Search NASA⌕ Search

SEARCH · Search NASA

Results for “Meteorologic data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integrated Hourly Meteorological Database of 20 Meteorological Stations (1981-2022) for Watershed Function SFA Hydrological Modeling

This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset.The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the `RCNEP` R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Assessing the impacts of assimilating IASI and MOPITT CO retrievals using CESM-CAM-chem and DART

We report on the results and evaluation with independent measurements from assimilating both MOPITT (Measurements Of Pollution In The Troposphere) and IASI (Infrared Atmospheric Sounding Interferometer) retrieved profiles into the Community Earth System Model (CESM). We used the Data Assimilation Research Testbed ensemble Kalman filter technique, with the full atmospheric chemistry CESM component Community Atmospheric Model with Chemistry. We first discuss the methodology and evaluation of the current data assimilation system with coupled meteorology and chemistry data assimilation. The various capabilities of MOPITT and IASI retrievals are highlighted, with specific attention to instrument vertical sensitivity and coverage and how these impact the analyses. MOPITT and IASI CO retrievals mostly constrain the CO fields close to the main anthropogenic, biogenic, and biomass burning CO sources. In the case of IASI CO assimilation, we also observe constraints on CO far from the sources. During the simulation time period (June and July 2008), CO assimilation of both instruments strongly improves the atmospheric CO state as compared to independent observations, with the higher spatial coverage of IASI providing better results on the global scale. Yet, the enhanced sensitivity of multispectral MOPITT observations to near surface CO over the main source regions provides synergistic effects at regional scales.

54 ENVIRONMENTAL SCIENCES↗
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 NTRS1973

Meteorological utility of high resolution multispectral data

There are no author-identified significant results in this report. This investigation is directed towards determining the meteorological information content of ERTS-1 MSS bulk data at 0.6 - 0.7 microns as a function of resolution. To accomplish this, the resolution of the MSS data is degraded incrementally to 0.5 min. Subsequent analysis of the processed photographs in conjunction with surface and upper air observations will then determine to what extent significant meteorological information has been lost in the degradation process.

Danko, J. M.↗
NASA NTRS1994

Lagrangian Transport Calculations Using UARS Data. Part I: Passive Tracers

The transport of passive tracers observed by UARS has been simulated using computed trajectories of thousands of air parcels initialized on a three-dimensional stratospheric grid. These trajectories are calculated in isentropic coordinates using horizontal winds provided by the United Kingdom Meteorological Office data assimilation system and vertical (cross-isentropic) velocities computed using a fast radiation code.

UARS computed trajectories↗
DOE OSTI2019

Wind direction estimation using SCADA data with consensus-based optimization

Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

17 WIND ENERGY↗
NASA NTRS2013

A Global Ozone Climatology from Ozone Soundings via Trajectory Mapping: A Stratospheric Perspective

This study explores a domain-filling trajectory approach to generate a global ozone climatology from sparse ozonesonde data. Global ozone soundings of 51,898 profiles at 116 stations over 44 years (1965-2008) are used, from which forward and backward trajectories are performed for 4 days, driven by a set of meteorological reanalysis data. Ozone mixing ratios of each sounding from the surface to 26 km altitude are assigned to the entire path along the trajectory. The resulting global ozone climatology is archived monthly for five decades from the 1960s to the 2000s with grids of 5 degree 5 degree 1 km (latitude, longitude, and altitude). It is also archived yearly from 1965 to 2008. This climatology is validated at 20 ozonesonde stations by comparing the actual ozone sounding profile with that found through the trajectories, using the ozone soundings at all the stations except one being tested. The two sets of profiles are in good agreement, both individually with correlation coefficients between 0.975 and 0.998 and root mean square (RMS) differences of 87 to 482 ppbv, and overall with a correlation coefficient of 0.991 and an RMS of 224 ppbv. The ozone climatology is also compared with two sets of satellite data, from the Satellite Aerosol and Gas Experiment (SAGE) and the Optical Spectrography and InfraRed Imager System (OSIRIS). Overall, the ozone climatology compares well with SAGE and OSIRIS data by both seasonal and zonal means. The mean difference is generally under 20 above 15 km. The comparison is better in the northern hemisphere, where there are more ozonesonde stations, than in the southern hemisphere; it is also better in the middle and high latitudes than in the tropics, where assimilated winds are imperfect in some regions. This ozone climatology can capture known features in the stratosphere, as well as seasonal and decadal variations of these features. Furthermore, it provides a wealth of detail about longitudinal variations in the stratosphere such as the spring ozone maximum over the Canadian Arctic. It also covers higher latitudes than current satellite data. The climatology shows clearly the depletion of ozone from the 1970s to the mid 1990s and ozone recovery in the 2000s. When this climatology is used as the upper boundary condition in an Environment Canada operational chemical forecast model, the forecast is improved in the vicinity of the upper tropospherelower stratosphere region. As this ozone climatology is neither dependent on a priori data or photochemical modeling, it provides independent information and insight that can supplement satellite data and model simulations and enhance our understanding of stratospheric ozone.

SAGE↗
NASA NTRSDate not supplied

Advancing NASA SatCORPS Global Data Products with Cloud Computing and Machine Learning

Operational satellite imager radiances are valuable for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. The use of cloud computing has enabled recent enhancements to process a constellation of geostationary satellites at higher spatiotemporal resolutions than previously possible that meet low latency and near real-time needs. Data taken from Meteosat-8 and -11, Himawari, GOES-16, and -17 are processed and combined with operational polar orbiting satellite data and composited on a 3-km grid to provide global coverage. To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. An update on recent SatCORPS enhancements is presented, highlighting the community benefits achieved with the use of cloud computing and machine learning.

William L Smith↗
NASA NTRS1983

Satellite data - Analysis techniques and applications to meteorology

The history of meteorological satellites is traced, together with current instrumental and data analysis capabilities. Photographs taken from rockets launched in the late 1950s encouraged placing spacecraft such as the Nimbus I in sunsynchronous orbit beginning in 1964, and eventually the GOES, GMS, TIROS and Meteosat spacecraft into GEO in the 1970s. Visible, UV, IR and microwave sensors provide data on cloud patterns, wind fields, vertical temperature and humidity profiles and the surface temperature. Corrections have been developed in the data analyses programs to account for the atmospheric effects on upwelling radiation, fractional cloudiness, cloud-top heights and cloud contamination factors.

Szejwach, G.↗
DOE OSTI2021

Surface Meteorological Station - Forks, WA (FKS) - Reviewed Data

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

17 WIND ENERGY↗
DOE OSTI2021

Surface Meteorological Station - Astoria, OR (AST) - Raw Data

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

17 WIND ENERGY↗
DOE OSTI2021

Surface Meteorological Station - Forks, WA (FKS) - Raw Data

A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.

17 WIND ENERGY↗
NASA NTRS2018

The Impact of Local Meteorological Conditions on Airframe Noise Flight Test Data

Phased microphone array measurements obtained during flight tests conducted in 2016 and 2017 are used to assess the importance of local meteorological measurements on the data. In particular, the effectiveness of atmospheric absorption corrections is evaluated under vastly different temperature and humidity conditions. The results indicate that, even under conditions with high absorption, sources can be visualized up to a frequency that is dependent on background noise levels, wind, and atmospheric turbulence. However, absolute levels were found to be problematic on days with high absorption rates, with the discrepancies most prevalent for aircraft positions further from the center of the array. Restricting the data to those days with favorable meteorological conditions generally resulted in a good collapse of the spectra, with differences less than a couple of decibels.

Lockard, David P.↗
DOE OSTI2016

GoAmazon 2014/15 Particle Sampling Field Campaign Report

As a part of the GoAmazon 2014/15 field deployments, samples of atmospheric particles were collected at the T3 field site (3° 12' 46.70" S, 60° 35' 53.0, Manacapuru, Brazil) established in the Amazonian region. The site was located downwind from Manaus and therefore, experienced clean conditions when winds were from northwest and polluted conditions when winds were from Manaus. During the second intensive operational period of GoAmazon 2014/15 (09/06/2014-10/02/2014), multiple samples of particles were collected using a cascade Multi-Orifice Uniform Deposition Impactor (MOUDI-110). Size-resolved samples by MOUDI (0.056-0.1, 0.1-0.18, 0.18-0.32, 0.32-0.56, 0.56-1.0, 1.0-1.8 μm) were collected for 6-12 hours. A variety of substrates, compatible with different spectro-microscopy methods of analysis, were arranged on the impactor stages. Collected aerosol samples are now stored pending laboratory analysis using multi-modal methods of chemical imaging. Our approach is to analyze only the samples of specific interest. We use the field campaign records and data on meteorology, as well as in situ measurements of gases and aerosols, to select the subset of samples for the in-depth laboratory analysis for selected episodes where changes in chemical composition of particles can be correlated with other data recorded at the site. Hence, we do not expect to perform detailed measurements on all samples—rather, only those samples with the greatest potential to provide scientific insight into aerosol chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗