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514 results for “meteorological data”
Meteorological & Oceanographic data from NW Galician coast
<p>File containing time series of daily values of coastal winds, upwelling index (offshore Ekman transport), continental runoff, solar irradiance and sea surface temperature for selected sites in the NW Galician coast (NW Spain). These data can be freely downloaded from the web sites of the Galician Meteorological Agency MeteoGalicia (http://www.meteogalicia.es), the Instituto Español de Oceanografía (http://www.indicedeafloramiento.ieo.es) and ICOADS (http://icoads.noaa.gov). We just put them together in a single file. The file includes metadata indicating units, the position of the selected stations and the origin of the data</p>
Aeolian saltation measurement and meteorological data over typical desert surfaces on the Alxa Plateau
<p>The data were collected from five meteorological stations situated over typical desert surfaces on the Alxa Plateau from November 2018 to December 2019. This dataset encompassed mean daily wind speed and direction, air temperature (℃), relative humidity (RH), cumulative saltation particle counts and kinetic energy. The measurement duration at the gravel Gobi site lasted over 9 months for uncontrollable reasons. Due to the involvement of unpublished articles in high-resolution data, this dataset only provides daily data as a reference. We will supplement high-resolution data in the future.</p>
Meteorological Data from Kardamyla, Chios: March 2024 Baseline Measurements for the MUSICA Project
<p>The present meteorological data is collected from the weather station in <strong>Kardamyla</strong>, a village in northern Chios, and is published on the <strong>Zenodo</strong> platform for open access. The Kardamyla area was selected due to its proximity to the location where the <strong>MUSICA</strong> project platform will be installed. The data includes measurements of temperature, rainfall, wind speed, and wind direction, and covers the period from March 1st to March 31st, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, with the goal of monitoring climate changes in the Kardamyla area and the broader region of Chios, particularly following the installation of the project's platform. The data presented here covers the period from March 2024, and new measurements will be regularly added as part of ongoing monitoring efforts to track changes in weather and climate conditions.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for March 2024</h3> <ul> <li><strong>Highest temperature</strong>: 25.1°C, recorded on March 31st, 2024, at 15:10.</li> <li><strong>Lowest temperature</strong>: 3.3°C, recorded on March 24th, 2024, at 05:50.</li> <li><strong>Highest daily rainfall</strong>: 28.2 mm, recorded on March 5th, 2024.</li> <li><strong>Highest wind speed</strong>: 77.2 km/h, recorded on March 12th, 2024, at 00:10.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>
Meteorological Data from Kardamyla, Chios: February 2024 Baseline Measurements for the MUSICA Project
<p>The present meteorological data is collected from the weather station in <strong>Kardamyla</strong>, a village in northern Chios, and is published on the <strong>Zenodo</strong> platform for open access. The Kardamyla area was selected due to its proximity to the location where the <strong>MUSICA</strong> project platform will be installed. The data includes measurements of temperature, rainfall, wind speed, and wind direction, and covers the period from February 1st to February 29th, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, with the goal of monitoring climate changes in the Kardamyla area and the broader region of Chios, particularly following the installation of the project's platform. The data presented here covers the period from February 2024, and new measurements will be regularly added as part of ongoing monitoring efforts to track changes in weather and climate conditions.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for February 2024</h3> <ul> <li><strong>Highest temperature</strong>: 21.9°C, recorded on February 29th, 2024, at 13:40.</li> <li><strong>Lowest temperature</strong>: 3.0°C, recorded on February 4th, 2024, at 07:30.</li> <li><strong>Highest daily rainfall</strong>: 49.4 mm, recorded on February 12th, 2024.</li> <li><strong>Highest wind speed</strong>: 82.1 km/h, recorded on February 11th, 2024, at 21:30.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>
Meteorological Data from Chios: March 2024 Baseline Measurements for the MUSICA Project
<p>The present meteorological data is collected from the weather station in <strong>Chiostown</strong>, located in Chios, and is published on the <strong>Zenodo</strong> platform for open access. The station is positioned at an elevation of 32 meters and the data includes measurements of temperature, rainfall, wind speed, and wind direction, covering the period from March 1st to March 31st, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, which aims to monitor climate changes in the Chiostown area and the broader region of Chios. The data presented here for March 2024 provides insight into the weather conditions leading up to the installation of the project's platform. Continuous monitoring efforts will track changes in weather and climate conditions as the project progresses.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for March 2024</h3> <ul> <li><strong>Highest temperature</strong>: 25.3°C, recorded on March 31st, 2024, at 15:50.</li> <li><strong>Lowest temperature</strong>: 7.1°C, recorded on March 24th, 2024, at 03:20.</li> <li><strong>Highest daily rainfall</strong>: 25.4 mm, recorded on March 5th, 2024.</li> <li><strong>Highest wind speed</strong>: 66.0 km/h, recorded on March 12th, 2024, at 10:50.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>
Comparison of Meteorological Data: Chios and Kardamyla, January – March 2024
<p>A comparison of the meteorological data from two nearby locations on Chios Island, <strong>Chios </strong>(town) and <strong>Kardamyla</strong>, for the months of January, February, and March 2024, reveals some interesting climatological patterns. The data includes temperature, rainfall, wind speed, and wind direction, providing a valuable overview of the weather conditions in these Aegean locations.</p> <h3>Temperature</h3> <p>The average temperature shows a steady increase in both areas over the three months:</p> <ul> <li><strong>Kardamyla</strong>: <ul> <li>January: 12.4°C</li> <li>February: 13.4°C</li> <li>March: 13.9°C</li> </ul> </li> <li><strong>Chios town</strong>: <ul> <li>January: 11.9°C</li> <li>February: 13.0°C</li> <li>March: 14.1°C</li> </ul> </li> </ul> <p>The temperatures in both areas are quite similar, with Chiostown recording slightly higher average temperatures in February and March compared to Kardamyla. This might be due to differences in local topography and exposure to wind patterns.</p> <h3>Rainfall</h3> <p>Rainfall varies across the three months, with significant precipitation recorded in both locations:</p> <ul> <li><strong>Kardamyla</strong>: <ul> <li>January: Total rainfall 87.6 mm (maximum daily: 19.2 mm)</li> <li>February: Total rainfall 70.4 mm (maximum daily: 49.4 mm on February 12)</li> <li>March: Total rainfall 63.2 mm (maximum daily: 28.2 mm on March 5)</li> </ul> </li> <li><strong>Chios town</strong>: <ul> <li>January: 54.8 mm</li> <li>February: 82.8 mm (maximum daily: 43.6 mm on February 12)</li> <li>March: 69.6 mm (maximum daily: 25.4 mm on March 5)</li> </ul> </li> </ul> <p>Both locations experienced heavy rainfall in February, with Chiostown recording a maximum daily rainfall of 43.6 mm on the 12th, close to the 49.4 mm recorded in Kardamyla on the same day. This indicates a shared weather event, likely an intense storm system affecting both areas.</p> <h3>Wind</h3> <p>Wind speeds in both locations show similar patterns of strong winds during winter and early spring, with some days registering high gusts:</p> <ul> <li><strong>Kardamyla</strong>: <ul> <li>January: Maximum wind speed 85.3 km/h</li> <li>February: Maximum wind speed 82.1 km/h</li> <li>March: Maximum wind speed 77.2 km/h</li> </ul> </li> <li><strong>Chios town</strong>: <ul> <li>January: Maximum wind speed 90.1 km/h</li> <li>February: Maximum wind speed 67.6 km/h</li> <li>March: Maximum wind speed 66.0 km/h</li> </ul> </li> </ul> <p>Chiostown recorded the highest wind speeds in January (90.1 km/h), while the wind intensity decreased slightly in February and March. In contrast, Kardamyla experienced consistently high wind speeds throughout the three months. Northerly and north-northeasterly winds dominate in both areas, with southern winds occurring during stormy conditions.</p> <h3>Conclusions</h3> <p>Several key conclusions can be drawn from this comparison:</p> <ol> <li><strong>Temperature</strong>: Temperatures rise gradually as spring approaches, with Chiostown showing slightly higher average temperatures than Kardamyla during February and March.</li> <li><strong>Rainfall</strong>: Both locations experienced significant rainfall, particularly in February. The high daily rainfall values on February 12 suggest a shared intense weather event affecting both locations.</li> <li><strong>Wind</strong>: Strong winds are a consistent feature in both areas, with Chiostown experiencing slightly higher maximum wind speeds in January but Kardamyla maintaining stronger winds throughout the three-month period.</li> </ol> <p>Overall, the weather conditions in Chios (town) and Kardamyla are quite similar, with local topographical and geographical factors influencing small variations in temperature, rainfall, and wind speeds.</p>
Madrid datasets (air quality, meteorological and traffic data) of 2019 and 2022
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2022. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (in degrees), Temperature, Humidity, Pressure, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed, Wind direction (after converting to categorical data (north, northeast, east, southeast, south, southwest, west, northwest) and passing through One Hot Encoder). The following are the names of the above features (the names of the columns in the files): 'NO2', 'windSpeed', 'windDir', 'Temp', 'Humidity', 'Pressure', 'SolarRad', 'intensidad', 'ocupacion', 'carga', 'vmed', 'windDir_Categ_east', 'windDir_Categ_north', 'windDir_Categ_northeast', 'windDir_Categ_northwest', 'windDir_Categ_south', 'windDir_Categ_southeast', 'windDir_Categ_southwest', 'windDir_Categ_west'.</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2019_winddir.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2022_winddir.csv</a> include the above features for the defined grid in the city of Madrid for January-June 2019 and January-June 2022, respectively, with the following dimension: 4344×340×19: (January-June 2019); 4343 × 340 × 19 (January-June 2022).</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2019.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2022.csv</a> include the above features with the following dimension: 4344 × 24 × 19: (January-June 2019); 4343 × 24 × 19 (January-June 2022), extracted from the defined grid for cells only where air quality monitoring stations exist.</p>
Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. Simulation and observational results of meteorological and air quality including four folders:</p> <p> Day_PBLH: Daily PBLH data</p> <p> Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO</p> <p> Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation</p> <p> Hour_radiation: Hourly surface radiation data</p> <p>2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:</p> <p> AOD: Yearly and seasonal AOD data</p> <p> CF: Yearly and seasonal CF data</p> <p> CO: Yearly and seasonal CO data</p> <p> LWP: Yearly and seasonal LWP data</p> <p> NO2: Yearly and seasonal NO2 data</p> <p> O3: Yearly and seasonal O3 data</p> <p> Precipitation: Yearly and seasonal precipitation data</p> <p> Radiation: Yearly and seasonal radiation data</p> <p> SO2: Yearly and seasonal SO2 data</p>
Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
ERA5 daily meteorological data (Puget Sound) for weather system identification
<p>This dataset includes the ERA5-based daily meteorological data over the US Puget Sound region. The data includes the following meteorological variables at 850hPa pressure level: temperature (T), relative humidity (RH), horizontal wind vector (U and V), vertical wind (W), and geopotential height (Z).</p> <p>The scripts here are used to establish the weather system classification model as in Chen et al. (submitted). More details, including the complete scripts for analysis/plotting will be updated here after the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, N. Sun, Weather Systems Connecting Modes of Climate Variability to Regional Hydroclimate Extremes. (submitted)</p>
Data products associated with "Multi-year glaciological and meteorological observations on debris-covered Kennicott Glacier, Alaska, 2016 - 2023"
<p>Data for Petersen, E., R. Hock, M. Loso, W. Guo, C. Markovsky, R. Yang, H. Han, D. Shangguan, and S. Kang, “Multi-year glaciological and meteorological observations on debris-covered Kennicott Glacier, Alaska, 2016-2023,” Geoscience Data Journal, Submitted January 2025.</p>
Data from: Sensitivity of simulated ammonia fluxes in Rocky Mountain National Park to measurement time resolution and meteorological inputs
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Environmental and meteorological data from: A habitat suitability analysis for three Culicoides species implicated in bluetongue virus transmission in the Southeastern United States
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Data from: Meteorological versus spatial drivers of the spatial synchrony of forest insect pest outbreaks in North America
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Southern Sierra Critical Zone Observatory (SSCZO), Wolverton Creek meteorological data, soil moisture and temperature
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Decades of water column temperature, lake level and meteorological data of Lake Lacawac, a pristine glacial lake at Lacawac Biological Field Station in the Pocono mountains, Pennsylvania USA (1992-2019)
Lake Lacawac weather and the lake water column were monitored to observe seasonal and interannual temperature and lake level patterns in response to solar heating, wind-driven water column mixing, precipitation, evaporation, watershed runoff and seepage, and outflow (L. Lacawac has an outflow stream but no inflow stream, and is surrounded on one side by peat bogs). Several studies have shown the lake to have a slow exchange by seepage (slightly more seeping in than out except during dry months). Over the years the raft data have been used to calibrate heat flux, mixing, and evaporation models for the lake, to study zooplankton and phytoplankton distribution and dissolved organic matter flux (photobleaching in the water column, influx from the watershed, and exchange with bottom sediments) and associated UV transparency. The data have also been used to accompany experimental manipulations of lake organisms (algae, zooplankton, fish) in relation to exposure to UV radiation. More recently these lake data have been useful in developing lake indices of climate change.
Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Meteorology Data (4/30/2007 - 8/5/2009)
Humans are creating significant global environmental change, including shifts in climate, increased nitrogen (N) deposition, and the facilitation of species invasions. A multi-factorial field experiment is being performed in an arid grassland within the Sevilleta National Wildlife Refuge (NWR) to simulate increased nighttime temperature, higher N deposition, and heightened El Niño frequency (which increases winter precipitation by an average of 50%). The purpose of the experiment is to better understand the potential effects of environmental change on grassland community composition and the growth of introduced creosote seeds and seedlings. The focus is on the response of three dominant species, all of which are near their range margins and thus may be particularly susceptible to environmental change. It is hypothesized that warmer summer temperatures and increased evaporation will favor growth of black grama (Bouteloua eriopoda), a desert grass, but that increased winter precipitation and/or available nitrogen will favor the growth of blue grama (Bouteloua gracilis), a shortgrass prairie species. Treatment effects on limiting resources (soil moisture, nitrogen mineralization, precipitation), species growth (photosynthetic rates, creosote shoot elongation), species abundance, and net primary production (NPP) are all being measured to determine the interactive effects of key global change drivers on arid grassland plant community dynamics.
meteorological data
<p>variable names are self-explanatory, otherwise see http://crea.uclm.es/siar/datmeteo/consulta.php?ip=2&ie=5</p>
Data from: Meteorological conditions influence short-term survival and dispersal in a reinforced bird population.
A high immediate mortality rate of released animals is an important cause of translocation failure ("release cost"). Post-release dispersal (i.e. the movements from the release site to the first breeding site) has recently been identified as another source of local translocation failure. In spite of their potential effects on conservation program outcomes, little is known about the quantitative effects of these two sources of translocation failure and their interactions with environmental factors and management designs. Based on long-term monitoring data of captive-bred North African houbara bustards Chlamydotis undulata undulata (hereafter, houbara) over large spatial scales, we investigated the relative effects of release (e.g. release group size, period of release), individual (e.g. sex and body condition) and meteorological (e.g. temperature and rainfall) conditions on post-release survival (N = 957 individuals) and dispersal (N = 436 individuals). We found that (i) rainfall and ambient air temperature had, respectively, a negative and a positive effect on houbara post-release dispersal distance, (ii) in interaction with the release period, harsh meteorological conditions had negative impact on the survival of houbara, (iii) density-dependent processes influenced the pattern of departure from the release site and (iv) post-release dispersal distance was male-biased, as natal dispersal of wild birds (although the dispersal patterns and movements may be influenced by different processes in captive-bred and in wild birds). Synthesis and applications. Our results demonstrate that post-release dispersal and mortality costs in translocated species may be mediated by meteorological factors, which in turn can be buffered by the release method. As the consequences of translocation programs on population dynamics depend primarily upon release costs and colonisation process, we suggest that their potential interactions with meteorological conditions must be carefully addressed in future programs (i) through monitoring of short-term post-release mortality to understand its link with environmental conditions; (ii) by carefully choosing the season of release to minimize exposition of inexperienced individuals to harsh conditions and (iii) generalising the use of long-term weather forecast to adapt release effort and staggering releases over several years to buffer meteorological effects.
Data from: DIY meteorology: use of citizen science to monitor snow dynamics in a data-sparse city
Cities are under pressure to operate their services effectively and project costs of operations across various timeframes. In high-latitude and high-altitude urban centers, snow management is one of the larger unknowns and has both operational and budgetary limitations. Snowfall and snow depth observations within urban environments are important to plan snow clearing and prepare for the effects of spring runoff on cities' drainage systems. In-house research functions are expensive, but one way to overcome that expense and still produce effective data is through citizen science. In this paper, we examine the potential to use citizen science for snowfall data collection in urban environments. A group of volunteers measured daily snowfall and snow depth at an urban site in Saskatoon (Canada) during two winters. Reliability was assessed with a statistical consistency analysis and a comparison with other data sets collected around Saskatoon. We found that citizen-science-derived data were more reliable and relevant for many urban management stakeholders. Feedback from the participants demonstrated reflexivity about social learning and a renewed sense of community built around generating reliable and useful data. We conclude that citizen science holds great potential to improve data provision for effective and sustainable city planning and greater social learning benefits overall.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.