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A diversity 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.
This data set contains the 15 minute resolution surface meteorology and soils data from the 15 NCAR/RAL weather stations that were operated around central Argentina during the RELAMPAGO (Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations) Extended Observing Period (EOP). Data providence, citation, and acknowledgement This ARM data set is a copy of v1.0 of the NCAR data set obtained in June 2024 from https://doi.org/10.26023/KW8Z-F2WX-H0Y. The citation for the original data source is: Gochis, D., et al. 2019. NCAR-RAL Surface Hydrometeorological Observation Network Data. Version 1.0. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.26023/KW8Z-F2WX-H0Y Accessed June 2024. In addition to the citation reference and any other acknowledgements, please acknowledge NCAR/EOL in your publications with text such as: “Data provided by NCAR/EOL under the sponsorship of the National Science Foundation. https://data.eol.ucar.edu/”
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
We evaluate the feasibility of using various clear-sky models or purchased satellite data for estimating the soiling of a reference cell irradiance sensor. We find results to be more accurate for models that consider local meteorological conditions. We conclude that given the data sets considered, and depending on the requirements of the data analyst, choosing to use purchased satellite irradiance data from Solargis to estimate the soiling of a reference cell sensor tends to yield more accurate results, although there are instances where a clear-sky model performs better. The SOLIS clear-sky model in PVLIB with variable P wat provided useful soiling results, implying that the general method of using a clear-sky model with local meteorological data may provide a low-cost tool for detecting soiling of irradiance sensors.
A diversity 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.
The use of social media, such as Twitter, has changed the information landscape for citizens’ participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise, such as how the usage of Twitter by a large population may change during the development of a major drought alongside how the changing usage facilitates drought detection. For this reason, contemporary analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Three different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is found that the integration of data resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Eight of the ten models tested showed quantifiable improvements in the performance over the control run model, suggesting that the Twitter-based model was superior in predicting drought severity. Future work lies in expanding this method to depict drought in the western U.S.
Abstract The marine boundary layer of the northeastern U.S. is studied with focus on wind speed, atmospheric stability, and turbulent kinetic energy (TKE), the three most relevant properties in the context of offshore wind power development. Two long‐term observational data sets are analyzed. The first one consists of multilevel meteorological variables measured up to 60 m during 2003–2011 at the offshore Cape Wind tower, located near the center of the Nantucket Sound. The second data set comes from the 2013–2014 IMPOWR campaign (Improving the Modeling and Prediction of Offshore Wind Resources), in which wind and wave data were collected with new instruments on the Cape Wind platform, in addition to meteorological data measured during 19 flight missions offshore of New York, Connecticut, Rhode Island, and Massachusetts. It is found that, in this region: (1) the offshore wind resource is remarkable, with monthly average wind speeds at 60 m exceeding 7 m s −1 all year round, highest winds in winter (10.1 m s −1 ) and lowest in summer (7.1 m s −1 ), and a distinct diurnal modulation, especially in summer; (2) the marine boundary layer is predominantly unstable (61% unstable vs. 21% neutral vs. 18% stable), meaning that mixing is strong, heat fluxes are positive, and the wind speed profile is often nonlogarithmic (~40% of the time); and (3) the shape of the wind speed profile (log versus nonlog) is an effective qualitative proxy for atmospheric stability, whereas TKE alone is not.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
Data centers are significant energy users and require continuous cooling to maintain high levels of computing performance. The majority of data centers have direct-expansion cooling which typically accounts for approximately 50% of the energy usage of data centers. However, using typical meteorological year 3 (TMY3) weather data and a simple psychometric bin analysis, alternative cooling strategies using a combination of economizer, evaporative, and supplemental DX cooling have been shown to be applicable in all climate zones in the United States. Average data center cooling energy savings across the U.S. was approximately 80%. Analysis of cooling energy savings is presented for various ASHRAE climate zones. The psychometric bin analysis is conducted for the ASHRAE recommended and allowable operating environment zones, as well as, a modified allowable operating environment. Control strategies are discussed. Finally, examples of energy efficient data centers using alternative cooling strategies are presented.
SAND2023-06610O The Nuclear Incident Response Program (NIRP) Core Software Suite is a set of code that supports multiple applications. It includes miscellaneous base code for data objects, mathematic equations, and user interface components that are used for developing large applications, including those used for data management. Among the types of applications: • Analyst Manager is used for managing contact information, which is also often included in reports and identifies the owners of calculations. • Radionuclide Viewer provides access to the Dose Coefficient File Package (DCFPAK) radiological data, and it complements the Mixture Manager tool. • Mixture Manager creates and manages radionuclides mixtures that are commonly used in other applications. • High Explosive Manager manages explosives and properties. • Chart Viewer is used for data charts such as meteorology charts and other charts specific to data needs. • Unit Converter converts numeric values to/from different units. This package includes object-oriented base code such as data objects describing the date, time, and location of an event; weather conditions; data types that support predefined ranges of values; a unit library, and others. The package also includes mathematic equations such as determining the distance between two points; erf functions; factorial functions; incidence fractions of probit curves; and others. Other features are customized user interface components for text fields; unit selectors (combo boxes); menus; panels; bug-tracking; and automated checks for software updates. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
In accordance with the developing economy and growing population, an increasing number of tall buildings have been constructed over the last 20 years. In 2017, there were 144 new buildings worldwide that were 200 m or greater in height; 50% of these buildings were in China. Due to the vertical gradient impact of meteorological parameters, the energy performance of tall buildings differs from that of general buildings. Few studies exist on vertical meteorological changes using measured data at different heights. Most studies on dynamic energy simulation simulate meteorological parameters using models. This study explores vertical meteorological patterns using hourly dry-bulb temperature, relative humidity, and wind speed data from 2007 to 2017 for a 325 m meteorological tower in Beijing. The temperature decreased significantly with increasing altitude (~0.9 °C per 100 m), while the daily temperature difference decreased with increasing altitude. The absolute humidity did not change significantly with height. The wind speed increased with altitude at approximately 2 m/s per 100 m. The building simulation showed that the annual heating load at a height of 320 m increased by 85% from that at 8 m; the annual cooling load decreased by 20%. Such significant differences in thermal loads for 300-m-tall buildings confirm the need to consider vertical meteorological differences in building performance simulations for tall buildings. A greater number of measurement points at different heights improve the simulation accuracy. Finally, guidance on selecting the heights for vertical metrological measurements is provided based on the influences of building thermal loads.
The standardized precipitation index (SPI), one of the most commonly used drought indicators, is widely used in the research areas of drought analysis and drought prediction in different fields such as meteorology, agriculture, and hydrology. However, its main disadvantage is the relatively coarse time resolution of one month. To improve the time resolution of SPI to identify flash droughts, we have refined the traditional SPI calculation method and developed a new multi-scale daily SPI dataset based on data from 484 meteorological stations in mainland China from 1961 to 2018. SPI data from three different sites (located in Henan, Yunnan, and Fujian Provinces) at the three-month timescale were analyzed by comparing with historically recorded drought events. We found that the new multi-scale daily SPI can effectively capture drought events in different periods and locations and identify the specific start and end times of drought events. In short, our SPI dataset appears reasonable and capable of facilitating drought research in different fields.