Search NASA⌕ Search

SEARCH · Search NASA

Results for “A-Train”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

A-Train Data Depot: Integrating and Visualizing Atmospheric Measurements Along the A-Train Tracks

The succession of US and international satellites that follow each other, seconds to minutes apart, across the local afternoon equator crossing is called the ATrain. The A-Train consists of the following satellites, in order of equator crossing: OCO, EOS Aqua, CloudSat, CALIPSO, PARASOL, and EOS Aura. Flying in such formation increases the number of observations, validates observations, and enables coordination between science observations, resulting in a more complete virtual science platform (Kelly, 2000) The goal of this project is to create the first ever A-Train virtual data portal/center, the A-Train Data Depot, to process, archive, access, visualize, analyze and correlate distributed atmosphere measurements from various A-Train instruments along A-Train tracks. The A-Train Data Depot (ATDD) will enable the free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth s Atmosphere along the A-Train tracks. Once the infrastructure of the ATDD is in place, it will be easily evolved to serve data from all A-Train data measurements: one stop shopping. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) will be accommodated by reusing the GSFC Atmospheric Composition Data and Information Services Center (ACDISC) visualization and analysis tool, GIOVANNI, existing data reduction tools, on-line archwing for fast data access, and Cooperative Institute for Research in the Atmosphere (CRA) data co-registration tools. Initial measurements utilized include CALIPSO lidar backscatter, CloudSat radar reflectivity, clear air relative humidity, water vapor and temperature from AIRS, and cloud properties and aerosols from both MODIS. This will be followed by associated measurements from MLS, OMI, HIRDLS, and TES. Given the independent nature of instrument/platform development, the ATDD project has been met with many interesting challenges that, once resolved, will provide a much greater understanding of the relative flight dynamics and data co-registration of the suite of A-Train instruments, thus greatly increasing the accuracy of A-Train data analysis. Some of these challenges will be discussed. The project s resulting visualizations and analysis illustrate the importance of managing data so that measurements from various missions can be combined to enhance the understanding of the atmosphere. A-Train data management coordination, as performed here, is extremely significant in facilitating the A-Train science of clouds, precipitation, aerosol and chemistry.

Kempler, Steven↗
NASA NTRS2006

A-Train Data Depot: Integrating, Visualizing, and Extracting Cloudsat, CALIPSO, MODIS, and AIRS Atmospheric Measurements Along the A-Train Tracks

The succession of US and international Earth observing satellites that follow each other, seconds to minutes apart, across the local afternoon equator crossing is called the ATrain. The A-Train consists of the following satellites, in order of equator crossing: OCO, EOS Aqua, CloudSat, CALIPSO, PARASOL, and EOS Aura. Flying in such formation increases the number of observations, validates observations, and enables coordination between science observations, resulting in a more complete virtual science platform (Kelly, 2000). The goal of this project is to create the first ever A-Train virtual data portal/center, the A-Train Data Depot (ATDD), to process, archive, access, visualize, analyze and correlate distributed atmosphere measurements from various A-Train instruments along A-Train tracks. The ATDD will enable the free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. Once the infrastructure of the ATDD is in place, it will be easily evolved to serve data from all A-Train data measurements: one stop shopping. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) will be accommodated by reusing the GSFC Atmospheric Composition Data and Information Services Center (ACDISC) visualization and analysis tool, GIOVANNI, existing data reduction tools, on-line archiving for fast data access, access to remote data without unnecessary data transfers, and data retrieval by users finding data desirable for further study. Initial measurements utilized include CALIPSO lidar backscatter, CloudSat radar reflectivity, clear air relative humidity, water vapor and temperature from AIRS, and cloud properties and aerosols from both MODIS. This will be foilowed by associated measurements from TVILS, =MI, HIRDLS, sad TES. Given the independent nature of instrumentlplatform development, the ATDD project has been met with many interesting challenges that, once resolved, will provide a much greater understanding of the relative flight dynamics and data co-registration of the suite of A-Train instruments, thus greatly increasing the accuracy of A-Train data analysis. Some of these challenges will be illustrated and discussed. The project's early visualizations and analysis efforts illustrate the importance of managing data so that measurements from various missions can be combined to enhance the understanding of the atmosphere. A-Train data management coordination, as performed here, is extremely significant in facilitating the A-Train science of clouds, precipitation, aerosol and chemistry.

Kempler, Steven↗
NASA NTRS2002

The Aura Mission and the "A-Train"

By the end of 2004 the A-Train will have been assembled in orbit. The A-Train consists of five low earth orbit satellites flying within 15 minutes of each other. The first in the train is Aqua, launched early this year, in an ascending node orbit with 1 :30 PM crossing time. In January 2004, Aura will be launched, following 15 minutes behind Aqua. Aura's focus is atmospheric chemistry, with both forward looking, and backward looking instruments along with nadir sounders. Aura's limb microwave instrument will also be able to make temperature and humidity measurements into the troposphere. By mid-2004, CALIPSO, a cloud lidar mission and CloudSat, a cloud radar mission will be co-manifested. These two spacecraft will follow, and maneuver about 1 minute behind Aqua. The lidar on CALIPSO will provide aerosol and cloud height information which can enhance the aerosol information from Aqua s MODIS instrument. CloudSat s radar will enhance the information from AMSR-E, AIRS and HSB on Aqua. The fifth satellite to be launched in 2004 is PARASOL which will use the POLDER instrument to provide polarization information which can be used to determine aerosol composition and size distribution.

Schoeberl, Mark R.↗
NASA NTRS2005

Towards an A-train Aerosol Assimilation System: Assimilation of MODIS Aerosol Optical Thickness Retrievals into a Global Aerosol Transport and Radiation Model

To understand the role of atmospheric aerosols in Earth's climate system we ideally require a global, three-dimensional, and temporally continuous system of measurements. The view of aerosols provided by a single polar-orbiting satellite instrument (e.g., MODIS), while providing the greatest spatial coverage of any measurement system available, can offer only a single relevant observation per day of a particular location. Furthermore, the quantitative interpretation of those observations is subject to considerable uncertainty because of we do not know the aerosol optical properties or vertical profiles well enough. Ground-based and in situ measurements can provide missing detail about aerosol vertical and optical properties, but lack the spatial (and sometimes temporal) coverage desired. Chemical transport models treating the aerosol lifecycle complement this picture by providing three-dimensional, global, and continuous aerosol fields, but suffer from poor constraints on their initial conditions (e.g., the aerosol source, sink, and transformation mechanisms). Here we present a global, three- dimensional aerosol transport model and assimilation system. The model assimilates the MODIS aerosol optical thickness product to constrain its aerosol distributions and burdens. We provide an analysis of the model aerosol distributions as constrained by ground-based sun photometer measurements and aerosol products from other satellite measurements.

Colarco, P.↗
NASA NTRS2005

Support for the Harvard University Water Vapor and Total Water Instruments for the 2004 NASA WB57 Middle Latitude Cirrus Experiment

In order to improve our understanding of the role clouds play in the climate system, NASA is investing considerable effort in characterizing clouds with instruments ranging from passive remote sensors on board the EOS platforms, to the forthcoming active remote sensors on Cloudsat and Calipso. These missions, when taken together, have the capacity to advance our understanding of the coupling between various components of the hydrologic cycle and the atmospheric circulation, and hold the additional potential of leading to significant improvements in the characterization of cloud feedbacks in global models. This is especially true considering that several of these platforms will be flown in an identical orbit within several minutes of one another-a constellation of satellites known as the A-Train. The algorithms that are being implemented and developed to convert these new data streams from radiance and reflectivity measurements into geophysical parameters invariably rely on some set of simplifymg assumptions and empirical constants. Uncertainties in these relationships lead to poorly understood random and systematic errors in the retrieved properties. This lack of understanding introduces ambiguity in interpreting the data and in using the global data sets for their intended purposes. In light of this, a series of flights with the W57F was proposed to address certain specific issues related to the basic properties of mid latitude cirrus clouds: the NASA WE357 Middle Latitude Cirrus Experiment ("MidCiX"). The science questions addressed are: 1) Can cloud property retrieval algorithms developed for A-Train active and passive remote sensing measurements accurately characterize the microphysical properties of synoptic and convectively generated cirrus cloud systems? 2) What are the relationships between the cirrus particle mass, projected area, and particle size spectrum in various genre of cirrus clouds? 3) Does the present compliment of state of the art in situ cloud probes provide the level of precision and accuracy needed to develop and validate algorithms and to contribute to our understanding of the characteristics and microphysical processes operating in cirrus clouds?

Anderson, James G.↗
NASA NTRS2006

Future NASA Earth Science Missions

Following on the successful launch of Aura in July 2004, NASA will continue to add to the A-train with the spring 2006 launch of the CALIPSO and Cloudsat missions, followed by the fall 2008 launch of the Orbiting Carbon Observatory (OCO). The CALIPSO mission has three nadir-viewing instruments: a cloud-aerosol lidar for measuring aerosol and cloud height and properties, a 3-channel thermal infrared imaging radiometer, and a wide field cloud camera. The Cloudsat mission has a 94 GHz cloud profiling radar, returning cloud structure vs. height profiles. Orbiting Carbon Observatory (OCO) is a mission focused on precision measurements of total column CO2 using reflected sunlight. These missions and how they relate to the other parts of the A-train will be presented. A brief report from a NASA workshop on future atmospheric science missions will also be presented.

Gleason, James↗
DOE OSTI2026

Neural network potentials with effective charge separation for non-equilibrium dynamics of ionic solids: a ZnO case study

Developing neural network potentials (NNPs) accurate under non-equilibrium dynamics is challenging, as such systems require extensive sampling beyond equilibrium phases. Here we construct high-fidelity NNPs for zinc oxide (ZnO), a polymorphic ionic solid, using density functional theory (DFT) reference data. To efficiently capture transitional configurations, we combine enhanced-sampling molecular dynamics with empirical potentials, data distillation, and pretraining on short-range atomic energies (A-Train), followed by transfer learning with DFT-relabeled datasets. This hierarchical approach improves transferability across polymorphs and stress states. We further introduce effective charge separation, treating long-range Coulombic terms analytically while short-range residual interactions are learned by the NNP. The optimal effective charges fall in the range 0.5–1.0 q e , consistent with dielectric-screened values derived from formal charges but distinct from Bader estimates. Motivated by this observation, we propose a simple data-driven protocol in which effective charges are optimized by comparing DFT reference energies with explicit Coulomb calculations, without additional NNP training. This strategy improves accuracy and transferability in DFT-level predictions of energies, forces, and stress. Together, these results provide a practical charge-selection framework for robust NNP development in ionic solids, enabling reliable simulation of polymorphic phase transformations and non-equilibrium dynamics.

Chemistry↗
NASA NTRS2001

The Aqua-Aura Train

This talk will focus on the afternoon constellation of EOS platforms and the scientific benefits that arise from this formation. The afternoon EOS constellation or the "A-train" will provide unprecedented information on clouds and aerosols. At 1:30 PM crossing time EOS-Aqua begins the train with the MODIS, CERES and AIRS instruments making aerosol, cloud, radiation budget , temperature and water vapor measurements. AMSR-E will also make total column water measurements. Following Aqua by one minute, Cloudsat will make active radar precipitation measurements as and PICASSOCENA will make lidar measurements of clouds and aerosols. Fourteen minutes later, EOS-Aura will pass through the same space making upper troposphere water vapor and ice profiles as well as some key trace gases associated with convective processes (MLS and HIRDLS). Additional measurements of aerosols will be made by Aura's OMI instrument.

Schoeberl, Mark↗
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 NTRS2004

Registration and Fusion of Multiple Source Remotely Sensed Image Data

Earth and Space Science often involve the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, global coverage of an area at multiple resolutions, map updating or validation of new instruments, as well as integration of data provided by multiple instruments carried on multiple platforms, e.g. in spacecraft constellations or fleets of planetary rovers. Our focus is on developing methods to perform fast, accurate and automatic image registration and fusion. General methods for automatic image registration are being reviewed and evaluated. Various choices for feature extraction, feature matching and similarity measurements are being compared, including wavelet-based algorithms, mutual information and statistically robust techniques. Our work also involves studies related to image fusion and investigates dimension reduction and co-kriging for application-dependent fusion. All methods are being tested using several multi-sensor datasets, acquired at EOS Core Sites, and including multiple sensors such as IKONOS, Landsat-7/ETM+, EO1/ALI and Hyperion, MODIS, and SeaWIFS instruments. Issues related to the coregistration of data from the same platform (i.e., AIRS and MODIS from Aqua) or from several platforms of the A-train (i.e., MLS, HIRDLS, OMI from Aura with AIRS and MODIS from Terra and Aqua) will also be considered.

LeMoigne, Jacqueline↗
NASA NTRS2005

Aerosol-Cloud-Precipitation Interactions over Indo-Gangetic Basin

About 60% of world population reside in Asia, in term of which sheer population density presents a major environmental stress. Economic expansion in this region is, in fact, accompanied by increases in bio-fuel burning, industrial pollution, and land cover and land use changes. With a growth rate of approx. 8%/yr for Indian economy, more than 600 million people from Lahore, Pakistan to Calcutta, India over the Indo-Gangetic Basin have particularly witnessed increased frequencies of floods and droughts as well as a dramatic increase in atmospheric loading of aerosols (i.e., anthropogenic and natural aerosol) in recent decades. This regional change (e.g., aerosol, cloud, precipitation, etc.) will constitute a vital part of the global change in the 21st century. Better understanding of the impacts of aerosols in affecting monsoon climate and water cycles is crucial in providing the physical basis to improve monsoon climate prediction and for disaster mitigation. Based on climate model simulations, absorbing aerosols (dust and black carbon) play a critical role in affecting interannual and intraseasonal variability of the Indian monsoon. An initiative on the integrated (aerosols, clouds, and precipitation) measurements approach over the Indo-Gangetic Basin will be discussed. An array of ground-based (e.g., AERONET, MPLNET, SMART-COMMIT, etc.) and satellite (e.g., Terra, A-Train, etc.) sensors will be utilized to acquire aerosol characteristics, sources/sinks, and transport processes during the pre-monsoon (April-May, aerosol forcing) season, and to obtain cloud and precipitation properties during the monsoon (May-June, water cycle response) season. Close collaboration with other international programs, such as ABC, CLIVAR, GEWEX, and CEOP in the region is anticipated.

Tsay, S.-C.↗
NASA NTRS2005

The Earth Science Afternoon Constellation: Preparing for Autonomous but Coordinated Operations

This paper describes how the challenges of coordinating the autonomous operations of geographically dispersed mission control centers for several small and large satellites are being overcome. The Earth Science Afternoon Constellation, also referred to as the "A-Train", is an international grouping of five NASA satellites (two major NASA EOS missions and three NASA/Earth System Science Pathfinder missions) and one French satellite orbiting in close proximity. This grouping of satellites provides scientists with the opportunity to perform coincident observations using data from two or more instruments on various satellites with measurements taken at approximately the same time. Three of the six missions are currently on-orbit, with the two missions expected to join the constellation later this year and one mission in 2007. The operational challenges are daunting for several reasons. There are several Mission Control Centers (widely separated on two continents), operating autonomously under tight budget constraints. All of the Mission Control Centers have reasons to be concerned about safety while flying in close proximity to other satellites, but most Centers did not have the resources or the desire to address this concern alone - the interfaces are too numerous and anticipated operations too costly. Clearly, an efficient approach was needed. This paper describes the steps taken to make this Earth science constellation a reality. Agreements were forged to allow the Mission Control Centers to maintain their autonomy, while ensuring their satellite's safety. Each member mission in the constellation operates independently in accordance with its own mission requirements, but the member missions have agreed to coordinate their operations, i.e., orbital positions and control to ensure the safety of the entire constellation. A centralized system was developed at NASA Goddard Space Flight Center to collect, analyze, and distribute ephemeris data used by each of the mission teams to determine the positions of the satellites in the constellation. The system issues warnings regarding possible dangerous configurations, eliminating the need for redundant capabilities at each Mission Control Center. On-orbit contingency situations were identified and analyzed; agreements were reached in advance of contingency operations to ensure that coordination between the Mission Control Centers can be handled expeditiously and fairly. In this manner, recovery from anomalous situations can be more quickly realized, thereby increasing the science return and reducing costs. The process used to develop these contingency procedures and the systems used to facilitate the contingency resolution are described as well.

Case, Warren↗
NASA NTRS2006

Monitoring CO2 sources and sinks from space : the Orbiting Carbon Observatory (OCO) Mission

NASA's Orbiting Carbon Observatory (OCO) will make the first space-based measurements of atmospheric carbon dioxide (CO2) with the precision, resolution, and coverage needed to characterize the geographic distribution of CO2 sources and sinks and quantify their variability over the seasonal cycle. OCO is currently scheduled for launch in 2008. The observatory will carry a single instrument that incorporates three high-resolution grating spectrometers designed to measure the near-infrared absorption by CO2 and molecular oxygen (O2) in reflected sunlight. OCO will fly 12 minutes ahead of the EOS Aqua platform in the Earth Observing System (EOS) Afternoon Constellation (A-Train). The in-strument will collect 12 to 24 soundings per second as the Observatory moves along its orbit track on the day side of the Earth. A small sampling footprint (<3 km2 at nadir) was adopted to reduce biases in each sounding associated with clouds and aerosols and spatial variations in surface topography. A comprehensive ground-based validation program will be used to assess random errors and biases in the XCO2 product on regional to continental scales. Measurements collected by OCO will be assimilated with other environmental measurements to retrieve surface sources and sinks of CO2. This information could play an important role in monitoring the integrity of large scale CO2 sequestration projects.

carbon dioxide sources and sinks↗
NASA NTRS2006

CloudSat Safety Operations at Vandenberg AFB

CloudSat safety operations at Vendenberg AFB is given. The topics include: 1) CloudSat Project Overview; 2) Vandenberg Ground Operations; 3) Delta II Launch Vehicle; 4) The A-Train; 5) System Safety Management; 6) CALIPSO Hazards Assessment; 7) CALIPSO Supplemental Safeguards; 8) Joint System Safety Operations; 9) Extended Stand-down; 10) Launch Delay Safety Concerns; and 11) Lessons Learned.

CloudSat↗
NASA NTRS2006

Using SCIAMACHY and Ground-based FTS Measurements to Test the OCO X(sub CO2) Retrieval and Validation Approach

Global, space-based observations of atmospheric CO2 with precision, resolution, and coverage needed to monitor sources and sinks: a) Spectra of reflected/scattered sunlight in NIR CO2 and O2 bands used to estimate X(sub CO2) with large sensitivity to surface; b) A-train orbit (1:15 PM polar sun sync); c) 16 day repeat cycle samples seasonal cycle on semi-monthly intervals; and d) NASA ESSP (Earth Space System Pathfinder) scheduled for Sept 2008 launch; 2 yrs lifetime. Initial comparison of SCIAMACHY and FTS retrievals for Park Falls: a) Positive bias in X(sub CO2) of approx. 10 ppm; and b) Negative bias in surface pressure After correction of spectral artifacts in O2A band: a) Largely improved agreement between SCIAMACHY and FTS X(sub CO2) (without clear bias) and in surface pressure; and b) Standard deviation of SCIAMACHY X(sub CO2 approx. 6 ppm. Good qualitative agreement with GEOS-CHEM, with GEOS-CHEM underestimating seasonal cycle. OCO is a dedicated CO2 instrument and will achieve much higher accuracy and precision: a) much higher spectral resolution (by factor of 20); and b) smaller ground pixels (by factor of 600).

CO2 measurements↗
NASA NTRS2006

First Results from the OMI Rotational Raman Scattering Cloud Pressure Algorithm

We have developed an algorithm to retrieve scattering cloud pressures and other cloud properties with the Aura Ozone Monitoring Instrument (OMI). The scattering cloud pressure is retrieved using the effects of rotational Raman scattering (RRS). It is defined as the pressure of a Lambertian surface that would produce the observed amount of RRS consistent with the derived reflectivity of that surface. The independent pixel approximation is used in conjunction with the Lambertian-equivalent reflectivity model to provide an effective radiative cloud fraction and scattering pressure in the presence of broken or thin cloud. The derived cloud pressures will enable accurate retrievals of trace gas mixing ratios, including ozone, in the troposphere within and above clouds. We describe details of the algorithm that will be used for the first release of these products. We compare our scattering cloud pressures with cloud-top pressures and other cloud properties from the Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. OMI and MODIS are part of the so-called A-train satellites flying in formation within 30 min of each other. Differences between OMI and MODIS are expected because the MODIS observations in the thermal infrared are more sensitive to the cloud top whereas the backscattered photons in the ultraviolet can penetrate deeper into clouds. Radiative transfer calculations are consistent with the observed differences. The OMI cloud pressures are shown to be correlated with the cirrus reflectance. This relationship indicates that OMI can probe through thin or moderately thick cirrus to lower lying water clouds.

Joiner, Joanna↗
NASA NTRS2003

The Earth System Science Pathfinder Orbiting Carbon Observatory (OCO) Mission

A viewgraph presentation describing the Earth System Science Pathfinder Orbiting Carbon Observatory (OCO) Mission is shown. The contents include: 1) Why CO2?; 2) What Processes Control CO2 Sinks?; 3) OCO Science Team; 4) Space-Based Measurements of CO2; 5) Driving Requirement: Precise, Bias-Free Global Measurements; 6) Making Precise CO2 Measurements from Space; 7) OCO Spatial Sampling Strategy; 8) OCO Observing Modes; 9) Implementation Approach; 10) The OCO Instrument; 11) The OCO Spacecraft; 12) OCO Will Fly in the A-Train; 13) Validation Program Ensures Accuracy and Minimizes Spatially Coherent Biases; 14) Can OCO Provide the Required Precision?; 15) O2 Column Retrievals with Ground-based FTS; 16) X(sub CO2) Retrieval Simulations; 17) Impact of Albedo and Aerosol Uncertainty on X(sub CO2) Retrievals; 18) Carbon Cycle Modeling Studies: Seasonal Cycle; 19) Carbon Cycle Modeling Studies: The North-South Gradient in CO2; 20) Carbon Cycle Modeling Studies: Effect of Diurnal Biases; 21) Project Status and Schedule; and 22) Summary.

carbon cycles↗