subpixel clouds are frequent, and are minimized in the warm sector, where subpixel clouds are infrequent. These results demonstrate the greater difficulty in interpreting remote sensing measurements in post–cold‐frontal conditions compared to quiescent or warm‐sector conditions. We show that the neglect of this observational uncertainty can qualitatively alter the interpretation of how cloud properties respond to large‐scale meteorology in GA7 and general circulation models in general. However, conservative application of the satellite data still allows robust evaluation of the simulated clouds. For overcast domains, Moderate Resolution Imaging Spectroradiometer observations show that overcast cloud heterogeneity is independent of large‐scale meteorology. However, heterogeneity increases when moving from warm sector to post‐cold‐frontal conditions in GA7. GA7 overestimates the heterogeneity of the low‐topped clouds that populate these regimes. For overcast domains, this bias is equivalent to a 15% underestimation in the mean cloud optical depth. These results suggest a systematic error in the subgrid‐scale variability parameterization, which, when corrected, will improve the simulation of the midlatitudes in GA7.

New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water

Abstract. A new method for retrieving aerosol optical depth (AOD) and its uncertainty from Multi-angle Imaging SpectroRadiometer (MISR) observations over dark water is outlined. MISR's aerosol retrieval algorithm calculates cost functions between observed and pre-simulated radiances for a range of AODs (from 0.0 to 3.0) and a prescribed set of aerosol mixtures. The previous version 22 (V22) operational algorithm considered only the AOD that minimized the cost function for each aerosol mixture and then used a combination of these values to compute the final, “best estimate” AOD and associated uncertainty. The new approach considers the entire range of cost functions associated with each aerosol mixture. The uncertainty of the reported AOD depends on a combination of (a) the absolute values of the cost functions for each aerosol mixture, (b) the widths of the cost function distributions as a function of AOD, and (c) the spread of the cost function distributions among the ensemble of mixtures. A key benefit of the new approach is that, unlike the V22 algorithm, it does not rely on empirical thresholds imposed on the cost function to determine the success or failure of a particular mixture. Furthermore, a new aerosol retrieval confidence index (ARCI) is established that can be used to screen high-AOD retrieval blunders caused by cloud contamination or other factors. Requiring ARCI ≥0.15 as a condition for retrieval success is supported through statistical analysis and outperforms the thresholds used in the V22 algorithm. The described changes to the MISR dark water algorithm will become operational in the new MISR aerosol product (V23), planned for release in 2017.

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements ( r 2 > 0.8), as well as the lowest average R M S E (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases ( r 2 > 0.85); the R M S E is lower by 4–5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average R M S E = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.

Microphysical Characteristics of Atmospheric Particulate Matter from NASA’s MODIS, MISR, and AERONET Observations

We present a comparative study of atmospheric particulate matter (also known as aerosols) observed by satellite remote sensing and ground-based observations. We compare satellite measurements obtained by NASA’s Moderate Resolution Imaging Spectro-Radiometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) instruments against the ground-based aerosol sun-photometer data from the Aerosol Robotic Network (AERONET) station in Cairo, Egypt from 2003 to 2014 to build a long-term database for climatological studies and to improve upon the accuracy and coverage achievable from the satellite data. We deduce microphysical and geometrical properties about the dominant aerosols based on key optical properties including aerosol optical depth (AOD), single scattering albedo (SSA), and Ångström exponent (AE). This has allowed us to place important constraints on the type of aerosols (natural, anthropogenic, and biogenic).

AOD trends during 2001–2010 from observations and model simulations

Abstract. The aerosol optical depth (AOD) trend between 2001 and 2010 is estimated globally and regionally from observations and results from simulations with the EMAC (ECHAM5/MESSy Atmospheric Chemistry) model. Although interannual variability is applied only to anthropogenic and biomass-burning emissions, the model is able to quantitatively reproduce the AOD trends as observed by the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor, while some discrepancies are found when compared to MISR (Multi-angle Imaging SpectroRadiometer) and SeaWIFS (Sea-viewing Wide Field-of-view Sensor) observations. Thanks to an additional simulation without any change in emissions, it is shown that decreasing AOD trends over the US and Europe are due to the decrease in the emissions, while over the Sahara Desert and the Middle East region, the meteorological changes play a major role. Over Southeast Asia, both meteorology and emissions changes are equally important in defining AOD trends. Additionally, decomposing the regional AOD trends into individual aerosol components reveals that the soluble components are the most dominant contributors to the total AOD, as their influence on the total AOD is enhanced by the aerosol water content.

Characterisation of dust aerosols in the infrared from IASI and comparison with PARASOL, MODIS, MISR, CALIOP, and AERONET observations

Abstract. Infrared Atmospheric Sounder Interferometer (IASI) observations covering the period from July 2007 to December 2011 are interpreted in terms of monthly mean, 1°×1°, 10 μm dust Aerosol Optical Depth (AOD), mean altitude and coarse mode effective radius. The geographical study area includes the northern tropical Atlantic and the northwest Arabian Sea, both characterised by strong, regular dust events. The method developed relies on the construction of Look-Up-Tables computed for a large selection of atmospheric situations and observing conditions. At a regional scale, a good agreement is found between IASI-retrieved 10 μm AOD and total visible optical depth at 550 nm from either the Moderate resolution Imaging Spectroradiometer (MODIS/Aqua or Terra), or the Multi-angle Imaging SpectroRadiometer (MISR), or the Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL). Taking into account the ratio existing between infrared and visible AODs, the diversity between the different 550 nm AODs is similar to the difference between these and the IASI AODs. The infrared AOD to visible AOD ratio, partly reflecting the varying distribution of the dust layer between the dust coarse mode particles seen by IASI, and the fine mode seen by the other instruments, is found to vary with the region observed with values close to already published values. Comparisons between the climatologies of the 10 μm IASI AOD and of the PARASOL non-spherical coarse mode AOD at 865 nm, both expected to be representative of the dust coarse mode, lead to conclusions differing according to the region considered. These differences are discussed in the light of the MODIS Angström exponent (865–550 nm). At local scale, around six Aerosol Robotic Network (AERONET) sites, close or far from the dust sources, a similar satisfactory agreement is found between IASI and the visible AODs and the differences between these products are shown and analysed. IASI-retrieved dust layer mean altitudes also compare well with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP/CALIPSO) aerosol mean layer altitude, both in terms of climatology and of zonal evolution throughout the Atlantic. Comparisons between the IASI-retrieved dust coarse mode effective radius and retrievals from AERONET at the six sites brings into evidence an almost systematic bias of about +0.35 μm (IASI-AERONET). Removing this bias leads to a satisfactory agreement between the climatologies of these two products. Overall, these results illustrate the dust westward transport characterised by a fast decrease of the dust optical depth, a somewhat slower decrease of the altitude, and an effective radius remaining almost constant during summer throughout the northern tropical Atlantic. They also demonstrate the capability of high resolution infrared sounders to contribute improving our understanding of processes related to the aerosols (transport, sources, cycles, effect of aerosols on the terrestrial radiation, etc.).

Tropical Atlantic dust and smoke aerosol variations related to the Madden‐Julian Oscillation in MODIS and MISR observations

AbstractIn this study, Moderate Resolution Imaging Spectroradiometer (MODIS) fine mode fraction and Multi‐angle Imaging SpectroRadiometer (MISR) nonspherical fraction data are used to derive dust and smoke aerosol optical thickness (τdust and τsmoke) over the tropical Atlantic in a complementary way: due to its wider swath, MODIS has 3–4 times greater sampling than MISR, but MISR dust discrimination is based on particle shape retrievals, whereas an empirical scheme is used for MODIS. MODIS and MISR show very similar dust and smoke winter climatologies. τdust is the dominant aerosol component over the tropical Atlantic, accounting for 40–70% of the total aerosol optical thickness (AOT), whereas τsmoke is significantly smaller than τdust. The consistency and high correlation between these climatologies and their daily variations lends confidence to their use for investigating the relative dust and smoke contributions to the total AOT variation associated with the Madden‐Julian Oscillation (MJO). The temporal evolution and spatial patterns of the τdus anomalies associated with the MJO are consistent between MODIS and MISR: the magnitude of MJO‐realted τdust anomalies is comparable to or even larger than that of the total τ, while the τsmoke anomaly represents about 15% compared to the total, which is quite different from their relative magnitudes to the total τ on the climatological time scale. This suggests that dust and smoke are not influenced by the MJO in the same way. Based on correlation analysis, dust is strongly influenced by the MJO‐modulated trade wind and precipitation anomalies, and can last as long as one MJO phase, whereas smoke is less affected.

Expanding MISR LAI Products to High Temporal Resolution With MODIS Observations

The Multi-Angle Imaging Spectroradiometer (MISR) is a powerful sensor for leaf area index (LAI) mapping with its simultaneous multi-angle observations. However, the LAI product derived from MISR observations has low temporal resolution, which is unsatisfactory for many applications. This paper presents an algorithm that expands the MISR LAI product to high temporal resolution with the aid of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The algorithm establishes relationships between the MISR LAI and the MODIS red/near-infrared band ratio (simple ratio (SR)) pixel by pixel using coincident data of these two sensors for the past nine years. Using these pixel-based SR-LAI relationships, a new LAI product with the merits of the original MISR product and high temporal resolution is obtained from MODIS surface reflectance. The expanded LAI series was compared with the original MISR and MODIS LAI products, as well as field LAI measurements made at the Baohe and Maoershan forest sites and the Hulunbeier grassland site, to assess the algorithm's performance. The results show that the temporal coverage of the MISR LAI improved from 15.5% to 65.2% in an 8-day composite, and the mean root-mean-square error is 0.74 for the vegetated pixels. This LAI product has similar temporal consistency and seasonal dynamics to the existing MODIS LAI product generated from the main algorithm, but is more robust against the low quality of reflectance inputs. The expanded LAI product differs with field measurements by about 11.5%, with agreement to field observations at all three sites within an accuracy of 0.8 LAI.

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.

Evaluation of detector-to-detector and mirror side differences for Terra MODIS reflective solar bands using simultaneous MISR observations

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the five Earth-observing instruments on-board the National Aeronautics and Space Administration (NASA) Earth-Observing System (EOS) Terra spacecraft, launched in December 1999. It has 36 spectral bands with wavelengths ranging from 0.41 to 14.4 μm and collects data at three nadir spatial resolutions: 0.25 km for 2 bands with 40 detectors each, 0.5 km for 5 bands with 20 detectors each and 1 km for the remaining 29 bands with 10 detectors each. MODIS bands are located on four separate focal plane assemblies (FPAs) according to their spectral wavelengths and aligned in the cross-track direction. Detectors of each spectral band are aligned in the along-track direction. MODIS makes observations using a two-sided paddle-wheel scan mirror. Its on-board calibrators (OBCs) for the reflective solar bands (RSBs) include a solar diffuser (SD), a solar diffuser stability monitor (SDSM) and a spectral-radiometric calibration assembly (SRCA). Calibration is performed for each band, detector, sub-sample (for sub-kilometre resolution bands) and mirror side. In this study, a ratio approach is applied to MODIS observed Earth scene reflectances to track the detector-to-detector and mirror side differences. Simultaneous observed reflectances from the Multi-angle Imaging Spectroradiometer (MISR), also onboard the Terra spacecraft, are used with MODIS observed reflectances in this ratio approach for four closely matched spectral bands. Results show that the detector-to-detector difference between two adjacent detectors within each spectral band is typically less than 0.2% and, depending on the wavelengths, the maximum difference among all detectors varies from 0.5% to 0.8%. The mirror side differences are found to be very small for all bands except for band 3 at 0.44 μm. This is the band with the shortest wavelength among the selected matching bands, showing a time-dependent increase for the mirror side difference. This study is part of the effort by the MODIS Characterization Support Team (MCST) in order to track the RSB on-orbit performance for MODIS collection 5 data products. To support MCST efforts for future data re-processing, this analysis will be extended to include more spectral bands and temporal coverage.

Online simulations of global aerosol distributions in the NASA GEOS‐4 model and comparisons to satellite and ground‐based aerosol optical depth

We have implemented a module for tropospheric aerosols (GOCART) online in the NASA Goddard Earth Observing System version 4 model and simulated global aerosol distributions for the period 2000–2006. The new online system offers several advantages over the previous offline version, providing a platform for aerosol data assimilation, aerosol‐chemistry‐climate interaction studies, and short‐range chemical weather forecasting and climate prediction. We introduce as well a methodology for sampling model output consistently with satellite aerosol optical thickness (AOT) retrievals to facilitate model‐satellite comparison. Our results are similar to the offline GOCART model and to the models participating in the AeroCom intercomparison. The simulated AOT has similar seasonal and regional variability and magnitude to Aerosol Robotic Network (AERONET), Moderate Resolution Imaging Spectroradiometer, and Multiangle Imaging Spectroradiometer observations. The model AOT and Angstrom parameter are consistently low relative to AERONET in biomass‐burning‐dominated regions, where emissions appear to be underestimated, consistent with the results of the offline GOCART model. In contrast, the model AOT is biased high in sulfate‐dominated regions of North America and Europe. Our model‐satellite comparison methodology shows that diurnal variability in aerosol loading is unimportant compared to sampling the model where the satellite has cloud‐free observations, particularly in sulfate‐dominated regions. Simulated sea salt burden and optical thickness are high by a factor of 2–3 relative to other models, and agreement between model and satellite over‐ocean AOT is improved by reducing the model sea salt burden by a factor of 2. The best agreement in both AOT magnitude and variability occurs immediately downwind of the Saharan dust plume.

Quantifying aerosol direct radiative effect with Multiangle Imaging Spectroradiometer observations: Top‐of‐atmosphere albedo change by aerosols based on land surface types

Using internally consistent albedo, aerosol, cloud, and surface data from the Multiangle Imaging Spectroradiometer (MISR) instrument onboard the Terra satellite, top‐of‐atmosphere (TOA) spectral albedo change (dα) in the presence of aerosols over land is estimated and its dependence on aerosol and surface properties is analyzed. Linear regressions between spectral TOA albedo and aerosol optical depth (AOD) for different surface types are examined to derive the aerosol‐free TOA albedo. MISR surface BiHemispherical Reflectance (BHR) values are used to differentiate surface types. We find relatively high correlations between spectral TOA albedo and AOD for BHR‐stratified data in 2° × 2° grid cells. The global mean values of cloud‐free dα over land for June–September 2007 are estimated to be 0.018 ± 0.003 (blue), 0.010 ± 0.003 (green), 0.007 ± 0.003 (red), and 0.008 ± 0.006 (near‐infrared). Individual regions show large variations from these values. Global patterns of dα are determined mainly by AOD and aerosol radiative efficiency. Large positive values of dα are observed over regions with high aerosol loading and large single‐scattering albedo, where the aerosol scattering effect is dominant. The presence of light‐absorbing aerosols reduces aerosol radiative efficiency and dα. Surface reflectance influences both aerosol scattering and absorbing effects. Generally, the aerosol radiative efficiency decreases with increasing BHR. We also examined dα‐AOD correlations over different vegetation types. We find the smallest dα values are over needleleaf forests and shrublands, whereas the largest values are over cropland and barren regions. The aerosol radiative efficiencies are lowest over needleleaf forests and barren regions and highest over grasslands and croplands.

Mineral dust plume evolution over the Atlantic from MISR and MODIS aerosol retrievals

We demonstrate how Multiangle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) space‐based aerosol products provide complementary information, characterizing (1) transported desert dust plume extent over water, (2) aerosol optical thickness (AOT) evolution, and (3) particle size sensitivity and fraction spherical evolution for the thicker parts of these plumes. MODIS provides more extensive coverage, whereas MISR's multiangle retrievals include dust properties and fill in areas where glint precludes MODIS optical depth retrievals, increasing by up to 50% dust plume surface area coverage compared to MODIS‐only observations. These results can be used to improve dust aerosol representations in climate, forecast, and transport models. Extensive comparison of MISR and MODIS AOT retrievals with Aerosol Robotic Network (AERONET) observations in dusty regions show accuracies of about 20%; MISR Angstrom exponent is predominantly less than 1, in reasonably good agreement with AERONET. For four selected North African dust transport events, combined MISR and MODIS observations map systematic changes in retrieved plume surface area, on the basis of AOT contours; these reflect differences in aerosol dispersion and removal rates that must be reproduced by models. Within MISR retrieval uncertainties, Angstrom exponent and AOT fraction spherical are lower in the optically thicker parts of the plume by up to 30% and increase as the plume is transported across the ocean, as would be expected; retrieved single‐scattering albedo values are ∼0.98 for all stages of plume evolution. For the cases studied, AERONET shows similar property patterns at points on either side of the Atlantic and indicates ∼15% decrease in aerosol coarse mode effective radius during transoceanic transport.

Retrieving photometric properties of desert surfaces in China using the Hapke model and MISR data

The retrieval of photometric properties of desert surfaces is an important first step in the parameterization of land surface components of regional dust emission and global radiation models and in Earth system modeling. In this study, the values of Hapke's photometric parameters ( ω, h, b, c, B0, and θ̄) were retrieved from the Multi-angle Imaging SpectroRadiometer (MISR) instrument at locations in China's deserts. Four pixels represented the typical surface characteristics of the Taklimakan Desert, sand dunes of Kumtag Desert, relatively smooth areas of the Kumtag Desert and the aeolian sandy soil of Loulan. In contrast to earlier studies, we found that the retrieved parameter values were largely affected by the initial value. To combat this problem we used a Monte Carlo method with physical constraints and a conformity indicator to ensure physically meaningful inversion. The results showed that the angular domain of MISR observations was sufficiently large to determine confidently the values of Hapke's photometric parameters with the exception of the opposition effect width ( h). Retrieved values for the single scattering albedo ( ω) and macroscopic roughness ( θ̄) were consistent with qualitative observations about the structure and composition of the surface material and the nature of the dune forms, respectively. At Loulan, where the surface was smoother than other sites, retrieved values exhibited the strongest backward scattering. These results indicated that at the sensor scale, a rough surface (e.g., dunes) does not necessarily mean more backward scattering than a smooth surface. This finding has significant implications for empirical methods (e.g., using the normalized index of backward-scattered radiance minus forward-scattered radiance as an indicator to indicate surface roughness) which should be used carefully for analyzing surface roughness from remote sensing data. Future research is needed to 1) understand how surface roughness at the sub-pixel scale modifies the angular characteristics of reflectance and to 2) find practical methods for rapid whole image processing for mapping the photometric parameters.