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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.

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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Gulf of Mexico Turbulence Intensity Model Data

The dataset is a subset of simulations carried out for the Gulf of Mexico region using the revised Weather Research and Forecasting (WRF) model version 4.2 that incorporates the implementation of online turbulence intensity (TI) calculations (Tai et al. 2023). The simulated atmospheric profiles near the Shell Exploration and Production Corporation's Tension Leg Platforms Ursa and Mars are archived. Physics parameterizations chosen for the simulations include the Thompson microphysics parameterization, Mellor-Yamada-Nakanishi Niino (MYNN) boundary layer parameterization, Mellor-Yamada-Janjic surface layer parameterization, Unified Noah land-surface parameterization, and the RRTMG longwave and shortwave radiation parameterization. Initial and boundary conditions are taken from the NOAA’s High-Resolution Rapid Refresh (HRRR) product. The JPL 0.01-degree Level 4 Multiscale Ultrahigh Resolution (MUR) Global Foundation Sea Surface Temperature (SST) Analysis (V4.1) data are used as the model’s SST forcing. Simulations are performed from January through June in 2020 and output every 10 minutes, which permits comparison with corresponding lidar observations.

17 WIND ENERGY↗
DOE OSTI2024

Mid-Atlantic Turbulence Intensity Model Data

The dataset is a subset of simulations carried out for the mid-Atlantic region using the revised Weather Research and Forecasting (WRF) model version 4.2 that incorporates the implementation of online turbulence intensity (TI) calculations (Tai et al. 2023). The simulated atmospheric profiles near the Air-Sea Interaction Tower (ASIT) of Woods Hole Oceanographic Institution’s Martha's Vineyard Coastal Observatory are archived. Physics parameterizations chosen for the simulations include the Thompson microphysics parameterization, Mellor-Yamada-Nakanishi Niino (MYNN) boundary layer parameterization, Mellor-Yamada-Janjic surface layer parameterization, Unified Noah land-surface parameterization, and the RRTMG longwave and shortwave radiation parameterization. Initial and boundary conditions are taken from NOAA’s High-Resolution Rapid Refresh (HRRR) product. The JPL 0.01-degree Level 4 Multiscale Ultrahigh Resolution (MUR) Global Foundation Sea Surface Temperature (SST) Analysis (V4.1) data are used as the model’s SST forcing. Simulations are performed from February through June in 2020 and output every 10 minutes, which permits comparison with corresponding lidar observations.

17 WIND ENERGY↗
NASA NTRS2012

The SPoRT-WRF: Evaluating the Impact of NASA Datasets on Convective Forecasts

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting entities, including a number of National Weather Service offices. SPoRT transitions real-time NASA products and capabilities to its partners to address specific operational forecast challenges. One challenge that forecasters face is applying convection-allowing numerical models to predict mesoscale convective weather. In order to address this specific forecast challenge, SPoRT produces real-time mesoscale model forecasts using the Weather Research and Forecasting (WRF) model that includes unique NASA products and capabilities. Currently, the SPoRT configuration of the WRF model (SPoRT-WRF) incorporates the 4-km Land Information System (LIS) land surface data, 1-km SPoRT sea surface temperature analysis and 1-km Moderate resolution Imaging Spectroradiometer (MODIS) greenness vegetation fraction (GVF) analysis, and retrieved thermodynamic profiles from the Atmospheric Infrared Sounder (AIRS). The LIS, SST, and GVF data are all integrated into the SPoRT-WRF through adjustments to the initial and boundary conditions, and the AIRS data are assimilated into a 9-hour SPoRT WRF forecast each day at 0900 UTC. This study dissects the overall impact of the NASA datasets and the individual surface and atmospheric component datasets on daily mesoscale forecasts. A case study covering the super tornado outbreak across the Ce ntral and Southeastern United States during 25-27 April 2011 is examined. Three different forecasts are analyzed including the SPoRT-WRF (NASA surface and atmospheric data), the SPoRT WRF without AIRS (NASA surface data only), and the operational National Severe Storms Laboratory (NSSL) WRF (control with no NASA data). The forecasts are compared qualitatively by examining simulated versus observed radar reflectivity. Differences between the simulated reflectivity are further investigated using convective parameters along with model soundings to determine the impacts of the various NASA datasets. Additionally, quantitative evaluation of select meteorological parameters is performed using the Meteorological Evaluation Tools model verification package to compare forecasts to in situ surface and upper air observations.

Zavodsky, Bradley↗
NASA NTRSDate not supplied

Forecasting with the GEOS-CF System and Other NASA Resources to Support Air Quality Management

Air quality (AQ) is a major and growing concern for public health around the world. Economic development, population growth, and climate change are all expected to exacerbate already poor AQ in many regions. Furthermore, AQ is often only sparsely monitored with reference-grade in-situ instruments. NASA resources and products have the potential to help in addressing this AQ data gap. The GEOS-CF (Goddard Earth Observing System – Composition Forecasting) global atmospheric composition modeling system is run each day at a global scale to provide recent estimates and five-day forecasts at hourly temporal resolution of atmospheric constituents relevant to AQ. NASA satellite missions (along with those of other space agencies) provide remotely-sensed estimates of atmospheric composition relevant to AQ. This paper gives a brief overview of these capabilities, and outlines the efforts underway to combine model forecasts, satellite retrievals, and surface-based measurements to provide more comprehensive and accurate estimates and forecasts of local AQ which will be broadly applicable and accessible globally.

GEOS-CF↗
DOE OSTI2017

Data-Driven Disease Forecasting

If disease outbreaks could be forecasted like the weather, communities could set up protective measures to mitigate their impact. At Los Alamos National Laboratory, scientists are improving disease-forecasting mathematical models by using clinical data--as well as internet data sources such as Wikipedia, Twitter, and Google--and coupling it with satellite imagery. The goal is to better understanding how diseases spread and, eventually, forecast disease outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A Pacific Tropical Decadal Variability Challenge for Climate Models

Understanding and forecasting Tropical Pacific Decadal-scale Variability (TPDV) strongly rely on climate model simulations. Using a Linear Inverse Modeling (LIM) diagnostic approach, we reveal Coupled Model Intercomparison Project Phase 6 models have significant challenges in reproducing the spatial structure and dominant mechanisms of TPDV. Specifically, while the models' ensemble mean pattern of TPDV resembles that of observations, the spread across models is very large and most models show significant differences from observations. In observations, removing the coupling between extratropics and tropics reduces TPDV by ~60%–70%, and removing the tropical thermocline variability makes the central tropical Pacific a key center of action for TPDV and El Niño Southern Oscillation variability. These characteristics are only confirmed in a subset of models. Differences between observations and simulations are outside the range of natural internal TPDV noise and pose important questions regarding our ability to model the impacts of natural internal low-frequency variability superimposed on long-term climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sensitivity of mesoscale modeling to urban morphological feature inputs and implications for characterizing urban sustainability

We examine the differences in meteorological output from the Weather Research and Forecasting (WRF) model run at 270 m horizontal resolution using 10 m, 100 m and 1 km resolution 3D neighborhood morphological inputs and with no morphological inputs. We find that the spatial variability in temperature, humidity, and other meteorological variables across the city can vary with the resolution and the coverage of the 3D urban morphological input, and that larger differences occur between simulations run without 3D morphological input and those run with some type of 3D morphology. We also find that the inclusion of input-building-defined roughness length calculations would improve simulation results further. We show that these inputs produce different patterns of heat wave spatial heterogeneity across the city of Washington, DC. These findings suggest that understanding neighborhood level urban sustainability under extreme heat waves, especially for vulnerable neighborhoods, requires attention to the representation of surface terrain in numerical weather models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

naturf: a package for generating urban parameters for numerical weather modeling

The Neighborhood Adaptive Tissues for Urban Resilience Futures tool (NATURF) is a Python workflow that generates files readable by the Weather Research and Forecasting (WRF) model. NATURF uses geopandas and hamilton to calculate 132 building parameters from shapefiles with building footprint and height information. These parameters can be collected and used in many formats, and the primary output is a binary file configured for input to WRF. This workflow is a flexible adaptation of the National/World Urban Database and Access Portal Tool (NUDAPT/WUDAPT) that can be used with any study area at any spatial resolution. The climate modeling community and urban planners can identify the effects of building/neighborhood morphology on the microclimate using the urban parameters and WRF-readable files produced by NATURF. More information on the urban parameters calculated can be found in the documentation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Assimilation of GPM GMI Rainfall Product with WRF GSI

The Global Precipitation Measurement (GPM) is an international mission to provide next-generation observations of rain and snow worldwide. The GPM built on Tropical Rainfall Measuring Mission (TRMM) legacy, while the core observatory will extend the observations to higher latitudes. The GPM observations can help advance our understanding of precipitation microphysics and storm structures. Launched on February 27th, 2014, the GPM core observatory is carrying advanced instruments that can be used to quantify when, where, and how much it rains or snows around the world. Therefore, the use of GPM data in numerical modeling work is a new area and will have a broad impact in both research and operational communities. The goal of this research is to examine the methodology of assimilation of the GPM retrieved products. The data assimilation system used in this study is the community Gridpoint Statistical Interpolation (GSI) system for the Weather Research and Forecasting (WRF) model developed by the Development Testbed Center (DTC). The community GSI system runs in independently environment, yet works functionally equivalent to operational centers. With collaboration with the NASA Short-term Prediction Research and Transition (SPoRT) Center, this research explores regional assimilation of the GPM products with case studies. Our presentation will highlight our recent effort on the assimilation of the GPM product 2AGPROFGMI, the retrieved Microwave Imager (GMI) rainfall rate data for initializing a real convective storm. WRF model simulations and storm scale data assimilation experiments will be examined, emphasizing both model initialization and short-term forecast of precipitation fields and processes. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to other GPM products. Further details of the methodology of data assimilation, preliminary result and test on the impact of GPM data and the influence on precipitation forecast will be presented at the conference.

Li, Xuanli↗
DOE OSTI2025

Evaluating Sea Breezes and Associated Convective Cloud Evolution in the Model Gray Zone

We characterize convective clouds associated with sea‐breeze circulations (SBC) using multi‐agency observations and multi‐case ensemble model simulations. The focus is on assessing convective cloud lifecycle properties and their merging behavior, as well as the environmental conditions they are embedded in, particularly SBC features. In total, 46 SBC days over the Houston‐Galveston region are selected and simulated using the Weather Research and Forecasting (WRF) model at a gray zone scale with a forecast‐like parameterization setup. Advanced techniques, including change‐point detection, a Lagrangian cloud tracking method, and a newly developed cell merging and splitting detection algorithm, are applied and/or developed for this study. Our findings indicate that the WRF model at 1 km grid spacing well represents the thermodynamic conditions over the region, as well as SBC timing and intensity. However, for the associated convective cells, WRF overestimates the 30‐dBZ echo top height, cell area, and maximum radar reflectivity compared to radar observations. This overestimation is potentially due to under‐resolved entrainment processes, an overestimated merging frequency, and the overestimation of updraft intensity. Furthermore, the model exhibits a deficiency in simulating congestus clouds, showing a more rapid transition from shallow to deep convection compared to observed behavior. Moreover, observations indicate stronger, deeper, and wider clouds when merging happens. Conversely, in simulations, the merging process does not necessarily lead to higher or longer‐lived cells, as many cases experience rapid and frequent merging and splitting which may result in more variance in convective updraft velocity during the convection lifetime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model

Abstract. Urban overheating and its ongoing exacerbation due to global warming and urban development lead to increased exposure to urban heat and increased thermal discomfort and heat stress. To quantify thermal stress, specific indices have been proposed that depend on air temperature, mean radiant temperature (MRT), wind speed, and relative humidity. While temperature and humidity vary on scales of hundreds of meters, MRT and wind speed are strongly affected by individual buildings and trees and vary on the meter scale. Therefore, most numerical thermal comfort studies apply microscale models to limited spatial domains (commonly representing urban neighborhoods with building blocks) with resolutions on the order of 1 m and a few hours of simulation. This prevents the analysis of the impact of city-scale adaptation and/or mitigation strategies on thermal stress and comfort. To solve this problem, we develop a methodology to estimate thermal stress indicators and their subgrid variability in mesoscale models – here applied to the multilayer urban canopy parameterization BEP-BEM within the Weather Research and Forecasting (WRF) model. The new scheme (consisting of three main steps) can readily assess intra-neighborhood-scale heat stress distributions across whole cities and for timescales of minutes to years. The first key component of the approach is the estimation of MRT in several locations within streets for different street orientations. Second, mean wind speed and its subgrid variability are downscaled as a function of the local urban morphology based on relations derived from a set of microscale LES and RANS simulations across a wide range of realistic and idealized urban morphologies. Lastly, we compute the distributions of two thermal stress indices for each grid square, combining all the subgrid values of MRT, wind speed, air temperature, and absolute humidity. From these distributions, we quantify the high and low tails of the heat stress distribution in each grid square across the city, representing the thermal diversity experienced in street canyons. In this contribution, we present the core methodology as well as simulation results for Madrid (Spain), which illustrate strong differences between heat stress indices and common heat metrics like air or surface temperature both across the city and over the diurnal cycle.

Geology↗
DOE OSTI2024

Applying double cropping and interactive irrigation in the North China Plain using WRF4.5

Abstract. Irrigated cultivation exerts a significant influence on the local climate and the hydrological cycle. The North China Plain (NCP) is known for its intricate agricultural system, marked by expansive cropland, high productivity, compact rotation, a semi-arid climate, and intensive irrigation practices. As a result, there has been considerable attention on the potential impact of this intensive irrigated agriculture on the local climate. However, studying the irrigation impact in this region has been challenging due to the lack of an accurate simulation of crop phenology and irrigation practices within the climate model. By incorporating double cropping with interactive irrigation, our study extends the capabilities of the Weather Research Forecast (WRF) model, which has previously demonstrated commendable performance in simulating single-cropping scenarios. This allows for two-way feedback between irrigated crops and climate, further enabling the inclusion of irrigation feedback from both ground and vegetation perspectives. The improved crop modeling system shows significant enhancement in capturing vegetation and irrigation patterns, which is evidenced by its ability to identify crop stages, estimate field biomass, predict crop yield, and project monthly leaf area index. The improved simulation of large-scale irrigated crops in the NCP can further enhance our understanding of the intricate relationship between agricultural development and climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Application of parametric weight and cost estimating relationships to future transport aircraft

A model comprised of system level weight and cost estimating relationships for transport aircraft is presented. In order to determine the production cost of future aircraft its weight is first estimated based on performance parameters, and then the cost is estimated as a function of weight. For initial evaluation CERs were applied to actual system weights of six aircraft (3 military and 3 commercial) with mean empty weights ranging from 30,000 to 300,000 lb. The resulting cost estimates were compared with actual costs. The average absolute error was only 4.3%. Then the model was applied to five aircraft still in the design phase (Boeing 757, 767 and 777, and BAC HS146-100 and HS146-200). While the estimates for the 757 and 767 are within 2 to 3 percent of their assumed break-even costs, it is recognized that these are very sensitive to the validity of the estimated weights, inflation factor, the amount assumed for nonrecurring costs, etc., and it is suggested that the model may be used in conjunction with other information such as RDT&E cost estimates and market forecasts. The model will help NASA evaluate new technologies and production costs of future aircraft.

Beltramo, M. N.↗
NASA NTRS2011

Integration of Airborne Aerosol Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems

This slide presentation reviews the study that used a model to forecast pollen to assist in warning for asthma populations. Using MODIS daily reflectances to input to a model, PREAM, adapted from the Dust REgional Atmospheric Modeling (DREAM) system, a product of predicted pollen is produced. Using the pollen from Juniper the PREAM model was shown to be an assist in alerting the public of pollen bursts, and reduce the health impact on asthma populations.

Luvall, Jeffrey C.↗
DOE OSTI2022

Forecasting battery capacity and power degradation with multi-task learning

Lithium-ion batteries degrade due to usage and exposure to environmental conditions, which affects their capability to store energy and supply power. Accurately predicting the capacity and power fade of lithium-ion battery cells is challenging due to intrinsic manufacturing variances and coupled nonlinear ageing mechanisms. In this paper, we propose a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning. The model is able to predict the degradation trajectory of both capacity and internal resistance together with knee-points and end-of-life points accurately at early-life stage. The validation shows an average percentage error of 2.37% and 1.24% for the prediction of capacity fade and resistance rise, respectively. The model's ability to accurately predict the degradation, facing capacity and resistance estimation errors, further demonstrates the model's robustness and generalizability. Compared with single-task learning models for forecasting capacity and power degradation, the model shows a significant prediction accuracy improvement and computational cost reduction. This work presents the highlights of multi-task learning in the degradation prognostics for lithium-ion batteries.

25 ENERGY STORAGE↗
NASA NTRS2011

Simulations of Cyclone Sidr in the Bay of Bengal with a High-Resolution Model: Sensitivity to Large-Scale Boundary Forcing

The predictability of Cyclone Sidr in the Bay of Bengal was explored in terms of track and intensity using the Advanced Research Hurricane Weather Research Forecast (AHW) model. This constitutes the first application of the AHW over an area that lies outside the region of the North Atlantic for which this model was developed and tested. Several experiments were conducted to understand the possible contributing factors that affected Sidr s intensity and track simulation by varying the initial start time and domain size. Results show that Sidr s track was strongly controlled by the synoptic flow at the 500-hPa level, seen especially due to the strong mid-latitude westerly over north-central India. A 96-h forecast produced westerly winds over north-central India at the 500-hPa level that were notably weaker; this likely caused the modeled cyclone track to drift from the observed actual track. Reducing the model domain size reduced model error in the synoptic-scale winds at 500 hPa and produced an improved cyclone track. Specifically, the cyclone track appeared to be sensitive to the upstream synoptic flow, and was, therefore, sensitive to the location of the western boundary of the domain. However, cyclone intensity remained largely unaffected by this synoptic wind error at the 500-hPa level. Comparison of the high resolution, moving nested domain with a single coarser resolution domain showed little difference in tracks, but resulted in significantly different intensities. Experiments on the domain size with regard to the total precipitation simulated by the model showed that precipitation patterns and 10-m surface winds were also different. This was mainly due to the mid-latitude westerly flow across the west side of the model domain. The analysis also suggested that the total precipitation pattern and track was unchanged when the domain was extended toward the east, north, and south. Furthermore, this highlights our conclusion that Sidr was influenced from the west side of the domain. The displacement error was significantly reduced after the domain size from the western model boundary was decreased. Study results demonstrate the capability and need of a high-resolution mesoscale modeling framework for simulating the complex interactions that contribute to the formation of tropical cyclones over the Bay of Bengal region

Kumar, Anil↗
NASA NTRS2011

Forecasting Lake-Effect Precipitation in the Great Lakes Region Using NASA Enhanced-Satellite Data

Lake-effect precipitation is common in the Great Lakes region, particularly during the late fall and winter. The synoptic processes of lake-effect precipitation are well understood by operational forecasters, but individual forecast events still present a challenge. Locally run, high resolution models can assist the forecaster in identifying the onset and duration of precipitation, but model results are sensitive to initial conditions, particularly the assumed surface temperature of the Great Lakes. The NASA Short-term Prediction Research and Transition (SPoRT) Center has created a Great Lakes Surface Temperature (GLST) composite, which uses infrared estimates of water temperatures obtained from the MODIS instrument aboard the Aqua and Terra satellites, other coarser resolution infrared data when MODIS is not available, and ice cover maps produced by the NOAA Great Lakes Environmental Research Lab (GLERL). This product has been implemented into the Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS), used within forecast offices to run local, high resolution forecasts. The sensitivity of the model forecast to the GLST product was analyzed with a case study of the Lake Effect Storm Echinacea, which produced 10 to 12 inches of snowfall downwind of Lake Erie, and 8 to 18 inches downwind of Lake Ontario from 27-29 January 2010. This research compares a forecast using the default Great Lakes surface temperatures from the Real Time Global sea surface temperature (RTG SST), in the WRF-EMS model to the enhanced NASA SPoRT GLST product to study forecast impacts. Results from this case study show that the SPoRT GLST contained less ice cover over Lake Erie and generally cooler water temperatures over Lakes Erie and Ontario. Latent and sensible heat fluxes over Lake Ontario were decreased in the GLST product. The GLST product decreased the quantitative precipitation forecast (QPF), which can be correlated to the decrease in temperatures and heat fluxes. A slight increase in precipitation coverage was noted over Lake Erie due to a decrease in ice cover. Both the RTG SST and the GLST products predicted the precipitation south of the actual location of precipitation. This single case study is the first part of an examination to determine how MODIS data can be applied to improve model forecasts in the Great Lakes region.

Cipullo, Michelle↗