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NASA NTRS2019

An Automatic Light Rain Detection Algorithm on NASA MPLNET Lidar Observations in the Frame of WMO GALION Project

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics. Results from the algorithm, besides filling a gap in precipitation and virga detection by radars, are of particular interest for the scientific community because will help to better understand long-term aerosol-cloud interactions and aerosol atmospheric removal (scavenging effect) by rain as multi-year database being available for several MPLNET permanent observational sites across the globe. Moreover, we developed the automatic algorithm at Universitat Politecnica de Catalunya (UPC) Barcelona, the unique permanent observation station member of MPLNET and the European Aerosol Lidar Network (EARLINET) In the future the algorithm can be then easily applied to any other lidar and/or ceilometer network infrastructure in the frame of World Meteorological Organization (WMO) Global Aerosol Watch (GAW) aerosol lidar observation network (GALION)

Simone Lolli↗

DOE OSTI2026

Improving the Confidence in Retrievals of Vertical Distributions of Cloud Condensation Nuclei Number Concentration from ARM Supported by Aircraft In Situ Observations

Accurate quantification of the vertical distribution of cloud condensation nuclei (CCN) number concentrations is critical for improving our understanding of aerosol–cloud interactions. Ground-based Raman lidars operated by the Atmospheric Radiation Measurement (ARM) program, together with surface CCN measurements, are used to retrieve vertically resolved CCN number concentrations (Retrieved Number concentration of CCN, RNCCN). These retrievals rely on several assumptions, including that aerosol composition is vertically homogeneous. To assess this assumption, we developed and tested a framework to infer the dominant aerosol classes/types at different altitudes. This was done by applying a k-Nearest-Neighbors (kNN) algorithm to lidar ratio and linear depolarization ratio measurements from Raman lidar. We evaluated the framework using aircraft aerosol and CCN measurements from the ARM Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) field campaign. The results show that RNCCN performance degrades as vertical aerosol complexity increases, i.e., RNCCN agrees with the aircraft CCN in vertically homogeneous conditions, but closure decreases in layered aerosol structures. To generalize beyond individual examples, we introduce a metric (heterogeneity index) that quantifies the vertical complexity by assessing the variation of inferred aerosol classes/types. Case-level statistics show a tendency for RNCCN and aircraft differences to increase with this metric. By detecting retrievals that are likely compromised by aerosol vertical heterogeneity, the proposed framework improves the interpretability and effective use of RNCCN used for long-term evaluation of models and aerosol–cloud interactions.

Tian, Jingjing↗

NASA NTRS2016

Validation and Expected Error Estimation of Suomi-NNP VIIRS Aerosol Optical Thickness and Angstrom Exponent with AERONET

The new-generation polar-orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET)measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of 0.0008 with root-mean-square error(RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06.The mean bias of VIIRS Ocean Angstrom Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S-NPP to future polar-orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations missions come to an end.

Huang, Jingfeng↗

NASA NTRS2010

Strategies for Improved CALIPSO Aerosol Optical Depth Estimates

In the spring of 2010, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) project will be releasing version 3 of its level 2 data products. In this paper we describe several changes to the algorithms and code that yield substantial improvements in CALIPSO's retrieval of aerosol optical depths (AOD). Among these are a retooled cloud-clearing procedure and a new approach to determining the base altitudes of aerosol layers in the planetary boundary layer (PBL). The results derived from these modifications are illustrated using case studies prepared using a late beta version of the level 2 version 3 processing code.

Vaughan, Mark A.↗

NASA NTRS1995

Nonsphericity of dust-like tropospheric aerosols: Implications for aerosol remote sensing and climate modeling

T-matrix computations of light scattering by polydispersions of randomly oriented nonspherical aerosols and Mie computations for equivalent spheres are compared. Findings show that even moderate nonsphericity results in suubstantial errors in the retrieved aerosol optical thickness if satellite reflectance measurements are analyzed using Mie theory. On the other hand, the use of Mie theory for nonspherical aerosols produces negligible errors in the computation of albedo and flux related quantities, provided that the aerosol size distribution and optical thickness are known beforehand. The first result can be explained by large nonspherical-spherical differences in scattering phase function, while the second result follows from small nonspherical-spherical differences in single-scattering albedo and asymmetry parameter. No cancellation of errors occurs if one consistently uses Mie theory in the retrieval algorithm and then in computing the albedo for the retrieved aerosol optical thickness.

Mishchenko, M. I.↗

NASA NTRS2000

Cloud and Aerosol Retrieval for the 2001 GLAS Satellite Lidar Mission

The Geoscience Laser Altimeter System (GLAS) is scheduled for launch in July of 2001 aboard the Ice, Cloud and Land Elevation Satellite (ICESAT). In addition to being a precision altimeter for mapping the height of the Earth's icesheets, GLAS will be an atmospheric lidar, sensitive enough to detect gaseous, aerosol, and cloud backscatter signals, at horizontal and vertical resolutions of 175 and 75m, respectively. GLAS will be the first lidar to produce temporally continuous atmospheric backscatter profiles with nearly global coverage (94-degree orbital inclination). With a projected operational lifetime of five years, GLAS will collect approximately six billion lidar return profiles. The large volume of data dictates that operational analysis algorithms, which need to keep pace with the data yield of the instrument, must be efficient. So, we need to evaluate the ability of operational algorithms to detect atmospheric constituents that affect global climate. We have to quantify, in a statistical manner, the accuracy and precision of GLAS cloud and aerosol observations. Our poster presentation will show the results of modeling studies that are designed to reveal the effectiveness and sensitivity of GLAS in detecting various atmospheric cloud and aerosol features. The studies consist of analyzing simulated lidar returns. Simulation cases are constructed either from idealized renditions of atmospheric cloud and aerosol layers or from data obtained by the NASA ER-2 Cloud Lidar System (CLS). The fabricated renditions permit quantitative evaluations of operational algorithms to retrieve cloud and aerosol parameters. The use of observational data permits the evaluations of performance for actual atmospheric conditions. The intended outcome of the presentation is that climatology community will be able to use the results of these studies to evaluate and quantify the impact of GLAS data upon atmospheric modeling efforts.

Hart, William D.↗

NASA NTRS2021

Extensive Characterization of Aerosol Optical Properties and Chemical Component Concentrations: Application of the GRASP/Component Approach to Long-term AERONET Measurements

A recently developed GRASP/Component approach (GRASP: Generalized Retrieval of Atmosphere and Surface Properties) was applied to AERONET (Aeronet Robotic Network) sun photometer measurements in this study. Unlike traditional aerosol component retrieval, this approach allows the inference of some information about aerosol composition directly from measured radiance, rather than indirectly through the inversion of optical parameters, and has been integrated into the GRASP algorithm. The newly developed GRASP/Component approach was applied to 13 AERONET sites for different aerosol types under the assumption of aerosol internal mixing rules to analyze the characteristics of aerosol components and their distribution patterns. The results indicate that the retrievals can characterize well the spatial and temporal variability of the component concentration for different aerosol types. A reasonable agreement between GRASP BC retrievals and MERRA-2 BC products is found for all different aerosol types. In addition, the relationships between aerosol component content and aerosol optical parameters such as aerosol optical depth (AOD), fine-mode fraction (FMF), absorption Ångström exponent (AAE), scattering Ångström exponent (SAE), and single scattering albedo (SSA) are also analyzed for indirect verifying the reliability of the component retrieval. It was demonstrated the GRASP/Component optical retrievals are in good agreement with AERONET standard products [e.g., correlation coefficient (R) of 0.93–1.0 for AOD, fine-mode AOD (AODF), coarse-mode AOD (AODC) and Ångström exponent (AE); R = ~ 0.8 for absorption AOD (AAOD) and SSA; RMSE (root mean square error) < 0.03 for AOD, AODF, AODC, AAOD and SSA]. Thus, it is demonstrated the GRASP/Component approach can provide aerosol optical products with comparable accuracy as the AERONET standard products from the ground-based sun photometer measurements as well as some additional important inside on aerosol composition.

Xindan Zhang↗

DOE OSTI2022

Quantification of Lanthanides in a Molten Salt Reactor Surrogate Off-Gas Stream Using Laser-Induced Breakdown Spectroscopy

To enable the deployment of molten salt reactor technology, the development of off-gas treatment systems and advanced monitoring tools capable of operating with high temperatures and radiation fields while delivering near real-time information is necessary. Here, we aim to fulfill this requirement and proposes laser-induced breakdown spectroscopy (LIBS) for monitoring molten salt aerosol streams. A sheath gas measuring method was developed to protect optical elements from aerosol particles and to ensure a relatively constant aerosol stream for measurement. An aqueous system was studied to demonstrate the utility of LIBS for monitoring possible fission products in an aerosol stream: Gd, Nd, and Sm up to 2000 parts per million (ppm). A calibration model was built using partial least squares (PLS) regression with five, six, and nine latent variables for Gd, Nd, and Sm, respectively. This calibration model successfully estimated the concentrations of three test samples, which were validated with inductively charged plasma optical emission spectroscopy measurements at a 99.9% confidence interval. To enhance these models, a genetic algorithm was used to filter the spectra before entering the PLS model, thereby limiting the spectral features being regressed to those with greater correlations to concentration. This allowed for the number of latent variables used in the PLS models to be reduced to four, three, and three for Gd, Nd, and Sm, respectively. Finally, the genetic algorithm-filtered PLS models were used to predict the concentrations of the aerosol stream on a real-time dataset and resulted in a 73%, 18%, and 25% improvement in root mean squared error of prediction compared to the original PLS models developed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2021

Evaluation of Novel NASA MODIS and VIIRS Aerosol Products and Assessment of Smoke Height Boundary Layer Ratio During Extreme Smoke Events in the Western U.S.

We analyze new aerosol products from NASA satellite retrievals over the western U.S. during August 2013, with special attention to locally generated wildfire smoke and downwind plume structures. Aerosol optical depth (AOD) at 550 nm from MODIS (Terra and Aqua Collections 6 and 6.1) and VIIRS Deep Blue (DB) and MODIS (Terra and Aqua) Multi‐Angle Implementation of Atmospheric Correction (MAIAC) retrievals are evaluated against ground‐based AErosol RObotic NETwork (AERONET) observations. We find a significant improvement in correlation with AERONET and other metrics in the latest DB AOD (MODIS C6.1 r^2=0.75, VIIRS r^2=0.79) compared to MODIS C6 (r2=0.62). In general, MAIAC (r^2=0.84) and DB (MODIS C6.1 and VIIRS) present similar statistical evaluation metrics for the western U.S. and are useful tools to characterize aerosol loading associated with wildfire smoke. We also evaluate three novel NASA MODIS plume injection height (PIH) products, one from MAIAC and two from the Aerosol Single scattering albedo and layer Height Estimation (ASHE) (MODIS and VIIRS) algorithm. Both Terra and Aqua MAIAC PIHs statistically agree with ground‐based and satellite lidar observations near the fire source, as do ASHE, although the latter is sensitive to assumptions about aerosol absorption properties. We introduce a first‐order approximation Smoke Height Boundary Layer Ratio (SHBLR) to qualitatively distinguish between aerosol pollution within the planetary boundary layer and the free troposphere. We summarize the scope, limitations, and suggestions for scientific applications of surface level aerosol concentrations specific to wildfire emissions and smoke plumes using these novel NASA MODIS and VIIRS aerosol products.

Plume injection height↗

NASA NTRS2011

Evaluating the Assumptions of Surface Reflectance and Aerosol Type Selection Within the MODIS Aerosol Retrieval Over Land: The Problem of Dust Type Selection

Aerosol Optical Depth (AOD) and Angstrom exponent (AE) values derived with the MODIS retrieval algorithm over land (Collection 5) are compared with ground based sun photometer measurements at eleven sites spanning the globe. Although, in general, total AOD compares well at these sites (R2 values generally over 0.8), there are cases (from 2 to 67% of the measurements depending on the site) where MODIS clearly retrieves the wrong spectral dependence, and hence, an unrealistic AE value. Some of these poor AE retrievals are due to the aerosol signal being too small (total AOD<0.3) but in other cases the AOD should have been high enough to derive accurate AE. However, in these cases, MODIS indicates AE values close to 0.6 and zero fine model weighting (FMW), i.e. dust model provides the best fitting to the MODIS observed reflectance. Yet, according to evidence from the collocated sun photometer measurements and back-trajectory analyses, there should be no dust present. This indicates that the assumptions about aerosol model and surface properties made by the MODIS algorithm may have been incorrect. Here we focus on problems related to parameterization of the land-surface optical properties in the algorithm, in particular the relationship between the surface reflectance at 660 and 2130 nm.

Mielonen, T.↗

NASA NTRS2017

Use of A-Train Aerosol Observations to Constrain Direct Aerosol Radiative Effects (DARE) Comparisons with Aerocom Models and Uncertainty Assessments

We describe a technique for combining multiple A-Train aerosol data sets, namely MODIS spectral AOD (aerosol optical depth), OMI AAOD (absorption aerosol optical depth) and CALIOP aerosol backscatter retrievals (hereafter referred to as MOC retrievals) to estimate full spectral sets of aerosol radiative properties, and ultimately to calculate the 3-D distribution of direct aerosol radiative effects (DARE). We present MOC results using almost two years of data collected in 2007 and 2008, and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the MODIS Collection 6 AOD data derived with the dark target and deep blue algorithms has extended the coverage of the MOC retrievals towards higher latitudes. The MOC aerosol retrievals agree better with AERONET in terms of the single scattering albedo (ssa) at 441 nm than ssa calculated from OMI and MODIS data alone, indicating that CALIOP aerosol backscatter data contains information on aerosol absorption. We compare the spatio-temporal distribution of the MOC retrievals and MOC-based calculations of seasonal clear-sky DARE to values derived from four models that participated in the Phase II AeroCom model intercomparison initiative. Overall, the MOC-based calculations of clear-sky DARE at TOA over land are smaller (less negative) than previous model or observational estimates due to the inclusion of more absorbing aerosol retrievals over brighter surfaces, not previously available for observationally-based estimates of DARE. MOC-based DARE estimates at the surface over land and total (land and ocean) DARE estimates at TOA are in between previous model and observational results. Comparisons of seasonal aerosol property to AeroCom Phase II results show generally good agreement best agreement with forcing results at TOA is found with GMI-MerraV3. We discuss sampling issues that affect the comparisons and the major challenges in extending our clear-sky DARE results to all-sky conditions. We present estimates of clear-sky and all-sky DARE and show uncertainties that stem from the assumptions in the spatial extrapolation and accuracy of aerosol and cloud properties, in the diurnal evolution of these properties, and in the radiative transfer calculations.

aerosol↗

NASA NTRS2011

On the Accuracy of Double Scattering Approximation for Atmospheric Polarization Computations

Interpretation of multi-angle spectro-polarimetric data in remote sensing of atmospheric aerosols require fast and accurate methods of solving the vector radiative transfer equation (VRTE). The single and double scattering approximations could provide an analytical framework for the inversion algorithms and are relatively fast, however accuracy assessments of these approximations for the aerosol atmospheres in the atmospheric window channels have been missing. This paper provides such analysis for a vertically homogeneous aerosol atmosphere with weak and strong asymmetry of scattering. In both cases, the double scattering approximation gives a high accuracy result (relative error approximately 0.2%) only for the low optical path - 10(sup -2) As the error rapidly grows with optical thickness, a full VRTE solution is required for the practical remote sensing analysis. It is shown that the scattering anisotropy is not important at low optical thicknesses neither for reflected nor for transmitted polarization components of radiation.

Korkin, Sergey V.↗

NASA NTRS2017

CATS Version 2 Aerosol Feature Detection and Applications for Data Assimilation

The Cloud Aerosol Transport System (CATS) lidar has been operating onboard the International Space Station (ISS) since February 2015 and provides vertical observations of clouds and aerosols using total attenuated backscatter and depolarization measurements. From February March 2015, CATS operated in Mode 1, providing backscatter and depolarization measurements at 532 and 1064 nm. CATS began operation in Mode 2 in March 2015, providing backscatter and depolarization measurements at 1064 nm and has continued to operate to the present in this mode. CATS level 2 products are derived from these measurements, including feature detection, cloud aerosol discrimination, cloud and aerosol typing, and optical properties of cloud and aerosol layers. Here, we present changes to our level 2 algorithms, which were aimed at reducing several biases in our version 1 level 2 data products. These changes will be incorporated into our upcoming version 2 level 2 data release in summer 2017. Additionally, owing to the near real time (NRT) data downlinking capabilities of the ISS, CATS provides expedited NRT data products within 6 hours of observation time. This capability provides a unique opportunity for supporting field campaigns and for developing data assimilation techniques to improve simulated cloud and aerosol vertical distributions in models. We additionally present preliminary work toward assimilating CATS observations into the NASA Goddard Earth Observing System version 5 (GEOS-5) global atmospheric model and data assimilation system.

data assimilation↗

NASA NTRS1989

A correction algorithm for particle size distribution measurements made with the forward-scattering spectrometer probe

A correction algorithm for evaluating the particle size distribution measurements of atmospheric aerosols obtained with a forward-scattering spectrometer probe (FSSP) is examined. A model based on Poisson statistics is employed to calculate the average diameter and rms width of the particle size distribution. The dead time and coincidence errors in the measured number density are estimated. The model generated data are compared with a Monte Carlo simulation of the FSSP operation. It is observed that the correlation between the actual and measured size distribution is nonlinear. It is noted that the algorithm permits more accurate calculation of the average diameter and rms width of the distribution compared to uncorrected measured quantities.

Lock, James A.↗

NASA NTRS2021

Application of Radon Transform to Multi-Angle Measurements Made by the Research Scanning Polarimeter: A New Approach to Cloud Tomography. Part I: Theory and Tests on Simulated Data.

The Research Scanning Polarimeter (RSP) is an airborne along-track scanner measuring the polarized and total reflectances in 9 spectral channels. The RSP was a prototype for the Aerosol Polarimetry Sensor (APS) launched on-board the NASA Glory satellite. Currently the retrieval algorithms developed for the RSP are being adopted for the measurements of the space-borne polarimeters on the upcoming NASA’s Plankton, Aerosol, Cloud Ocean Ecosystem (PACE)satellite mission. The RSP’s uniquely high angular resolution coupled with the high frequency of measurements allows for characterization of liquid water cloud droplet sizes using the polarized rainbow structure. It also provides geometric constraints on the cumulus cloud’s 2D cross section yielding the cloud’s geometric shape estimates. In this study we further build on the latter technique to develop a new tomographic approach to retrieval of cloud internal structure from remote sensing measurements. While tomography in the strict definition is a technique based on active measurements yielding a tomogram (directional optical thickness as a function of angle and offset of the view ray), we developed a “semi-tomographic” approach in which tomogram of the cloud is estimated from passive observations instead of being measured directly. This tomogram is then converted into 2D spatial distribution of the extinction coefficient using inverse Radon transform (filtered back projection) which is the standard tomographic procedure used e.g., in medical CT scans. This algorithm is computationally inexpensive compared to techniques relying on highly-multi-dimensional least-square fitting; it does not require iterative 3D RT simulations. The resulting extinction distribution is defined up to an unknown constant factor, so we discuss the ways to calibrate it using additional independent measurements. In the next step we use the profile of the droplet size distribution parameters from the cloud’s side (derived by fitting the polarized rainbows) to convert the 2D extinction distribution into that of the droplet number concentration. We illustrate and validate the proposed technique using 3D-RT-simulatedRSP observations of a LES-generated Cu cloud. Quantitative comparisons between the retrieved and the original optical and microphysical parameters are presented.

clouds↗

NASA NTRS2000

Effects of Whitecaps on Satellite-Derived Ocean Color

During the 3.25 years of the project, various aspects of satellite ocean-color remote sensing were investigated, including effect of whitecaps on atmospheric correction, validity of aerosol models, and evaluation of ocean-color products. Algorithms to estimate pigment concentration and photo-synthetically active radiation (PAR) were developed, and studies of geophysical phenomena, such as the 1998 Asian Dust event, were performed. The influence of solar radiation absorption by phytoplankton on mixed layer dynamics, ocean circulation, and climate was also investigated. The project's results and findings are described.

Frouin, Robert↗

NASA NTRS2012

Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.

tropical ecosystems↗

NASA NTRS2018

Swelling of Transported Smoke from Savanna Fires over the Southeast Atlantic Ocean

We use the recently released Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Version 4.1 (V4) lidar data to study the smoke plumes transported from Southern African biomass burning areas. Significant improvements in the CALIPSO V4 Level 1 calibration and V4 Level 2 algorithms lead to a better representation of their optical properties, with the aerosol subtype improvements being particularly relevant to smoke over this area. For the first time, we show evidence of smoke particles increasing in size, evidenced in their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. We hypothesize that this is due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is also relevant to the ORACLES field mission.

Kar, J.↗