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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Volcanic Signatures in Estimates of Stratospheric Aerosol Size, Distribution Width, Surface Area, and Volume Deduced from Global Satellite-Based Observations

Volcanic signatures in the stratospheric aerosol layer are revealed by two independent techniques which retrieve aerosol information from global satellite-based observations of particulate extinction. Both techniques combine the 4-wavelength Stratospheric Aerosol and Gas Experiment (SAGE) II extinction measurements (0.385 <= lambda <= 1.02 microns) with the 7.96 micron and 12.82 micron extinction measurements from the Cryogenic Limb Array Etalon Spectrometer (CLAES) instrument. The algorithms use the SAGE II/CLAES composite extinction spectra in month-latitude-altitude bins to retrieve values and uncertainties of particle effective radius R(sub eff), surface area S, volume V and size distribution width sigma(sub R). The first technique is a multi-wavelength Look-Up-Table (LUT) algorithm which retrieves values and uncertainties of R(sub eff) by comparing ratios of extinctions from SAGE II and CLAES (e.g., E(sub lambda)/E(sub 1.02) to pre-computed extinction ratios which are based on a range of unimodal lognormal size distributions. The pre-computed ratios are presented as a function of R(sub eff) for a given sigma(sub g); thus the comparisons establish the range of R(sub eff) consistent with the measured spectra for that sigma(sub g). The fact that no solutions are found for certain sigma(sub g) values provides information on the acceptable range of sigma(sub g), which is found to evolve in response to volcanic injections and removal periods. Analogous comparisons using absolute extinction spectra and error bars establish the range of S and V. The second technique is a Parameter Search Technique (PST) which estimates R(sub eff) and sigma(sub g) within a month-latitude-altitude bin by minimizing the chi-squared values obtained by comparing the SAGE II/CLAES extinction spectra and error bars with spectra calculated by varying the lognormal fitting parameters: R(sub eff), sigma(sub g), and the total number of particles N(sub 0). For both techniques, possible biases in retrieved-parameters caused by assuming a unimodal functional form are removed using correction factors computed from representative in situ measurements of bimodal size distributions. Some interesting features revealed by the LUT and PST retrievals include: (1) Increases in S and V (but not R(sub eff)) after the Ruiz and Kelut injections, (2) Increases in S, V, R(sub eff) after Pinatubo, (3) Post-Pinatubo increases in S, V, and R(sub eff) that are more rapid in the tropics than elsewhere, (4) Mid-latitude post-Pinatubo increases in R(sub eff) that lag increases in S and V, (5) S and V returning to pre-Pinatubo values sooner than R(sub eff) does, (6) Sharp increases in sigma(sub g), after Pinatubo and slight increases in sigma(sub g) after Ruiz, Etna, Kelut, Spurr and Rabaul, and (7) Gradual declines in the heights at which R(sub eff), S and V peak after Pinatubo.

Bauman, J. J.↗
NASA NTRS2006

Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia

During the ACE-Asia field campaign, unprecedented amounts of aerosol property data in East Asia during springtime were collected from an array of aircraft, shipboard, and surface instruments. However, most of the observations were obtained in areas downwind of the source regions. In this paper, the newly developed satellite aerosol algorithm called "Deep Blue" was employed to characterize the properties of aerosols over source regions using radiance measurements from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS). Based upon the ngstr m exponent derived from the Deep Blue algorithm, it was demonstrated that this new algorithm is able to distinguish dust plumes from fine-mode pollution particles even in complex aerosol environments such as the one over Beijing. Furthermore, these results were validated by comparing them with observations from AERONET sites in China and Mongolia during spring 2001. These comparisons show that the values of satellite-retrieved aerosol optical thickness from Deep Blue are generally within 20%-30% of those measured by sunphotometers. The analyses also indicate that the roles of mineral dust and anthropogenic particles are comparable in contributing to the overall aerosol distributions during spring in northern China, while fine-mode particles are dominant over southern China. The spring season in East Asia consists of one of the most complex environments in terms of frequent cloudiness and wide ranges of aerosol loadings and types. This paper will discuss how the factors contributing to this complexity influence the resulting aerosol monthly averages from various satellite sensors and, thus, the synergy among satellite aerosol products.

Hsu, N. Christina↗
NASA NTRS1999

Principles in Remote Sensing of Aerosol from MODIS Over Land and Ocean

The well-calibrated spectral radiances measured by MODIS will be processed to retrieve daily aerosol properties that include optical thickness and mass loading over land and optical thickness, the mean particle size of the dominant mode and the ratio between aerosol modes over ocean. In addition, after launch, aerosol single scattering albedo will be calculated as an experimental product. The retrieval process over land is based on a dark target method that identifies appropriate targets in the mid-IR channels and uses an empirical relationship found between the mid-ER and the visible channels to estimate surface reflectance in the visible from the mid-HZ reflectance measured by satellite. The method employs new aerosol models for industrial, smoke and dust aerosol. The process for retrieving aerosol over the ocean makes use of the wide spectral band from 0.55-2.13 microns and a look-up table constructed from combinations of five accumulation modes and five coarse modes. Both the over land and over ocean algorithms have been validated with satellite and airborne radiance measurements. We estimate that MODIS will be able to measure aerosol optical thickness (t) to within 0.05 +/- 0.2t over land and to within 0.05 +/- 0.05t over ocean. Much of the earth's surface is located far from aerosol sources and experience very low aerosol optical thickness. Will the accuracy expected from MODIS retrievals be sufficient to measure the global aerosol direct and indirect forcing? We are attempting to answer this question using global model results and cloud climatology.

Remer, L. A.↗
NASA NTRS1995

Raman lidar and sun photometer measurements of aerosols and water vapor during the ARM RCS experiment

The first Atmospheric Radiation Measurement (ARM) Remote Cloud Study (RCS) Intensive Operations Period (IOP) was held during April 1994 at the Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site near Lamont, Oklahoma. This experiment was conducted to evaluate and calibrate state-of-the-art, ground based remote sensing instruments and to use the data acquired by these instruments to validate retrieval algorithms developed under the ARM program. These activities are part of an overall plan to assess general circulation model (GCM) parameterization research. Since radiation processes are one of the key areas included in this parameterization research, measurements of water vapor and aerosols are required because of the important roles these atmospheric constituents play in radiative transfer. Two instruments were deployed during this IOP to measure water vapor and aerosols and study their relationship. The NASA/Goddard Space Flight Center (GSFC) Scanning Raman Lidar (SRL) acquired water vapor and aerosol profile data during 15 nights of operations. The lidar acquired vertical profiles as well as nearly horizontal profiles directed near an instrumented 60 meter tower. Aerosol optical thickness, phase function, size distribution, and integrated water vapor were derived from measurements with a multiband automatic sun and sky scanning radiometer deployed at this site.

Ferrare, R. A.↗
NASA NTRS2014

Remote Sensing of Cloud Top Height from SEVIRI: Analysis of Eleven Current Retrieval Algorithms

The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) - a crucial parameter to estimate the thermal cloud radiative forcing - can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 kilometers lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from −0.8 kilometers to 0.6 kilometers. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.

Hamann, U.↗
NASA NTRS2014

Cloud Thermodynamic Phase Detection with Polarimetrically Sensitive Passive Sky Radiometers

The primary goal of this project has been to investigate if ground-based visible and near-infrared passive radiometers that have polarization sensitivity can determine the thermodynamic phase of overlying clouds, i.e. if they are comprised of liquid droplets or ice particles. While this knowledge is important by itself for our understanding of the global climate, it can also help improve cloud property retrieval algorithms that use total (unpolarized) radiance to determine Cloud Optical Depth (COD). This is a potentially unexploited capability of some instruments in the NASA Aerosol Robotic Network (AERONET), which, if practical, could expand the products of that global instrument network at minimal additional cost. We performed simulations that found, for zenith observations, cloud thermodynamic phase is often expressed in the sign of the Q component of the Stokes polarization vector. We chose our reference frame as the plane containing solar and observation vectors, so the sign of Q indicates the polarization direction, parallel (negative) or perpendicular (positive) to that plane. Since the quantity of polarization is inversely proportional to COD, optically thin clouds are most likely to create a signal greater than instrument noise. Besides COD and instrument accuracy, other important factors for the determination of cloud thermodynamic phase are the solar and observation geometry (scattering angles between 40 and 60 degrees are best), and the properties of ice particles (pristine particles may have halos or other features that make them difficult to distinguish from water droplets at specific scattering angles, while extreme ice crystal aspect ratios polarize more than compact particles). We tested the conclusions of our simulations using data from polarimetrically sensitive versions of the Cimel 318 sun photometerradiometer that comprise AERONET. Most algorithms that exploit Cimel polarized observations use the Degree of Linear Polarization (DoLP), not the individual Stokes vector elements (such as Q). For this reason, we had no information about the accuracy of Cimel observed Q and the potential for cloud phase determination. Indeed, comparisons to ceilometer observations with a single polarized spectral channel version of the Cimel at a site in the Netherlands showed little correlation. Comparisons to Lidar observations with a more recently developed, multi-wavelength polarized Cimel in Maryland, USA, show more promise. This divergence between simulations and observations has prompted us to begin the development of a small test instrument called the Sky Polarization Radiometric Instrument for Test and Evaluation (SPRITE). This instrument is specifically devoted to the accurate observation of Q, and the testing of calibration and uncertainty assessment techniques, with the ultimate goal of understanding the practical feasibility of these measurements.

Knobelspiesse, Kirk D.↗
NASA NTRSDate not supplied

Mapping Modeled Aerosol Species to Measured Lidar Ratios for the MIRA Project

Overarching goal: Create Climatological Lidar Ratio Maps for CALIPSO - Since CALIPSO is an elastic backscatter lidar, it is necessary to select a priori aerosol lidar ratios prior to computing extinction profiles. - Presently, the CALIPSO lidar ratio selection process uses a single lidar ratio for each of the 7 CALIPSO aerosol types in the troposphere. - Consequently, regional and seasonal variability of the lidar ratio is very limited in the present CALIPSO algorithm. - Here, we describe an approach for creating new climatological lidar ratio maps for each of the CALIPSO aerosol types using MODIS-CALIPSO constrained retrievals and global model simulations. - The purpose is to eventually replace the seven single-valued lidar ratios for the CALIPSO types with latitude- and longitude-dependent maps for each of the seven types.

G. L. Schuster↗
NASA NTRS2017

Advanced Characterisation of Aerosol Size Properties from Measurements of Spectral Optical Depth Using the GRASP Algorithm

This study evaluates the potential of using aerosol optical depth (τ (sub a)) measurements to characterise the microphysical and optical properties of atmospheric aerosols. With this aim, we used the recently developed GRASP (Generalized Retrieval of Aerosol and Surface Properties) code for numerical testing of six different aerosol models with different aerosol loads. The direct numerical simulations (self-consistency tests) indicate that the GRASP-AOD retrieval provides modal aerosol optical depths (fine and coarse) to within 0.01 of the input values. The retrieval of the fine-mode radius, width and volume concentration are stable and precise if the real part of the refractive index is known. The coarse-mode properties are less accurate, but they are significantly improved when additional a priori information is available. The tests with random simulated errors show that the uncertainty in the bimodal log-normal size distribution parameters increases as the aerosol load decreases. Similarly, the reduction in the spectral range diminishes the stability of the retrieved parameters. In addition to these numerical studies, we used optical depth observations at eight AERONET locations to validate our results with the standard AERONET inversion products. We found that bimodal log-normal size distributions serve as useful input assumptions, especially when the measurements have inadequate spectral coverage and/or limited accuracy, such as moon photometry. Comparisons of the mode median radii between GRASP-AOD and AERONET indicate average differences of 0.013µm for the fine mode and typical values of 0.2–0.3µm for the coarse mode. The dominant mode (i.e. fine or coarse) indicates a 10 % difference in mode radii between the GRASP-AOD and AERONET inversions, and the average of the difference in volume concentration is around 17 % for both modes. The retrieved values of the fine-mode τ(sub a)(500) using GRASP-AOD are generally between those values obtained by the standard AERONET inversion and the values obtained by the AERONET spectral deconvolution algorithm (SDA), with differences typically lower than 0.02 between GRASP-AOD and both algorithms. Finally, we present some examples of application of GRASP-AOD inversion using moon photometry and the airborne PLASMA sun photometer during the ChArMEx summer 2013 campaign in the western Mediterranean.

Torres, Benjamin↗
NASA NTRS2020

Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (r(e): R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of r(e) (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.

SPECTRAL-RESOLUTION LIDAR↗
NASA NTRS2011

MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning Algorithms

To monitor the earth atmosphere and its surface changes, satellite based instruments collect continuous data. While some of the data is directly used, some others such as aerosol properties are indirectly retrieved from the observation data. While retrieved variables (RV) form very powerful products, they don't come without obstacles. Different satellite viewing geometries, calibration issues, dynamically changing atmospheric and earth surface conditions, together with complex interactions between observed entities and their environment affect them greatly. This results in random and systematic errors in the final products.

Albayrak, Arif↗
NASA NTRS2016

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations: 2. Retrieval Evaluation

An infrared-based optimal estimation (OE-IR) algorithm for retrieving ice cloud properties is evaluated. Specifically, the implementation of the algorithm with MODerate resolution Imaging Spectroradiometer (MODIS) observations is assessed in comparison with the operational retrieval products from MODIS on the Aqua satellite (MYD06), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and the Imaging Infrared Radiometer (IIR); the latter two instruments fly on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in the Afternoon Constellation (A-Train) with Aqua. The results show that OE-IR cloud optical thickness (tau) and effective radius (r(sub eff)) retrievals perform best for ice clouds having 0.5 < tau< 7 and r(sub eff) < 50microns. For global ice clouds, the averaged retrieval uncertainties of tau and r(sub eff) are 19% and 33%, respectively. For optically thick ice clouds with tau larger than 10, however, the tau and r(sub eff) retrieval uncertainties can exceed 30% and 50%, respectively. For ice cloud top height (h), the averaged global uncertainty is 0.48km. Relatively large h uncertainty (e.g., > 1km) occurs for tau < 0.5. Analysis of 1month of the OE-IR retrievals shows large tau and r(sub eff) uncertainties in storm track regions and the southern oceans where convective clouds are frequently observed, as well as in high-latitude regions where temperature differences between the surface and cloud top are more ambiguous. Generally, comparisons between the OE-IR and the operational products show consistent tau and h retrievals. However, obvious differences between the OE-IR and the MODIS Collection 6 r(sub eff) are found.

cloud optics↗
NASA NTRSDate not supplied

The PACE-MAPP Polarized Ocean Bio-optical Model

The MAPP polarimeter retrieval algorithm, originally developed for the airborne NASA GISS Research Scanning Polarimeter, is being adapted to the PACE observing system to allow for a simultaneous retrieval of aerosol, thin cirrus, and ocean products using both PACE polarimeters and the OCI shortwave infrared channels. Here, we describe the polarized ocean bio-optical model we have developed for PACE-MAPP, which is designed to use a combination of homogenous and coated spheres to more accurately model the absorption and backscattering properties of algal and non-algal particles, and which also includes absorption by colored dissolved organic matter. We first use Lorenz-Mie computations to create lookup tables to accurately and efficiently characterize the optical properties of homogeneous and coated spheres embedded in water for PACE. We then use these lookup tables to develop a bio-optical model that consists of an external mixture of homogeneous (non-algal) and coated (algal) spheres. Lastly, we use the bio-optical model in forward radiative transfer computations to perform inversions of MODIS remote sensing reflectance for a variety of ocean regions. We explore the sensitivity of these inversions to ocean constituent inherent optical properties.

James Allen↗
NASA NTRS2021

A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products from MODIS

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03),central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals thanMOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereasMOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

MAIAC↗
NASA NTRS2014

Retrieval, Inter-Comparison, and Validation of Above-Cloud Aerosol Optical Depth from A-train Sensors

Absorbing aerosols produced from biomass burning and dust outbreaks are often found to overlay lower level cloud decks and pose greater potentials of exerting positive radiative effects (warming) whose magnitude directly depends on the aerosol loading above cloud, optical properties of clouds and aerosols, and cloud fraction. Recent development of a 'color ratio' (CR) algorithm applied to observations made by the Aura/OMI and Aqua/MODIS constitutes a major breakthrough and has provided unprecedented maps of above-cloud aerosol optical depth (ACAOD). The CR technique employs reflectance measurements at TOA in two channels (354 and 388 nm for OMI; 470 and 860 nm for MODIS) to retrieve ACAOD in near-UV and visible regions and aerosol-corrected cloud optical depth, simultaneously. An inter-satellite comparison of ACAOD retrieved from NASA's A-train sensors reveals a good level of agreement between the passive sensors over the homogeneous cloud fields. Direct measurements of ACA such as carried out by the NASA Ames Airborne Tracking Sunphotometer (AATS) and Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) can be of immense help in validating ACA retrievals. We validate the ACA optical depth retrieved using the CR method applied to the MODIS cloudy-sky reflectance against the airborne AATS and 4STAR measurements. A thorough search of the historic AATS-4STAR database collected during different field campaigns revealed five events where biomass burning, dust, and wildfire-emitted aerosols were found to overlay lower level cloud decks observed during SAFARI-2000, ACE-ASIA 2001, and SEAC4RS- 2013, respectively. The co-located satellite-airborne measurements revealed a good agreement (RMSE less than 0.1 for AOD at 500 nm) with most matchups falling within the estimated uncertainties in the MODIS retrievals. An extensive validation of satellite-based ACA retrievals requires equivalent field measurements particularly over the regions where ACA are often observed from satellites, i.e., south-eastern Atlantic Ocean, tropical Atlantic Ocean, northern Arabian Sea, South-East and North-East Asia.

validation↗
DOE OSTI2020

Improving Predictability of Mixed-Phase Clouds and Aerosol Interactions in the Community Earth System Model (CESM) with ARM Measurements

The objective of this project is to improve the simulation and predictability of mixed-phase clouds and aerosol interactions in the Community Earth System Model (CESM) through comparisons with the DOE ARM observations. There are four major goals of the proposed study: (1) Improve the representation of ice microphysical processes in mixed-phase clouds; (2) Develop a long-term multi-sensor mixed-phase cloud dataset; (3) Test the performance of improved ice microphysics in CESM-CAM5 with the ARM data; and (4) Examine mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5. In this project, we have (1) Improved the representation of ice microphysical processes in mixed-phase clouds that include ice nucleation, ice depositional growth through the Wegener–Bergeron–Findeisen (WBF) process, feedbacks between cloud microphysics, dynamics and radiation, and provides links to aerosol properties in the Community Earth System Model (CESM)-Community Atmosphere Model version 5 (CAM5); (2) Developed a long-term multi-sensor mixed-phase cloud dataset. The mixed-phase cloud retrieval algorithms are improved by refining the phase determinations and ice number concentration retrievals. A multi-year mixed-phase cloud observation dataset (1999-2004; October 2013-February 2017) is regenerated at the ARM Barrow site with the improved algorithms. The dataset has been used in the global model evaluation to improve the model parameterizations; (3) Tested the performance of improved ice microphysics in CESM-CAM5 with the ARM data. CESM-CAM5 is run in the DOE Cloud-Associated Parameterizations Testbed (CAPT) and in the single-column model (SCM) mode to facilitate comparison with ARM observations. Multi-year CAPT simulations of seasonal variations of cloud microphysical properties, cloud liquid water path (LWP) and ice water path (IWP), cloud longwave and shortwave forcings, cloud occurrence frequency are evaluated against the ARM multi-sensor mixed-phase cloud retrievals; and (4) Examined mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5 that include the impacts of different ice nucleation mechanisms, sensitivity to dust IN concentration, treatment of the WBF process on mixed-phase cloud properties. Aerosol effect on mixed-phase clouds through the liquid phase (droplet activation) and ice phase processes (e.g., the glaciation indirect effect) is investigated.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Study of MPLNET-Derived Aerosol Climatology over Kanpur, India, and Validation of CALIPSO Level 2 Version 3 Backscatter and Extinction Products

The level 2 aerosol backscatter and extinction profiles from the NASA Micropulse Lidar Network (MPLNET) at Kanpur, India, have been studied from May 2009 to September 2010. Monthly averaged extinction profiles from MPLNET shows high extinction values near the surface during October March. Higher extinction values at altitudes of 24 km are observed from April to June, a period marked by frequent dust episodes. Version 3 level 2 Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol profile products have been compared with corresponding data from MPLNET over Kanpur for the above-mentioned period. Out of the available backscatter profiles, the16 profiles used in this study have time differences less than 3 h and distances less than 130 km. Among these profiles, four cases show good comparison above 400 m with R2 greater than 0.7. Comparison with AERONET data shows that the aerosol type is properly identified by the CALIOP algorithm. Cloud contamination is a possible source of error in the remaining cases of poor comparison. Another source of error is the improper backscatter-to-extinction ratio, which further affects the accuracy of extinction coefficient retrieval.

extinction profiles↗
NASA NTRS2004

Ozone and Aerosol Retrieval from Backscattered Ultraviolet Radiation

In this presentation we will discuss the techniques to estimate total column ozone and aerosol absorption optical depth from the measurements of backscattered ultraviolet (buv) radiation. The total ozone algorithm has been used to create a unique record of the ozone layer, spanning more than 3 decades, from a series of instruments (BUV, SBUV, TOMS, SBUV/2) flown on NASA, NOAA, Japanese and Russian satellites. We will discuss how this algorithm can be considered a generalization of the well-known Dobson/Brewer technique that has been used to process data from ground-based instruments for many decades, and how it differs from the DOAS techniques that have been used to estimate vertical column densities of a host of trace gases from data collected by GOME and SCIAMACHY instruments. The BUV aerosol algorithm is most suitable for the detection of UV absorbing aerosols (smoke, desert dust, volcanic ash) and is the only technique that can detect aerosols embedded in clouds. This algorithm has been used to create a quarter century record of aerosol absorption optical depth using the BUV data collected by a series of TOMS instruments. We will also discuss how the data from the OM1 instrument launched on July 15,2004 will be combined with data from MODIS and CALIPSO lidar data to enhance the accuracy and information content of satellite-derived aerosol measurements. The OM1 and MODIS instruments are currently flying on EOS Aura and EOS Aqua satellites respectively, part of a constellation of satellites called the "A-train". The CALIPSO satellite is expected to join this constellation in mid 2005.

Bhartia, Pawan K.↗
NASA NTRS2011

Analysis of MAIAC Dust Aerosol Retrievals from MODIS Over North Africa

An initial comparison of aerosol optical thickness over North Africa for year 2007 was performed between the Deep Blue and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms complimented with MISR and OMI data. The new MAIAC algorithm has a better sensitivity to the small dust storms than the DB algorithm, but it also has biases in the brightest desert regions indicating the need for improvement. The quarterly averaged AOT values in the Bodele depression and western downwind transport region show a good agreement among MAIAC, MISR and OMI data, while the DB algorithm shows a somewhat different seasonality.

Lyapustin, A.↗