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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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13 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Creating a Realistic Weather Environment for Motion-Based Piloted Flight Simulation

A flight simulation environment is being enhanced to facilitate experiments that evaluate research prototypes of advanced onboard weather radar, hazard/integrity monitoring (HIM), and integrated alerting and notification (IAN) concepts in adverse weather conditions. The simulation environment uses weather data based on real weather events to support operational scenarios in a terminal area. A simulated atmospheric environment was realized by using numerical weather data sets. These were produced from the High-Resolution Rapid Refresh (HRRR) model hosted and run by the National Oceanic and Atmospheric Administration (NOAA). To align with the planned flight simulation experiment requirements, several HRRR data sets were acquired courtesy of NOAA. These data sets coincided with severe weather events at the Memphis International Airport (MEM) in Memphis, TN. In addition, representative flight tracks for approaches and departures at MEM were generated and used to develop and test simulations of (1) what onboard sensors such as the weather radar would observe; (2) what datalinks of weather information would provide; and (3) what atmospheric conditions the aircraft would experience (e.g. turbulence, winds, and icing). The simulation includes a weather radar display that provides weather and turbulence modes, derived from the modeled weather along the flight track. The radar capabilities and the pilots controls simulate current-generation commercial weather radar systems. Appropriate data-linked weather advisories (e.g., SIGMET) were derived from the HRRR weather models and provided to the pilot consistent with NextGen concepts of use for Aeronautical Information Service (AIS) and Meteorological (MET) data link products. The net result of this simulation development was the creation of an environment that supports investigations of new flight deck information systems, methods for incorporation of better weather information, and pilot interface and operational improvements for better aviation safety. This research is part of a larger effort at NASA to study the impact of the growing complexity of operations, information, and systems on crew decision-making and response effectiveness; and then to recommend methods for improving future designs.

Daniels, Taumi S.↗
NASA NTRS2019

The WRF Lightning Forecasting Algorithm: Sensitivities to Microphysics and Boundary Layer Physics Schemes

As with WRF model simulated convection in general, LFA output is sensitive to cloud and PBL physics. WRF convection is also sensitive to model initialization uncertainties and unresolved errors.  Large sensitivity exists in HRRR-like Thompson microphysics and PBL, for which original LFA calibration constant needs to be multiplied by 1.13 to give proper FRD amplitudes. Thompson microphysics scheme also shows poorest correlations relative to reference data, suggesting low predictability of HRRR LFA output. Need to validate HRRR LFA against GLM observations.

weather prediction↗
NASA NTRS2015

An Integrated 0-1 Hour First-Flash Lightning Nowcasting, Lightning Amount and Lightning Jump Warning Capability

Lightning one of the most dangerous weather-related phenomena, especially as many jobs and activities occur outdoors, presenting risk from a lightning strike. Cloud-to-ground (CG) lightning represents a considerable safety threat to people at airfields, marinas, and outdoor facilities-from airfield personnel, to people attending outdoor stadium events, on beaches and golf courses, to mariners, as well as emergency personnel. Holle et al. (2005) show that 90% of lightning deaths occurred outdoors, while 10% occurred indoors despite the perception of safety when inside buildings. Curran et al. (2000) found that nearly half of fatalities due to weather were related to convective weather in the 1992-1994 timeframe, with lightning causing a large component of the fatalities, in addition to tornadoes and flash flooding. Related to the aviation industry, CG lightning represents a considerable hazard to baggage-handlers, aircraft refuelers, food caterers, and emergency personnel, who all become exposed to the risk of being struck within short time periods while convective storm clouds develop. Airport safety protocols require that ramp operations be modified or discontinued when lightning is in the vicinity (typically 16 km), which becomes very costly and disruptive to flight operations. Therefore, much focus has been paid to nowcasting the first-time initiation and extent of lightning, both of CG and of any lightning (e.g, in-cloud, cloud-to-cloud). For this project three lightning nowcasting methodologies will be combined: (1) a GOESbased 0-1 hour lightning initiation (LI) product (Harris et al. 2010; Iskenderian et al. 2012), (2) a High Resolution Rapid Refresh (HRRR) lightning probability and forecasted lightning flash density product, such that a quantitative amount of lightning (QL) can be assigned to a location of expected LI, and (3) an algorithm that relates Pseudo-GLM data (Stano et al. 2012, 2014) to the so-called "lightning jump" (LJ) methodology (Shultz et al. 2011) to monitor lightning trends and to anticipate/forecast severe weather (hail > or =2.5 cm, winds > or =25 m/s, tornadoes). The result will be a time-continuous algorithm that uses GOES satellite, radar fields, and HRRR model fields to nowcast first-flash LI and QL, and subsequently monitors lightning trends on a perstorm basis within the LJ algorithm for possible severe weather occurrence out to > or =3 hours. The LI-QL-LJ product will also help prepare the operational forecast community for Geostationary Lightning Mapper (GLM) data expected in late 2015, as these data are monitored for ongoing convective storms. The LI-QL-LJ product will first predict where new lightning is highly probable using GOES imagery of developing cumulus clouds, followed by n analysis of NWS (dual-polarization) radar indicators (reflectivity at the -10 C altitude) of lightning occurrence, to increase confidence that LI is immanent. Once lightning is observed, time-continuous lightning mapping array and Pseudo-GLM observations will be analyzed to assess trends and the severe weather threat as identified by trends in lightning (i.e. LJs). Additionally, 5- and 15-min GOES imagery will then be evaluated on a per-storm basis for overshooting and other cloud-top features known to be associated with severe storms. For the processing framework, the GOES-R 0-1 hour convective initiation algorithm's output will be developed within the Warning Decision Support System - Integrated Information (WDSS-II) tracking tool, and merged with radar and lightning (LMA/Psuedo-GLM) datasets for active storms. The initial focus of system development will be over North Alabama for select lightning-active days in summer 2014, yet will be formed in an expandable manner. The lightning alert tool will also be developed in concert with National Weather Service (NWS) forecasters to meet their needs for real-time, accurate first-flash LI and timing, as well as anticipated lightning trends, amounts, continuation and cessation, so to provide key situational awareness and decision support information. The NASA Short-term Prediction Research and Transition (SPoRT) Center will provide important logistical and collaborative support and training, involving interactions with the NWS and broader user community.

Mecikalski, John↗
NASA NTRSDate not supplied

Boundary-Layer Cloud Modeling Challenges on the North Slope of Alaska

The accurate modeling and prediction of cloud base heights is critical for energy balance calculations and aviation operations, alike. Low-level (i.e., boundary-layer) Arctic clouds can be difficult to model, making prediction of formation and dissipation challenging. Primarily mixed-phase, these clouds typically contain low quantities of supercooled liquid water and often slowly precipitate relatively small amounts of moderately and heavily rimed snow particles. While this appears to be the predominant cloudy state on the North Slope of Alaska (NSA), the delicate balance of microphysical, dynamical, radiative, surface coupling, and advective processes can rapidly shift to heavy snow (with various degrees of riming) or to a complete dissipation of the cloud layer without any precipitation, depending on the dominant processes. Here we strive to disentangle these various processes. First, we compare the predictive performances of four different numerical weather models in forecasting the presence and base-heights of low-level clouds: the High-Resolution Rapid Refresh - Alaska (HRRR-AK) model, the Polar Weather Research and Forecasting (Polar WRF) model, the Unified Model (UM), and the European Centre for Medium-range Weather Forecasting (ECMWF) model. Initial results comparing model output at two U.S. Department of Energy Atmospheric Radiation Measurement (AMT) NSA sites, during the fall season in 2019 and 2022, show that the UM slightly outperforms the HRRR-AK in terms of accurately forecasting the presence of a low-level cloud layer (89% of the time). All models have a significant bias of 300 to 800 meters in forecasting cloud base height (lower than is observed); however, the UM and ECMWF models have the lowest biases. Finally, a case study for a particularly challenging April 2017 thin-cloud event is presented, wherein we compare the performance of four different bulk microphysical parameterization schemes using a higher-resolution large eddy simulation (LES) model, the WRF-LES. Initial results show that the Thompson scheme was the only one able to reproduce and sustain a substantial supercooled liquid layer, but it was unable to reproduce the transition from a deep, liquid-rich cloud to a thin layer with moderately and heavily rimed precipitation. This is the first step in linking simulated LES-scale riming processes with those parameterized at a coarser mesoscale model scale. This has important implications for forecasting low-level clouds in an operational environment, given the efficiency of the riming process.

cloud base heights↗
NASA NTRS2016

Using 3-D Numerical Weather Data in Piloted Simulations

This report describes the process of acquiring and using 3-D numerical model weather data sets in NASA Langley's Research Flight Deck (RFD). A set of software tools implement the process and can be used for other purposes as well. Given time and location information of a weather phenomenon of interest, the user can download associated numerical weather model data. These data are created by the National Oceanic and Atmospheric Administration (NOAA) High Resolution Rapid Refresh (HRRR) model, and are then processed using a set of Mathworks' Matlab(TradeMark) scripts to create the usable 3-D weather data sets. Each data set includes radar re ectivity, water vapor, component winds, temperature, supercooled liquid water, turbulence, pressure, altitude, land elevation, relative humidity, and water phases. An open-source data processing program, wgrib2, is available from NOAA online, and is used along with Matlab scripts. These scripts are described with sucient detail to make future modi cations. These software tools have been used to generate 3-D weather data for various RFD experiments.

Daniels, Taumi S.↗
NASA NTRS2016

Satellite Maps and Relevant Compositional Properties of PM2.5 in Difficult Winter Situations and Comparisons to DISCOVER-AQ Airborne Sampling

Mediterranean-climate regions like California's San Joaquin Valley are subject to severe wintertime particulate pollution affecting public health. We present maps of episodes and particulate diagnostics to aid diagnosis and amelioration. For abatement at sources, we require an understanding of sources and transport. Remote sensing should be of aid, but radiance-to-particle relationships are far different from methods which have been of use in the Eastern USA, Northern and Central Europe. Here are the problems: (a) Thin if very polluted mixed layers (MLs) yield optical depths, AOD, near the detection level, (b) bright and quite variegated surfaces (c) Unusual particle composition (e.g., predominance of NH4NO3 and fireplace buning aerosol), which complicate the relationship of AOD to PM2.5. Specialized analysis of MODIS-Aqua data to obtain AOD using the multi-angle (MAIAC) technique employed by Lyapustin and Wang. Meteorological analyses like NOAA's Rapid Analysis Product (RAP, or newer products like HRRR), which are formulated to remain close to observations (e.g. of water), provide useful ML information corroborated by DISCOVER-AQ in-situ and lidar observations. The many PM2.5 measurements allow a calibration of these products and thus maps of aerosol on many successive aerosol buildups. These calibrations also allow insight into compositional information relevant to MODIS retrievals, the product of aerosol density and specific scattering. We have found that the rich in-situ, lidar, and sun-photometer data sets of NASA'S DISCOVER-AQ data set (2013) of great aid. We will highlight the most interesting of many intercomparisons possible with this rich data set. We conclude with a description of new work to extend these insights to similar regions, e.g. the Imperial Valley of California, the Po Valley and maritime Southern Europe, and the litoral regions of Israel.

aerosols↗
NASA NTRS2016

Feasibility of Varying Geo-Fence Around an Unmanned Aircraft Operation Based on Vehicle Performance and Wind

Managing trajectory separation is critical to ensuring accessibility, efficiency, and safety in the unmanned airspace. The notion of geo-fences is an emerging concept, where distance buffers enclose individual trajectories and areas of operation in order to manage the airspace. Currently, the Air Traffic Management system for commercial travel defines static distance buffers around the aircraft; however, commercial UASs are envisioned to operate in significantly closer proximity to other UAS requiring a geo-fence for spacing operations. The geo-fence size can be determined based on vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Calculation of the geo-fence size could be determined as part of pre-flight planning and during real-time operations. A largely non-homogeneous fleet of UASs will be operating in low altitude and will likely be commercially developed. Due to intellectual property concerns, the operators may not provide detailed specifications of the control system to UTM. In addition, the huge variety of UAS makes modeling each control system prohibitive and flight data for these vehicles may not exist. Therefore, a generalized, simple geo-fence sizing algorithm must be developed such that it does not rely on detailed knowledge of the vehicle control system, accounts for the presence of urban winds, and is sufficiently accurate. In this work, two simple models are investigated to determine its feasibility as an adequate means for calculating the geo-fence size. The vehicle data used in this work are provided by UAS manufactures who have partnered with NASA's UTM project and some publicly available websites. The first model utilizes wind data processed from the NOAA HRRR (Hourly Rapid Refresh) product and Sonar Annemometer data provided by San Jose State. The second model utilizes OpenFOAM which is a CFD code used to generate a wind field for flow around a single building. The key vehicle performance parameters can include UAS response time to disturbances, command to actuation latency, control system rate limits, time to recovery to desired path, and aerodynamics. It was found that the first model provides an initial understanding of geo-fence sizing, but does not provide enough accuracy to provide UTM with an efficient means of scheduling vehicles. The results of the second model reveal that modeling UAS controls systems with a linearized plant and gain scheduled PID controller does not allow capture the UAS flight dynamics within a significant envelope of the wind disturbances.

uncertainty↗
NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
NASA NTRSDate not supplied

Pre-Tropical Cyclone Squall Lines and the Connection to the Diurnal Cycle during Hurricane Laura (2020)

Previous studies on the tropical cyclone (TC) diurnal cycle have shown in observations and modeling the presence of outward propagating squall lines. These squall lines were observed in several TCs during the 2020 Atlantic hurricane season. Of these squall lines, one observed in Hurricane Laura made landfall over 14 hours prior to the hurricane’s eye. This squall was responsible for numerous tornado warnings, which disrupted preparations and evacuations ahead of the storm’s landfall. This study seeks to characterize these squall lines and their environmental and thermodynamic characteristics, determine whether they behave more like midlatitude squall lines or tropical cyclone rain bands, and assess the performance of the convection-allowing High Resolution Rapid Refresh model in forecasting them over the ocean and over land. By using a combination of operational radar data, a network of surface weather observations from land and sea, satellite data, and HRRR model output, this case will undergo an extensive evaluation based on criteria from past literature in order to better understand these features as a whole and in relation to the diurnal cycle.

Vivian L Brasfield↗
NASA NTRSDate not supplied

A Study of the Kinematic and Dynamic Processes associated with Mesoscale Snowbands in a Midwest United States Snowstorm on 5 February 2020

Mesoscale snowbands are frequently responsible for dumping large amounts of snow over a relatively small region and pose a considerable forecasting challenge. The present study focuses on the mesoscale snowbands observed on 5 February 2020 over the Midwest United States. In order to improve the forecasting of mesoscale snowbands, it is important to understand the processes that organize these features. To this end, we are investigating the kinematic and dynamic processes that shape the mesoscale snowbands using a combination of high-altitude airborne radar, in-situ aircraft measurements, surface observations, and model simulations. This presentation will primarily focus on the three-dimensional structure of the mesoscale snowbands in terms of EXRAD reflectivity and horizontal winds. The horizontal winds are retrieved from the EXRAD scanning-beam Doppler velocities using a VAD technique. These winds, in combination with virtual potential temperature taken from the HRRR operational analysis, are used to compute the bulk Richardson number in order to investigate any contributions to banding features that might be tied to Kelvin-Helmholtz instability. Our analysis does not support the presence of Kelvin-Helmholtz instability. That said, low, but not subcritical, Richardson numbers were found near the top of some banding features in this event and it is possible that the model analysis is smoothing out the virtual potential temperature fields. Finally, we present arguments for using a flight module designed to sample the along-band structures with particular emphasis on observing generating cells and low-level microphysics.

Charles N. Helms↗
NASA NTRSDate not supplied

Pre-Tropical Cyclone Squall Lines and the Connection to the Diurnal Cycle in Hurricane Laura (2020)

Previous studies on the tropical cyclone (TC) diurnal cycle have shown in observations and modeling the presence of outward propagating squall lines. These squall lines were observed in several TCs during the 2020 Atlantic hurricane season. Of these squall lines, one observed in Hurricane Laura made landfall over 13 hours prior to the hurricane’s eye. This squall was responsible for numerous tornado warnings, which disrupted preparations and evacuations ahead of the storm’s landfall. This study seeks to characterize these squall lines and their environmental and thermodynamic characteristics, determine whether they behave more like midlatitude squall lines or tropical cyclone rain bands, and assess the performance of the convection-allowing High Resolution Rapid Refresh model in forecasting them over the ocean and over land. By using a combination of operational radar data, a network of surface weather observations from land and sea, satellite data, and HRRR model output, this case will undergo an extensive evaluation based on criteria from past literature in order to better understand these features as a whole and in relation to the diurnal cycle.

Vivian L Brasfield↗
NASA NTRSDate not supplied

Observational Analysis of Atlantic Basin Tropical Cyclone Squall Lines and Relationship to the Diurnal Cycle

Previous studies on the tropical cyclone (TC) diurnal cycle have shown in observations and modeling the presence of outward propagating squall lines. These squall lines were observed in several TCs during recent Atlantic hurricane seasons. Of these squall lines, one observed in Hurricane Laura made landfall over 14 hours prior to the hurricane’s eye. This squall was responsible for 40 tornado warnings, which disrupted preparations and evacuations ahead of the storm’s landfall. This study seeks to characterize these squall lines and their environmental and thermodynamic characteristics, determine whether they behave more like midlatitude squall lines or tropical cyclone rain bands, and assess the performance of the convection-allowing High Resolution Rapid Refresh model in forecasting them over the ocean and over land. By using a combination of operational radar data, a network of surface weather observations from land and sea, satellite data, and HRRR model output, this case will undergo an extensive evaluation based on criteria from past literature in order to better understand these features as a whole and in relation to the diurnal cycle.

Vivian Brasfield↗