SEARCH · Engineering Papers

Results for “Weather”

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 883 records · Page 49

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Quantifying sensitivity in numerical weather prediction-modeled offshore wind speeds through an ensemble modeling approach

A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP-modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. In conclusion, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble-based wind resource data sets.

17 WIND ENERGY↗

DOE OSTI2015

A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part 1. Wind and Turbulence

We found that numerical-weather-prediction models are often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense horizontally- and vertically-resolved geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City and the simulated fields important to transport and dispersion models were compared to measurements from a number of sodars, tower-based sonic anemometers, and balloon soundings located in the greater metropolitan area. Time histories of computed wind speed, wind direction, turbulent kinetic energy (e), friction velocity (u* ), and reciprocal Obukhov length (1 / L) were compared to measurements over the 1-month field campaign. Vertical profiles of wind speed, potential temperature (θ ), and e were compared during short intensive operating periods. The WRF model was typically able to replicate the measured diurnal variation of the wind fields, but with an average absolute wind direction and speed difference of 35° and 1.9 m s -1 , respectively. Then, using the Mellor-Yamada-Janjic (MYJ) surface-layer scheme, the WRF model was found to generally underpredict surface-layer TKE but overpredict u* that was observed above a suburban region of Oklahoma City. The TKE-threshold method used by the WRF model’s MYJ surface-layer scheme to compute the boundary-layer height (h) consistently overestimated h derived from a θ gradient method whether using observed or modelled θ profiles.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part III: Boundary-Layer Parametrizations

Numerical weather prediction is often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model 4.0 was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City. We compare three different simulations in their ability to reproduce the observations, each using a different boundary-layer parametrization. Specifically, we examine the Mellor–Yamada–Janjic (MYJ), Yonsei University (YSU), and Mellor–Yamada–Nakanishi–Niino (MYNN) boundary-layer parametrizations. All three predict the wind speed well during the day but overpredict it at night. The MYNN parametrization is better than MYJ at predicting the daytime turbulence in the surface layer, but both underpredict the nocturnal turbulence. Additionally, the MYJ parametrization is best at predicting the reciprocal Obukhov length, while MYNN and YSU both significantly overpredict thermal stability. Reconstructing the reciprocal Obukhov length from other simulated parameters produces more accurate values for both parametrizations. All three models overpredict the boundary-layer height, particularly under convective conditions. The MYJ parametrization overestimates boundary-layer height the most, while YSU and MYNN have comparable performance with MYNN having an advantage in predicting the stable boundary-layer height. Several days were found where the WRF simulations predict significant deviations from the prevailing diurnal pattern in wind direction, which are not found in the observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Effects of Weathering on Mechanical, Antimicrobial Properties and Biodegradation Process of Silver Loaded TPE Compounds

The incorporation of antimicrobial metals such as silver is an alternative to protect the material against microbial attack. However, loaded polymer can lose its antimicrobial properties after some time of use, and the additive may even leak out into the environment becoming harmful to non-target organisms. This study aims to evaluate the mechanical properties and antimicrobial activity of silver containing thermoplastic elastomer (TPE) samples exposed to weathering and the influence of additive incorporation in material biodegradation in the soil. For this purpose, silver ions (Ag{sup +}-bentonite, Ag{sup +}-phosphate) and silver nanoparticles (AgNp-silica) based additives were blended in a formulation of SEBS, polypropylene and mineral oil. The test samples were exposed to natural ageing over nine months, and were then evaluated according to their mechanical properties, antimicrobial activity, and degree of crystallinity and surface characteristics. The biodegradation process before and after natural ageing was evaluated through the generation of carbon dioxide. The results show that the action of natural ageing reduced the mechanical properties of loaded and unloaded TPE, and modified the degree of crystallinity and the chemical characteristic of the TPE surface. The presence or type of additive did not influence material resistance after being exposed to weathering. A decrease in antimicrobial activity in samples after natural ageing was observed. At a variable level and according to the chemical content, generation of carbon dioxide from TPE samples was greater in aged samples than in unexposed ones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Household energy resilience in extreme weather events: An investigation of energy service importance, HVAC usage behaviors, and willingness to pay

Household energy resilience, particularly maintaining essential energy services within an acceptable and affordable level, is critical during extreme weather events (e.g., winter storms, heat waves) with power shortages or outages. However, the research on the importance and acceptable compromise levels of energy services, HVAC usage behavior changes, and willingness to pay (WTP) for energy services during disasters is limited. Therefore, this study investigates the importance and compromise of household energy services, HVAC usage behaviors, and WTP, as three key dimensions of household energy resilience, in both normal days and disaster scenarios through surveys (n=485). First, 12 common residential end-uses are grouped into high, mid, and low-priority services using hierarchical clustering, in which high-priority services (perceived as important and un-compromisable) include food cooking and storage (refrigerator and freezer) and thermal comfort (space heating and cooling), mid-priority services involve water heating and lighting, while low-priority services are mostly related to entertainment and housework (e.g., TV, laundry). Also, HVAC use patterns demonstrate that occupants become more energy conservative during disasters, e.g., reducing durations of running HVAC and adjusting temperature setpoints to save energy. In terms of energy cost, approximately 60% of respondents are willing to pay more to maintain energy services during disasters, although only 13% are prepared to pay over double the normal energy prices. The regression models further reveal that household energy resilience varies across different sociodemographic groups (e.g., age, income, family size) and is impacted by disaster experience and beliefs in clean energy and climate. In conclusion, these research findings provide important perspectives to understand household energy resilience and inform measures for disaster adaptation of households in extreme weather events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study

Highlights: • Earth observation data has potential for use in epidemiology but is seldom validated. • The performance of two EO products varied depending on numerous factors. • Epidemiologists should use their own weather stations to record meteorological data. • Daily estimates of meteorological variables are associated with rotavirus infection. • Choice of climate data source should be determined by the research question. Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluating precipitation, streamflow, and inundation forecasting skills during extreme weather events: A case study for an urban watershed

Integrated forecasting systems for precipitation, streamflow, and floodplain inundation are of critical importance in mitigating the impacts of destructive floods caused by extreme weather events. However, the skills of streamflow and floodplain inundation forecasts derived from various Quantitative Precipitation Forecasts (QPF) require a greater level of understanding. In this paper, a set of QPF developed by the National Weather Service (NWS) were used to drive a flood modeling system obtained utilizing offline coupling of a physics-based distributed hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM), and a hydrodynamic model, the Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON). This flood modeling system was used to produce forecasts of streamflow and floodplain inundation maps during three major flood events in the Brays Bayou Watershed (Houston, Texas, USA) for a range of QPF durations (6–72 h). Then, to investigate the effects of increasing QPF durations on the forecasts, the forecasting skills of precipitation, streamflow, and floodplain inundation were quantified. The results show that: 1) QPF skills for more intense and sustained events such as hurricanes and tropical storms are higher than for shorter, less intense events; 2) while QPF and streamflow forecasting skills decrease as QPF durations increase, inundation forecasts under longer QPF durations (24 or 72 h) show higher skills; 3) extending the maximum QPF duration in operational hydrologic modeling from 24 h (under normal circumstances) to 72 h (for extreme events) may increase the skills of long lead time forecasts for large-scale events like Hurricane Harvey.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Defining weather scenarios for simulation-based assessment of thermal resilience of buildings under current and future climates: A case study in Brazil

In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized. Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’ objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Landscape Age as a Major Control on the Geography of Soil Weathering

Global-scale models of rock-derived nutrient availability often assume that physical erosion drives soils toward an approximate “steady state” over geologic timescales. By definition, steady-state models do not represent landscape age—that is, the time elapsed since soil formation is initiated by major erosional or depositional events. We hypothesize that this steady-state assumption has large consequences on estimates of soil fertility because landscape age can mediate the retention of mobile elements in soil, particularly in low-relief landscapes and humid climates. We quantified the effect of landscape age on soil fertility by estimating Na retention in soils across the United States and explicitly resolving landscape age in regions that experienced significant deposition or glacial retreat after the Last Glacial Maximum (LGM). We then used a simple one-compartment model to simulate soil formation and weathering, comparing predictions that incorporated landscape age with those based on the steady-state assumption. We found that soils formed in LGM deposits in low-relief, humid settings generally retain 10 times more Na than soils formed outside of LGM deposits. Furthermore, the model that accounted for landscape age outperformed a steady-state model across the United States and increased globally averaged estimates of Na retention by 17%. These results reinforce the idea that landscape age is a major control on weathering and should not be ignored in simulations of nutrient cycling.

58 GEOSCIENCES↗

DOE OSTI2023

Forecasting for the Weather Driven Energy System - A New Task under IEA Wind

The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast that has been the focus of considerable recent attention. Therefore, the group behind the former IEA Wind Task 36 Forecasting for Wind Energy has initiated a new IEA Wind Task with a much broader perspective, which includes prospective interaction with other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. In the new IEA Wind Task 51 (entitled "Foreacsting for the Weather Drive Energy System") the existing Work Packages (WPs) are complemented by work streams in a matrix structure. The Task is divided in three WPs according to the stakeholders: WP1 is mainly aimed at meteorologists, providing the weather forecast basis for the power forecasts. In WP2, the forecast service vendors are the main stakeholders, while the end users populate WP3. The new Task 51 started in January 2022. Planned activities include 4 workshops. The first will focus on the state of the art in forecasting for the energy system plus related research issues and be held during September 2022 in Dublin. The other three workshops will be held later during the 4-year Task period and address (1) seasonal forecasting with emphasis on Dunkelflaute, storage and hydro, (2) minute-scale forecasting, and (3) extreme power system events. The issues and conclusions of each of the workshops will be documented by a published paper. Additionally, the Recommended Practice on Forecast Solution Selection will be updated to reflect the broader perspective.

geophysics computing↗

DOE OSTI2022

Trends in surface equivalent potential temperature: A more comprehensive metric for global warming and weather extremes

Significance The Earth has warmed by 1.2 ± 0.1 °C since the preindustrial era. The most common metric to measure the ongoing global warming is surface air temperature since it has long and reliable observational records. However, surface air temperature alone does not fully describe the nature of global warming and its impact on climate and weather extremes. Here we show that surface equivalent potential temperature, which combines the surface air temperature and humidity, is a more comprehensive metric not only for the global warming but also for its impact on climate and weather extremes including tropical deep convection and extreme heat waves. We recommend that it should be used more widely in future climate change studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Predicting Storm Outages Through New Representations of Weather and Vegetation

This paper presents new developments in an outage prediction model (OPM) for an electric distribution network in the Northeastern United States and assesses their significance to the OPM performance. The OPM uses regression tree models fed by numerical weather prediction outputs, spatially distributed information on soil, vegetation, electric utility assets, and historical power outage data to forecast the number and spatial distribution of outages across the power distribution grid. New modules introduced hereby consist in 1) a storm classifier based on weather variables; 2) a multimodel optimization of regression tree output; and 3) a post-processing routine for more accurately describing tree-leaf conditions. Model implementations are tested through leave-one-storm-out cross-validations performed on 120 storms of varying intensity and characteristics. The results demonstrate that the median absolute percentage error of the new OPM version decreased from 130% to 59% for outage predictions at the service territory level, and the OPM skills for operational forecasts are consistent with the skills based on historical storm analyses.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Improved net carbon budgets in the US Midwest through direct measured impacts of enhanced weathering

Abstract Terrestrial enhanced weathering (EW) through the application of Mg‐ or Ca‐rich rock dust to soil is a negative emission technology with the potential to address impacts of climate change. The effectiveness of EW was tested over 4 years by spreading ground basalt (50 t ha −1 year −1 ) on maize/soybean and miscanthus cropping systems in the Midwest US. The major elements of the carbon budget were quantified through measurements of eddy covariance, soil carbon flux, and biomass. The movement of Mg and Ca to deep soil, released by weathering, balanced by a corresponding alkalinity flux, was used to measure the drawdown of CO 2 , where the release of cations from basalt was measured as the ratio of rare earth elements to base cations in the applied rock dust and in the surface soil. Basalt application stimulated peak biomass and net primary production in both cropping systems and caused a small but significant stimulation of soil respiration. Net ecosystem carbon balance (NECB) was strongly negative for maize/soybean (−199 to −453 g C m −2 year −1 ) indicating this system was losing carbon to the atmosphere. Average EW (102 g C m −2 year −1 ) offset carbon loss in the maize/soybean by 23%–42%. NECB of miscanthus was positive (63–129 g C m −2 year −1 ), indicating carbon gain in the system, and EW greatly increased inorganic carbon storage by an additional 234 g C m −2 year −1 . Our analysis indicates a co‐deployment of a perennial biofuel crop (miscanthus) with EW leads to major wins—increased harvested yields of 29%–42% with additional carbon dioxide removal (CDR) of 8.6 t CO 2 ha −1 year −1 . EW applied to maize/soybean drives a CDR of 3.7 t CO 2 ha −1 year −1 , which partially offsets well‐established carbon losses from soil from this crop rotation. EW applied in the US Midwest creates measurable improvements to the carbon budgets perennial bioenergy crops and conventional row crops.

enhanced weathering↗

DOE OSTI2023

Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site

Abstract The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias in downward shortwave and negative bias in longwave radiation suggests that the model is underestimating cloud fraction in the study domain. The study concludes by showing a brief comparison with version 3 of the HRRR and shows that version 4 has better performance in almost all near-surface variables. Significance Statement A correct representation of the near-surface variables is important for numerical weather prediction models. This study investigates the capability of the latest NOAA High-Resolution Rapid Refresh (HRRRv4) model in simulating the near-surface variables by comparing against the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) in situ observations. Among others, we find that the surface heat fluxes, such as sensible and latent heat fluxes, are the most difficult variables to be reproduced. This study also shows that the diurnal cycle has the dominant impact on the model’s performance, which means the majority of the outputted near-surface variables have the strong diurnal cycle in their bias errors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Evaluation of the Fitch Wind-Farm Wake Parameterization with Large-Eddy Simulations of Wakes Using the Weather Research and Forecasting Model

Abstract Wind-farm parameterizations in weather models can be used to predict both the power output and farm effects on the flow; however, their correctness has not been thoroughly assessed. We evaluate the wind-farm parameterization of the Weather Research and Forecasting Model with large-eddy simulations (LES) of the wake performed with the same model. We study the impact on the velocity and turbulence kinetic energy (TKE) of inflow velocity, roughness, resolution, number of turbines (one or two), and inversion height and strength. We compare the mesoscale with the LES by spatially averaging the LES within areas correspondent to the mesoscale horizontal spacing: one covering the turbine area and two downwind. We find an excellent agreement of the velocity within the turbine area between the two types of simulations. However, within the same area, we find the largest TKE discrepancies because in mesoscale simulations, the turbine-added TKE has to be highest at the turbine position to be advected downwind. Within the downwind areas, differences between velocities increase as the wake recovers faster in the LES, whereas for the TKE both types of simulations show similar levels. From the various configurations, the impact of inversion height and strength is small for these heights and inversion levels. The highest impact for the one-turbine simulations appears under the low-speed case due to the higher thrust, whereas the impact of resolution is low for the large-eddy simulations but high for the mesoscale simulations. Our findings demonstrate that higher-fidelity simulations are needed to validate wind-farm parameterizations.

17 WIND ENERGY↗

DOE OSTI2021

Evaluation of the Rapid Refresh Numerical Weather Prediction Model over Arctic Alaska

Abstract Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision-making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Extreme Weather Events and Climate Change Attribution

A report from the National Academies of Sciences, Engineering, and Medicine concludes it is now possible to estimate the influence of climate change on some types of extreme events. The science of extreme event attribution has advanced rapidly in recent years, giving new insight to the ways that human-caused climate change can influence the magnitude or frequency of some extreme weather events. This report examines the current state of science of extreme weather attribution, and identifies ways to move the science forward to improve attribution capabilities. Confidence is strongest in attributing types of extreme events that are influenced by climate change through a well-understood physical mechanism, such as, the more frequent heat waves that are closely connected to human-caused global temperature increases, the report finds. Confidence is lower for other types of events, such as hurricanes, whose relationship to climate change is more complex and less understood at present. For any extreme event, the results of attribution studies hinge on how questions about the event's causes are posed, and on the data, modeling approaches, and statistical tools chosen for the analysis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Q-6 DARHT Weather Enclosure Project Upgrades Design Review

The Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility is adding a weather enclosure for the vessel area that will require the Detonation Science and Technology Group’s (Q-6) support for installing new cabling infrastructure to support capacitive discharge unit (CDU) firing operations and diagnostics. The requirement’s work scope is described in Q-6-TR-0034U*, Q-6 Support for the DARHT Weather Enclosure Upgrade Project, Customer Requirements Review (CRR).

42 ENGINEERING↗