A novel method of urban land type identification utilizing meteorological station network temperature data, as developed for the Baltimore-Washington region
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Cassie A. Stearns↗
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A portable real-time data processing system for standard meteorological radiosondes
The UMET-1 is a microprocessor-based portable system for automatic real-time processing of flight data transmitted from the standard RAWINSONDE upper atmosphere meteorological balloonsonde. The first 'target system' is described which was designed to receive data from a mobile tracking and telemetry receiving station (TRADAT), as the balloonsonde ascends to apogee. After balloon-burst, the UMET-1 produces user-ready hardcopy.
Staffanson, F. L.↗
Downscaled precipitation and mean air temperature datasets; East-Taylor subbasin; 2008-2019; daily temporal resolution; 400 m spatial resolution
This dataset provides gridded meteorological forcing data (specifically, daily precipitation and daily mean air temperature). The dataset has been generated by downscaling the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset from a spatial resolution of 800 m to 400 m. The time period is 2008-2019, and the mapped area is East Taylor subbasin in Upper Colorado. The data are in the form of NetCDF files, arranged by year. Compared to the PRISM dataset, the dates of the downscaled dataset are shifted backwards by one (e.g., downscaled data for May 25 corresponds to PRISM data for May 26). This temporal shifting makes the meteorological forcing correspond more closely to the prescribed date. The NetCDF format is a standard raster format that can be read using any Geographic Information System (GIS) software; plenty of modules exist in popular scripting languages (such as Python, R and Matlab) that can also be used to read NetCDF files.East_Taylor_Tavg_PRISM400 corresponds to mean air temperature.East_Taylor_Precip_PRISM400.zip corresponds to precipitation.The proprietary PRISM data (800 m resolution) were purchased with funding from the Watershed Function Scientific Focus Area supported by U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award no. DE-AC02-05CH11231.Dataset update on March 9, 2022:Uploaded new data files that are identical to the earlier files but are now in the NetCDF format, arranged by year. Earlier, the files were in the GeoTiff format, arranged by date.
54 ENVIRONMENTAL SCIENCES↗
Data flow in the Synchronous Meteorological Satellite system
Synchronous meteorological satellite mission goals and system design, describing imaging, data collection, facsimile, space environment, trilateration, ranging and transmission
Ames, S. A.↗
Summary of aircraft results for 1978 southeastern Virginia urban plume measurement study of ozone, nitrogen oxides, and methane
Ozone production was determined from aircraft and surface in situ measurements, as well as from an airborne laser absorption spectrometer. Three aircraft and approximately 10 surface stations provided air-quality data. Extensive meteorological, mixing-layer-height, and ozone-precursor data were also measured. Approximately 50 hrs (9 flight days) of data from the aircraft equipped to monitor ozone, nitrogen oxides, dewpoint temperature, and temperature are presented. In addition, each experiment conducted is discussed.
Gregory, G. L.↗
Preliminary analysis of amplitude and phase fluctuations in the JAPE multiple tone data to distances of 500 meters
The JAPE short range data provide a good opportunity for studying phase and amplitude fluctuations of acoustic signals in the atmosphere over distances of several hundred meters. Several factors contribute to the usefulness of these data: extensive meteorological measurements were made, controlled sources were used, the data were recorded with a high dynamic range digital system that preserved phase information and a significant number of measurement points were obtained allowing both longitudinal and transverse studies. Further, Michigan Tech, in cooperation with the U.S. Army TARDEC, has developed phase tracking algorithms for studying vehicle acoustic signals. These techniques provide an excellent tool for analyzing the amplitude and phase fluctuations of the JAPE data. The results of studies such as those reported here have application at several levels: the mechanisms of signal amplitude and phase fluctuations in propagating acoustic signals are not well understood nor are the mathematical models highly developed, acoustic arrays depend strongly on signal coherence and signal amplitude stability in order to perform to their design specifications and active noise control implementation in regions considerably removed from the primary and secondary sources depends upon signal amplitude and phase stability. Work reported here is preliminary in nature but it does indicate the utility of the phase tracking and amplitude detection algorithms. The results obtained indicate that the phase fluctuations of the JAPE continuous multiple tone data (simultaneous transmission of 80, 200 and 500 Hz) are in general agreement with existing theories but the amplitude fluctuations are seen to be less well behaved and show less consistency.
Rogers, James↗
Critical Climate-Sensitive and Important Grain-Producing Regions: Grain Production/Yield Variations Due to Climate Fluctuations
Ideally, the Crop Country Inventory, CCI, is a methodology for the pre-harvest prediction of large variations in a country s crop production. This is accomplished by monitoring the historical climatic fluctuations, especially during the crop calendar period, in a climate sensitive large crop production region or sub-country, rather than the entire country. The argument can be made that the climatic fluctuations in the climatic sensitive region are responsible for the major annual crop country variations and that the remainder of the country, without major climatic fluctuations for a given year, can be assumed to be a steady-state crop producer. The principal data set that has been used is the Global Climate Mode (GCM) data from the National Center for Environmental Prediction (NCEP), taken over the last half century. As a test of its accuracy, GCM data can and has been correlated with the actual meteorological station data at the station site.
Welker, J. E.↗
Radar - TTU radar - Raw Data
This dataset coincides with a selected atmospheric case from the 200-m meteorological tower data.
17 WIND ENERGY↗
Remote Sensing Data from CLARET: A Prototype Cart Data Set
A data set containing radiation, meteorological, and cloud sensor observations is documented. It was prepared for use by the Department of Energy's Atmospheric Radiation Measurement (ARM) program and other interested scientists. These data are a precursor of the types of data that ARM Cloud And Radiation Testbed (CART) sites will provide. The data are from the Cloud Lidar And Radar Exploratory Test (CLARET) conducted by the Wave Propagation Laboratory during autumn 1989 in the Denver-Boulder area of Colorado primarily for the purpose of developing new cloud-sensing techniques on cirrus. After becoming aware of this experiment, ARM scientists requested archival of subsets or the data to assist in the developing ARM program. Five CLARET cases were selected: two with cirrus, one with stratus, one with mixed-phase clouds, and one with clear skies. The cases range from 2 to 9.5 h in length. A pyranometer, pyrgeometer, pyrheliometer, and an infrared radiometer constituted the ensemble of instruments that provided surface radiation data. A lidar, radar, and ceilometer observed the cloud geometrical structure, and visual reports and all-sky camera observations were assimilated to provide cloud cover data. Radiosondes, wind profiler, RASS (profiling virtual temperature), microwave radiometers (observing column integrated liquid water and water vapor), and standard surface measurements provided meteorological data. Satellite data from the stratus case and one cirrus case were analyzed for statistics on cloud cover and top height. The main body of the selected data are available on diskette from the Wave Propagation Laboratory or Los Alamos National Laboratory. In addition to documenting the data set, this report describes CLARET and gives a bibliography of publications associated with the project. Some preliminary results of CLARET' research are also summarized. Simultaneous CO 2 lidar and radar backscatter measurements were shown to provide estimates of the effective radius of ice particles. Simultaneous radar and infrared radiometer data appear useful for estimating column-integrated numbers and average sizes of ice cloud particles. Ice water content obtained with this method compared favorably with values from another empirical technique using radar data alone. Depolarization of the CO 2 lidar signal from ice clouds was surprisingly small, suggesting that calculation of backscatter from nonspherical particles for this lidar is a tractable problem. Examples are also cited of CO 2 lidar measurements of the effective radius of water cloud drop size distributions and of inference of the size of pristine ice crystals that assume a particular orientation in the air. These parameters are all important to radiative transfer through clouds.
Clouds (Meteorology)↗
Novel Hourly-Resolved Global Cloud Property Composite from Operational Satellites Imagers
Numerous applications in satellite remote sensing of surface properties, atmospheric state, composition, and radiation require accurate knowledge of the location and characteristics of clouds. Operational satellite imager radiances are valuable for cloud detection and 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. This paper describes a new global high-resolution dataset of cloud properties made available for community use that is constructed from analyses of a constellation of meteorological satellite imagers. Data taken from Meteosat-8/9 and -11, Himawari-8/9, GOES-16, and -17/-18, Aqua, Terra, Suomi-NPP and NOAA-20 are processed and composited on a 3-km grid to provide hourly global coverage. A historical multi-year dataset is available to serve various needs including modeling challenges related to cloud evaluations and parameterizations. Near real-time data products are also currently available between 60N and 60S. Efforts are underway to operationalize polar orbiting satellite cloud detection methods for low-latency applications over polar regions. The cloud detection and retrieval algorithms have been developed over many years to support NASA weather and climate programs such as the Clouds and Earth’s Radiant Energy System (CERES). 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. A brief description of the methods highlighting the unique aspects of the SatCORPS data products will be presented along with information on their status and availability.
SatCORPS↗
Vegetation monitoring and classification using NOAA/AVHRR satellite data
A vegetation gradient model, based on a new surface hydrologic index and NOAA/AVHRR meteorological satellite data, has been analyzed along a 1300 km east-west transect across the state of Texas. The model was developed to test the potential usefulness of such low-resolution data for vegetation stratification and monitoring. Normalized Difference values (ratio of AVHRR bands 1 and 2, considered to be an index of greenness) were determined and evaluated against climatological and vegetation characteristics at 50 sample locations (regular intervals of 0.25 deg longitude) along the transect on five days in 1980. Statistical treatment of the data indicate that a multivariate model incorporating satellite-measured spectral greenness values and a surface hydrologic factor offer promise as a new technique for regional-scale vegetation stratification and monitoring.
Greegor, D. H., Jr.↗
PLUSWIND Hourly Plant-Level Power
These data contain the hourly plant-level power time series from PLUSWIND. The time series is derived from the HRRR model and corrected for density and losses. The PLUSWIND dataset hosted on the Wind Data Hub here (https://a2e.energy.gov/project/pluswind) contains additional versions with meteorological corrections. Data are provided for years 2018 - 2021. The data hosted at this location include conversion from capacity factor output (normalized output) to total hourly production (units of MW).
17 WIND ENERGY↗
Contribution of Lake-Effect Snow to the Catskill Mountains Snowpack
Meltwater from snow that falls in the Catskill Mountains in southern New York contributes to reservoirs that supply drinking water to approximately nine million people in New York City. Using the NOAA National Ice Centers Interactive Multisensor Snow and Ice Mapping System (IMS) 4km snow maps, we have identified at least 32 lake-effect (LE) storms emanating from Lake Erie andor Lake Ontario that deposited snow in the CatskillDelaware Watershed in the Catskill Mountains of southern New York State between 2004 and 2017. This represents a large underestimate of the contribution of LE snow to the Catskills snowpack because many of the LE snowstorms are not visible in the IMS snow maps when they travel over snow-covered terrain. Most of the LE snowstorms that we identified originate from Lake Ontario but quite a few originate from both Erie and Ontario, and a few from Lake Erie alone. Using satellite, meteorological and reanalysis data we identify conditions that contributed to LE snowfall in the Catskills. Clear skies following some of the storms permitted measurement of the extent of snow cover in the watershed using multiple satellite sensors. IMS maps tend to overestimate the extent of snow compared to MODerate resolution Imaging Spectroradiometer (MODIS) and Landsat-derived snow-cover extent maps. Using this combination of satellite and meteorological data, we can begin to quantify the important contribution of LE snow to the Catskills Mountain snowpack. Changes that are predicted in LE snowfall from the Great Lakes could impact the distribution of rain vs snow in the Catskills which may affect future reservoir operations in the NYC Water Supply System.
Lake Ontario↗
SOME ASPECTS OF STRATOSPHERIC CIRCULATION DERIVED FROM METEOROLOGICAL ROCKET FIRINGS OVER THE UNITED STATES DURING THE WINTER OF 1961
Wind and temperature data from meteorological rocket firings over the united states during the winter of 1961 and the relationship of the polar vortex
ATMOSPHERIC CIRCULATION↗
Some aspects of stratospheric circulation derived from meteorological rocket firings over the united states during the winter of 1961.
Wind and temperature data from meteorological rocket firings over united states during winter 1961 and relationship of polarized vortex
ATMOSPHERIC CIRCULATION↗
Applications of ISES for meteorology
The results are summarized from an initial assessment of the potential real-time meteorological requirements for the data from Eos systems. Eos research scientists associated with facility instruments, investigator instruments, and interdisciplinary groups with data related to meteorological support were contacted, along with those from the normal operational user and technique development groups. Two types of activities indicated the greatest need for real-time Eos data: technology transfer groups (e.g., NOAA's Forecasting System Laboratory and the DOD development laboratories), and field testing groups with airborne operations. A special concern was expressed by several non-U.S. participants who desire a direct downlink to be sure of rapid receipt of the data for their area of interest. Several potential experiments or demonstrations are recommended for ISES which include support for hurricane/typhoon forecasting, space shuttle reentry, severe weather forecasting (using microphysical cloud classification techniques), field testing, and quick reaction of instrumented aircraft to measure such events as polar stratospheric clouds and volcanic eruptions.
Try, Paul D.↗
McIDAS III - A modern interactive data access and analysis system
A powerful facility for meteorological analysis called the Man Computer Interactive Data Access System (McIDAS) was designed and implemented in the early 1970's at the Space Science and Engineering Center of the University of Wisconsin-Madison. Hardware and software experience gained via extensive use of that facility and its derivatives have led to a newer implementation of McIDAS on a larger computer with significant enhancements to the supporting McIDAS software. McIDAS allows remote and local access to a wide range of data from satellites and conventional observations, time lapse displays of imagery data, overlaid graphics, and current and past meteorological data. Available software allows one to perform analysis of a wide range of digital images as well as temperature and moisture sounding data obtained from satellites. McIDAS can generate multicolor composites of conventional and satellite weather data, radar and forecast data in a wide variety of two- and three-dimensional displays as well as time lapse movies of these analyses. These and other capabilities are described in this paper.
Suomi, V. E.↗
Crop weather models of corn and soybeans for Agrophysical Units (APU's) in Iowa using monthly meteorological predictors
Models based on multiple regression were developed to estimate corn and soybean yield from weather data for agrophysical units (APU) in Iowa. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for crop reporting districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU's were selected to be more homogeneous with respect crop to production than the CRDs. The APU models are quite similar to the CRD models, similar explained variation and number of predictor variables. The APU models are to be independently evaluated and compared to the previously evaluated CRD models. That comparison should indicate the preferred model area for this application, i.e., APU or CRD.
Leduc, S.↗