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NASA NTRS1999

A Global Data Assimilation System for Atmospheric Aerosol

We will give an overview of an aerosol data assimilation system which combines advances in remote sensing of atmospheric aerosols, aerosol modeling and data assimilation methodology to produce high spatial and temporal resolution 3D aerosol fields. Initially, the Goddard Aerosol Assimilation System (GAAS) will assimilate TOMS, AVHRR and AERONET observations; later we will include MODIS and MISR. This data assimilation capability will allows us to integrate complementing aerosol observations from these platforms, enabling the development of an assimilated aerosol climatology as well as a global aerosol forecasting system in support of field campaigns. Furthermore, this system provides an interactive retrieval framework for each aerosol observing satellites, in particular TOMS and AVHRR. The Goddard Aerosol Assimilation System (GAAS) takes advantage of recent advances in constituent data assimilation at DAO, including flow dependent parameterizations of error covariances and the proper consideration of model bias. For its prognostic transport model, GAAS will utilize the Goddard Ozone, Chemistry, Aerosol, Radiation and Transport (GOCART) model developed at NASA/GSFC Codes 916 and 910.3. GOCART includes the Lin-Rood flux-form, semi-Langrangian transport model with parameterized aerosol chemistry and physical processes for absorbing (dust and black carbon) and non-absorbing aerosols (sulfate and organic carbon). Observations and model fields are combined using a constituent version of DAO's Physical-space Statistical Analysis System (PSAS), including its adaptive quality control system. In this talk we describe the main components of this assimilation system and present preliminary results obtained by assimilating TOMS data.

daSilva, Arlindo↗

NASA NTRS2021

Columnar Aerosol Types and Compositions Over Peninsular Southeast Asia Based on Long-Term AERONET Data

Aerosol chemical components such as black carbon (BC) and brown carbon (BrC) regulate aerosol optical properties, which in turn drive the atmospheric radiative forcing estimations due to aerosols. In this study, we used the long-term measurements from AERONET (Aerosol Robotic Network) to better understand the aerosol types and composition with respect to their seasonal and spatial variabilities in peninsular Southeast Asia (PSEA, here defined as Vietnam, Cambodia, Thailand, Laos, and Myanmar). Two methods (i.e., aerosol type cluster and aerosol component retrieval) were applied to determine the aerosol type and chemical composition during the biomass-burning (BB) season. AERONET sites in northern PSEA showed a higher AOD (aerosol optical depth) compared to that of southern PSEA. Differences in land use pattern, geographic location, and weather regime caused much of the aerosol variability over PSEA. Lower single-scattering albedo (SSA) and higher fine-mode fraction (FMF) values were observed in February and March, suggesting the predominance of BB type aerosols with finer and stronger absorbing particles during the dry season. However, we also found that the peak BB month (i.e., March) in northern PSEA may not coincide with the lowest SSA once dust particles have mixed with the other aerosols. Furthermore, we investigated two severe BB events in March of 2014 and 2015, revealing a significant BrC fraction during BB event days. On high AOD days, although the BC fraction was high, the BrC fraction remained low due to lack of aerosol aging. This study highlights the dominance of carbonaceous aerosols in the PSEA atmosphere during the BB season, while also revealing that transported dust particles and BrC aerosol aging may introduce uncertainties into the aerosol radiative forcing calculation.

Sheng-Hsiang Wang↗

NASA NTRS2012

Estimating Marine Aerosol Particle Volume and Number from Maritime Aerosol Network Data

As well as spectral aerosol optical depth (AOD), aerosol composition and concentration (number, volume, or mass) are of interest for a variety of applications. However, remote sensing of these quantities is more difficult than for AOD, as it is more sensitive to assumptions relating to aerosol composition. This study uses spectral AOD measured on Maritime Aerosol Network (MAN) cruises, with the additional constraint of a microphysical model for unpolluted maritime aerosol based on analysis of Aerosol Robotic Network (AERONET) inversions, to estimate these quantities over open ocean. When the MAN data are subset to those likely to be comprised of maritime aerosol, number and volume concentrations obtained are physically reasonable. Attempts to estimate surface concentration from columnar abundance, however, are shown to be limited by uncertainties in vertical distribution. Columnar AOD at 550 nm and aerosol number for unpolluted maritime cases are also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) data, for both the present Collection 5.1 and forthcoming Collection 6. MODIS provides a best-fitting retrieval solution, as well as the average for several different solutions, with different aerosol microphysical models. The average solution MODIS dataset agrees more closely with MAN than the best solution dataset. Terra tends to retrieve lower aerosol number than MAN, and Aqua higher, linked with differences in the aerosol models commonly chosen. Collection 6 AOD is likely to agree more closely with MAN over open ocean than Collection 5.1. In situations where spectral AOD is measured accurately, and aerosol microphysical properties are reasonably well-constrained, estimates of aerosol number and volume using MAN or similar data would provide for a greater variety of potential comparisons with aerosol properties derived from satellite or chemistry transport model data.

Sayer, A. M.↗

NASA NTRS1989

Light scattering characteristics of various aerosol types derived from multiple wavelength lidar observations

The present study demonstrates the potential of a multiple-wavelength lidar for discriminating between several aerosol types on the basis of the wavelength dependence of the aerosol backscatter coefficient. The two-component lidar equation was solved under the assumption of similarity in the derived profiles of backscatter coefficients for each wavelength. It is shown that a three-wavelength lidar system operating at 300, 600, and 1064nm can provide unique information for discriminating between various aerosol types (continental, maritime, Saharan-dust, stratospheric aerosols in a tropopause fold event, and tropical forest aerosols). Mie calculations were made using in situ aerosol data and aerosol models to compare with the lidar results. The disagreement between the theoretical and empirical results in some cases was substantial. These differences may be partly due to uncertainties in the lidar data analysis and aerosol characteristics and also due to the conventional assumption of aerosol sphericity for the aerosol Mie calculations.

Sasano, Yasuhiro↗

NASA NTRS1980

Analysis and interpretation of lidar observations of the stratospheric aerosol

Data obtained with a 48 in. telescope lidar system are compared with results obtained using a one-dimensional stratospheric aerosol model to analyze various microphysical processes influencing the formation of this aerosol. Special attention is given to the following problems: (1) how lidar data can help determine the composition of the aerosol particles and (2) how the layer corresponds to temperature profile variations. The lidar record during the period 1974 to 1979 shows a considerable decrease of the peak value of the backscatter ratio. Seasonal variations in the aerosol layer and a gradual decrease in stratospheric loading are observed. The aerosol model simulates a background stratospheric aerosol layer, and it predicts stratospheric aerosol concentrations and compositions. Numerical experiments are carried out by using the model and by comparing the theoretical results with the experimentally obtained lidar record. Comparisons show that the backscatter profile is consistent with the composition when the particles are sulfuric acid and water; it is not consistent with an ammonium sulfate composition. It is shown that the backscatter ratio is not sensitive to the composition or stratospheric loading of condensation nuclei such as meteoritic debris.

Hamill, P.↗

NASA NTRS1994

The post-pinatubo evolution of stratospheric aerosol surface area density as inferred from SAGE 2

Following the eruption of Mount Pinatubo in June of 1991, the aerosol mass loading of the stratosphere increased from -1 Mt to approximately 30 Mt. This change in aerosol loading was responsible for numerous radiative and chemical changes observed within the stratosphere. As a result, the ability to quantify aerosol properties on a global basis during this period is important. Aerosol surface area density is a critical parameter in governing the rates of heterogeneous reactions, such as ClONO2 plus H2O yields HNO3 plus HOCl, which influence the stratospheric abundance of ozone. Following the eruption of Mt. Pinatubo, measurements by the Stratospheric Aerosol and Gas Experiment (SAGE 2) indicated that the stratospheric aerosol surface area density increased by as much as a factor of 100. Using SAGE 2 multi-wavelength aerosol extinction data, aerosol surface area density as well as mass are derived for the period following the eruption of Mt. Pinatubo through the present.

Poole, L. R.↗

NASA NTRS1985

A comparison of SAGE I data during the stratospheric warming of February-March, 1979

The fine scale vertical structure of SAGE I ozone and aerosol data during a stratospheric warming is investigated using meteorological and SBUV ozone data. By stratifying the ozone and aerosol data for a limited time period, a comparison of the structure of profiles becomes possible under different meteorological conditions. For example, the cold air region shows more laminated structures than the other regions. In addition, vertical motions calculated at the same locations as the SAGE profiles show that they are consistent with variances found in the ozone and aerosol data.

Nagatani, R. M.↗

NASA NTRS2007

Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

Leptoukh, Gregory↗

NASA NTRS1984

Sulphur-rich volcanic eruptions and stratospheric aerosols

Data from direct measurements of stratospheric optical depth, Greenland ice-core acidity, and volcanological studies are compared, and it is shown that relatively small but sulfur-rich volcanic eruptions can have atmospheric effects equal to or even greater than much larger sulfur-poor eruptions. These small eruptions are probably the most frequent cause of increased stratospheric aerosols. The possible sources of the excess sulfur released in these eruptions are discussed.

Rampino, M. R.↗

NASA NTRS2004

Optimal Aerosol Parameterization for Remote Sensing Retrievals

We have developed a new algorithm for the retrieval of aerosol and gases from SAGE It1 solar transmission measurements. This algorithm improves upon the NASA operational algorithm in several key aspects, including solving the problem non-linearly and incorporating a new methodology for separating the contribution of aerosols and gases. In order to extract aerosol information we have built a huge database of aerosol models for both stratospheric and tropospheric aerosols, and polar stratospheric cloud particles. This set of models allows us to calculate a vast range of possible extinction spectra for aerosols. and from these, derive a set of eigenvectors which then provide the basis set used in our inversion algorithm. Our aerosol algorithm and retrievals are described in several articles (listed in References Section) published under this grant. In particular they allow us to analyze the spectral properties of aerosols and PSCs and ultimately derive their microphysical properties. We have found some considerable differences between our spectra and the ones derived from the SAGE III operational algorithm. These are interesting as they provide an independent check on the validity of published aerosol data and, in particular, on their associated uncertainties. In order to understand these differences, we are assembling independent aerosol data from other sources with which to make comparisons. We have carried out extensive comparisons of our ozone retrievals with both SAGE III and independent lidar, ozonesonde, and satellite measurements (Polyakov et al., 2004). These show very good agreement throughout the stratosphere and help to quantify differences which can be attributed to natural variation in ozone versus that produced by algorithmic differences. In the mid - upper stratosphere, agreement with independent data was generally within 5 - 20%. but in the lower stratosphere the differences were considerably larger. We believe that a large proportion of this discrepancy in the lower stratosphere is attributable to natural variation, and is also seen in comparisons between lidar and ozonesonde measurements. NO2 profiles obtained with our algorithm were compared to those obtained through the SAGE III operational algorithm and exhibited differences of 20 - 40%. Our retrieved profiles agree with the HALOE NO2 measurements significantly better than those of the operational retrieval. In other work (described below), we are extending our aerosol retrievals into the infrared regime and plan to perform retrievals from combined uv-visible-infrared spectra. This work will allow us to use the spectra to derive the size and composition of aerosols, and we plan to employ our algorithms in the analysis of PSC spectra. We are presently also developing a limb-scattering algorithm to retrieve aerosol data from limb measurements of solar scattered radiation.

Newchurch, Michael J.↗

NASA NTRS2003

MODIS Satellite Data and GOCART Model Characterization of the Global Aerosol

Recently produced daily MODIS aerosol data for the whole year of 2001 are used to show the concentration and dynamics of aerosol over ocean and large parts of the continents. The data were validated against the Aerosol Robotic Network (AERONET) measurements over land and ocean. Monthly averages and a movie based on the daily data are produced and used to demonstrate the spatial and temporal evolution of aerosol. The MODIS wide spectral range is used to distinguish fine smoke and pollution aerosol from coarse dust and salt. The aerosol is observed above ocean and land. The movie produced from the MODIS data provides a new dimension to aerosol observations by showing the dynamics of the system. For example in February smoke and dust emitted from the Sahel and West Africa is shown to travel to the North-East Atlantic. In April heavy dust and pollution from East Asia is shown to travel to North America. In May-June pollution and dust play a dynamical dance in the Arabian Sea and Bay of Bengal. In Aug-September smoke from South Africa and South America is shown to pulsate in tandem and to periodically to be transported to the otherwise pristine Southern part of the Southern Hemisphere. The MODIS data are compared with the GOCART model and used to estimate the first observation based direct anthropogenic radiative forcing of climate by aerosol.

Kaufman, Yoram↗

NASA NTRS2010

Large Scale Covariability in the Aerosol-SST-Precipitation Relation: A Satellite Perspective

Although large scale aerosol effects on precipitation have attracted increasing attention in recent years, significant uncertainties still remain in the current understanding of'such effects. One of the factors contributing to these uncertainties is the availability of aerosol data sets that are sufficiently long, stable, and comprehensive to be used for climate analysis. Such data sets are crucial for any long-term studies that are needed to demonstrate the statistical significance of large scale aerosol effects on precipitation when compared to the effects from other climate factors, such as remote or local sea surface temperature (SST) variability (ENSO. etc.). A longterm consistent aerosol product that complements the existing MODIS and AVHRR aerosol products is therefore needed to minimize aerosol data uncertainty issues for such Studies. The SeaWiFS instrument was primarily designed to measure ocean color, and it has been the mayor source for providing a comprehensive global data set of such measurements since its launch in 1997. Besides being used in this capacity, however, the well-calibrated set of radiances measured in the wavelength range from the visible (0.41 m) to the near infrared (0.87 m) is also well suited to creating long-term climate data records of aerosols. Recently, a new 13-year SeaWiFS aerosol optical depth dataset has been developed that expands its capacity to be used for such atmospheric studies. This SeaWiFS aerosol product provides us with an unprecedented opportunity to examine the covariability of aerosol, SST and precipitation on both a regional and global scale. We also use concurrent independent SST (NOAA Optimum Interpolation, OI, SST V2) and precipitation (GPCP V2) datasets for this study. We will present and discuss results from this study over several aerosol hot spot regions ( -,North Atlantic. South Atlantic, Western Pacific, Indian Ocean etc.) during the SeaWIFS data period (1997 -- current) from this study will be presented and discussed. These results will highlight the value and impact of this unique product to climate studies

Hsu, N. Christina↗

NASA NTRS1987

SAGE II - An overview

The Stratospheric Aerosol and Gas Experiment II (SAGE II) aboard the Earth Radiation Budget Satellite was launched from Shuttle in October 1984. SAGE II is a seven-channel sun-photometer measuring stratospheric aerosols, ozone, water vapor, and nitrogen dioxide during each spacecraft sunrise and sunset. In addition to stratospheric information, mid-tropospheric and higher water vapor, ozone, and aerosol data are being produced in cloud-free regions, and cloud data everywhere else. Aerosol information is being produced at three wavelengths and, together with water vapor data, is providing a global microphysical description of the aerosol.

Mccormick, M. P.↗

NASA NTRSDate not supplied

The Plankton, Aerosol, Cloud, Ocean Ecosystem (Pace) Mission Overview: From Concept to Launch

Abstract for Technical Review: The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). NASA Headquarters directed the mission development and implementation to the NASA Goddard Space Flight Center (GSFC) with guidance to utilize a Design-to-Cost (DTC) process. Mission studies were conducted to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. The PACE project office at NASA’s GSFC was responsible for the satellite development, launch and commissioning. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which includes field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The PACE observatory is comprised of three instruments, the Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone) and the PACE spacecraft. The observatory has been commissioned and is performing on-orbit science data collection operations. Summary for Program: The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024 under the leadership of the NASA Goddard Space Flight Center (GSFC), the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s. The PACE observatory is comprised of the Ocean Color Instrument (OCI), the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone), along with the PACE spacecraft.

Robert H Estep, Jr↗

NASA NTRSDate not supplied

Mission Overview and Status from Concept to Launch

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). NASA Headquarters directed the mission development and implementation to the NASA Goddard Space Flight Center (GSFC) with guidance to utilize a Design-to-Cost (DTC) process. Mission studies were conducted to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. The PACE project office at NASA’s GSFC was responsible for the satellite development, launch and commissioning. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which includes field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The PACE observatory is comprised of three instruments, the Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone) and the PACE spacecraft. The observatory has been commissioned and is performing on-orbit science data collection operations. Summary for Program: The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024 under the leadership of the NASA Goddard Space Flight Center (GSFC), the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s. The PACE observatory is comprised of the Ocean Color Instrument (OCI), the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone), along with the PACE spacecraft.

Robby Estep↗

NASA NTRS2013

GEOS-5 Aerosol Modeling Data Assimilation: Update on Recent and Future Development

No abstract available

Data Assimilation↗

NASA NTRS2006

Using global aerosol models and satellite data for air quality studies: Challenges and data needs

Aerosol particles, also known as PM2.5 (particle diameter less than 2.5 pm) and PM10 (particle diameter less than 10 pm), are one of the key atmospheric components that determines air quality. Yet, air quality forecasts for PM are still in their infancy and remain a challenging task. It is difficult to simply relate PM levels to local meteorological conditions, and large uncertainties exist in regional air quality model emission inventories and initial and boundary conditions. Especially challenging are periods when a significant amount of aerosol comes from outside the regional modeling domain through long-range transport. In the past few years, NASA has launched several satellites with global aerosol measurement capabilities, providing large-scale chemical weather pictures. NASA has also supported development of global models which simulate atmospheric transport and transformation processes of important atmospheric gas and aerosol species. I will present the current modeling and satellite capabilities for PM2.5 studies, the possibilities and challenges in using satellite data for PM2.5 forecasts, and the needs of future remote sensing data for improving air quality monitoring and modeling.

Chin, Mian↗

NASA NTRS1998

LASE measurements of aerosols and water vapor during TARFOX

The TARFOX (Tropospheric Aerosol Radiative Forcing Observational Experiment) intensive field campaign was designed to reduce uncertainties in estimates of the effects of anthropogenic aerosols on climate by measuring direct radiative effects and the optical, physical, and chemical properties of aerosols [1]. TARFOX was conducted off the East Coast of the United States between July 10-31, 1996. Ground, aircraft, and satellite-based sensors measured the sensitivity of radiative fields at various atmospheric levels to aerosol optical properties (i.e., optical thickness, phase function, single-scattering albedo) and to the vertical profile of aerosols. The LASE (Lidar Atmospheric Sensing Experiment) instrument, which was flown on the NASA ER-2 aircraft, measured vertical profiles of total scattering ratio and water vapor during a series of 9 flights. These profiles were used in real-time to help direct the other aircraft to the appropriate altitudes for intensive sampling of aerosol layers. We have subsequently used the LASE aerosol data to derive aerosol backscattering and extinction profiles. Using these aerosol extinction profiles, we derived estimates of aerosol optical thickness (AOT) and compared these with measurements of AOT from both ground and airborne sun photometers and derived from the ATSR-2 (Along Track and Scanning Radiometer 2) sensor on ERS-2 (European Remote Sensing Satellite-2). We also used the water vapor mixing ratio profiles measured simultaneously by LASE to derive precipitable water vapor and compare these to ground based measurements.

Ferrare, Richard A.↗