SEARCH · Search NASA

Results for “transformer based forecasting”

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.

SourceResearch connectionFrom yearThrough year

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

At least 55 records · Page 3

All sources NASA NTRS DOE OSTI

NASA NTRS2006

Establishing Lagrangian Connections between Observations within Air Masses Crossing the Atlantic during the ICARTT Experiment

The International Consortium for Atmospheric Research on Transport and Transformation (ICARTT)-Lagrangian experiment was conceived with an aim to quantify the effects of photochemistry and mixing on the transformation of air masses in the free troposphere away from emissions. To this end attempts were made to intercept and sample air masses several times during their journey across the North Atlantic using four aircraft based in New Hampshire (USA), Faial (Azores) and Creil (France). This article begins by describing forecasts using two Lagrangian models that were used to direct the aircraft into target air masses. A novel technique is then used to identify Lagrangian matches between flight segments. Two independent searches are conducted: for Lagrangian model matches and for pairs of whole air samples with matching hydrocarbon fingerprints. The information is filtered further by searching for matching hydrocarbon samples that are linked by matching trajectories. The quality of these coincident matches is assessed using temperature, humidity and tracer observations. The technique pulls out five clear Lagrangian cases covering a variety of situations and these are examined in detail. The matching trajectories and hydrocarbon fingerprints are shown and the downwind minus upwind differences in tracers are discussed.

Methven, J.↗

NASA NTRS2023

Automated Fiber Placement Through Thickness Defect Stacking Optimization

In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to produce large-scale composite parts. After a design has been created, a manufacturing strategy has to be developed based on the working material, part geometry, and machine capabilities. This process planning stage is essential to the AFP workflow and currently requires a high level of manual input from an experienced process planner. In an effort to automate and optimize this stage, the Computer Aided Process Planning (CAPP) module was developed. CAPP assists process planners in identifying optimal starting point location and layup strategy for each ply of a laminate. This Ply-Level Optimization (PLO) phase operates on the quantification of ply quality through predictable geometry-based defects such as gaps, overlaps, angle deviation, and steering. As you move from PLO to Laminate-Level Optimization (LLO) the design space grows exponentially, emphasizing the need for automated optimization. The work presented in this thesis expands CAPP’s functionality by comparing the planned fiber paths through the thickness of the laminate to mitigate stacked area defects and achieve an optimal laminate-level manufacturing strategy. Within CAPP, predicted gap and overlap defects are imported from Vericut Composites Programming (VCP) and then discretized to streamline the through-thickness comparison. Two objective functions are used to score different combinations of ply layup strategies based on defect stacking both globally and locally. Four combinatorial optimization algorithms were coupled with these objective functions to investigate the laminate-level manufacturing strategy design space and converge on the optimal plan. These algorithms were evaluated based on accuracy and efficiency through virtual testing on a complex tool surface. A separate LLO approach was developed to achieve near-optimal laminates in significantly less time. The end result is a software package which greatly reduces the required input from process planners, shortening the design-build cycle time and improving part quality.

AFP↗

NASA NTRS2008

Distributed Aviation Concepts and Technologies

Aviation has experienced one hundred years of evolution, resulting in the current air transportation system dominated by commercial airliners in a hub and spoke infrastructure. While the first fifty years involved disruptive technologies that required frequent vehicle adaptation, the second fifty years produced a stable evolutionary optimization of decreasing costs with increasing safety. This optimization has resulted in traits favoring a centralized service model with high vehicle productivity and cost efficiency. However, it may also have resulted in a system that is not sufficiently robust to withstand significant system disturbances. Aviation is currently facing rapid change from issues such as environmental damage, terrorism threat, congestion and capacity limitations, and cost of energy. Currently, these issues are leading to a loss of service for weaker spoke markets. These catalysts and a lack of robustness could result in a loss of service for much larger portions of the aviation market. The impact of other competing transportation services may be equally important as casual factors of change. Highway system forecasts indicate a dramatic slow down as congestion reaches a point of non-linearly increasing delay. In the next twenty-five years, there is the potential for aviation to transform itself into a more robust, scalable, adaptive, secure, safe, affordable, convenient, efficient and environmentally friendly system. To achieve these characteristics, the new system will likely be based on a distributed model that enables more direct services. Short range travel is already demonstrating itself to be inefficient with a centralized model, providing opportunities for emergent distributed services through air-taxi models. Technologies from the on-demand revolution in computers and communications are now available as major drivers for aviation on-demand adaptation. Other technologies such as electric propulsion are currently transforming the automobile industry, and will also significantly alter the functionality of future distributed aviation concepts. Many hurdles exist, including technology, regulation, and perception. Aviation has an inherent governmental role not present in other recent on-demand transformations, which may pose a risk of curtailing aviation democratization .

Moore, Mark D.↗

DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1999

Constructing Synoptic Maps of Stratospheric Column Ozone from HALOE, SAGE and Balloonsonde Data Using Potential Vorticity Isentropic Coordinate Transformations

In this study we utilize potential vorticity - isentropic (PVI) coordinate transformations as a means of combining ozone data from different sources to construct daily, synthetic three-dimensional ozone fields. This methodology has been used successfully to reconstruct ozone maps in particular regions from aircraft data over the period of the aircraft campaign. We expand this method to create high-resolution daily global maps of profile ozone data, particularly in the lower stratosphere, where high-resolution ozone data are sparse. Ozone climatologies in PVI-space are constructed from satellite-based SAGE II and UARS/HALOE data, both of which-use solar occultation techniques to make high vertical resolution ozone profile measurements, but with low spatial resolution. A climatology from ground-based balloonsonde data is also created. The climatologies are used to establish the relationship between ozone and dynamical variability, which is defined by the potential vorticity (in the form of equivalent latitude) and potential temperature fields. Once a PVI climatology has been created from data taken by one or more instruments, high-resolution daily profile ozone field estimates are constructed based solely on the PVI fields, which are available on a daily basis from NCEP analysis. These profile ozone maps could be used for a variety of applications, including use in conjunction with total ozone maps to create a daily tropospheric ozone product, as input to forecast models, or as a tool for validating independent ozone measurements when correlative data are not available. This technique is limited to regions where the ozone is a long-term tracer and the flow is adiabatic. We evaluate the internal consistency of the technique by transforming the ozone back to physical space and comparing to the original profiles. Biases in the long-term average of the differences are used to identify regions where the technique is consistently introducing errors. Initial results show the technique is useful in the lower stratosphere at most latitudes throughout the year,and in the winter hemisphere in the middle stratosphere. The results are problematic in the summer hemisphere middle stratosphere due to increased ozone photochemistry and weak PV gradients. Alternate techniques in these regions will be discussed. An additional limitation is the quality and resolution of the meteorological data.

Hollandsworth, Stacey M.↗

NASA NTRS2013

Approach to Integrate Global-Sun Models of Magnetic Flux Emergence and Transport for Space Weather Studies

The Sun lies at the center of space weather and is the source of its variability. The primary input to coronal and solar wind models is the activity of the magnetic field in the solar photosphere. Recent advancements in solar observations and numerical simulations provide a basis for developing physics-based models for the dynamics of the magnetic field from the deep convection zone of the Sun to the corona with the goal of providing robust near real-time boundary conditions at the base of space weather forecast models. The goal is to develop new strategic capabilities that enable characterization and prediction of the magnetic field structure and flow dynamics of the Sun by assimilating data from helioseismology and magnetic field observations into physics-based realistic magnetohydrodynamics (MHD) simulations. The integration of first-principle modeling of solar magnetism and flow dynamics with real-time observational data via advanced data assimilation methods is a new, transformative step in space weather research and prediction. This approach will substantially enhance an existing model of magnetic flux distribution and transport developed by the Air Force Research Lab. The development plan is to use the Space Weather Modeling Framework (SWMF) to develop Coupled Models for Emerging flux Simulations (CMES) that couples three existing models: (1) an MHD formulation with the anelastic approximation to simulate the deep convection zone (FSAM code), (2) an MHD formulation with full compressible Navier-Stokes equations and a detailed description of radiative transfer and thermodynamics to simulate near-surface convection and the photosphere (Stagger code), and (3) an MHD formulation with full, compressible Navier-Stokes equations and an approximate description of radiative transfer and heating to simulate the corona (Module in BATS-R-US). CMES will enable simulations of the emergence of magnetic structures from the deep convection zone to the corona. Finally, a plan will be summarized on the development of a Flux Emergence Prediction Tool (FEPT) in which helioseismology-derived data and vector magnetic maps are assimilated into CMES that couples the dynamics of magnetic flux from the deep interior to the corona.

Mansour, Nagi N.↗

DOE OSTI2025

Novel Deep Learning Transformer Model for Short to Sub‐Seasonal Streamflow Forecast

Accurate short-to-subseasonal streamflow forecasts are becoming crucial for effective water management in an increasingly variable climate. However, streamflow forecast remains challenging over extended lead times, uncertainty in meteorological inputs, and increased frequency and variability in extreme weather and climate events. We implemented a Future Time Series Transformer (FutureTST) model for streamflow forecasting that separately integrates past meteorological and streamflow data while incorporating future weather conditions. FutureTST achieves a mean Nash-Sutcliffe Efficiency (NSE) of 0.82 to 0.67 for 1- to 30-day streamflow forecasts. Incorporating upstream streamflow information improved forecast accuracy by up to 10%. During real-time forecast, FutureTST maintains higher forecast skills of 9.03 for 1-day and 5.74 for 14-day forecasts. In contrast, calibrated process-based hydrological model forecasts become unreliable beyond a 4-day lead time. Our findings demonstrate the potential of FutureTST as a reliable streamflow forecasting tool that offers a valuable addition to operational flood monitoring systems and climate-resilient decision-making.

Ambika, Anukesh Krishnankutty [Oak Ridge National ↗

NASA NTRS2011

Hyperspectral Remote Sensing of Atmosphere and Surface Properties

Atmospheric Infrared Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), and Cross-track Infrared Sounder (CrIS) are all hyper-spectral satellite sensors with thousands of spectral channels. Top of atmospheric radiance spectra measured by these sensors contain high information content on atmospheric, cloud, and surface properties. Exploring high information content contained in these high spectral resolution spectra is a challenging task due to computation e ort involved in modeling thousands of spectral channels. Usually, only very small fractions (4{10 percent) of the available channels are included in physical retrieval systems or numerical weather forecast (NWP) satellite data assimilations. We will describe a method of simultaneously retrieving atmospheric temperature, moisture, cloud, and surface properties using all available spectral channels without sacrificing computational speed. The essence of the method is to convert channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. Because the EOFs are orthogonal to each other, about 100 super-channels are adequate to capture the information content of the radiance spectra. A Principal Component-based Radiative Transfer Model (PCRTM) developed at NASA Langley Research Center is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. There is no need to perform EOF transformations to convert super channels back to spectral space at each iteration step for a one-dimensional variational retrieval or a NWP data assimilation system. The PCRTM forward model is also capable of calculating radiative contributions due to multiple-layer clouds. The multiple scattering effects of the clouds are efficiently parameterized. A physical retrieval algorithm then performs an inversion of atmospheric, cloud, and surface properties in super channel domain directly therefore both reducing the computational need and preserving the information content of the IASI measurements. The inversion algorithm is based on a non-linear Levenberg-Marquardt method with climatology covariance matrices and a priori information as constraints. One advantage of this approach is that it uses all information content from the hyper-spectral data so that the retrieval is less sensitive to instrument noise and eliminates the need for selecting a sub-set of the channels.

Liu, Xu↗

NASA NTRS2009

Hydrogen Cyanide in the Upper Troposphere: GEM-AQ Simulation and Comparison with ACE-FTS Observations

We investigate the spatial and temporal distribution of hydrogen cyanide (HCN) in the upper troposphere through numerical simulations and comparison with observations from a space-based instrument. To perform the simulations, we used the Global Environmental Multiscale Air Quality model (GEM-AQ), which is based on the threedimensional Gobal multiscale model developed by the Meteorological Service of Canada for operational weather forecasting. The model was run for the period 2004-2006 on a 1.5deg x 1.5deg global grid with 28 hybrid vertical levels from the surface up to 10 hPa. Objective analysis data from the Canadian Meteorological Centre were used to update the meteorological fields every 24 h. Fire emission fluxes of gas species were generated by using year-specific inventories of carbon emissions with 8-day temporal resolution from the Global Fire Emission Database (GFED) version 2. The model output is compared with HCN profiles measured by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) instrument onboard the Canadian SCISAT-1 satellite. High values of up to a few ppbv are observed in the tropics in the Southern Hemisphere; the enhancement in HCN volume mixing ratios in the upper troposphere is most prominent in October. Low upper-tropospheric mixing ratios of less than 100 pptv are mostly recorded at middle and high latitudes in the Southern Hemisphere in May-July. Mixing ratios in Northern Hemisphere peak in the boreal summer. The amplitude of the seasonal variation is less pronounced than in the Southern Hemisphere. The comparison with the satellite data shows that in the upper troposphere GEM-AQ perform7s well globally for all seasons, except at northern hi gh and middle latitudes in surnmer, where the model has a large negative bias, and in the tropics in winter and spring, where it exhibits large positive bias. This may reflect inaccurate emissions or possible inaccuracies in the emission profile. The model is able to explain most of the observed variability in the upper troposphere HCN field, includin g the interannual variations in the observed mixing ratio. A complementary comparison with daily total columns of HCN from two middle latitude ground-based stations in Northern Japan for the same simulation period shows that the model captures the observed seasonal variation and also points to an underestimation of model emissions in the Northern Hemisphere in the summer. The estimated average global emission equals 1.3 Tg N/yr. The average atmospheric burden is 0.53 Tg N, and the corresponding lifetime is 4.9 months.

Lupu, A.↗

NASA NTRS2018

Integrated Real-Time Modeling System for Heliospheric Space Weather Forecasting

The Sun lies at the center of space weather and is the source of its variability. The primary input to coronal and solar wind models is the activity of the magnetic field in the solar photosphere. What is needed is to develop physics-based models for the dynamics of the magnetic field from the deep convection zone of the Sun to the corona with the goal of providing robust near real-time boundary conditions at the base of space weather forecast models. NASA strategic capabilities efforts toward this goal will be summarized during the talk. These have the objective to enable characterizing and predicting the magnetic field structure and flow dynamics of the Sun by assimilating data from helioseismology and magnetic field observations into physics-based realistic magnetohydrodynamics (MHD) simulations. It is argued that an effort in integrating first-principle modeling of solar magnetism and flow dynamics with real-time observational data via advanced data assimilation methods is needed for transformative steps in space weather research and prediction.

Mansour, Nagi N.↗

DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2007

The Stellar Imager (SI) Project: Resolving Stellar Surfaces, Interiors, and Magnetic Activity

The Stellar Imager (SI) is a UV/Optical. Space-Based Interferometer designed to enable 0.1 milli-arcsec (mas) spectral imaging of stellar surfaces and, via asteroseismology, stellar interiors and of the Universe in general. The ultra-sharp images of SI will revolutionize our view of many dynamic astrophysical processes by transforming point sources into extended sources, and snapshots into evolving views. The science of SI focuses on the role of magnetism in the Universe, particularly on magnetic activity on the surfaces of stars like the Sun. Its prime goal is to enable long-term forecasting of solar activity and the space weather that it drives. SI will also revolutionize our understanding of the formation of planetary systems, of the habitability and climatology of distant planets, and of many magneto-hydrodynamically controlled processes in the Universe. In this paper we discuss the science goals, technology needs, and baseline design of the SI mission.

Carpenter, Kenneth G.↗

NASA NTRS1985

Comparison of Land Cover Information from LANDSAT Multispectral Scanner (MSS) and Airborne Thematic Mapper Simulator (TMS) Data for Hydrologic Applications

Thematic mapper simulator (TMS) data produced a more accurate and spatially contiguous classification than MSS for the Clinton River Basin in Michigan. While the accuracy of the 4-band TMS data set was as good as the 7-band, the 3-band TMS data sets were also better than the MSS. The combination of bands selected based on the transformed divergence technique provided one band in each of the major regions of the spectrum: visible (band 3), near IR (band 4), middle IR (band 5) and thermal IR (band 7). These results should be viewed with some caution, since the data are from a TMS rather than the actual TM and the MSS data were obtained in early summer while the TMS was flown in late summer. The higher accuracies for the developed categories (residential and commercial) should improve the predictions of runoff in flood forecasting models and of flood damage for damage calculation models appreciably.

Gervin, J. C.↗

NASA NTRS2007

The Stellar Imager (SI) - A Mission to Resolve Stellar Surfaces, Interiors, and Magnetic Activity

The Stellar Imager (SI) is a UV/Optical, Space-Based Interferometer designed to enable 0.1 milli-arcsecond (mas) spectral imaging of stellar surfaces and, via asteroseismology, stellar interiors and of the Universe in general. The ultra-sharp images of the Stellar Imager will revolutionize our view of many dynamic astrophysical processes by transforming point sources into extended sources, and snapshots into evolving views. SI's science focuses on the role of magnetism in the Universe, particularly on magnetic activity on the surfaces of stars like the Sun. SI's prime goal is to enable long-term forecasting of solar activity and the space weather that it drives. SI will also revolutionize our understanding of the formation of planetary systems, of the habitability and climatology of distant planets, and of many magneto-hydrodynamically controlled processes in the Universe. SI is included as a 'Flagship and Landmark Discovery Mission' in the 2005 NASA Sun Solar System Connection (SSSC) Roadmap and as a candidate for a 'Pathways to Life Observatory' in the NASA Exploration of the Universe Division (EUD) Roadmap (May, 2005). In this paper we discuss the science goals and technology needs of, and the baseline design for, the SI Mission (http://hires.gsfc.nasa.gov/si/) its ability to image the 'Biggest, Baddest, Coolest Stars'.

Carpenter, Kenneth↗

NASA NTRS2008

The Stellar Imager (SI) Vision Mission and the Benefits of an Ares V Launch

The Stellar Imager (SI) is a UV/Optical, Space-Based Interferometer designed to enable 0.1 milli-arcsecond (mas) spectral imaging of stellar surfaces and, via asteroseismology, stellar interiors and of the Universe in general. The ultra-sharp images of the Stellar Imager will revolutionize our view of many dynamic astrophysical processes by transforming point sources into extended sources, and snapshots into evolving views. SI's science focuses on the role of magnetism in the Universe, particularly on magnetic activity on the surfaces of stars like the Sun. SI's prime goal is to enable long-term forecasting of solar activity and the space weather that it drives. SI will also revolutionize our understanding of the formation of planetary systems, of the habitability and climatology of distant planets, and of many magneto-hydrodynamically controlled processes in the Universe. SI is a "Flagship and Landmark Discovery Mission" in the 2005 Heliophysics Roadmap and a potential implementation of the UVOI in the 2006 Science Program for NASA's Astronomy and Physics Division. In this paper we briefly discuss the science goals, technology needs, and baseline design of the SI Mission, and then describe the benefits to the mission that a launch on an Ares V, with its larger payload shroud, would produce. Additional information on SI can be found at: http://hires.gsfc.nasa.gov/si/.

Carpenter, Kenneth F.↗

NASA NTRS2018

GEOS S2S-2_1 File Specification: GMAO Seasonal and Sub-Seasonal Forecast Output

The NASA GMAO seasonal (9 months) and subseasonal (45 days) forecasts are produced with the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model and Data Assimilation System Version S2S-2_1. The new system replaces version S2S-1.0 described in Borovikov et al (2017), and includes upgrades to many components of the system. The atmospheric model includes an upgrade from a pre-MERRA-2 version running on a latitude-longitude grid at approx. 1 degree resolution to a current version running on a cubed sphere grid at approximately 1/2 degree resolution. The important developments are related to the dynamical core (Putman et al., 2011), the moist physics (''two-moment microphysics'' of Barahona et al., 2014) and the cryosphere (Cullather et al., 2014). As in the previous GMAO S2S system, the land model is that of Koster et al (2000). GMAO S2S-2_1 now includes the Goddard Chemistry Aerosol Radiation and Transport (GOCART, Colarco et al., 2010) single moment interactive aerosol model that includes predictive aerosols including dust, sea salt and several species of carbon and sulfate. The previous version of GMAO S2S specified aerosol amounts from climatology, which were used to inform the atmospheric radiation only. The ocean model includes an upgrade from MOM4 to MOM5 (Griffies 2012), and continues to be run on the tripolar grid at approximately 1/2 degree resolution in the tropics with 40 vertical levels. As in S2S-1.0, the sea ice model is from the Los Alamos Sea Ice model (CICE4, Hunke and Lipscomb 2010). The Ocean Data Assimilation System (ODAS) has been upgraded from the one described in Borovikov et al., 2017 to one that uses a modified version of the Penny, 2014 Local Ensemble Transform Kalman Filter (LETKF), and now assimilates along-track altimetry. The ODAS also does a nudging to MERRA-2 SST and sea ice boundary conditions. The atmospheric data assimilation fields used to constrain the atmosphere in the ODAS have been upgraded from MERRA to a MERRA-2 like system. The system is initialized using a MERRA-2-like atmospheric reanalysis (Gelaro et al. 2017) and the GMAO S2S-2_1 ocean analysis. Additional ensemble members for forecasts are produced with initial states at 5-day intervals, with additional members based on perturbations of the atmospheric and ocean states. Both subseasonal and seasonal forecasts are submitted to the National MultiModel Ensemble (NMME) project, and are part of the US/Canada multimodel seasonal forecasts (http://www.cpc.ncep.noaa.gov/products/NMME/). A large suite of retrospective forecasts (''hindcasts'') have been completed, and contribute to the calculation of the model's baseline climatology and drift, anomalies from which are the basis of the seasonal forecasts.

Seasonal Forecast↗

NASA NTRS2016

Predicting Real-Time Safety of the National Airspace System

To gain the situational awareness necessary for informed decision making regarding avoidance of airspace hazards, each operator must consolidate operations-relevant information from disparate sources and apply extensive domain knowledge to correctly interpret not just the current state of the NAS but forecast its (combined) evolution over the duration of the operation. This time- and workload-intensive process is periodically repeated throughout the operation so that changes can be managed in a timely manner.The imprecision, inaccuracies, inconsistency, and incompleteness of the incoming data further challenges the process. To facilitate informed decision making, this paper presents a model-based framework for the textitautomated real-time monitoring and prediction of possible effects of airspace hazards on the safety of the National Airspace System (NAS). First, hazards to flight are identified and transformed into sms, that is, quantities of interest that could be evaluated based on available data and are predictive of an unsafe event. The sms and associated thresholds that specify when an event transitions from emphsafe to emphunsafe are combined with models of airspace operations and aircraft dynamics. The framework can include any hazard to flight that can be modeled quantitatively. Models can be detailed and complex, or they can be considerably simplifed, as appropriate to the application. Real-time NAS safety monitoring and prediction begins with an estimate of the state of the NAS using the dynamic models. Given the state estimate and a probability distribution of future inputs to the NAS, we can then predict the evolution of the NAS - the future state - and the occurrence of hazards and unsafe events. The entire probability distribution of airspace sms is computed, not just point estimates, without significant assumptions regarding the distribution type andor parameters. We demonstrate our overall approach through a simulated scenario in which we predict the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress. Predictions accounting for common sources of uncertainty are included and it is shown how the predictions improve in time, become more confident, and change dynamically as new information is made available to the prediction algorithm.

safety↗