Multi-angle Imaging SpectroRadiometer Research Articles

When Will MISR Detect Rising High Clouds?

AbstractIt is predicted by both theory and models that high‐altitude clouds will occur higher in the atmosphere as a result of climate warming. This produces a positive longwave feedback and has a substantial impact on the Earth's response to warming. This effect is well established by theory, but is poorly constrained by observations, and there is large spread in the feedback strength between climate models. We use the NASA Multi‐angle Imaging SpectroRadiometer (MISR) to examine changes in Cloud‐Top‐Height (CTH). MISR uses a stereo‐imaging technique to determine CTH. This approach is geometric in nature and insensitive to instrument calibration and therefore is well suited for trend analysis and studies of variability on long time scales. In this article we show that the current MISR record does have an increase in CTH for high‐altitude cloud over Southern Hemisphere (SH) oceans but not over Tropical or the Northern Hemisphere (NH) oceans. We use climate model simulations to estimate when MISR might be expected to detect trends in CTH, that include the NH. The analysis suggests that according to the models used in this study MISR should detect changes over the SH ocean earlier than the NH, and if the model predictions are correct should be capable of detecting a trend over the Tropics and NH very soon (3–10 years). This result highlights the potential value of a follow‐on mission to MISR, which no longer maintains a fixed equator crossing time and is unlikely to be making observations for another 10 years.

Long-term spatiotemporal trends in aerosol optical depth and its relationship with enhanced vegetation index and meteorological parameters over South Asia.

Satellite-based aerosol optical depth (AOD) is columnar light extinction by aerosol absorption and scattering and has become the most important variable for the assessment of the spatiotemporal distribution of aerosols at a regional and global level. In this paper, we have used AOD observations of multiangle imaging spectroradiometer (MISR) from September 2002 to May 2017, moderate resolution imaging spectroradiometer (MODIS) from September 2002 to December 2020, and sea-viewing wide field-of-view sensor (SeaWiFS) from September 2002 to December 2010 over South Asia. We have observed the association of AOD with enhanced vegetation index (EVI) and meteorological variables (temperature (TEMP), wind speed (WS), and relative humidity (RH)) acquired from Giovanni during the period September 2002-December 2020. The satellite observations of Terra-, MISR-, and SeaWiFS-AOD were also compared with Aqua-AOD. The findings show that AOD in eastern Pakistan is higher than in the western Pakistan due to increase in population density and biomass burning. Mean annual peak AOD (˃ 0.7) has been observed over the IGB region because of the significant increase in economical, industrial, and agricultural activities while AOD of ˃ 0.6 is observed over Bangladesh. The lowest mean annual AOD of ˂ 0.3 is observed over northeastern Afghanistan, western Nepal, and Bhutan whereas the AOD of 0.3 is seen over Sri Lanka. The highest seasonal mean AOD of 0.8 has been seen over Bihar, India, and AOD of ~ 0.7 is observed over Bangladesh while the lowest AOD is observed over Afghanistan, Sri Lanka, Nepal, and Bhutan during the winter season. However, the mean AOD over eastern Pakistan is maximum in both monsoon and post-monsoon season but relatively low in pre-monsoon and winter. The highest positive seasonal AOD anomalies were observed over South Asia in winter season followed by post-monsoon, pre-monsoon, and least being monsoon. The higher mean AOD anomaly value is found to be 0.2 over eastern Pakistan and western India. In northeastern Pakistan and central India, AOD and RH are positively correlated (r ˃ 0.54) while negatively correlated over Afghanistan, southwestern region of Pakistan, eastern India, Nepal, Bhutan, and Bangladesh. AOD is negatively correlated (r = ~ - 0.3) with EVI over eastern Pakistan and western India. The highest correlation coefficient (r) obtained among Terra and Aqua is 0.97, MISR and Aqua is 0.93, and SeaWiFS and Aqua is 0.58 over South Asia.

A global-scale analysis of the MISR Level-3 aerosol optical depth (AOD) product: Comparison with multi-platform AOD data sources

This study analyses the applicability of the recently released Level-3 (L3) daily and monthly aerosol optical depth (AOD) products (version F15_0032) over land retrieved from the Multiangle Imaging Spectroradiometer (MISR) instrument. For this purpose, daily AOD data from 427 Aerosol Robotic Network (AERONET) sites worldwide during 2001–2018 and 39 China Aerosol Remote Sensing Network (CARSNET) sites across China during 2002–2014 were selected for comparison. Also, MISR-based size-segregated AODs were collected to compare with the coarse- and fine-mode AODs retrieved from AERONET to reveal the particles size modes that mainly contribute to the offset of MISR total AOD relative to observations. By comparing with five other monthly AOD datasets, including two MODIS (Moderate resolution Imaging Spectroradiometer) retrieval products, a multi-satellite merged product, and two aerosol reanalysis, MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications) and CAMS (Copernicus Atmosphere Monitoring Service), we then explored the applicability of MISR for characterizing the climatology, interannual variations, and long-term (2003–2017) trends in regional aerosol loadings over the 12 regions of interest. Overall, about 80% (60%) of the daily AOD values retrieved by MISR fall within expected error bounds of ± [0.05 + 0.2 × AOD] (±[0.03 + 0.1 × AOD]), with a high correlation (R = 0.87). Our comparison results show that although the V23 algorithm addresses several issues in L2 AOD retrieval relative to the previous version, the L3 data aggregated from L2 data tends to on average overestimate low AOD values and underestimate high AOD values. These offset relative to observations are mainly attributed to the overestimation of coarse-mode AOD and the underestimation of fine-mode AOD by MISR. Intercomparison of 2003–2017 AOD trends from multiple data sources indicate that MISR can capture well the increases occurring in South Asia, as well as the decreases occurring in Eastern China, Eastern United States, and Western Europe. Our study confirmed that MISR L3 product performs reliably at regional scales, particularly in typically polluted areas, but caution is still needed when applied to areas where sparse sampling is encountered.

Vegetation Angular Signatures of Equatorial Forests From DSCOVR EPIC and Terra MISR Observations

In vegetation canopies cross-shading between finite dimensional leaves leads to a peak in reflectance in the retro-illumination direction. This effect is called the hot spot in optical remote sensing. The hotspot region in reflectance of vegetated surfaces represents the most information-rich directions in the angular distribution of canopy reflected radiation. This paper presents a new approach for generating hot spot signatures of equatorial forests from synergistic analyses of multiangle observations from the Multiangle Imaging SpectroRadiometer (MISR) on Terra platform and near backscattering reflectance data from the Earth Polychromatic Imaging Camera (EPIC) onboard NOAA’s Deep Space Climate Observatory (DSCOVR). A canopy radiation model parameterized in terms of canopy spectral invariants underlies the theoretical basis for joining Terra MISR and DSCOVR EPIC data. The proposed model can accurately reproduce both MISR angular signatures acquired at 10:30 local solar time and diurnal courses of EPIC reflectance (NRMSE < 9%, R2 > 0.8). Analyses of time series of the hot spot signature suggest its ability to unambiguously detect seasonal changes of equatorial forests.

Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East

Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measurements are scarce and extreme meteorological events such as sandstorms are frequent. In order to supplement exposure modeling efforts under such conditions, satellite-retrieved aerosol optical depth (AOD) has proven to be useful due to its global coverage. By using AODs from the Multiangle Implementation of Atmospheric Correction (MAIAC) of the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) combined with meteorological and assimilated aerosol information from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we constructed machine learning models to predict PM2.5 in the area surrounding the Persian Gulf, including Kuwait, Bahrain, and the United Arab Emirates (U.A.E). Our models showed regional differences in predictive performance, with better results in the U.A.E. (median test R2 = 0.66) than Kuwait (median test R2 = 0.51). Variable importance also differed by region, where satellite-retrieved AOD variables were more important for predicting PM2.5 in Kuwait than in the U.A.E. Divergent trends in the temporal and spatial autocorrelations of PM2.5 and AOD in the two regions offered possible explanations for differences in predictive performance and variable importance. In a test of model transferability, we found that models trained in one region and applied to another did not predict PM2.5 well, even if the transferred model had better performance. Overall the results of our study suggest that models developed over large geographic areas could generate PM2.5 estimates with greater uncertainty than could be obtained by taking a regional modeling approach. Furthermore, development of methods to better incorporate spatial and temporal autocorrelations in machine learning models warrants further examination.

Situation of Urban Mobility in Pakistan: Before, during, and after the COVID-19 Lockdown with Climatic Risk Perceptions

The coronavirus pandemic (COVID-19) has impacted the usual global movement patterns, atmospheric pollutants, and climatic parameters. The current study sought to assess the impact of the COVID-19 lockdown on urban mobility, atmospheric pollutants, and Pakistan’s climate. For the air pollution assessment, total column ozone (O3), sulphur dioxide (SO2), and tropospheric column nitrogen dioxide (NO2) data from the Ozone Monitoring Instrument (OMI), aerosol optical depth (AOD) data from the Multi-angle Imaging Spectroradiometer (MISR), and dust column mass density (PM2.5) data from the MERRA-2 satellite were used. Furthermore, these datasets are linked to climatic parameters (temperature, precipitation, wind speed). The Kruskal–Wallis H test (KWt) is used to compare medians among k groups (k > 2), and the Wilcoxon signed-rank sum test (WRST) is for analyzing the differences between the medians of two datasets. To make the analysis more effective, and to justify that the variations in air quality parameters are due to the COVID-19 pandemic, a Generalized Linear Model (GLM) was used. The findings revealed that the limitations on human mobility have lowered emissions, which has improved the air quality in Pakistan. The results of the study showed that the climatic parameters (precipitation, Tmax, Tmin, and Tmean) have a positive correlation and wind speed has a negative correlation with NO2 and AOD. This study found a significant decrease in air pollutants (NO2, SO2, O3, AOD) of 30–40% in Pakistan during the strict lockdown period. In this duration, the highest drop of about 28% in NO2 concentrations has been found in Karachi. Total column O3 did not show any reduction during the strict lockdown, but a minor decline was depicted as 0.38% in Lahore and 0.55% in Islamabad during the loosening lockdown. During strict lockdown, AOD was reduced up to 23% in Islamabad and 14.46% in Lahore. The results of KWt and WRST evident that all the mobility indices are significant (p < 0.05) in nature. The GLM justified that restraining human activities during the lockdown has decreased anthropogenic emissions and, as a result, improved air quality, particularly in metropolitan areas.

Introducing the MISR level 2 near real-time aerosol product

Abstract. Atmospheric aerosols are an important element of Earth's climate system and have significant impacts on the environment and on human health. Global aerosol modeling has been increasingly used for operational forecasting and as support for decision making. For example, aerosol analyses and forecasts are routinely used to provide air quality information and alerts in both civilian and military applications. The growing demand for operational aerosol forecasting calls for additional observational data that can be assimilated into models to improve model accuracy and predictive skill. These factors have motivated the development, testing, and release of a new near real-time (NRT) level 2 (L2) aerosol product from the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra platform. The NRT product capitalizes on the unique attributes of the MISR aerosol retrieval approach and product contents, such as reliable aerosol optical depth as well as aerosol microphysical information. Several modifications are described that allow for rapid product generation within a 3 h window following acquisition of the satellite observations. Implications for the product quality and consistency are discussed and compared to the current operational L2 MISR aerosol product. Several ways of implementing additional use-specific retrieval screenings are also highlighted.

Cloud-Top Height Comparison from Multi-Satellite Sensors and Ground-Based Cloud Radar over SACOL Site

Cloud-top heights (CTH), as one of the representative variables reflecting cloud macro-physical properties, affect the Earth–atmosphere system through radiation budget, water cycle, and atmospheric circulation. This study compares the CTH from passive- and active-spaceborne sensors with ground-based Ka-band zenith radar (KAZR) observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site for the period 2013–2019. A series of fundamental statistics on cloud probability in different limited time and areas at the SACOL site reveals that there is an optimal agreement for both cloud frequency and fraction derived from space and surface observations in a 0.5° × 0.5° box area and a 40-min time window. Based on the result, several facets of cloud fraction (CF), cloud overlapping, seasonal variation, and cloud geometrical depth (CGD) are investigated to evaluate the CTH retrieval accuracy of different observing sensors. Analysis shows that the CTH differences between multi-satellite sensors and KAZR decrease with increasing CF and CGD, significantly for passive satellite sensors in non-overlapping clouds. Regarding passive satellite sensors, e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua, the Multi-angle Imaging SpectroRadiometer (MISR) on Terra, and the Advanced Himawari Imager on Himawari-8 (HW8), a greater CTH frequency difference exists between the upper and lower altitude range, and they retrieve lower CTH than KAZR on average. The CTH accuracy of HW8 and MISR are susceptible to inhomogeneous clouds, which can be reduced by controlling the increase of CF. Besides, the CTH from active satellite sensors, e.g., Cloud Profiling Radar (CPR) on CloudSat, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), agree well with KAZR and are less affected by seasonal variation and inhomogeneous clouds. Only CALIPSO CTH is higher than KAZR CTH, mainly caused by the low-thin clouds, typically in overlapping clouds.

The influence of fire aerosols on surface climate and gross primary production in the Energy Exascale Earth System Model (E3SM)

AbstractFire-emitted aerosols play an important role in influencing Earth’s climate, directly by scattering and absorbing radiation and indirectly by influencing cloud microphysics. The quantification of fire-aerosol interactions, however, remains challenging and subject to uncertainties in emissions, plume parameterizations, and aerosol properties. Here we optimized fire-associated aerosol emissions in the Energy Exascale Earth System Model (E3SM) using the Global Fire Emissions Database (GFED) and AERONET aerosol optical depth (AOD) observations during 1997-2016. We distributed fire emissions vertically using smoke plume heights from Multi-angle Imaging SpectroRadiometer (MISR) satellite observations. From the optimization, we estimate that global fires emit 45.5 Tg y-1 of primary particulate organic matter and 3.9 Tg y-1 of black carbon. We then performed two climate simulations with and without the optimized fire emissions. We find that fire aerosols significantly increase global AOD by 14 ± 7% and contribute to a reduction in net shortwave radiation at the surface (-2.3 ± 0.5 W m-2). Together, fire-induced direct and indirect aerosol effects cause annual mean global land surface air temperature to decrease by 0.17 ± 0.15°C, relative humidity to increase by 0.4 ± 0.3%, and diffuse light fraction to increase by 0.5 ± 0.3%. In response, GPP declines by 2.8 Pg C y-1, as a result of large positive drivers (decreases in temperature and increases in humidity and diffuse light) nearly cancelling out large negative drivers (decreases shortwave radiation and soil moisture). Our analysis highlights the importance of fire aerosols in modifying surface climate and photosynthesis across the tropics.

Assessing the contribution of the ENSO and MJO to Australian dust activity based on satellite- and ground-based observations

Abstract. Despite Australian dust's critical role in the regional climate and surrounding marine ecosystems, the controlling factors of the spatiotemporal variations of Australian dust are not fully understood. Here we assess the connections between observed spatiotemporal variations of Australian dust with key modes of large-scale climate variability, namely the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO). Multiple dust observations from the Aerosol Robotic Network (AERONET), weather stations, and satellite instruments, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR), are examined. The assessed multiple dust observations consistently identify the natural and agricultural dust hotspots in Australia, including the Lake Eyre basin, Lake Torrens basin, Lake Frome basin, Simpson Desert, Barwon–Darling basin, Riverina, Barkly Tableland, and the lee side of the Great Dividing Range, as well as a country-wide, austral spring-to-summer peak in dust activity. Our regression analysis of observed dust optical depth (DOD) upon an ocean Niño index confirms previous model-based findings on the enhanced dust activity in southern and eastern Australia during the subsequent austral spring and summer dust season following the strengthening of austral wintertime El Niño. Our analysis further indicates the modulation of the ENSO–dust relationship with the MJO phases. During sequential MJO phases, the dust-active center moves from west to east, associated with the eastward propagation of MJO, with the maximum enhancement in dust activity at about 120, 130, and 140∘ E, corresponding to MJO phases 1–2, 3–4, and 5–6, respectively. MJO phases 3–6 are favorable for enhanced ENSO modulation of dust activity, especially the occurrence of extreme dust events, in southeastern Australia, currently hypothesized to be attributed to the interaction between MJO-induced anomalies in convection and wind and ENSO-induced anomalies in soil moisture and vegetation.

Calibration of the DSCOVR EPIC Visible and NIR Channels using Multiple LEO Radiometers

The first five years of operation of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange one point have produced results that uniquely complement the data from currently operating low orbit Earth-observing instruments. In this paper we describe an updated unified approach to EPIC calibration. In this approach, calibration coefficients and their trends were obtained by comparing EPIC observations to the measurements from polar orbiting radiometers. In this study L1B reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites, Multi-angle Imaging Spectroradiometer (MISR) onboard Terra and Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were used to infer calibration coefficients for four EPIC visible and near-infrared channels: 443 nm, 551 nm, 680 nm, and 780 nm. EPIC Version three measurements made between June 2015 and August 2020 were used for comparison. The calibration procedure identifies the most homogeneous low Earth orbit radiometer scenes matching scattering angles that are temporarily and spatially collocated with EPIC observations. These scenes are used to determine reflectance to count (R/C) ratios in spectrally analogous channels. Seasonal average R/C ratios were analyzed to obtain EPIC calibration gains and trends. The trends for the full dataset period are not statistically significant except in the 443 nm channel. No significant changes in calibration were found after the instrument’s exit from safe hold in March 2020. The R/C ratios were also used to determine the differences in EPIC gains resulting from separate calibrations: against MODIS Aqua or Terra, as well as against forward or aftward MISR cameras. Statistical tests indicate that the differences between the two datasets are not significant except in the 780 nm channels where Aqua-derived coefficients may be around 2% lower compared to Terra. The dependence of EPIC calibration gains on the instrument scattering angle and on DSCOVR-Earth distance were investigated. Lastly, model Low Earth Orbit (LEO) reflectances calculated to match the EPIC viewing geometry were employed to study how EPIC calibration coefficients depend on EPIC-LEO viewing geometry differences. The effect of LEO and EPIC angular mismatch on calibration was shown to be small.

Seasonal variability and trends in global type-segregated aerosol optical depth as revealed by MISR satellite observations

This study utilized a long-term (2001–2018) aerosol optical component dataset retrieved from the Multiangle Imaging Spectroradiometer (MISR), Version 23, to perform comprehensive analyses of the global climatology of seasonal AODs, partitioned by aerosol types (including small-size, medium-size, large-size, spherical, and non-spherical). By dividing eight different AOD bins and performing trend analysis, the seasonal variability and trends in these type-segregated AODs, as well as in the frequency occurrences (FOs) for different AOD bins, globally and over 12 regions of interest, were also investigated. In terms of particle size, small-size aerosol particles (diameter < 0.7 μm) contribute the largest to global extinction in all three seasons except winter. A similar globally dominant role is exhibited by spherical aerosols, which contribute 68.5%, 73.3%, 71.6% and 70.2% to the global total AOD (TAOD) in spring, summer, autumn and winter, respectively, on a global average scale. FOs with different aerosol loading levels suggested that the seasonal FOs tend to decrease progressively with increasing aerosol loading, except for Level 1 (TAOD< 0.05). Examination of the seasonal distribution of FOs revealed that the FO at Level 1 (Level 2, 0.05 < TAOD< 0.15) is much larger in summer/winter (winter/autumn) than in spring/autumn (spring/summer) over most areas of the world. However, the FOs for Level 3 (0.15 < TAOD< 0.25) to Level 8 (TAOD> 1.0) generally exhibit greater intensity in spring/summer than in autumn/winter. Temporal trend analyses showed that the seasonal TAOD experiences a significant decline during 2001–2018 in most regions globally, except in South Asia, the Middle East, and North Africa. Opposite seasonal trends in the above regions are closely related to the increase in FOs in the range of 0.4 < TAOD< 1.0. The global average TAOD shows the most pronounced decline in spring, falling by −10.4% (P < 0.05). Examination of the trends in type-segregated AODs further revealed that the decreases in size-segregated (shape-segregated) AODs all contribute to the decrease in seasonal TAOD, with small-size AOD (spherical AOD) contributing most significantly.

Aerosols optical depth and Ångström exponent over different regions in Garhwal Himalaya, India.

Aerosol optical depth (AOD) and Ångström exponent (AE) are observed to be important parameters in understanding the status of ambient aerosol concentration over a particular location and depend not only upon the local but also on the large-scale dynamics of the atmosphere. The present article analyses the AOD and AE parameters retrieved with Moderate Resolution Imaging Spectrometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) instruments onboard satellites, for the upper (Chamoli) and foothill (Dehradun) regions of Garhwal Himalaya in Uttarakhand, India, from 2006 to 2015. Aerosol properties are investigated at monthly, seasonal, and annual scales. The monthly mean values of MODIS-derived AOD and AE were observed to be 0.18 (± 0.14) and 1.05 (± 0.43) respectively over the Dehradun region. The seasonal maximums in AOD with MODIS and MISR were observed as 0.23 ± 0.06 and 0.29 ± 0.07 respectively in the pre-monsoon season, and the minimum values (0.099 ± 0.02) were observed in the post-monsoon season, over the Dehradun region. In contrast, in the Chamoli region, the maximum AOD (MODIS) was 0.21 ± 0.06 observed in the monsoon season and the minimum was 0.036 ± 0.007 in the post-monsoon season. Over a decade, the AE for Chamoli and Dehradun was found to vary from 0.07 to 0.17 and from 0.14 to 0.20 respectively. The median AE for Chamoli and Dehradun was found to be 1.49 and 1.47 respectively, marking the dominance of fine mode particles of anthropogenic origin. Observations show the presence of dust and polluted dust resulting from the long-range transport from the west. The comparison of AOD values from the two sensors shows a significant correlation (0.73) with slightly higher values from MISR over the year. The results obtained are important in understanding the climatic implications due to the atmospheric aerosols over the abovementioned Himalayan region of Uttarakhand, India.

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.

Assessment and Error Analysis of Terra-MODIS and MISR Cloud-Top Heights Through Comparison With ISS-CATS Lidar.

Cloud‐top heights (CTH) from the Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra constitute our longest‐running single‐platform CTH record from a stable orbit. Here, we provide the first evaluation of the Terra Level 2 CTH record against collocated International Space Station Cloud‐Aerosol Transport System (CATS) lidar observations between 50ºN and 50ºS. Bias and precision of Terra CTH relative to CATS is shown to be strongly tied to cloud horizontal and vertical heterogeneity and altitude. For single‐layered, unbroken, optically thick clouds observed over all altitudes, the uncertainties in MODIS and MISR CTH are −540 ± 690 m and −280 ± 370 m, respectively. The uncertainties are generally smaller for lower altitude clouds and larger for optically thin clouds. For multi‐layered clouds, errors are summarized herein using both absolute CTH and CATS‐layer‐altitude proximity to Terra CTH. We show that MISR detects the lower cloud in a two‐layered system, provided top‐layer optical depth <∼0.3, but MISR low‐cloud CTH errors are unaltered by the presence of thin cirrus. Systematic and random errors are propagated to explain inter‐sensor disagreements, as well as to provide the first estimate of the MISR stereo‐opacity bias. For MISR, altitude‐dependent wind‐retrieval bias (−90 to −110 m) and stereo‐opacity bias (−60 to −260 m) and for MODIS, CO2‐slicing bias due to geometrically thick cirrus leads to overall negative CTH bias. MISR’s precision is largely driven by precision in retrieved wind‐speed (3.7 m s−1), whereas MODIS precision is driven by forward‐modeling uncertainty.

Global Wildfire Plume‐Rise Data Set and Parameterizations for Climate Model Applications

AbstractThe fire plume height (smoke injection height) is an important parameter for calculating the transport and lifetime of smoke particles, which can significantly affect regional and global air quality and atmospheric radiation budget. To develop an observation‐based global fire plume‐rise data set, a modified one‐dimensional plume‐rise model was used with observation‐based fire size and Maximum Fire Radiative Power (MFRP) data, which are derived from satellite fire hotspot measurements. The resulting data set captured well the observed plume height distribution derived from the Multiangle Imaging SpectroRadiometer (MISR) measurements. The fraction of fire plumes penetrating above the boundary layer is relatively low at 20% at the time of MISR observation (10:30 am LT) but increases to an average of ∼55% in the late afternoon, implying that the MISR observation data sampled in late morning underestimate the average daytime fire plume heights and plume mixing into the free troposphere. Therefore, adjustments are required through dynamic modeling or parameterization of fire plume height as a function of meteorological and fire conditions when the MISR data set is applied in climate model simulations. We conducted sensitivity simulations using the Community Atmospheric Models version 5 (CAM5). Model results show that the incorporation of fire plume rise in the model tends to significantly increase fire aerosol impacted regions. We applied the offline plume‐rise data to develop an online fire plume height parameterization, allowing for simulating the feedbacks of climate/weather on fire plume rise in climate models.

Two decades of aerosol observations by AATSR, MISR, MODIS and MERRA-2 over India and Indian Ocean

Atmospheric aerosols play key roles in radiation budget, ecosystem dynamics, air quality and cloud microphysics of a region and thus, they greatly influence the global climate, ecosystem and public health. We present the temporal variability of atmospheric aerosols over India and north Indian Ocean (NIO) for the past two decades (2000–2019). Here, the measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra, Multi-angle Imaging Spectroradiometer (MISR) on Terra, and Advanced Along Track Scanning Radiometer (AATSR) measurements from Envisat, and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data are considered. On average, the Indo Gangetic Plain (IGP) shows the largest (0.5–1.2), and NIO and Himalaya show the smallest (0.1 or smaller) Aerosol Optical depth (AOD) values. The peak AOD loading is observed in June–July-August, and IGP has a secondary peak in March owing to the stubble and biomass burning in winter months. In contrast, the peak aerosol loading in the northeast is in winter due to forest and biomass burning during the period there. The inter-annual variability is very small in NIO, Himalaya and Bay of Bengal. The trends estimated from the combined (AATSR, MISR, MODIS and MERRA-2) data show the highest positive trends at the lower IGP and east central regions, about 0.8–1.2/dec, and are statistically significant. This is consistent with the urban activity, industries and dense population there. However, the Desert, northern Himalaya and northern Arabian Sea show insignificant negative trends, from −0.2 to −0.4/dec, as the anthropogenic sources of aerosols are very limited there. The bias estimate shows that most satellite and reanalysis data are in very good agreement at all regions (within 0.1–0.2). Even though the bias in the measurements are considered, the trends estimated are still large enough to be statistically significant. The analyses, therefore, caution the increasing aerosol loading and their plausible climate feedback in these regions. The assessment also demonstrate the potential of synergetic use of multiple-platform measurements for climate system studies.

The Association Between Ambient PM2.5 Exposure and Anemia Outcomes Among Children Under Five Years of Age in India.

The ecological analysis assessed average hemoglobin levels and anemia prevalence (hemoglobin < 11 g/dL considered anemic) by district using multiple linear regression models. The individual-level analysis assessed average individual hemoglobin level and anemia status (yes/no) using generalized linear mixed models to account for clustering by district. Ambient PM2.5 exposure data were derived from the Multiangle Imaging SpectroRadiometer (MISR) level 2 aerosol optical depth (AOD) data and averaged from birth date to date of interview. The district-level ecological analysis found that, for every 10 μg m-3 increase in ambient PM2.5 exposure, average anemia prevalence increased by 1.90% (95% CI = 1.43, 2.36) and average hemoglobin decreased by 0.07 g/dL (95% CI = 0.09, 0.05). At the individual level, for every 10 μg m-3 increase in ambient PM2.5 exposure, average hemoglobin decreased by 0.14 g/dL (95% CI = 0.12, 0.16). The odds ratio associated with a 10-μg m-3 increase in ambient PM2.5 exposure was 1.09 (95% CI = 1.06, 1.11). There was evidence of effect modification by wealth index, maternal anemia status, and child BMI. Our results suggest that ambient PM2.5 exposure could be linked to anemia in Indian children, although additional research on the underlying biologic mechanisms is needed. Future studies on this association should specifically consider interactions with dietary iron deficiency, maternal anemia status, and child BMI.Keywords: Anemia; Children; Ambient PM2.5 exposure; India; Association.

Influence of observation angle change on satellite retrieval of aerosol optical depth

Use of multi-angle sensors, which can perform ground observations in different view directions, provides an important technical means to acquire more comprehensive and detailed remote sensing information. This study explored the effect of angle changes on satellite-based aerosol optical depth (AOD) retrieval using 2016–2018 Multi-angle Imaging SpectroRadiometer (MISR) data in the Yangtze River Delta. The Moderate resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by the MODIS V5.2 algorithm and MODIS BRDF data are used to obtain MISR surface reflectance at different angles after a spectral conversion between MODIS and MISR, and then MISR AOD with different angles were retrieved based on the MISR surface reflectance at different angles. Moreover, the errors and their formation mechanism in retrieving AOD using MISR remote sensing data at different angles were analyzed based on the Global Aerosol Robotic Network (AERONET) ground observations. We used six indicators, including correlation coefficient (R), significant level (P value), mean absolute error (MAE), mean relative error (MRE) root mean square error (RMSE) and relative mean bias (RMB), and AERONET AOD is used to verify the MISR AOD. The results show that at Taihu and Xuzhou stations, the highest R values are 0.81 (Aa AOD) and 0.75 (An AOD), and the minimum RMSE values at Taihu and Xuzhou-CUMT stations are 0.11 (Aa AOD) and 0.20 (An AOD), respectively. Incorrect surface reflectance measurement is the root cause of the angle effect in MISR AOD retrieval. With the same surface reflectance deviation, the larger the MISR observation angle, the closer the retrieved MISR AOD is to the AOD observed by AERONET. The AOD retrieval of the forward observation angle is closer to AERONET AOD than that of backward observation angle, and the retrieved error is greatest at the vertical angle (nadir). Therefore, sensors for vertical ground observation are not suitable for AOD retrieval, and sensors in non-vertical directions, especially forward to the ground, are more favorable to MISR AOD retrieval.

Constraining the relationships between aerosol height, aerosol optical depth and total column trace gas measurements using remote sensing and models

Abstract. Proper quantification of the aerosol vertical height is essential to constrain the atmospheric distribution and lifetime of aerosols, as well as their impact on the environment. We use globally distributed, daily averaged measurements of aerosol stereo heights of fire aerosols from the Multi-angle Imaging SpectroRadiometer (MISR) to understand the aerosol distribution. We also connect these results with a simple plume rise model and a new multi-linear regression model approach based on daily measurements of NO2 from OMI and CO from MOPITT to understand and model the global aerosol vertical height profile over biomass burning regions. First, plumes associated with the local dry-burning season at midlatitudes to high latitudes frequently have a substantial fraction lofted into the free troposphere and in some cases even the stratosphere. Second, plumes mainly associated with less-polluted regions in developing countries and heavily forested areas tend to stay closer to the ground, although they are not always uniformly distributed throughout the boundary layer. Third, plumes associated with more serious loadings of pollution (such as in Africa, Southeast Asia and northeast China) tend to have a substantial amount of smoke transported uniformly through the planetary boundary layer and up to around 3 km. Fourth, the regression model approach yields a better ability to reproduce the measured heights compared to the plume rise model approach. This improvement is based on a removal of the negative bias observed from the plume model approach, as well as a better ability to work under more heavily polluted conditions. However, over many regions, both approaches fail, requiring deeper work to understand the physical, chemical and dynamical reasons underlying the failure over these regions.