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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Data Communications Performance of AOCDL and AUTOMET Over a VDL Mode 2 Link

Airline Operational Control Data Link (AOCDL) is a service that allows for downlink of in-flight data to help an airline operate efficiently. Automated Meteorological Transmission (AUTOMET) is a weather reporting system that provides forecasts and atmospheric conditions to pilots during flight. These are two examples of new or extended services that will be enabled by implementation of the ATN and its digital air/ground communications data links. One of the possible data links identified for these services is VDL Mode 2. This research focuses on the data link performance of AOCDL and AUTOMET services in a 2015 scenario using VDL Mode 2 as the data link. In the near term, it is expected that the Controller-Pilot Data Link Communication (CPDLC) and Decision Support System Data Link (DSSDL) services will also use the VDL Mode 2 link. CPDLC and DSSDL are high-priority services that communicate aircraft control information and require low end-to-end delays for safety. VDL Mode 3, which is the preferred link for these services because it provides priority-based link access, will not be deployed in time for earlier implementations of these services to use. As such, the research also investigates the impact of CPDLC and DSSDL services on the same VDL link as AOCDL and AUTOMET. Performance of these services over the data link will be separately examined for the airport, terminal, and en route domains. OPNET simulations are performed for each domain. The number of aircraft for each domain is representative of the expected peak aircraft in 2015. Results are collected during a steady-state condition, with aircraft neither entering nor exiting the domain. The study explores the services in terms of the traffic load they present to the VDL Mode 2 radio. The amount of load each service contributes to the data link will be shown for each domain. The end-to-end delays for each service will be discussed. Data link metrics such as subnetwork and queuing delays, channel utilization, and protocol overhead will be used to show the impact of these services on VDL Mode 2. All results will be presented both with and without the inclusion of CPDLC and DSSDL services.

Bretmersky, Steven↗
NASA NTRS2004

Single-column Model Intercomparison for a Stably Stratified Atmospheric Boundary Layer

The parameterization of the stably stratified atmospheric boundary layer is a difficult issue, which has a large impact on the medium-range weather forecasts and on climate integrations. A non-strongly stratified arctic case is simulated by nineteen single-column turbulence schemes. The statistics from the Large-eddy simulation (LES) intercomparison made for the same case by eight different models are used as a guiding reference. The single-column parameterizations include research schemes and operational schemes from major forecast and climate research centres. First order schemes, a large number of turbulence kinetic energy closures, and other proposals have submitted results. There is a large spread in the results; in general, the operational schemes mix more efficiently than the research ones, and the TKE and other higher order closures give results closer to the LES statistics. The sensitivities of the schemes to the parameters of their turbulence closures are partially explored.

Cuxart, J.↗
NASA NTRS2023

Space Weather Investigation Frontier (SWIFT)

The Space Weather Investigation Frontier (SWIFT) mission will aim at making major discoveries on the three-dimensional structure and dynamics of heliospheric structures that drive space weather. The focus will be on Interplanetary Coronal Mass Ejections (ICMEs) that originate from massive expulsions of plasma and magnetic flux from the solar corona. They cause the largest geomagnetic storms and solar energetic particle events, threatening to endanger life and disrupt technology on Earth and in space. A big current problem, both regarding fundamental solar-terrestrial physics and space weather, is that we do not yet understand spatial characteristics and temporal evolution of ICMEs and that the existing remote-sensing and in-situ observatories are not suited for resolving multi-layered and evolutionary structures in these massive storm drivers. Here, we propose a groundbreaking mission concept study using solar sail technology that, for the first time, will make continuous, in-situ multi-point observations along the Sun-Earth line beyond the Lagrange point L1 (sub-L1). This unique position, in combination with L1 assets, will allow distinguishing between local and global processes, spatial characteristics, temporal evolution, and particle energization mechanisms related to ICMEs. In addition, measurements of the magnetic field in earthbound ICMEs and their sub-structures from the SWIFT location will double the current forecasting lead-times from L1. This concept also paves the way for missions with increasingly longer forecasting lead-times, addressing NASA and NOAA’s space weather goals, as set forth by the Decadal Survey. The objective of this communication is to inform the community of the ongoing effort, including plans to further develop the mission concept, supported by the Heliophysics Flight Opportunities Studies (HFOS) program under NASA’s Research Opportunities in Space and Earth Sciences (ROSES).

M. Akhavan-Tafti↗
NASA NTRS1996

Understanding the Role of Biology in the Global Environment: NASA'S Mission to Planet Earth

NASA has long used the unique perspective of space as a means of expanding our understanding of how the Earth's environment functions. In particular, the linkages between land, air, water, and life-the elements of the Earth system-are a focus for NASA's Mission to Planet Earth. This approach, called Earth system science, blends together fields like meteorology, biology, oceanography, and atmospheric science. Mission to Planet Earth uses observations from satellites, aircraft, balloons, and ground researchers as the basis for analysis of the elements of the Earth system, the interactions between those elements, and possible changes over the coming years and decades. This information is helping scientists improve our understanding of how natural processes affect us and how we might be affecting them. Such studies will yield improved weather forecasts, tools for managing agriculture and forests, information for fishermen and local planners, and, eventually, an enhanced ability to predict how the climate will change in the future. NASA has designed Mission to Planet Earth to focus on five primary themes: Land Cover and Land Use Change; Seasonal to Interannual Climate Prediction; Natural Hazards; Long-Term Climate Variability; and Atmosphere Ozone.

Townsend, William F.↗
NASA NTRS1982

Medium-range objective predictions of thunderstorms on the McIDAS/CSIS interactive computer system

Until recently, all operational meteorological data has been made available to forecasters in a variety of different forms. Predictions based upon these different data formats have been complicated by the inability of forecasters to easily assimilate, in real-time, all data to provide an optimum decision regarding future weather occurrences. By March 1980, a joint NASA/NOAA effort had been initiated to develop the Centralized Storm Information System (CSIS). The primary objectives of this joint project are related to an improvement of the overall severe storm forecast and warning procedure and to a demonstration of the operational utility of techniques developed within the applied research community. CSIS is to utilize the Man Computer Interactive Data Access System (McIDAS). The present investigation is concerned with one of the first attempts to employ the CSIS system for the evaluation of a new research technique involving the prediction of thunderstorms over a forecast period of 12-48 hours.

Wilson, G. S.↗
NASA NTRS1996

Precipitable Water Variability Using SSM/I and GOES VAS Pathfinder Data Sets

Determining moisture variability for all weather scenes is critical to understanding the earth's hydrologic cycle and global climate changes. Remote sensing from geostationary satellites provides the necessary temporal and spatial resolutions necessary for global change studies. Due to antenna size constraints imposed with the use of microwave radiometers, geostationary satellites have carried instruments passively measuring radiation at infrared wavelengths or shorter. The shortfall of using infrared instruments in moisture studies lies in its inability to sense terrestrial radiation through clouds. Microwave emissions, on the other hand, are mostly unaffected by cloudy atmospheres. Land surface emissivity at microwave frequencies exhibit both high temporal and spatial variability thus confining moisture retrievals at microwave frequencies to over marine atmospheres (a near uniform cold background). This study intercompares the total column integrated water content Precipitable Water, (PW) as derived from both the Special Sensor Microwave Imager (SSM/I) and the Geostationary Operational Environmental Satellite (GOES) VISSR Atmospheric Sounder (VAS) pathfinder data sets. PW is a bulk parameter often used to quantify moisture variability and is important to understanding the earth's hydrologic cycle and climate system. This research has been spawned in an effort to combine two different algorithms which together can lead to a more comprehensive quantification of global water vapor. The approach taken here is to intercompare two independent PW retrieval algorithms and to validate the resultant retrievals against an existing data set, namely the European Center for Medium range Weather Forecasts (ECMWF) model analysis data.

Lerner, Jeffrey A.↗
NASA NTRS1982

The 1981 current research on aviation weather (bibliography)

Current and ongoing research programs related to various areas of aviation meteorology are presented. Literature searches of major abstract publications, were conducted. Research project managers of various government agencies involved in aviation meteorology research provided a list of current research project titles and managers, supporting organizations, performing organizations, the principal investigators, and the objectives. These are tabulated under the headings of advanced meteorological instruments, forecasting, icing, lightning and atmospheric electricity; fog, visibility, and ceilings; low level wind shear, storm hazards/severe storms, turbulence, winds, and ozone and other meteorological parameters. This information was reviewed and assembled into a bibliography providing a current readily useable source of information in the area of aviation meteorology.

Daniel, J.↗
NASA NTRS2000

Section on Observed Impacts on El Nino

Agricultural applications of El Nino forecasts are already underway in some countries and need to be evaluated or re-evaluated. For example, in Peru, El Nino forecasts have been incorporated into national planning for the agricultural sector, and areas planted with rice and cotton (cotton being the more drought-tolerant crop) are adjusted accordingly. How well are this and other such programs working? Such evaluations will contribute to the governmental and intergovernmental institutions, including the Inter-American Institute for Global Change Research and the US National Ocean and Atmospheric Agency that are fostering programs to aid the effective use of forecasts. As El Nino climate forecasting grows out of the research mode into operational mode, the research focus shifts to include the design of appropriate modes of utilization. Awareness of and sensitivity to the costs of prediction errors also grow. For example, one major forecasting model failed to predict the very large El Nino event of 1997, when Pacific sea-surface temperatures were the highest on record. Although simple correlations between El Nino events and crop yields may be suggestive, more sophisticated work is needed to understand the subtleties of the interplay among the global climate system, regional climate patterns, and local agricultural systems. Honesty about the limitations of an forecast is essential, especially when human livelihoods are at stake. An end-to-end analysis links tools and expertise from the full sequence of ENSO cause-and-effect processes. Representatives from many disciplines are needed to achieve insights, e.g, oceanographers and atmospheric scientists who predict El Nino events, climatologists who drive global climate models with sea-surface temperature predictions, agronomists who translate regional climate connections in to crop yield forecasts, and economists who analyze market adjustments to the vagaries of climate and determine the value of climate forecasts. Methods include historical studies to understand past patterns and to test hindcasts of the prediction tools, crop modeling, spatial analysis and remote sensing. This research involves expanding, deepening, and applying the understanding of physical climate to the fields of agronomy and social science; and the reciprocal understanding of crop growth and farm economics to climatology. Delivery of a regional climate forecast with no information about how the climate forecast was derived limits its effectiveness. Explanation of a region's major climate driving forces helps to place a seasonal forecast in context. Then, a useful approach is to show historical responses to previous El Nino events, and projections, with uncertainty intervals, of crop response from dynamic process crop growth models. Regional ID forecasts should be updated with real-time weather conditions. Since every El Nino event is different, it is important to track, report and advise on each new event as it unfolds. The stability of human enterprises depends on understanding both the potentialities and the limits of predictability. Farmers rely on past experience to anticipate and respond to fluctuations in the biophysical systems on which their livelihoods depend. Now scientists are improving their ability to predict some major elements of climate variability. The improvements in the reliability of El Nino forecasts are encouraging, but seasonal forecasts for agriculture are not, and will probably never be completely infallible, due to the chaotic nature of the climate system. Uncertainties proliferate as we extend beyond Pacific sea-surface temperatures to climate teleconnections and agricultural outcomes. The goal of this research is to shed as a clear light as possible on these inherent uncertainties and thus to contribute to the development of appropriate responses to El Nino and other seasonal forecasts for a range of stakeholders, which, ultimately, includes food consumers everywhere.

Rosenzweig, Cynthia↗
NASA NTRS2012

Short-Term Prediction Research and Transition (SPoRT) Center: Transitioning Satellite Data to Operations

The Short-term Prediction Research and Transition (SPoRT) Center located at NASA Marshall Space Flight Center has been conducting testbed activities aimed at transitioning satellite products to National Weather Service operational end users for the last 10 years. SPoRT is a NASA/NOAA funded project that has set the bar for transition of products to operational end users through a paradigm of understanding forecast challenges and forecaster needs, displaying products in end users decision support systems, actively assessing the operational impact of these products, and improving products based on forecaster feedback. Aiming for quality partnerships rather than a large quantity of data users, SPoRT has become a community leader in training operational forecasters on the use of up-and-coming satellite data through the use of legacy instruments and proxy data. Traditionally, SPoRT has supplied satellite imagery and products from NASA instruments such as the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS). However, recently, SPoRT has been funded by the GOES-R and Joint Polar Satellite System (JPSS) Proving Grounds to accelerate the transition of selected imagery and products to help improve forecaster awareness of upcoming operational data from the Visible Infrared Imager Radiometer Suite (VIIRS), Cross-track Infrared Sounder (CrIS), Advanced Baseline Imager (ABI), and Geostationary Lightning Mapper (GLM). This presentation provides background on the SPoRT Center, the SPoRT paradigm, and some example products that SPoRT is excited to work with forecasters to evaluate.

Zavodsky, Bradley↗
NASA NTRSDate not supplied

Comparison of the CALIPSO Level 3 Ice Cloud Product with the DARDAR and 2C-ICE products

Understanding the vertical distribution of ice clouds is crucial for climate modeling and weather forecasting. Since its launch to space in 2006, the Cloud-Aerosol Lidar with Orthogonal Polarization instrument (CALIOP) onboard the CALIPSO spacecraft has been providing unprecedented high-quality profiles of ice clouds, especially optically thin cirrus clouds, during both day and night, on a global scale. Recently the lidar science working group at the NASA Langley Research Center delivered a level 3 (L3) ice cloud product which reports monthly statistics of ice cloud extinction coefficient and ice water content on a uniform 3-dimensional spatial grid. This presentation compares the ice cloud climatology derived from this product with two other ice cloud products, namely the raDAR/liDAR(DARDAR) ice cloud product and the CloudSat-CALIPSO Ice Cloud Property Product (2C-ICE). Using processing similar to the CALIPSO L3 product, granules of DARDAR and 2C-ICE data have been processed into monthly statistics for a better comparison. Similarities and differences will be presented and discussed. The findings illustrate the strengths of the different products and help to identify which product might be most appropriate for a given research topic.

Xia Cai↗
NASA NTRS1992

Monthly means of selected climate variables for 1985 - 1989

Meteorologists are accustomed to viewing instantaneous weather maps, since these contain the most relevant information for the task of producing short-range weather forecasts. Climatologists, on the other hand, tend to deal with long-term means, which portray the average climate. The recent emphasis on dynamical extended-range forecasting and, in particular measuring and predicting short term climate change makes it important that we become accustomed to looking at variations on monthly and longer time scales. A convenient toll for researchers to familiarize themselves with the variability which occurs in selected parameters on these time scales is provided. The format of the document was chosen to help facilitate the intercomparison of various parameters and highlight the year-to-year variability in monthly means.

Schubert, S.↗
NASA NTRS2011

Space Weather, Geomagnetic Disturbances and Impact on the High-Voltage Transmission Systems

Geomagnetically induced currents (GIC) affecting the performance of high-voltage power transmission systems are one of the most significant hazards space weather poses on the operability of critical US infrastructure. The severity of the threat was emphasized, for example, in two recent reports: the National Research Council (NRC) report "Severe Space Weather Events--Understanding Societal and Economic Impacts: A Workshop Report" and the North American Electric Reliability Corporation (NERC) report "HighImpact, Low-Frequency Event Risk to the North American Bulk Power System." The NRC and NERC reports demonstrated the important national security dimension of space weather and GIC and called for comprehensive actions to forecast and mitigate the hazard. In this paper we will give a brief overview of space weather storms and accompanying geomagnetic storm events that lead to GIC. We will also review the fundamental principles of how GIC can impact the power transmission systems. Space weather has been a subject of great scientific advances that have changed the wonder of the past to a quantitative field of physics with true predictive power of today. NASA's Solar Shield system aimed at forecasting of GIC in the North American high-voltage power transmission system can be considered as one of the ultimate fruits of those advances. We will review the fundamental principles of the Solar Shield system and provide our view of the way forward in the science of GIC.

Pullkkinen, A.↗
NASA NTRS2021

Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

Research in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

Ryan A Zamora↗
NASA NTRSDate not supplied

Designing and Evaluating NASA SPoRT Center’s DustTracker-AI Model for Detecting Dust in NASA/NOAA Geostationary Satellite Imagery

- Near real-time identification of airborne dust in satellite imagery is important for mitigating the adverse effects of dust storms on human activities. - False color Red-Green-Blue (RGB) imagery has been used for dust detection, but it has limitations and can be difficult to interpret. - The NASA Short-term Research and Transition (SPoRT) center has developed a night-time dust detection random forest (NT-DustTracker-AI, Berndt et al. 2021) model using NASA/NOAA Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery as inputs. - The SPoRT center has partnered with the NOAA National Weather Service to evaluate the model for use in weather forecasting operations, and preliminary results have been positive. - The SPoRT center has expanded the model to cover both day and night, continuing to use infrared imagery as inputs. This new model, known as DustTracker-AI, has shown good agreement with available dust observations

Robert A. Junod↗
DOE OSTI2026

Short‐Term Hourly Weather Forecasting Using PredRNN With Image Preprocessing

Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.

Tran, Hoang [Pacific Northwest National Laboratory↗
NASA NTRS2000

Design of an Aircraft Vortex Spacing System for Airport Capacity Improvement

The National Aeronautics and Space Administration (NASA) is addressing airport capacity enhancements through the Terminal Area Productivity (TAP) program. Within TAP, the Reduced Spacing Operations element at the NASA Langley Research Center is developing an Aircraft VOrtex Spacing System (AVOSS). AVOSS will integrate the output of several systems to produce weather dependent, dynamic wake vortex spacing criteria. These systems provide current and predicted weather conditions, models of wake vortex transport and decay in these weather conditions, and real-time feedback of wake vortex behavior from sensors. The goal of the NASA program is to provide the research and development to demonstrate an engineering model AVOSS, in real-time operation, at a major airport. A wake vortex system test facility was established at the Dallas-Fort Worth International Airport (DFW) in 1997 and tested in 1998. Results from operation of the initial AVOSS system, plus advances in wake vortex prediction and near-term weather forecast models, "nowcast", have been integrated into a second-generation system. This AVOSS version is undergoing final checkout in preparation for a system demonstration in 2000. This paper describes the revised AVOSS system architecture, subsystem enhancements, and initial results with AVOSS version 2 from a deployment at DFW in the fall of 1999.

Hinton, David A.↗
DOE OSTI2023

Development of Multiresolution Capabilities for the Holistic Energy Resource Optimization Network (HERON) tool A progress update

INL researchers work on technoeconomic analyses for integrated energy systems (IES) using the Framework for Optimization of ResourCes and Economics (FORCE). Within FORCE, researchers use the Holistic Energy Resource Optimization Network (HERON) tool to conduct optimization of grid portfolios under uncertain market conditions. These optimizations determine optimal capacities for all IES components and strategies for resource dispatch which maximize some economic metric (e.g., net present value). Resource dispatch occurs on finer timescales (typically hours) and thus are asked to respond to a given time series (e.g. hourly load demand profiles for a grid, or pre-determined electricity prices). Volatile and complex bidding dynamics as well as poorly forecasted weather events within deregulated markets add uncertainty to the time series; FORCE can address this uncertainty by training a reduced order model on historical time series and generate unique synthetic time series which represent individual scenarios or realizations of the market. The IES configuration can be simulated under these different sampled realizations and a stochastic optimization is conducted which optimizes the expected value of the desired economic metric. The training of a synthetic time series generator is limited by the chosen time resolution; dynamics can occur on different time scales. Seasonal demand trends can dominate faster dynamical events (such as power outages from certain sectors or severe weather events) which might not get captured correctly by the trained model. In this report, we investigate different ways of addressing the training and generation of time series on multiple time scales using three main algorithms: wavelet decomposition, dynamic mode decomposition, and generative adversarial networks for time series. We demonstrate a time series analysis that yields information on not just the frequency space but also temporal space: where a fast Fourier transform can provide what frequencies dominate, the new algorithms can provide when the frequencies dominate as well. These analyses can help improve IES optimization by allowing researchers to couple simulations at different timescales when it is most needed - seasonal, day-ahead, and real time optimization - with greater computational efficiency. Future work will include implementation of a subset of the proposed algorithms into the FORCE toolset and application of these analyses into multiple timescale optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2018

Using Reanalysis in Crop Monitoring and Forecasting Systems

Weather observations are essential for crop monitoring and forecasting but they are not always available and in some cases they have limited spatial representativeness. Thus, reanalyses represent an alternative source of information to be explored. In this study, we assess the feasibility of reanalysis-based crop monitoring and forecasting by using the system developed and maintained by the European Commission- Joint Research Centre, its gridded daily meteorological observations, the biased-corrected reanalysis AgMERRA and the ERA-Interim reanalysis. We focus on Europe and on two crops, wheat and maize, in the period 1980-2010 under potential and water-imited conditions. In terms of inter-annual yield correlation at the country scale, the reanalysis-driven systems show a very good performance for both wheat and maize (with correlation values higher than 0.6 in almost all EU28 countries) when compared to the observations-driven system. However, significant yield biases affect both crops. All simulations show similar correlations with respect to the FAO reported yield time series. These findings support the integration of reanalyses in current crop monitoring and forecasting systems and point to the emerging opportunities linked to the coming availability of higher-resolution reanalysis updated at near real time.

Reanalysis↗