Constrained Retrievals of Aerosol Optical Properties Using Combined Lidar and Imager Measurements During the FIREX-AQ Campaign

Smoke aerosols arise from a variety of regional sources and fuel types dependent on the properties of the fire, leading to spatial variability in smoke composition and optical properties. After emission, these aerosols age and mix in the atmosphere with other aerosol species, such as sulfates, altering the optical, and microphysical properties of the smoke aerosols over time. Thus, lidar ratio (extinction to backscatter ratio) derived from lidar sensors exhibit spatiotemporal variability for smoke. Traditional backscatter lidar processing algorithms employ a signal loss method that utilizes the reduction of signals below and above cloud layers, enabling simultaneous retrievals of both layer-averaged lidar ratio and particulate extinction, which avoids the need for assigning lidar ratios based on layer type as is typically used for backscatter lidar algorithms. In this study, the signal loss method, which is traditionally designed for cloud property retrievals, is attempted for elevated smoke plume property retrievals using NASA’s Cloud Physics Lidar (CPL) observations from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Good agreement (linear correlation coefficient of 0.67) is found between aerosol optical depth (AOD) derived from the signal loss method and the constrained method, utilizing collocated GOES MAGARA AOD values as constraints for lidar ratio retrievals, for the Williams Flats smoke event. Differences in derived lidar ratios from the signal loss method and the constrained method (13.6 and 7.4%) are found to be smaller than the expected signal loss lidar ratio error estimate of ∼17–23%. A good agreement is also found in lidar ratios derived from this study and from using Differential Absorption Lidar-High Spectral Resolution Lidar (DIAL-HSRL) measurements for the Williams Flats Fire. The lidar ratio statistics of smoke plumes presented in this analysis (51 ± 13 sr) also compare favorably with lidar ratio values found in previous studies; however, they remain lower than the assumed smoke lidar ratio of 70 sr (at 532 nm) used by CALIPSO and CPL, and vary with plume transport distance. These findings suggest lidar ratio is likely to be regionally specific and evolve with plume transport. Thus, innovative methods for simultaneous retrieval of lidar ratio and aerosol extinction, such as the signal loss method proposed in this study, are needed for accurate aerosol retrievals from standard backscatter lidars in the future

Wildfire Smoke Particle Properties and Evolution, from Space-Based Multi-Angle Imaging

Emitted smoke composition is determined by properties of the biomass burning source and ambient ecosystem. However, conditions that mediate the partitioning of black carbon (BC) and brown carbon (BrC) formation, as well as the spatial and temporal factors that drive particle evolution, are not understood adequately for many climate and air-quality related modeling applications. In situ observations provide considerable detail about aerosol microphysical and chemical properties, although sampling is extremely limited. Satellites offer the frequent global coverage that would allow for statistical characterization of emitted and evolved smoke, but generally lack microphysical detail. However, once properly validated, data from the National Aeronautics and Space Administration (NASA) Earth Observing Systems Multi-Angle Imaging Spectroradiometer (MISR) instrument can create at least a partial picture of smoke particle properties and plume evolution. We use in situ data from the Department of Energys Biomass Burning Observation Project (BBOP) field campaign to assess the strengths and limitations of smoke particle retrieval results from the MISR Research Aerosol (RA) retrieval algorithm. We then use MISR to characterize wildfire smoke particle properties and to identify the relevant aging factors in several cases, to the extent possible. The RA successfully maps qualitative changes in effective particle size, light absorption, and its spectral dependence, when compared to in situ observations. By observing the entire plume uniformly, the satellite data can be interpreted in terms of smoke plume evolution, including size-selective deposition, new-particle formation, and locations within the plume where BC or BrC dominates

Eyjafjallajokull Volcano Plume Particle-Type Characterization from Space-Based Multi-angle Imaging

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 from the spring 2010 eruption of the Eyjafjallaj kull volcano, 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 overwater 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

MISR Eyjafjallajokull and Grimsvotn Retrievals

No abstract availabl

MISR Update

The document was presented at the 2013 AEROCENTER Annual Meeting held at the GSFC Visitors Center, May 31, 2013. The organizers of the meeting are posting the talks to the public Aerocenter website

Updated MISR Dark Water Research Aerosol Retrieval Algorithm - Part 1: Coupled 1.1 km Ocean Surface Chlorophyll a Retrievals with Empirical Calibration Corrections

As aerosol amount and type are key factors in the 'atmospheric correction' required for remote-sensing chlorophyll alpha concentration (Chl) retrievals, the Multi-angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo (SSA) retrievals. We introduce a coupled, self-consistent retrieval of Chl together with aerosol over dark water. There are time-varying MISR radiometric calibration errors that significantly affect key spectral reflectance ratios used in the retrievals. Therefore, we also develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, based on comparisons with coincident MODIS (Moderate Resolution Imaging Spectroradiometer) observations and trend analysis of the MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites. We run the MISR research retrieval algorithm (RA) with the corrected MISR reflectances to generate MISR-retrieved Chl and compare the MISR Chl values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and Storage System) in situ observations. Where Chl(sub in situ) less than 1.5 mg m(exp -3), the results from our Chl model are expected to be of highest quality, due to algorithmic assumption validity. Comparing MISR RA Chl to the 49 coincident SeaBASS observations, we report a correlation coefficient (r) of 0.86, a root-mean-square error (RMSE) of 0.25, and a median absolute error (MAE) of 0.10. Statistically, a two-sample Kolmogorov- Smirnov test indicates that it is not possible to distinguish between MISR Chl and available SeaBASS in situ Chl values (p greater than 0.1). We also compare MODIS-Terra and MISR RA Chl statistically, over much broader regions. With about 1.5 million MISR-MODIS collocations having MODIS Chl less than 1.5 mg m(exp -3), MISR and MODIS show very good agreement: r = 0.96, MAE = 0.09, and RMSE = 0.15. The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS-Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo

Refined Use of Satellite Aerosol Optical Depth Snapshots to Constrain Biomass Burning Emissions in the GOCART Model

Simulations of biomass burning (BB) emissions in global chemistry and aerosol transport models depend on external inventories, which provide location and strength of burning aerosol sources. Our previous work (Petrenko et al., 2012) shows that satellite snapshots of aerosol optical depth (AOD) near the emitted smoke plume can be used to constrain model-simulated AOD, and effectively, the assumed source strength. We now refine the satellite-snapshot method and investigate applying simple multiplicative emission correction factors for the widely used Global Fire Emission Database version 3 (GFEDv3) emission inventory can achieve regional-scale consistency between MODIS AOD snapshots and the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model. The model and satellite AOD are compared over a set of more than 900 BB cases observed by the MODIS instrument during the 2004, and 2006-2008 biomass burning seasons. The AOD comparison presented here shows that regional discrepancies between the model and satellite are diverse around the globe yet quite consistent within most ecosystems. Additional analysis of including small fire emission correction shows the complimentary nature of correcting for source strength and adding missing sources, and also indicates that in some regions other factors may be significant in explaining model-satellite discrepancies. This work sets the stage for a larger intercomparison within the Aerosol Inter-comparisons between Observations and Models (AeroCom) multi-model biomass burning experiment. We discuss here some of the other possible factors affecting the remaining discrepancies between model simulations and observations, but await comparisons with other AeroCom models to draw further conclusions

Constraining Chemical Transport PM2.5 Modeling Outputs Using Surface Monitor Measurements and Satellite Retrievals: Application over the San Joaquin Valley

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM(2.5), its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM(2.5) fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM(2.5), 0.88 and 0.65 for NO3(), 0.78 and 0.23 for SO4(2), 1.00 and 1.01 for NH4(+), 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM(2.5) improve by 30% and 13%, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4(2) cross-validation values showed the largest spatial and spatiotemporal R(2) improvement, with a 43% increase. Assessing this physical technique in a well-instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent

MISR Radiance Anomalies Induced by Stratospheric Volcanic Aerosols

The 16-year MISR monthly radiances are analyzed in this study, showing significant enhancements of anisotropic scattering at high latitudes after several major volcanic eruptions with injection heights greater than 14 km. The anomaly of deseasonalized radiance anisotropy between MISRs DF and DA views (70.5 forward and aft) is largest in the blue band with amplitudes amounting to 515% of the mean radiance. The anomalous radiance anisotropy is a manifestation of the stronger forward scattering of reflected sunlight due to the direct and indirect effects of stratospheric volcanic aerosols (SVAs). The perturbations of MISR radiance anisotropy from the Kasatochi (August 2008), Sarychev (June 2009), Nabro (June 2011) and Calbuco (April 2015) eruptions are consistent with the poleward transported SVAs observed by CALIOP and OMPS-LP. In a particular scene over the Arctic Ocean, the stratospheric aerosol mid-visible optical depth can reach as high as 0.20.5. The enhanced global forward scattering by SVAs has important implications for the shortwave radiation budge

Data associated with "Constraining aerosol phase function using dual-view geostationary satellites"

The dataset includes the data for figures shown in the paper. Two opposing GEOS satellites capture a dust event in the Gulf of Mexico in June 2019. We demonstrate a first-guess phase function to reconstruct dust phase function by leveraging GEOS satellites. We then evaluate our methodology using a different dust event over the Gulf of Mexico in June 2020.Passive satellite observations play an important role in monitoring global aerosol properties and helping quantify aerosol radiative forcing in the climate system. The quality of aerosol retrievals from the satellite platform relies on well-calibrated radiance measurements from multiple spectral bands, and the availability of appropriate particle optical models. Inaccurate scattering phase function assumptions can introduce large retrieval errors. High-spatial resolution, dual-view observations from the Advanced Baseline Imagers (ABI) on board the two most recent Geostationary Operational Environmental Satellites (GOES), East and West, provide a unique opportunity to better constrain the aerosol phase function. Using dual GOES reflectance measurements for a dust event in the Gulf of Mexico in 2019, we demonstrate how a first-guess phase function can be reconstructed by considering the variations in observed scattering angle throughout the day. Using the reconstructed phase function, aerosol optical depth retrievals from the two satellites are self-consistent and agree well with surface-based optical depth estimates. We evaluate our methodology and reconstructed phase function against independent retrievals made from low-Earth-orbit multi-angle observations for a different dust event in 2020. Our new aerosol optical depth retrievals have a root-mean-square-difference of 0.019– 0.047. Furthermore, the retrievals between the two geostationary satellites for this case agree within about 0.059±0.072, as compared to larger discrepancies between the operational GOES products at times, which do not employ the dual-view technique.This work is based upon research supported by the U. S. Office of Naval Research under Multidisciplinary University Research Initiative (MURI) Grant N00014-16-1-2040. JW and XX are supported by the same MURI grant to the University of Iowa. RK and JAL are supported by NASA’s Climate and Radiation Research and Analysis Program under Hal Maring, the Atmospheric Composition Program under Richard Eckman, and the EOS Terra and MISR projects. NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) program (NNH17ZDA001N-MEASURES) provided partial support of LR and RL