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At least 235 records · Page 13

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DOE OSTI2023

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1979

Recommendations of the panels: Panel on flight planning to avoid high ozone

Flights planned or accomplished during certain months of the year at the higher latitudes and altitudes at or above the tropopause are discussed. Cabin ozone level limitations are established, and additional information is required for more accurate and qualtitative forecasting and design data base for operational utilization. Better tropopause heights, ozone concentration and corresponding meteorological data along selected flight routes, and meteorological data were investigated.

Mohnen, V. A.↗

NASA NTRS1986

The atmospheric excitation of earth orientation changes during MERIT

Geodetic estimates of earth orientation variations detected during the MERIT campaign (September 1983-November 1984) are compared with corresponding meteorological data. The geodetic data were obtained using VLBI, lunar laser ranging, and satellite laser ranging and the meteorological data were from the NMC in the U.S. and the European Centre for Medium Range Weather Forecasting (ECMRWF) in the U.K. The effects of changes in pressure, wind, and the inverted barometer ocean response on the excitation of the polar motion and the length of day are examined. The comparison between the meteorological and geodetic data reveals that the equatorial vector component of the atmospheric angular momentum (AAM) has significant annual and semiannual variations. Good correlation is detected between the geodetic polar motion data and the NMC pressure inverted barometer data combined with the ECMRWF wind estimates and semiannual agreement with the NMC pressure data is observed. There is also good correlation between the ECMRWF and NMC polar vector component of the AAM data and the geodetic length of day estimates, and good semiannual agreement with NMC pressure data is noted.

Eubanks, T. M.↗

NASA NTRS1982

Spectral estimates of intercepted solar radiation by corn and soybean canopies

Attention is given to the development of methods for combining spectral and meteorological data in crop yield models which are capable of providing accurate estimates of crop condition and yields throughout the growing season. The present investigation is concerned with initial tests of these concepts using spectral and agronomic data acquired in controlled experiments. The data were acquired at the Purdue University Agronomy Farm, 10 km northwest of West Lafayette, Indiana. Data were obtained throughout several growing seasons for corn and soybeans. Five methods or models for predicting yields were examined. On the basis of the obtained results, it is concluded that estimating intercepted solar radiation using spectral data is a viable approach for merging spectral and meteorological data in crop yield models.

Gallo, K. P.↗

NASA NTRS2005

TAMDAR Sensor Validation in 2003 AIRS II

This study entails an assessment of TAMDAR in situ temperature, relative humidity and winds sensor data from seven flights of the UND Citation II. These data are undergoing rigorous assessment to determine their viability to significantly augment domestic Meteorological Data Communications Reporting System (MDCRS) and the international Aircraft Meteorological Data Reporting (AMDAR) system observational databases to improve the performance of regional and global numerical weather prediction models. NASA Langley Research Center participated in the Second Alliance Icing Research Study from November 17 to December 17, 2003. TAMDAR data taken during this period is compared with validation data from the UND Citation. The data indicate acceptable performance of the TAMDAR sensor when compared to measurements from the UND Citation research instruments.

Daniels, Taumi S.↗

DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗

DOE OSTI2022

Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.

17 WIND ENERGY↗

DOE OSTI2024

Convolutional Non-Homogeneous Poisson Process and its Application to Wildfire Ignition Risk Quantification for Power Delivery Networks

To quantify wildfire ignition risks on power delivery networks, the current practice predominantly relies on the empirically calculated fire danger indices, which may not well capture the effects of dynamically changing environmental factors. This article proposes a spatio-temporal point process model, known as the Convolutional Non-homogeneous Poisson Process (cNHPP), and applies the model to quantify wildfire ignition risks for power delivery networks. The proposed model captures both the current (i.e., instantaneous) and cumulative (i.e., historical) effects of key environmental processes (i.e., covariates) on wildfire risks, as well as the spatio-temporal dependency among different segments of the power delivery network. The computation and interpretation of the intensity function are thoroughly investigated. We apply the proposed approach to estimate wildfire ignition risks on major transmission lines in California, using historical fire data, meteorological and vegetation data obtained from the National Oceanic and Atmospheric Administration and National Aeronautics and Space Administration. Here, a comprehensive comparison study is performed to show the applicability and predictive capability of the proposed approach.

Non-homogeneous Poisson Process↗

DOE OSTI2020

Characterization with dense array data of seismic sources in the shallow part of the San Jacinto fault zone

SUMMARY We analyse dominant sources identified in a catalogue of more than 156 000 localizations performed using a 26-d data set recorded by a dense array set on the San Jacinto fault near Anza, in California. Events were localized using an array processing technique called Match Field Processing. As for all array processing techniques, the quality of the event position decrease when the events are outside of the array. We thus separate localizations in and outside the array using simple geometrical conditions. We compare the time distribution of the localization to additional data such as meteorological data, day of human activity as well as existing catalogues to determine the nature of the dominant events located using our method. We find that most of the events located outside of the array could be attributed to a surface structure excited by wind. On the other hand, part of the localizations under the array occur during regional earthquakes and could correspond to diffraction on the fault's heterogeneities. The rest of the localizations inside the array could be generated by the fault itself.

Geochemistry & Geophysics↗

DOE OSTI2018

A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. In conclusion, the normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.

14 SOLAR ENERGY↗

DOE OSTI2019

Realized ecological forecast through an interactive Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models

Abstract. Predicting future changes in ecosystem services is not only highly desirable but is also becoming feasible as several forces (e.g., available big data, developed data assimilation (DA) techniques, and advanced cyber-infrastructure) are converging to transform ecological research into quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models. EcoPAD (v1.0) is a web-based software system that automates data transfer and processing from sensor networks to ecological forecasting through data management, model simulation, data assimilation, forecasting, and visualization. It facilitates interactive data–model integration from which the model is recursively improved through updated data while data are systematically refined under the guidance of model. EcoPAD (v1.0) relies on data from observations, process-oriented models, DA techniques, and the web-based workflow. We applied EcoPAD (v1.0) to the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment in northern Minnesota. The EcoPAD-SPRUCE realizes fully automated data transfer, feeds meteorological data to drive model simulations, assimilates both manually measured and automated sensor data into the Terrestrial ECOsystem (TECO) model, and recursively forecasts the responses of various biophysical and biogeochemical processes to five temperature and two CO 2 treatments in near-real time (weekly). Forecasting with EcoPAD-SPRUCE has revealed that mismatches in forecasting carbon pool dynamics are more related to model (e.g., model structure, parameter, and initial value) than forcing variables, opposite to forecasting flux variables. EcoPAD-SPRUCE quantified acclimations of methane production in response to warming treatments through shifted posterior distributions of the CH 4 :CO 2 ratio and the temperature sensitivity ( Q 10 ) of methane production towards lower values. Different case studies indicated that realistic forecasting of carbon dynamics relies on appropriate model structure, correct parameterization, and accurate external forcing. Moreover, EcoPAD-SPRUCE stimulated active feedbacks between experimenters and modelers to identify model components to be improved and additional measurements to be taken. It has become an interactive model–experiment (ModEx) system and opens a novel avenue for interactive dialogue between modelers and experimenters. Altogether, EcoPAD (v1.0) acts to integrate multiple sources of information and knowledge to best inform ecological forecasting.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Atmospheric turbulence affects wind turbine nacelle transfer functions

Despite their potential as a valuable source of individual turbine power performance and turbine array energy production optimization information, nacelle-mounted anemometers have often been neglected because complex flows around the blades and nacelle interfere with their measurements. This work quantitatively explores the accuracy of and potential corrections to nacelle anemometer measurements to determine the degree to which they may be useful when corrected for these complex flows, particularly for calculating annual energy production (AEP) in the absence of other meteorological data. Using upwind meteorological tower measurements along with nacelle-based measurements from a General Electric (GE) 1.5sle model, we calculate empirical nacelle transfer functions (NTFs) and explore how they are impacted by different atmospheric and turbulence parameters. This work provides guidelines for the use of NTFs for deriving useful wind measurements from nacelle-mounted anemometers. Corrections to the nacelle anemometer wind speed measurements can be made with NTFs and used to calculate an AEP that comes within 1% of an AEP calculated with upwind measurements. We also calculate unique NTFs for different atmospheric conditions defined by temperature stratification as well as turbulence intensity, turbulence kinetic energy, and wind shear. During periods of low stability as defined by the Bulk Richardson number ( R B ), the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of high stability at some wind speed bins below rated speed, leading to a steeper NTF during periods of low stability. Similarly, during periods of high turbulence, the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of low turbulence at most wind bins between cut-in and rated wind speed. Based on these results, we suggest different NTFs be calculated for different regimes of atmospheric stability and turbulence for power performance validation purposes.

17 WIND ENERGY↗

DOE OSTI2016

aafnavdsm10hz (a1)

This umbrella contains data from all instrumentation associated with the aircraft’s navigational location, motion, and attitude data. Navigational instrumentation on the Gulfstream-159 includes navigational data recorded from the AIMMS-20 (for other AIMMS produced data see the instrument category MET-AIR), CMigits global positioning system (GPS) & Inertial Navigational System (INS) Tactical System, Differential Survey Module (DSM), and VectorNav VN-200 INS. For a higher-order product that contains navigational data integrated with meteorological data and sampling masks/flags to aid analysis please see the categorical value added product NAVMET-AIR.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

aafnavcmig (a1)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

aafnavdsm1hz (a1)

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1988

The breakup of the Southern Hemisphere spring polar ozone and temperature minimums from 1979 to 1987

The purpose of this study is to quantify the observations of the polar vortex breakup. The data used in this study consist of Total Ozone Mapping Spectrometer (TOMS) data, and National Meteorological Center (NMC) analyses. The final warming is diagnosed using the difference between zonal means at 80 degrees and 50 degrees S for temperature, ozone, and layer mean temperature. The polar vortex breakup can also be diagnosed by the onset of weak zonal mean zonal winds (i.e., u, overbar denotes a zonal average) at 60 degrees S. Computations of the polar vortex breakdown date using NMC meteorological data and TOMS total ozone data indicate that the breakdown is occurring later in the spring in the lowest portion of the stratosphere. At altitudes above 100 mb, the large interannual variance of the breakdown date renders any trend determination of the breakdown date difficult. Individual plots of TOMS total ozone indicate that the total ozone minimum remains intact for a longer period of time than is observed in earlier years.

Newman, Paul A.↗

NASA NTRS1990

Vehicular sources in acoustic propagation experiments

One of the most important uses of acoustic propagation models lies in the area of detection and tracking of vehicles. Propagation models are used to compute transmission losses in performance prediction models and to analyze the results of past experiments. Vehicles can also provide the means for cost effective experiments to measure acoustic propagation conditions over significant ranges. In order to properly correlate the information provided by the experimental data and the propagation models, the following issues must be taken into consideration: the phenomenology of the vehicle noise sources must be understood and characterized; the vehicle's location or 'ground truth' must be accurately reproduced and synchronized with the acoustic data; and sufficient meteorological data must be collected to support the requirements of the propagation models. The experimental procedures and instrumentation needed to carry out propagation experiments are discussed. Illustrative results are presented for two cases. First, a helicopter was used to measure propagation losses at a range of 1 to 10 Km. Second, a heavy diesel-powered vehicle was used to measure propagation losses in the 300 to 2200 m range.

Prado, Gervasio↗

NASA NTRS1973

Tropospheric Transmissivity Measurements Using the Raman Nitrogen Lidar Technique

LIDAR measurements in Azusa, California, during October 1972, were made in which the backscattered Raman-shifted nitrogen return was ratioed at different altitudes in order to obtain transmissivity. Rawinsonde data from nearby El Monte were used to determine the temperature and nitrogen number density altitude profiles. These data and other meteorological data are compared to the vertical aerosol and transmissivity structure determined by LIDAR. Also data analysis techniques are shown for obtaining q2 (transmissivity) and beta (attenuation coefficient) as a function of altitude.

McCormick, M. P.↗