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At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Assimilation of Satellite-Derived Soil Moisture for Improved Forecasts of the Great Plains Low-Level Jet

In the context of forecasting societally impactful Great Plains low-level jets (GPLLJs), the potential added value of satellite soil moisture (SM) data assimilation (DA) is high. GPLLJs are both sensitive to regional soil moisture gradients and frequent drivers of severe weather, including mesoscale convective systems. An untested hypothesis is that SM DA is more effective in forecasts of weakly synoptically forced, or uncoupled GPLLJs, than in forecasts of cyclone-induced coupled GPLLJs. Using the NASA Unified Weather Research and Forecasting (NU-WRF) Model, 75 GPLLJs are simulated at 9-km resolution both with and without NASA Soil Moisture Active Passive SM DA. Differences in modeled SM, surface sensible (SH) and latent heat (LH) fluxes, 2-m temperature (T2), 2-m humidity (Q2), PBL height (PBLH), and 850-hPa wind speed (W850) are quantified for individual jets and jet-type event subsets over the south-central Great Plains, as well as separately for each GPLLJ sector (entrance, core, and exit). At the GPLLJ core, DA-related changes of up to 5.4 kg m–2 in SM can result in T2, Q2, LH, SH, PBLH, and W850 differences of 0.68°C, 0.71 g kg –2 , 59.9 W m –2 , 52.4 W m –2 , 240 m, and 4 m s –1 , respectively. W850 differences focus along the jet axis and tend to increase from south to north. Further, jet-type differences are most evident at the GPLLJ exit where DA increases and decreases W850 in uncoupled and coupled GPLLJs, respectively. Data assimilation marginally reduces negative wind speed bias for all jets, but the correction is greater for uncoupled GPLLJs, as hypothesized.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region

This data package contains observation-based and model-simulated datasets (all provided in NetCDF format) for evaluating how wintertime lake-ice representation affects regional weather and climate over the Laurentian Great Lakes (freshwater lake ecosystem) during the high–ice-cover winter of 2009. The observational component includes: (1) Stage IV gridded precipitation at 4 km, hourly resolution for January–February 2009 over the Great Lakes region (radar–gauge multisensor precipitation analyses); (2) Great Lakes Surface Environmental Analysis (GLSEA) satellite-derived lake-ice coverage at 1.3 km, daily resolution for the 2009 winter months, providing ice coverage over Lakes Superior, Michigan, Huron, Erie, and Ontario; and (3) in situ measurements at the Standard Rock site on Lake Superior from the Great Lakes Evaporation Network (GLEN) at hourly resolution, including near-surface atmospheric variables and sensible and latent heat fluxes (air–lake exchange) at a fixed point location. The modeling component provides corresponding fields from two simulations, both archived at 4 km, hourly resolution: a standalone Weather Research Forecasting model (WRF) run driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), and a two-way coupled model using WRF and the Finite Volume Community Ocean Model (WRF-FVCOM, a 3-D hydrodynamic lake model). These outputs include variables relevant to air–lake interaction and lake-effect processes (e.g., near-surface temperature, humidity, wind, precipitation, and surface turbulent fluxes), enabling direct comparison with the observational datasets. Users can analyze and visualize these NetCDF files with common tools such as Python (e.g., xarray, netCDF4, numpy, pandas), NCO/CDO, Panoply, or ncview; NetCDF variables can also be converted to other formats (e.g., CSV, GeoTIFF) using these utilities.

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗
NASA NTRS2004

Role of Meteorology in Flights of a Solar-Powered Airplane

In the summer of 2001, the Helios prototype solar-powered uninhabited aerial vehicle (UAV) [a lightweight, remotely piloted airplane] was deployed to the Pacific Missile Range Facility (PMRF), at Kauai, Hawaii, in an attempt to fly to altitudes above 100,000 ft (30.48 km). The goal of flying a UAV to such high altitudes has been designated a level-I milestone of the NASA Environmental Research Aircraft and Sensor Technology (ERAST) program. In support of this goal, meteorologists from NASA Dryden Flight Research Center were sent to PMRF, as part of the flight crew, to provide current and forecast weather information to the pilots, mission directors, and planners. Information of this kind is needed to optimize flight conditions for peak aircraft performance and to enable avoidance of weather conditions that could adversely affect safety. In general, the primary weather data of concern for ground and flight operations are wind speeds (see Figure 1). Because of its long wing span [247 ft (.75 m)] and low weight [1,500 to 1,600 lb (about 680 to 726 kg)], the Helios airplane is sensitive to wind speeds exceeding 7 kn (3.6 m/s) at the surface. Also, clouds are of concern because they can block sunlight needed to energize an array of solar photovoltaic cells that provide power to the airplane. Vertical wind shear is very closely monitored in order to prevent damage or loss of control due to turbulence.

Donohue, Casey↗
DOE OSTI2013

USING COORDINATED OBSERVATIONS IN POLARIZED WHITE LIGHT AND FARADAY ROTATION TO PROBE THE SPATIAL POSITION AND MAGNETIC FIELD OF AN INTERPLANETARY SHEATH

Coronal mass ejections (CMEs) can be continuously tracked through a large portion of the inner heliosphere by direct imaging in visible and radio wavebands. White light (WL) signatures of solar wind transients, such as CMEs, result from Thomson scattering of sunlight by free electrons and therefore depend on both viewing geometry and electron density. The Faraday rotation (FR) of radio waves from extragalactic pulsars and quasars, which arises due to the presence of such solar wind features, depends on the line-of-sight magnetic field component B{sub ∥} and the electron density. To understand coordinated WL and FR observations of CMEs, we perform forward magnetohydrodynamic modeling of an Earth-directed shock and synthesize the signatures that would be remotely sensed at a number of widely distributed vantage points in the inner heliosphere. Removal of the background solar wind contribution reveals the shock-associated enhancements in WL and FR. While the efficiency of Thomson scattering depends on scattering angle, WL radiance I decreases with heliocentric distance r roughly according to the expression I∝r {sup –3}. The sheath region downstream of the Earth-directed shock is well viewed from the L4 and L5 Lagrangian points, demonstrating the benefits of these points in terms of space weather forecasting. The spatial position of the main scattering site r{sub sheath} and the mass of plasma at that position M{sub sheath} can be inferred from the polarization of the shock-associated enhancement in WL radiance. From the FR measurements, the local B{sub ∥sheath} at r{sub sheath} can then be estimated. Simultaneous observations in polarized WL and FR can not only be used to detect CMEs, but also to diagnose their plasma and magnetic field properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

CWRF performance at downscaling China climate characteristics

The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980–2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities.

Meteorology & Atmospheric Sciences↗
DOE OSTI2014

Morphological and kinematic evolution of three interacting coronal mass ejections of 2011 February 13-15

During 2011 February 13-15, three Earth-directed coronal mass ejections (CMEs) launched in succession were recorded as limb CMEs by STEREO/SECCHI coronagraphs (COR). These CMEs provided an opportunity to study their geometrical and kinematic evolution from multiple vantage points. In this paper, we examine the differences in geometrical evolution of slow and fast CMEs during their propagation in the heliosphere. We also study their interaction and collision using STEREO/SECCHI COR and Heliospheric Imager (HI) observations. We have found evidence of interaction and collision between the CMEs of February 15 and 14 in the COR2 and HI1 field of view (FOV), respectively, while the CME of February 14 caught up with the CME of February 13 in the HI2 FOV. By estimating the true mass of these CMEs and using their pre- and post-collision dynamics, the momentum and energy exchange between them during the collision phase are studied. We classify the nature of the observed collision between the CMEs of February 14 and 15 as inelastic, reaching close to the elastic regime. Relating imaging observations with in situ WIND measurements at L1, we find that the CMEs move adjacent to each other after their collision in the heliosphere and are recognized as distinct structures in in situ observations. Our results highlight the significance of HI observations in studying CME-CME collision for the purpose of improved space weather forecasting.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning

Abstract We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, an LR and convolutional neural network (CNN) are more accurate than the index-based models. However, only the CNN can produce reliable predictions that can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and grid points of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850-hPa geopotential heights and zonal winds to making skillful, high-probability predictions. Corresponding composite anomalies identify connections with El Niño–Southern Oscillation during winter and the Atlantic multidecadal oscillation and North Atlantic subtropical high during summer.

Pegion, Kathy↗
NASA NTRS1981

A split explicit reformulation of the regional numerical weather prediction model of the Japan Meteorological Agency

The split explicit integration scheme for numerical weather prediction models is employed in a version of the regional numerical weather prediction model of the Japan Meteorological Agency. The finite-difference scheme of the model is designed in the manner proposed by Okamura (1975). The horizontal advection terms in the governing equations are integrated with a time step limited by the wind speed while the terms which describe inertial-gravity oscillations are integrated in a succession of shorter time steps. The physical processes included within the model are precipitation, small-scale convection, surface exchanges of sensible and latent heat, and radiative heating and cooling. An example of a surface pressure forecast over Europe is shown for initial data observed at 0000 GMT 29 December 1979. Quantitative precipitation forecasts over Europe and North America for the 24 h period beginning at 0000 GMT 30 December 1979 are also shown. It is concluded that the model is capable of realistically depicting the evolution of synoptic-scale systems.

Duffy, D. G.↗
NASA NTRS2008

Solar Probe Plus: Report of the Science and Technology Definition Team

Solar Probe+ will be an extraordinary and historic mission, exploring what is arguably the last region of the solar system to be visited by a spacecraft, the Sun s outer atmosphere or corona as it extends out into space. Approaching as close as 9.5 RS* (8.5 RS above the Sun s surface), Solar Probe+ will repeatedly sample the near-Sun environment, revolutionizing our knowledge and understanding of coronal heating and of the origin and evolution of the solar wind and answering critical questions in heliophysics that have been ranked as top priorities for decades. Moreover, by making direct, in-situ measurements of the region where some of the most hazardous solar energetic particles are energized, Solar Probe+ will make a fundamental contribution to our ability to characterize and forecast the radiation environment in which future space explorers will work and live.

Source record↗
DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

The Importance of Convective Stage on Out-of-Cloud Convectively Induced Turbulence from High-Resolution Simulations

Turbulence (clear-air, mountain wave, convectively induced) is an aviation hazard that is a challenge to forecast due to the coarse resolution ultilized in operational weather models. Turbulence indices are commonly used to aid pilots in avoiding turbulence, but these indices have been designed and calibrated for midlatitude clear-air turbulence prediction (e.g., the Ellrod index). A significant limitation with current convectively induced turbulence (CIT) prediction is the lack of storm stage dependency. In this study, six high-resolution simulations of tropical oceanic and midlatitude continental convection are performed to characterize the turbulent environment near various convective types during the developing and mature stages. Second-order structure functions, a diagnostic commonly used to identify turbulence in turbulence prediction systems, are used to characterize the probability of turbulence for various convective types. Turbulence likelihood was found to be independent of region (i.e., tropical vs midlatitude) but dependent on convective stage. The probability of turbulence increased near developing convection for the majority of cases. Additional analysis of static stability and vertical wind shear, indicators of turbulence potential, showed that the convective environment near developing convection was more favorable for turbulence production than mature convection. Near developing convection, static stability decreased and vertical wind shear increased. Vertical wind shear near mature and developing convection was found to be weakly correlated to turbulence intensity in both the tropics and the midlatitudes. Finally, this study emphasizes the need for turbulence avoidance guidelines for the aviation community that are dependent on convective stage.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

Satellite sensing capabilities for surface temperature and meteorological parameters over the ocean

Remote meteorological measurements over the oceans have been used extensively for weather forecasting, ship safety, and global-scale studies of climate and sea conditions. A review is conducted regarding the satellite-sensing capabilities for sea-surface temperature and tropospheric marine meteorological mesurements. The tropospheric measurements are concerned with vertical temperature profiles, integrated water vapor and liquid water, rain, aerosols and surface winds. Attention is also given to several environmental-monitoring applications where satellite sensed synoptic data might be combined with ground and airborne chemical measurements to provide improved assessments of regional and global ennvironmental quality and climatology. Special emphasis is given to current satellite measurement accuracies (errors) and to programs involving U.S. technology. Aspects of sea-surface temperature are treated in detail in connection with extensive studies concentrated in this area.

Darnell, W. L.↗
NASA NTRS2001

Operational Performance of Sensor Systems Used to Determine Atmospheric Boundary Layer Properties as Part of the NASA Aircraft Vortex Spacing System Project

There has been a renewed interest in the application of remote sensor technology to operational aviation and airport-related activities such as Aircraft Vortex Spacing System (AVOSS). Radio Acoustic Sounding Systems (RASS), Doppler-acoustic sodars, Ultrahigh Frequencies (UHF) profilers and lidars have many advantages in measuring wind and temperature profiles in the lower atmospheric boundary layer since they can operate more or less continuously and unattended; however, there are limitations in their operational use at airports. For example, profilers deteriorate (limited altitude coverage or missing) in moderate or greater rain and can be affected by airplane targets in their field of view. Sodars can handle precipitation better but are affected by the high noise environments of airports and strong winds. Morning temperature inversions typically limit performance of RASS, sodars and profilers. Fog affects sonic anemometers. Lidars can have difficulties in clouds, fog or heavy precipitation. Despite their limitations these sensors have proven useful to provide wind and temperature profiles for AVOSS. Capabilities and limitations of these and other sensors used in the AVOSS program are discussed, parameter settings for the sensor systems are documented, and recommendations are made for the most cost-effective group of sensors for the future. The potential use of specially tuned dynamic forecast models and measurements from landing and departing aircraft are addressed.

Zak, J. Allen↗
NASA NTRS2018

A Realization of Bias Correction Method in the GMAO Coupled System

Over the past several decades, a tremendous effort has been made to improve model performance in the simulation of the climate system. The cold or warm sea surface temperature (SST) bias in the tropics is still a problem common to most coupled ocean atmosphere general circulation models (CGCMs). The precipitation biases in CGCMs are also accompanied by SST and surface wind biases. The deficiencies and biases over the equatorial oceans through their influence on the Walker circulation likely contribute the precipitation biases over land surfaces. In this study, we introduce an approach in the CGCM modeling to correct model biases. This approach utilizes the history of the model's short-term forecasting errors and their seasonal dependence to modify model's tendency term and to minimize its climate drift. The study shows that such an approach removes most of model climate biases. A number of other aspects of the model simulation (e.g. extratropical transient activities) are also improved considerably due to the imposed pre-processed initial 3-hour model drift corrections. Because many regional biases in the GEOS-5 CGCM are common amongst other current models, our approaches and findings are applicable to these other models as well.

Chang, Yehui↗
DOE OSTI2023

PNNL - Domain - d02 - Physics Site #12

This dataset is model output from one-way nested Weather Research and Forecasting (WRF) to WRF-large eddy simulations (LES) used to assess parametric uncertainty of LES to turbulence model parameters. As specified in the attachment "mmc_microscale_UQ_parameters.xlsm," 64 different parameter combinations are used.

17 WIND ENERGY↗
DOE OSTI2023

PNNL - Domain - d02 - Wasco Airport

This dataset is model output from one-way nested Weather Research and Forecasting (WRF) to WRF-large eddy simulations (LES) used to assess parametric uncertainty of LES to turbulence model parameters. As specified in the attachment "mmc_microscale_UQ_parameters.xlsm," 64 different parameter combinations are used.

17 WIND ENERGY↗
DOE OSTI2023

PNNL - Domain - d03 - Physics Site #12

This dataset is model output from one-way nested Weather Research and Forecasting (WRF) to WRF-large eddy simulations (LES) used to assess parametric uncertainty of LES to turbulence model parameters. As specified in the attachment "mmc_microscale_UQ_parameters.xlsm," 64 different parameter combinations are used.

17 WIND ENERGY↗