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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Feasibility and Prospects of the High Resolution Rapid Refresh Model For Dynamic Line Rating

The ampacity of transmission lines is defined as the maximum amount of current the conductor can safely carry. It is necessary for transmission line operators to apply ampacity limits due to the thermal properties of the conductor. Dynamic Line Rating (DLR) is a technology and technique that uses the environmental conditions or a set of the conditions to calculate the ampacity of the conductor. The way the DLR is calculated has depended upon some amount of physical technology to implement the solution. The High Resolution Rapid Refresh (HRRR) Model is an atmospheric model that may provide enough resolution to perform dynamic line rating without any hardware at all. The objective is to use this model to perform Dynamic Line Rating. This will solve two of the most pervasive problems in Dynamic Line Rating, how to instrument a transmission line hundreds of miles long and then how to predict near future ampacity sufficient for transmission operators to effectively use, This technique will use the Idaho National Laboratory site as the test location for analysis. The High Resolution Rapid Refresh model is a reliable, freely available, and well-maintained weather forecasting model for the entire United States of America. The model provides enough special resolution that additional local weather monitoring devices at location are not necessary to conduct dynamic line rating. The HRRR ratings are lower on average than the locally measured ratings which allows for an additional margin of safety. The adoption of using purely digital methods for dynamic line rating could allow for wide scale adoption of the technology with very little overhead expenditure of implementation.

17 WIND ENERGY↗
NASA NTRS2005

Lightning Mapping and the Nowcasting of Severe Storms

This paper describes a successful research and operational collaboration between NASA scientists and NWS forecasters to improve severe stor m warnings using real-time data from a regional VHF total lightning mapping array (LMA). Key objectives of our research using LMA data ar e: a) Identification of intensifying and weakening storms using the time rate-of-change of total flash rate; b) Improved severe storm poten tial situational awareness; c) Evaluation of the potential of total f lash rate trend to improve severe storm probability of detection (POD ) and lead time; and d) Validation of mesoscale model forecasts of th understorm initiation. The LMA data are distributed for ingest and di splay in the WFO AWIPS decision support system, and archived at each WFO for case studies, event playbacks, and assessments using the NWS Warning Event Simulator. The Huntsville WFO has upgraded severe thund erstorm warnings to verified tornado warnings and avoided a false ala rm on a severe storm through the added information on storm growth, intensification, and decay that can be deduced from the magnitude and temporal trend of total flash rates. We present detailed case studies of the observed relationships between lightning activity and tornadi c storm development as determined by radar reflectivity and velocity fields, and thunderstorms forecast by the Weather Research and Foreca st (WRF) model. From these collaborative studies, forecasters can eva luate the added value of total lightning data within the forecast and warning decision-making process (http://weather.msfc.nasa.gov/sport) .

Goodman, S.↗
DOE OSTI2025

Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators

Abstract. In Part 1, we created an ensemble based on spherical Fourier neural operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics that assess the ensemble's physical fidelity, our ensemble has comparable performance to operational weather forecasting systems. However, it requires orders-of-magnitude fewer computational resources. Here in Part 2, we generate a huge ensemble (HENS), with 7424 members initialized each day of summer 2023. We enumerate the technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents a detailed sampling of internal variability. HENS has two primary applications: (1) as a large dataset with which to study the statistics and drivers of extreme weather and (2) as a weather forecasting system. For extreme climate statistics, HENS samples events 4σ away from the ensemble mean. At each grid cell, HENS increases the skill of the most accurate ensemble member and enhances coverage of possible future trajectories. As a weather forecasting model, HENS issues extreme weather forecasts with better uncertainty quantification. It also reduces the probability of outlier events, in which the verification value lies outside the ensemble forecast distribution.

Mahesh, Ankur↗
NASA NTRS2013

Tethered Satellites as Enabling Platforms for an Operational Space Weather Monitoring System

Space weather nowcasting and forecasting models require assimilation of near‐real time (NRT) space environment data to improve the precision and accuracy of operational products. Typically, these models begin with a climatological model to provide "most probable distributions" of environmental parameters as a function of time and space. The process of NRT data assimilation gently pulls the climate model closer toward the observed state (e.g. via Kalman smoothing) for nowcasting, and forecasting is achieved through a set of iterative physics‐based forward‐prediction calculations. The issue of required space weather observatories to meet the spatial and temporal requirements of these models is a complex one, and we do not address that with this poster. Instead, we present some examples of how tethered satellites can be used to address the shortfalls in our ability to measure critical environmental parameters necessary to drive these space weather models. Examples include very long baseline electric field measurements, magnetized ionospheric conductivity measurements, and the ability to separate temporal from spatial irregularities in environmental parameters. Tethered satellite functional requirements will be presented for each space weather parameter considered in this study.

Krause, L. Habash↗
DOE OSTI2020

Probabilistic Prediction of Geomagnetic Storms and the K p index

Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index K p in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic K p predictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-ahead K p prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (K p ≥ 5 – ) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1979

Spares-optimized model

Computerized spares optimization for Space Shuttle Project comprises analytical process for developing spares quantification and budget forecasts. Model, which assesses risk associated with recommended spares quantities, is enconomical way to determine best mix of large number of spare types.

Cain, A. W.↗
NASA NTRS1986

Research Review, 1984

A variety of topics relevant to global modeling and simulation are presented. Areas of interest include: (1) analysis and forecast studies; (2) satellite observing systems; (3) analysis and forecast model development; (4) atmospheric dynamics and diagnostic studies; (5) climate/ocean-air interactions; and notes from lectures.

Source record↗
NASA NTRS2010

Applied Meteorology Unit (AMU) Quarterly Report - Fourth Quarter FY-10

Three AMU tasks were completed in this Quarter, each resulting in a forecast tool now being used in operations and a final report documenting how the work was done. AMU personnel completed the following tasks (1) Phase II of the Peak Wind Tool for General Forecasting task by delivering an improved wind forecasting tool to operations and providing training on its use; (2) a graphical user interface (GUI) she updated with new scripts to complete the ADAS Update and Maintainability task, and delivered the scripts to the Spaceflight Meteorology Group on Johnson Space Center, Texas and National Weather Service in Melbourne, Fla.; and (3) the Verify MesoNAM Performance task after we created and delivered a GUI that forecasters will use to determine the performance of the operational MesoNAM weather model forecast.

Bauman, William↗
NASA NTRS2013

Tethered Satellites as an Enabling Platform for Operational Space Weather Monitoring Systems

Tethered satellites offer the potential to be an important enabling technology to support operational space weather monitoring systems. Space weather "nowcasting" and forecasting models rely on assimilation of near-real-time (NRT) space environment data to provide warnings for storm events and deleterious effects on the global societal infrastructure. Typically, these models are initialized by a climatological model to provide "most probable distributions" of environmental parameters as a function of time and space. The process of NRT data assimilation gently pulls the climate model closer toward the observed state (e.g., via Kalman smoothing) for nowcasting, and forecasting is achieved through a set of iterative semi-empirical physics-based forward-prediction calculations. Many challenges are associated with the development of an operational system, from the top-level architecture (e.g., the required space weather observatories to meet the spatial and temporal requirements of these models) down to the individual instruments capable of making the NRT measurements. This study focuses on the latter challenge: we present some examples of how tethered satellites (from 100s of m to 20 km) are uniquely suited to address certain shortfalls in our ability to measure critical environmental parameters necessary to drive these space weather models. Examples include long baseline electric field measurements, magnetized ionospheric conductivity measurements, and the ability to separate temporal from spatial irregularities in environmental parameters. Tethered satellite functional requirements are presented for two examples of space environment observables.

Gilchrist, Brian E.↗
DOE OSTI2026

1995 OMPO Model Development Project

The 1995 Oahu Metropolitan Planning Organization (OMPO) Model Development Project for Oahu Island was intended to document how Oahu residents use the streets, highways, and transit services in the region. These data were used to develop the Travel Demand Forecasting Model, which is used to forecast the traffic impacts of various land use changes. The survey collected complete travel information of 4,060 Oahu households for a 24-hour period, including demographic and socioeconomic data.

1Hz data↗
NASA NTRS2018

Prediction Skill of the MJO Teleconnection Signals in the NASA GEOS Subseasonal Reforecasts

Tropical-extratropical teleconnections are considered key to advancing subseasonal prediction. The Madden Julian oscillation (MJO), characterized by large scale convective envelopes propagating along the tropical Indo-Pacific sector, is known to modulate midlatitude circulation and associated weather patterns. Although there is a general consensus on the MJO's influence on the midlatitude circulation, which is thought to be due to modulations of the North Atlantic Oscillation (NAO) and the Pacific North America (PNA) pattern, relatively less is known about the predictability of these teleconnection signals in dynamical forecast models. The composite evolution of the midlatitude circulation anomalies and associated wave train structure as the delayed response to tropical heating are reported in many studies that have examined reanalyses and long model simulations. However, it is yet to be determined whether they lend any beneficial subseasonal forecast skill, especially to weekly mean surface temperature and precipitation over North America. Investigating useful predictable signals from the MJO teleconnections is also complicated by the fact that the MJO is a moving heat source with an approximate periodicity of 30-60 days, and that the structure and amplitude of the midlatitude response can be sensitive to the longitudinal positioning of the heating anomaly as well as the propagation speed of the MJO. The objective of this study is to investigate the impact of MJO teleconnections on forecast accuracy at 2-3 week lead over North America, with an emphasis on the above-mentioned lesser known aspects of these teleconnections. To this end, we utilize a suite of subseasonal reforecasts performed with the latest NASA GEOS-5 seasonal-to-subseasonal (S2S) system. These reforecasts were performed as part of the NOAA SubX project, wherein the NASA GEOS-5 atmosphere-ocean coupled model was run at degree horizontal resolution, initialized every 5 days for the period 1999-2016. The GEOS-5 model shows skillful predictions of the MJO, with the correlation coefficient based on the real-time multivariate MJO (RMM) index staying at or above 0.5 up to forecast lead 26-36 days. The system is thus a useful tool for investigating MJO teleconnection processes.

Achuthavarier, Deepthi↗
NASA NTRS2002

The Impact of Soil Moisture Initialization On Seasonal Precipitation Forecasts

Some studies suggest that the proper initialization of soil moisture in a forecasting model may contribute significantly to the accurate prediction of seasonal precipitation, especially over mid-latitude continents. In order for the initialization to have any impact at all, however, two conditions must be satisfied: (1) the initial soil moisture anomaly must be "remembered" into the forecasted season, and (2) the atmosphere must respond in a predictable way to the soil moisture anomaly. In our previous studies, we identified the key land surface and atmospheric properties needed to satisfy each condition. Here, we tie these studies together with an analysis of an ensemble of seasonal forecasts. Initial soil moisture conditions for the forecasts are established by forcing the land surface model with realistic precipitation prior to the start of the forecast period. As expected, the impacts on forecasted precipitation (relative to an ensemble of runs that do not utilize soil moisture information) tend to be localized over the small fraction of the earth with all of the required land and atmosphere properties.

Koster, R. D.↗
NASA NTRS2000

Forecasting disease risk for increased epidemic preparedness in public health

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

Disease Outbreaks↗
NASA NTRS2010

Improving the Representation of Snow Crystal Properties with a Single-Moment Mircophysics Scheme

Single-moment microphysics schemes are utilized in an increasing number of applications and are widely available within numerical modeling packages, often executed in near real-time to aid in the issuance of weather forecasts and advisories. In order to simulate cloud microphysical and precipitation processes, a number of assumptions are made within these schemes. Snow crystals are often assumed to be spherical and of uniform density, and their size distribution intercept may be fixed to simplify calculation of the remaining parameters. Recently, the Canadian CloudSat/CALIPSO Validation Project (C3VP) provided aircraft observations of snow crystal size distributions and environmental state variables, sampling widespread snowfall associated with a passing extratropical cyclone on 22 January 2007. Aircraft instrumentation was supplemented by comparable surface estimations and sampling by two radars: the C-band, dual-polarimetric radar in King City, Ontario and the NASA CloudSat 94 GHz Cloud Profiling Radar. As radar systems respond to both hydrometeor mass and size distribution, they provide value when assessing the accuracy of cloud characteristics as simulated by a forecast model. However, simulation of the 94 GHz radar signal requires special attention, as radar backscatter is sensitive to the assumed crystal shape. Observations obtained during the 22 January 2007 event are used to validate assumptions of density and size distribution within the NASA Goddard six-class single-moment microphysics scheme. Two high resolution forecasts are performed on a 9-3-1 km grid, with C3VP-based alternative parameterizations incorporated and examined for improvement. In order to apply the CloudSat 94 GHz radar to model validation, the single scattering characteristics of various crystal types are used and demonstrate that the assumption of Mie spheres is insufficient for representing CloudSat reflectivity derived from winter precipitation. Furthermore, snow density and size distribution characteristics are allowed to vary with height, based upon direct aircraft estimates obtained from C3VP data. These combinations improve the representation of modeled clouds versus their radar-observed counterparts, based on profiles and vertical distributions of reflectivity. These meteorological events are commonplace within the mid-latitude cold season and present a challenge to operational forecasters. This study focuses on one event, likely representative of others during the winter season, and aims to improve the representation of snow for use in future operational forecasts.

Molthan, Andrew L.↗
DOE OSTI2017

High‐Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States

Abstract The aim of this study is to examine projections of extreme temperatures over the continental United States (CONUS) for the 21st century using an ensemble of high spatial resolution dynamically downscaled model simulations with different boundary conditions. The downscaling uses the Weather Research and Forecast model at a spatial resolution of 12 km along with outputs from three different Coupled Model Intercomparison Project Phase 5 global climate models that provide boundary conditions under two different future greenhouse gas (GHG) concentration trajectories. The results from two decadal‐length time slices (2045–2054 and 2085–2094) are compared with a historical decade (1995–2004). Probability density functions of daily maximum/minimum temperatures are analyzed over seven climatologically cohesive regions of the CONUS. The impacts of different boundary conditions as well as future GHG concentrations on extreme events such as heat waves and days with temperature higher than 95°F are also investigated. The results show that the intensity of extreme warm temperature in future summer is significantly increased, while the frequency of extreme cold temperature in future winter decreases. The distribution of summer daily maximum temperature experiences a significant warm‐side shift and increased variability, while the distribution of winter daily minimum temperature is projected to have a less significant warm‐side shift with decreased variability. Using “business‐as‐usual” scenario, 5‐day heat waves are projected to occur at least 5–10 times per year in most CONUS and ≥95°F days will increase by 1–2 months by the end of the century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Climate-driven trends of biogenic volatile organic compound emissions and their impacts on summertime ozone and secondary organic aerosol in China in the 2050s

As a precursor of ozone (03) and secondaty organic aerosol (SOA), volatile organic compounds (VOCs) largely derive from natural sources, which may va1y with future climate change patterns. In this study, the emissions and impacts of biogenic VOCs (BVOCs) were projected into summertime of the 2050s (average for 2048-2052) for China using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Community Multiscale Air Quality Model (CMAQ) driven by future meteorological fields simulated with the Weather Research and Forecasting Model (WRF) for two future climate scenarios (RCP4.5 and RCP8.5). Sutface temperatures were projected to increase by 0.18 K yr 1 and 0.34 K yr 1 under RCP4.5 and RCP8.5, respectively. Such increases in future surface temperatures will increase BVOC emissions, as emissions of isoprene and monoterpene (ac­counting for 77.73% of all BVOCs in China at the current time) are projected to increase by 11.13% and 25.20% under the two RCP scenarios in the 2050s, respectively. Consequently, such increases in BVOCs will also have considerable impacts on concentrations of 03 and SOA. BVOCs contribute 10.11% of 03 and 62.59% of SOA concentrations in eastern China at current time. Under the RCP8.5 scenario, climate-driven BVOC changes will enhance 03 and SOA concentrations by 0.90% and 7.33% in eastern China from ClUTent to the 2050s and account for 31.83% and 52.80% of total 03 and SOA changes, respectively, on average across five typical regions. Such increases in 03 and SOA concentrations resulting from climate-driven BVOC changes decline considerably under the RCP4.5 scenario to less than 0.80% and 6.50%, respectively, implying that climate mitigation can facilitate air pollution control by alleviating an increase in BVOCs.

Liu, Song↗
DOE OSTI2023

Techno-economic life cycle assessment of CO 2 -EOR operations towards net negative emissions at farnsworth field unit

Optimizations of CO 2 Water Alternating Gas(WAG)- systems with multi-objectives of incremental recovery and maximization of CO 2 storage are challenging. Here, the incorporation of a total Greenhouse gas (GHG) life cycle assessment is mostly ignored leading to inaccurate estimation of overall net carbon emissions of their operations. In this study, the effect of a total GHG life cycle assessment on a multi-objective CO 2 -WAG optimization with integrated techno-economic assessment (TEA) which factors carbon tax credit is conducted. A life cycle assessment (LCA) was conducted utilizing a 20 -year optimized post history matched data from a high fidelity reservoir simulation model. Using data generated from the optimum result, a techno-economic life cycle analysis was further conducted. The first scenario classified as the base model had an estimated 81% of purchased CO 2 sequestered. The results through a comprehensive techno-economic LCA model yielded a net estimate of 73% of purchased CO 2 . The optimized forecasted model which considered key operational and reservoir factors such as WAG ratio, injection rates and periods, and well specification resulted in an improved sequestration of 92% of purchased CO 2 . However, this also dropped to 84% after taking it through LCA. These results clearly indicate a significant amount of net CO 2 is not accounted for when operations are not analyzed through LCA. From the LCA, direct flaring volumes of CO 2 , energy consumption and efficiency of unit equipment were noticed to be the major causes of these reductions. Considering ten main sources of energy as source of energy generation, a comparative techno-eco LCA was conducted. The results confirmed a lower net volume and NPV for energy sources with higher carbon footprints and vice versa. Thus, a total LCA of CO 2 -WAG greatly influences net storage factor of purchased CO 2 and hence project NPV where tax credit/incentives per ton of CO 2 sequestered is considered. Although operational conditions are optimized for best results, there are significant factors that leads to minimization of net storage factor. This study therefore provides an insightful information for optimizing CO 2 -WAG multi-objectives to achieve minimum GHG emission.

02 PETROLEUM↗
DOE OSTI2025

High-resolution climate model datasets for energy infrastructure planning in a renewable-dependent future

Electrification and renewables deployment efforts are amplifying the interdependence of the climate and energy systems. Increases in climate model resolution, which is now approaching that of reanalysis datasets and operational weather forecast models, present a unique opportunity to use future climate projections for energy infrastructure planning. In this Perspective, we review recent developments in high-resolution climate modeling, which have been driven by increased computing power and advanced software tools. We then look ahead to discuss how high-resolution climate data can be used to plan for a renewable-dependent future, and envision a unified climate-energy model framework that captures the two-way feedbacks between these interdependent systems.

climate change↗