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NASA NTRS1991

A lidar equipped with photon counter for measuring stratospheric aerosols

A large lidar system was developed at the Anhui Institute of Optics and Fine Mechanics. It is composed of a high power frequency doubled Nd:YAG laser (120 mJ at 532 nm, 1 mrad), a 651 mm diameter receiving telescope, and a 64 channel photon counter. The lidar observations are controlled by an IBM PC/AT computer. One of the projects is to use the lidar system to make regular observations of stratospheric aerosols. Profiles of scattering ratio have been obtained since November 1989. Meanwhile, an Xe-Cl laser (308 nm wavelength) was assembled to allow the lidar to make measurements of vertical ozone distributions.

Zhou, Jun↗
NASA NTRS1992

Lidar Observations of the Pinatubo Volcanic Cloud over Hampton, Virginia

A series of eruptions of Mt. Pinatubo climaxed in a cataclysmic eruption on June 15-16, which greatly perturbed the stratospheric aerosol layer. These eruptions yielded an estimated 20 megatons of SO2, which is three times the amount produced by El Chichon. Lidar measurements taken by the 48-inch lidar system at Langley Research Center show the vertical distribution, intensity and spread of the Pinatubo aerosol layers over the middle latitude locations. A few observations have also been made using an airborne lidar system which measures scattering and depolarization. The magnitude and transport time of the volcanic aerosols following Pinatubo and El Chichon are compared. In addition, comparisons are made between the ground-based lidar measurements and near-coincident Stratospheric Aerosol and Gas Experiment (SAGE) 2 satellite observations of the Pinatubo aerosol layers.

Osborn, M. T.↗
NASA NTRS2001

Full-Time, Eye-Safe Cloud and Aerosol Lidar Observation at Atmospheric Radiation Measurement Program Sites: Instruments and Data Analysis

Atmospheric radiative forcing, surface radiation budget, and top of the atmosphere radiance interpretation involves a knowledge of the vertical height structure of overlying cloud and aerosol layers. During the last decade, the U.S. Department of Energy through I the Atmospheric Radiation Measurement (ARM) program has constructed four long- term atmospheric observing sites in strategic climate regimes (north central Oklahoma, In Barrow. Alaska, and Nauru and Manus Islands in the tropical western Pacific). Micro Pulse Lidar (MPL) systems provide continuous, autonomous observation of all significant atmospheric cloud and aerosol at each of the central ARM facilities. Systems are compact and transmitted pulses are eye-safe. Eye-safety is achieved by expanding relatively low-powered outgoing Pulse energy through a shared, coaxial transmit/receive telescope. ARM NIPL system specifications, and specific unit optical designs are discussed. Data normalization and calibration techniques are presented. A multiple cloud boundary detection algorithm is also described. These techniques in tandem represent an operational value added processing package used to produce normalized data products for Cloud and aerosol research and the historical ARM data archive.

Campbell, James R.↗
NASA NTRS1989

Aerosol backscatter studies supporting LAWS

Optimized Royal Signals and Radar Establishment (RSRE), Laser True Airspeed System (LATAS) algorithm for low backscatter conditions was developed. The algorithm converts backscatter intensity measurements from focused continuous-wave (CW) airborne Doppler lidar into backscatter coefficients. The performance of optimized algorithm under marginal backscatter signal conditions was evaluated. The 10.6 micron CO2 aerosol backscatter climatologies were statistically analyzed. Climatologies reveal clean background aerosol mode near 10(exp -10)/kg/sq m/sr (mixing ratio units) through middle and upper troposhere, convective mode associated with planetary boundary layer convective activity, and stratospheric mode associated with volcanically-generated aerosols. Properties of clean background mode are critical to design and simulation studies of Laser Atmospheric Wind Sounder (LAWS), a MSFC facility Instrument on the Earth Observing System (Eos). Previous intercomparisons suggested correlation between aerosol backscatter at CO2 wavelength and water vapor. Field measurements of backscatter profiles with MSFC ground-based Doppler lidar system (GBDLS) were initiated in late FY-88 to coincide with independent program of local rawinsonde releases and overflights by Multi-spectral Atmospheric Mapping Sensor (MAMS), a multi-channel infrared radiometer capable of measuring horizontal and vertical moisture distributions. Design and performance simulation studies for LAWS would benefit from the existence of a relationship between backscatter and water vapor.

Rothermel, Jeffry↗
NASA NTRS2000

Preliminary Results from an Assimilation of Saharan Dust Using TOMS Radiances and the GOCART Model

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions. Surface mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from .08-10 microns and only simulates Saharan dust. We draw the assimilation to two observables in this study: the TOMS aerosol index (Al) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance alone. The forward model that simulates the observables requires the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer from the GOCART fields. The forward model also requires a refractive index for the dust. We test three index values to see which best fits the TOMS observables. These are 1) for Saharan dust reported by Patterson, 2) for a mixture of Saharan dust and a highly reflective material (sea salt or sulfate) and 3) for pure illite. The assimilation works best assuming either pure illite or the dust mixture. Our assimilation cycle first determines values of the aerosol index (Al) and the radiance at 380 nm based on the GOCART aerosol fields. Differences between the observed and GOCART model calculated Al and 380 nm radiance are first analyzed horizontally using the Physical-space Statistical Analysis System (PSAS). A quasi-Newton iteration is then performed to produce analyzed 3D aerosol fields according to parameterized background and observation error covariances. We only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols are dominant and carbonaceous aerosols are minimal.

Weaver, C. J.↗
NASA NTRS2003

Observations of Dust Using the NASA Geoscience Laser Altimeter System (GLAS): New New Measurements of Aerosol Vertical Distribution From Space

On January 12, 2003 NASA launched the first satellite-based lidar, the Geoscience Laser -Altimeter System (GLAS), onboard the ICESat spacecraft. The GLAS atmospheric measurements introduce a fundamentally new and important tool for understanding the atmosphere and climate. In the past, aerosols have only been studied from space using images gathered by passive sensors. Analysis of this passive data has lead to an improved understanding of aerosol properties, spatial distribution, and their effect on the earth's climate. However, these images do not show the aerosol's vertical distribution. As a result, a key piece of information has been missing. The measurements now obtained by GLAS will provide information on the vertical distribution of aerosols and clouds, and improve our ability to study their transport processes and aerosol-cloud interactions. Here we show an overview of GLAS, provide an update of its current status, and present initial observations of dust profiles. In particular, a strategy of characterizing the height profile of dust plumes over source regions will be presented.

Welton, Ellsworth↗
NASA NTRS2016

Multi-Sensor Cloud and Aerosol Retrieval Simulator and Remote Sensing from Model Parameters : Aerosols - Part 2

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a simulated radiance product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers. This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled. In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model sub grid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms. Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (MOYD04). TheMOYD04 product is of course normally produced from MOYD021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a MOYD021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source. We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

aerosol retrieval↗
NASA NTRS2017

CATS Near Real Time Data Products: Applications for Assimilation Into the NASA GEOS-5 AGCM

From February 2015 through October 2017, the NASA Cloud-Aerosol Transport System (CATS) backscatter lidar operated on the International Space Station (ISS) as a technology demonstration for future Earth Science Missions, providing vertical measurements of cloud and aerosols properties. Owing to its location on the ISS, a cornerstone technology demonstration of CATS was the capability to acquire, process, and disseminate near-real time (NRT) data within 6 hours of observation time. CATS NRT data has several applications, including providing notification of hazardous events for air traffic control and air quality advisories, field campaign flight planning, as well as for constraining cloud and aerosol distributions in via data assimilation in aerosol transport models. Â Recent developments in aerosol data assimilation techniques have permitted the assimilation of aerosol optical thickness (AOT), a 2-dimensional column integrated quantity that is reflective of the simulated aerosol loading in aerosol transport models. While this capability has greatly improved simulated AOT forecasts, the vertical position, a key control on aerosol transport, is often not impacted when 2-D AOT is assimilated. Here, we present preliminary efforts to assimilate CATS aerosol observations into the NASA Goddard Earth Observing System version 5 (GEOS-5) atmospheric general circulation model and assimilation system using a 1-D Variational (1-D VAR) ensemble approach, demonstrating the utility of CATS for future Earth Science Missions.

Hlavka, D. L.↗
NASA NTRS2013

Evaluation of GEOS-5 Sulfur Dioxide Simulations During the Frostburg, MD 2010 Field Campaign.

Sulfur dioxide (SO2) is a major atmospheric pollutant with a strong anthropogenic component mostly produced by the combustion of fossil fuel and other industrial activities. As a precursor of sulfate aerosols that affect climate, air quality, and human health, this gas needs to be monitored on a global scale. Global climate and chemistry models including aerosol processes along with their radiative effects are important tools for climate and air quality research. Validation of these models against in-situ and satellite measurements is essential to ascertain the credibility of these models and to guide model improvements. In this study the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) module running on-line inside the Goddard Earth Observing System version 5 (GEOS-5) model is used to simulate aerosol and SO2 concentrations. Data taken in November 2010 over Frostburg, Maryland during an SO2 field campaign involving ground instrumentation and aircraft are used to evaluate GEOS-5 simulated SO2 concentrations. Preliminary data analysis indicated the model overestimated surface SO2 concentration, which motivated the examination of mixing processes in the model and the specification of SO2 anthropogenic emission rates. As a result of this analysis, a revision of anthropogenic emission inventories in GEOS-5 was implemented, and the vertical placement of SO2 sources was updated. Results show that these revisions improve the model agreement with observations locally and in regions outside the area of this field campaign. In particular, we use the ground-based measurements collected by the United States Environmental Protection Agency (US EPA) for the year 2010 to evaluate the revised model simulations over North America.

Air Quality Campaign↗
NASA NTRSDate not supplied

Observing System Simulations for the AOS Mission

The Earth System Observatory (ESO) is NASA’s response to the recommendations of the 2017 Earth Sciences Decadal Survey conducted by the US National Academy of Sciences, Engineering and Medicine. The ESO is being conceived as a set of fully integrated missions addressing 4 main Earth science focus areas including aerosols, clouds, convection and precipitation (jointly re-ferred to as AOS, the Atmosphere Observing System). ESO ground breaking observations will provide critical measurements to address societally relevant problems in climate change, natural hazard mitiga-tion, fighting forest fires, and improving real-time agricultural processes. A critical element of the AOS observing strategy is to make extensive use of new passive and active sen-sors as well as of the so-called Program-of-Record (PoR), complemented by a fully integrated sub-orbital component. In order to achieve maximum benefit, all these observations need to be integrated into comprehensive observing and modeling/data assimilation systems. Such an approach requires compre-hensive model-data synthesis capabilities that needs to be conceived in conjunction with the space-based and suborbital components of AOS. In this presentation we will summarize the major science goals of AOS including cloud feedbacks, at-mospheric convection, emphasizing aerosol processes and aerosol radiative effects, and the synergistic aspects of clouds-precipitation-aerosol interactions. We will describe examples of the observing system simulation capabilities being developed for AOS, including global storm resolving nature runs, detailed instrument and retrieval simulators, as well as fast retrieval emulators for instrumenting climate models. This simulation environment, being developed under NASA’s open-source science initiative, will permit us to explore how AOS data will be used across space and time to better initialize forecasts and train modeling systems, and to infuse models and data assimilation systems with AOS data, well before launch.

Arlindo da Silva↗
NASA NTRSDate not supplied

Simulation of the Aerosol Size Distribution Using a Neural Network Surrogate for the Modal Aerosol Module (MAM7)

One objective of atmospheric simulations is to quantify the distribution of aerosols and their properties. Accurate parameterizations of the processes governing aerosol mass, particle number, and particle size distribution are important for predicting the Earth’s net radiative balance and aerosol-cloud interactions. The Modal Aerosol Module (MAM7) is a two-moment aerosol model that simulates mass, number, and size distribution of seven modes comprised of internally mixed aerosol species. The two-moment scheme adds significant computational expense but allows for the prediction of varying particle size distribution relative to the bulk method which predicts only total mass. In this work, we developed a neural network surrogate model for MAM7 (MAMnet) to predict the aerosol number concentration in NASA’s Global Earth Observing System (GEOS) without adding prohibitive computational expense. MAMnet, can be driven by output from a single moment, mass-based, aerosol scheme (Goddard Chemistry Aerosol and Radiation model (GOCART)) or from reanalysis products (Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)). MAMnet was trained using number concentrations from a 5-year GEOS/MAM7 simulation at 1-degree horizontal resolution and using the total mass calculated across modes as inputs, as well as temperature and air density. The model architecture for MAMnet was based on AlexNet, the 2012 winner of the ImageNet Large Scale Visual Recognition Challenge. While some modifications were necessary to accommodate our problem, important aspects of the network were preserved. MAMnet was able to reproduce zonal dynamics and spatial distributions of the aerosol number concentration however predictability in the upper troposphere was poor.

Katherine H Breen↗
NASA NTRS2006

Non Radiation Hardened Microprocessors in Spaced Based Remote Sensing Systems

The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) mission is a comprehensive suite of active and passive sensors including a 20Hz 230mj Nd:YAG lidar, a visible wavelength Earth-looking camera and an imaging infrared radiometer. CALIPSO flies in formation with the Earth Observing System Post-Meridian (EOS PM) train, provides continuous, near-simultaneous measurements and is a planned 3 year mission. CALIPSO was launched into a 98 degree sun synchronous Earth orbit in April of 2006 to study clouds and aerosols and acquires over 5 gigabytes of data every 24 hours. The ground track of one CALIPSO orbit as well as high and low intensity South Atlantic Anomaly outlines is shown. CALIPSO passes through the SAA several times each day. Spaced based remote sensing systems that include multiple instruments and/or instruments such as lidar generate large volumes of data and require robust real-time hardware and software mechanisms and high throughput processors. Due to onboard storage restrictions and telemetry downlink limitations these systems must pre-process and reduce the data before sending it to the ground. This onboard processing and realtime requirement load may mean that newer more powerful processors are needed even though acceptable radiation-hardened versions have not yet been released. CALIPSO's single board computer payload controller processor is actually a set of four (4) voting non-radiation hardened COTS Power PC 603r's built on a single width VME card by General Dynamics Advanced Information Systems (GDAIS). Significant radiation concerns for CALIPSO and other Low Earth Orbit (LEO) satellites include the South Atlantic Anomaly (SAA), the north and south poles and strong solar events. Over much of South America and extending into the South Atlantic Ocean the Van Allen radiation belts dip to just 200-800km and spacecraft entering this area are subjected to high energy protons and experience higher than normal Single Event Upset (SEU) and Single Event Latch-up (SEL) rates. Although less significant, spacecraft flying in the area around the poles experience similar upsets. Finally, powerful solar proton events in the range of 10MeV/10pfu to 100MeV/1pfu as are forecasted and tracked by NOAA's Space Environment Center in Colorado can result in Single Event Upset (SEU), Single Event Latch-up (SEL) and permanent failures such as Single Event Gate Rupture (SEGR) in some technologies. (Galactic Cosmic Rays (GCRs) are another source, especially for gate rupture) CALIPSO mitigates common radiation concerns in its data handling through the use of redundant processors, radiation-hardened Application Specific Integrated Circuits (ASIC), hardware-based Error Detection and Correction (EDAC), processor and memory scrubbing, redundant boot code and mirrored files. After presenting a system overview this paper will expand on each of these strategies. Where applicable, related on-orbit data collected since the CALIPSO initial boot on May 4, 2006 will be noted.

Decoursey, Robert J.↗
NASA NTRS2008

Climate Model Evaluation using New Datasets from the Clouds and the Earth's Radiant Energy System (CERES)

There are some in the science community who believe that the response of the climate system to anthropogenic radiative forcing is unpredictable and we should therefore call off the quest . The key limitation in climate predictability is associated with cloud feedback. Narrowing the uncertainty in cloud feedback (and therefore climate sensitivity) requires optimal use of the best available observations to evaluate and improve climate model processes and constrain climate model simulations over longer time scales. The Clouds and the Earth s Radiant Energy System (CERES) is a satellite-based program that provides global cloud, aerosol and radiative flux observations for improving our understanding of cloud-aerosol-radiation feedbacks in the Earth s climate system. CERES is the successor to the Earth Radiation Budget Experiment (ERBE), which has widely been used to evaluate climate models both at short time scales (e.g., process studies) and at decadal time scales. A CERES instrument flew on the TRMM satellite and captured the dramatic 1998 El Nino, and four other CERES instruments are currently flying aboard the Terra and Aqua platforms. Plans are underway to fly the remaining copy of CERES on the upcoming NPP spacecraft (mid-2010 launch date). Every aspect of CERES represents a significant improvement over ERBE. While both CERES and ERBE measure broadband radiation, CERES calibration is a factor of 2 better than ERBE. In order to improve the characterization of clouds and aerosols within a CERES footprint, we use coincident higher-resolution imager observations (VIRS, MODIS or VIIRS) to provide a consistent cloud-aerosol-radiation dataset at climate accuracy. Improved radiative fluxes are obtained by using new CERES-derived Angular Distribution Models (ADMs) for converting measured radiances to fluxes. CERES radiative fluxes are a factor of 2 more accurate than ERBE overall, but the improvement by cloud type and at high latitudes can be as high as a factor of 5. Diurnal cycles are explicitly resolved by merging geostationary satellite observations with CERES and MODIS. Atmospheric state data are provided from a frozen version of the Global Modeling and Assimilation Office- Data Assimilation System at the NASA Goddard Space Flight Center. In addition to improving the accuracy of top-of-atmosphere (TOA) radiative fluxes, CERES also produces radiative fluxes at the surface and at several levels in the atmosphere using radiative transfer modeling, constrained at the TOA by CERES (ERBE was limited to the TOA). In all, CERES uses 11 instruments on 7 spacecraft all integrated to obtain climate accuracy in TOA to surface fluxes. This presentation will provide an overview of several new CERES datasets of interest to the climate community (including a new adjusted TOA flux dataset constrained by estimates of heat storage in the Earth system), show direct comparisons between CERES ad ERBE, and provide a detailed error analysis of CERES fluxes at various time and space scales. We discuss how observations can be used to reduce uncertainties in cloud feedback and climate sensitivity and strongly argue why we should NOT "call off the quest".

Loeb, Norman G.↗
NASA NTRS2003

Promise and Capability of NASA's Earth Observing System to Monitor Human-Induced Climate Variations

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. The Moderate Resolution Imaging Spectroradiometer (MODIS), developed as part of the Earth Observing System (EOS) and launched on Terra in December 1999 and Aqua in May 2002, is designed to meet the scientific needs for satellite remote sensing of clouds, aerosols, water vapor, and land and ocean surface properties. This sensor and multi-platform observing system is especially well suited to observing detailed interdisciplinary components of the Earth s surface and atmosphere in and around urban environments, including aerosol optical properties, cloud optical and microphysical properties of both liquid water and ice clouds, land surface reflectance, fire occurrence, and many other properties that influence the urban environment and are influenced by them. In this presentation I will summarize the current capabilities of MODIS and other EOS sensors currently in orbit to study human-induced climate variations.

King, M. D.↗
NASA NTRSDate not supplied

Satellite-Assisted Particulate Matter (SAPM) for the Models, In situ, and Remote sensing of Aerosols (MIRA) Working Group

Models, In situ, and Remote sensing of Aerosols (MIRA) is an international working group that aims to foster collaborations amongst these three major aerosol specialties. It consists of a collection of interdisciplinary and independently funded projects/topics with clearly-defined goals. Currently there are four MIRA topic areas, including Satellite-Assisted Particulate Matter (SAPM), which is focused specifically on fine particulate matter (PM2.5). The study of PM2.5 is important due to its significant contribution to air pollution and resultant, adverse human health effects. The goal of the SAPM topic group is to conduct and provide intercomparisons of various methods and techniques to obtain surface PM2.5 estimates assisted by satellite remote sensors, global aerosol models, and in situ aerosol measurements. Some benefits of space-based/model-assisted PM2.5 retrievals include providing PM2.5 approximations in regions with poor in situ ground station coverage and a means for assessing spatial/temporal PM2.5 variations on regional and/or global scales. Furthermore, benefits of spaceborne lidar/model-assisted PM2.5 retrievals include providing nighttime PM2.5 estimates and characterization of near-surface aerosol vertical structure. SAPM is seeking international datasets of mass scattering/absorption coefficient and aerosol hygroscopic properties for different aerosol species, as well as ground-based in situ PM2.5 measurements. In this poster presentation, we give an overview of SAPM, and show results from three recent/ongoing PM2.5 studies. These efforts involve deriving surface PM2.5 concentrations from two spaceborne lidars, the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud-Aerosol Transport System (CATS), and investigating key variables that impact the estimation of PM2.5 from model simulated aerosol optical depth (AOD) using the Goddard Earth Observing System (GEOS) model.

Travis D. Toth↗
NASA NTRS2007

Improving Public Health DSSs by Including Saharan Dust Forecasts Through Incorporation of NASA's GOCART Model Results

Approximately 2-3 billion metric tons of soil dust are estimated to be transported in the Earth's atmosphere each year. Global transport of desert dust is believed to play an important role in many geochemical, climatological, and environmental processes. This dust carries minerals and nutrients, but it has also been shown to carry pollutants and viable microorganisms capable of harming human, animal, plant, and ecosystem health. Saharan dust, which impacts the eastern United States (especially Florida and the southeast) and U.S. Territories in the Caribbean primarily during the summer months, has been linked to increases in respiratory illnesses in this region and has been shown to carry other human, animal, and plant pathogens. For these reasons, this candidate solution recommends integrating Saharan dust distribution and concentration forecasts from the NASA GOCART global dust cycle model into a public health DSS (decision support system), such as the CDC's (Centers for Disease Control and Prevention's) EPHTN (Environmental Public Health Tracking Network), for the eastern United States and Caribbean for early warning purposes regarding potential increases in respiratory illnesses or asthma attacks, potential disease outbreaks, or bioterrorism. This candidate solution pertains to the Public Health National Application but also has direct connections to Air Quality and Homeland Security. In addition, the GOCART model currently uses the NASA MODIS aerosol product as an input and uses meteorological forecasts from the NASA GEOS-DAS (Goddard Earth Observing System Data Assimilation System) GEOS-4 AGCM. In the future, VIIRS aerosol products and perhaps CALIOP aerosol products could be assimilated into the GOCART model to improve the results.

Berglund, Judith↗