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907 results for “meteorology”
Raw meteorological dataset from the Southern Ocean collected on board the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>A Vaisala MAWS240 meteorological station was installed on the R/V Akademik Tryoshnikov during a circumnavigation of Antarctica in the austral summer season of 2016/2017. This dataset contains the raw text files of meteorology data collected in the Southern Ocean and Atlantic Ocean as part of the Antarctic Circumnavigation Expedition (ACE). Data coverage is from 17th November 2016 until 11th April 2016.</p> <p>Data files have undergone no processing or quality-checking and are as-recorded, directly from the instrumentation.</p> <p>Wind speed and direction parameters were recorded with a resolution of three seconds. Air temperature, relative humidity, dew point, solar radiation, ultraviolet radiation, cloud level and sky cover were recorded with a resolution of 30 seconds.</p> <p>Time of the measurement should be used with the TIMEDIFF to convert it to UTC. Latitude and longitude recorded are not corrected. Underway seawater measurements were recorded as null values.</p> <p><strong>Dataset contents</strong></p> <ul> <li>MAWS__SMSAWS__YYYYMMDD.txt, data file, ASCII tab-separated</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>ace_raw_meteorology_data_change_log.txt, metadata, text format</li> </ul> <p>Data files contain data for one day and are named by that date.</p> <p>Null values are recorded as ///, // or /</p> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - Added additional data files with coverage from 2016-11-17 - 2016-11-22 inclusive. Updated README.txt with information about data coverage. Added this change_log file.</p> <p><strong>v1.0</strong> - Initial release of raw meteorological data.</p> <p><strong>Dataset license</strong></p> <p>This raw meteorological dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Meteorology, environment and surface flux data for grassland sites in Germany
<p>Observation and model data for locations Fendt (DE-Fen), Rottenbuch (DE-RbW) and Graswang (DE-Gwg), in conjunction with selected journal publications. These data have primarily been used for investigation of surface carbon fluxes (Net Ecosystem Exchange, Gross Primary Productivity), seasonal climatic trends and land management. </p> <p>The sites are part of TERENO, a network of observatories in Germany. The TERENO Data Portal should provide other and more up-to-date information. The time period includes the ScaleX intensive observation campaigns that took place in 2015 and 2016. The data format is NetCDF4. A Jupyter notebook is available (see Related identifiers, GitLab) with technical notes and examples. </p>
Rye microgrid load and generation data, and meteorological forecasts.
<p>This dataset contains timeseries for Rye Microgrid, Trondheim, Norway. The timeseries include solar and wind power generation, consumption and historical weather forecasts.</p> <p>From <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a>:</p> <p><em>"The Rye microgrid is a pilot within the EU research project REMOTE. It is a small microgrid placed at Langørgen, in the outskirts of Trondheim, and is a small energy system designed to supply electricity to a modern farm and three households. The REMOTE projects goal for Rye Microgrid is to run the system in islanded mode.</em></p> <p><em>The system has two sources of generation – a wind turbine and a rack of PV panels. In addition, the system has two storages – a battery with high charge and discharge response, but with limited storage and losses, and a hydrogen energy system, with lower charge and discharge rates, higher losses and storage capacity. When you want to charge the hydrogen system, electricity is used to run an electrolyser that makes hydrogen from water and stores the resulting hydrogen in a tank. The process can be reversed by producing electricity from hydrogen using a fuel cell. (...)</em></p> <p><em>Morover, when local production or discharges from storages are not sufficient to cover the demand, the microgrid can draw electricity from the grid at some costs."</em></p> <p> </p> <p>For further details, see: <a href="https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf">https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf</a> and <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a></p> <p>rye_generation_and_load.csv is a comma-separated csv-file with the following columns (all values in <em>kW </em>and time as UTC):</p> <ul> <li>Consumption: Consumption of loads in system (residential and agriculture).</li> <li>Solar: Total production from all solar PV racks.</li> <li>Wind: Power production from wind turbine.</li> </ul> <p>met_data.h5: Contains historical weather forecasts data from The Norwegian Meteorological Institute (met.no) updated every 6 hours for the given location. The file is in hdf5 format. The forecasts include the following parameters: air_pressure_at_sea_level [Pa], air_temperature_2m [K], cloud_area_fraction [pu], integral_of_surface_downwelling_shortwave_flux_in_air_wrt_time [J/m<sup>2</sup>s], wind_direction_10m [deg], wind_speed_10m [m/s]</p> <p>The structure of the file is as follows:</p> <ul> <li>lat63_41_lon10_11 (coordinates) <ul> <li>[forecasted parameter] <ul> <li>forecast <ul> <li>2020-01-01T00Z (time forecast was issued) <ul> <li>axis0 (columns, index where each represent a point in a geographical grid. For example if axis=0,1,2,3, the tables contains the forecasts for the four closes points to the microgrid.)</li> <li>axis1 (rows, timestamps)</li> <li>block0_items (equal to axis0)</li> <li>block0_values (matrix, forecast values)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Meteorological and ground observations in the Qinghai-Tibet Engineering Corridor
<p>Observation of meteorological factors was conducted at two permanent meteorological stations (Golmud and Wudaoliang) and one field meteorological station (Xidatan) with daily meteorological records. All three meteorological stations contain ground observations.</p>
Deep learning to extract the meteorological by-catch of wildlife cameras: Supporting data, models and code
<p>This repository contains the data, models and code to train and deploy deep learning models related to the paper "Deep learning to extract the meteorological by-catch of wildlife cameras" published in the journal Global Change Biology (<a href="https://doi.org/10.1111/gcb.17078"><strong>https://doi.org/10.1111/gcb.17078</strong></a>).</p>
Copper Wire Corrosion and Meteorological Conditions in a Hangar
<p>Measurement of copper wire corrosion in a hangar (Kbely, Prague) with meteorological data (indoor and outdoor temperature, indoor and outdoor humidity, indoor and outdoor dew point, wind direction, wind speed, pressure above mean sea level). The dataset also includes measurements of pollution (SO2) from a meteorological station (Libuš, Prague) and NO2, PM10, and PM2.5 from Holešovice, Prague. The sampling period is one hour, and the measurements were collected for a duration of 390 days.</p>
Dataset for 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea'
<p>This dataset complements the paper 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea' accepted <span>for publication in Journal of Geophysical Research - Earth Surface</span>.</p> <p> </p> <p>This dataset contains Digital Terrain Maps (DTMs) of specific areas offshore from Dunkirk, on the northern coast of France, opening to the Southern Bight of the North Sea. These areas host marine dunes (sand waves) that have been numerically investigated in this research to identify the parameters influencing their migration. The openTELEMAC system (version v8p4) can be downloaded from <a href="https://opentelemac.org/">https://opentelemac.org/</a>. The model development, calibration and validation is described in Durand (2024).</p> <p> </p> <p>The site-specific data collected by France Energies Marines (2021) are currently proprietary. To protect these data, DTMs of bathymetric changes are provided, calculated as the difference in metres between the final and initial seabed levels. Negative values indicate lowering of the seabed (erosion) and positive values indicate rising (accretion).</p> <p>The initial and final periods are:</p> <ul> <li>S1: 17-Nov-2019</li> <li>S2: 17-Mar-2020</li> <li>S5: 5-Dec-2020</li> </ul> <p>The DTMs are provided for two areas (refer to paper for locations):</p> <ul> <li>Tile #1</li> <li>Tile #3</li> </ul> <p> </p> <p>Included in the dataset are observations, Case I model output (without wind and atmospheric pressure), and Case II model output (with wind and atmospheric pressure).</p>
Meteorological Data from Chios: May 2024 Baseline Measurements for the MUSICA Project
<h2><strong>May 2024 – Chios (Chiostown)</strong></h2> <h3>Introduction</h3> <p>The present meteorological data is collected from the weather station in Chiostown, located in Chios, and is published on the Zenodo 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 May 1st to May 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 for May 2024 captures the transition from spring to early summer, offering insights into the warming trend and dry conditions typical for the region during this period.</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 May 2024</h3> <ul> <li><strong>Highest temperature</strong>: 28.8°C, recorded on May 19th, 2024, at 18:20.</li> <li><strong>Lowest temperature</strong>: 12.3°C, recorded on May 15th, 2024, at 05:00.</li> <li><strong>Total rainfall</strong>: 1.2 mm, with the highest daily rainfall of 1.19 mm recorded on May 11th, 2024.</li> <li><strong>Highest wind speed</strong>: 56.3 km/h, recorded on May 27th, 2024, at 10:40.</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>
CAMELS-ES: Catchment Attributes and Meteorology for Large-Sample Studies – Spain
<p>CAMELS-ES is a hydrometeorological dataset covering 269 catchments in Spain and the time period from 1991 to 2020. It is a contribution to the Caravan initiative, a global community that collects open hydrometeorological data to support global hydrological modelling. As other datasets in Caravan, CAMELS-ES includes both catchment attributes extracted from HydroATLAS and ERA5-Land, meteorological time series from ERA5-Land and discharge records from the Spanish Ministry of the Environment. In addition, CAMELS-ES includes information from the European Flood Awareness System (EFASv5): catchment attributes extracted from the input static maps used in the hydrological model LISFLOOD, and the simulated discharge from EFASv5 long run.</p>
Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic
<ul> <li>Supporting datasets for paper "Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic". </li> <li>Those are a subset of the (analyzed) datasets from WRF control simulation "ERA5" in netcdf format. See manuscript for more details. <ul> <li>cld_size.nc: cloud object size</li> <li>cld_ort_2020-03-01_15_00_00.nc: cloud object at 15:00 UTC</li> <li>hydro-02-2020-03-01_15/00/00.nc: water path sample data at 15:00 UTC</li> <li>wrfout_d02_2020-03-01_15/00/00: wrf output sample data at 15:00 UTC</li> </ul> </li> </ul>
Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)
<p>This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows:</p> <p>1. Basic information of literatures (Table S1.xlsx)</p> <p>2. Model performance metrics (Table S2.xlsx)</p> <p>3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx)</p> <p>4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx)</p> <p>5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx)</p> <p>.</p>
CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2020 (IGG-CNR-CZO@NIVOLET)
<p>CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2020 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance.</p> <p>During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020).</p> <p>Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.</p>
CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2021 (IGG-CNR-CZO@NIVOLET)
<p>CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2021 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance.</p> <p>During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020).</p> <p>Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.</p>
Data files for: Meteorological factors in the production of Gigantic Jets by tropical thunderstorms in Colombia
<p>Data includes:</p> <ul> <li>Gigantic jet locations and times</li> <li>Vertical profiles for GJ and null cases</li> <li>CSV files with meteorological variables per GJ event and null case</li> </ul>
A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA
<p>This dataset contains meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains during water years 2019-2021. Data collection had an emphasis on paired open-forest sites and included three forested elevations. In total, we present 270 snow pit observations, 4,019 snow depth measurements, and three years of meteorological forcing from two weather stations (one in a meadow, the other in an adjacent forest). The dataset is described in a forthcoming publication of the same name: <em>A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA</em> (Bonner et al., 2022).</p> <p>All snow observation and meteorological forcing data are available as both .nc and .mat files.<br> Additionally, original digitized copies of snow pit observations are provided as .gsheet/.xlxs files.</p> <p>This dataset will continue to be updated, via this repository, as additional years of data are collected.</p>
Air pollution, atmospheric and local meteorological data for Graz, Austria from 2014 to end of 2021
<p>The data covers a timeframe from January 2014 to November 2021 in a daily frequency, and covers two sources:</p> <ul> <li>The environmental and pollutant data was provided by the Austrian government under the following license: CC-BY-4.0: Land Steiermark - <a href="http://data.steiermark.gv.at">data.steiermark.gv.at</a> <ul> <li>Air quality (<em>Lovric_et_al_air_pollutants.csv</em>) by means of NO<sub>2</sub>, NO, NO<sub>x</sub>, PM<sub>10</sub> and O<sub>3</sub> was measured at five sites in Graz, Austria (Süd (<em>eng. South</em>) - S, Nord (<em>eng. North</em>) - N, West (<em>eng. West</em>) - W, Don Bosco – D, Ost (<em>eng. East</em>) – O). In addition weather conditions like temperature, percipitation, relative humidity, pressure, wind speed and direction are added (<em>Lovric_et_al_local_meteorology.csv</em>)</li> </ul> </li> <li>The ERA5-Land data (<em>Lovric_et_al_era5_recalculated.csv</em>) is subject to the Copernicus licence from following source <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcds.climate.copernicus.eu%2Fcdsapp%23!%2Fdataset%2F10.24381%2Fcds.e2161bac%3Ftab%3Doverview&data=05%7C01%7Cmlovric%40know-center.at%7C2ba06457329349623a5608da631632c9%7C0d3c92e977ae4f49bd126ff29e8f1c37%7C0%7C0%7C637931244242754711%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=lt5NcIfbIRGse01Naha8bolxEkdtLmyp2VNcrz38Rk8%3D&reserved=0">https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview</a> <ul> <li>it includes following variables : <ul> <li>Cloud_Cover_Mean</li> <li>Temperature_Air_2m_Max_Day_Time</li> <li>Temperature_Air_2m_Min_Night_Time</li> <li>Wind_Speed_10m_Mean</li> </ul> </li> </ul> </li> </ul>
ERA-NUTS: meteorological time-series based on C3S ERA5 for European regions (1980-2021)
<p><strong># ERA-NUTS (1980-2021)</strong></p> <p>This dataset contains a set of time-series of meteorological variables based on <a href="https://climate.copernicus.eu/climate-reanalysis">Copernicus Climate Change Service (C3S) ERA5 reanalysis</a>. The data files can be downloaded from here while notebooks and other files can be found on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>.</p> <p>This data has been generated with the aim of providing hourly time-series of the <strong>meteorological variables</strong> commonly used for power system modelling and, more in general, studies on energy systems.</p> <p>An example of the analysis that can be performed with ERA-NUTS is shown <a href="https://youtu.be/zVeF8Dv6jlE">in this video</a>.</p> <p><strong>Important</strong>: <em>this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us <a href="mailto:matteo.de-felice@ec.europa.eu">sending an email</a> or opening an Issue in the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>.</em></p> <p><strong>## Data</strong><br> The time-series have hourly/daily/monthly frequency and are aggregated following the <a href="https://ec.europa.eu/eurostat/web/nuts/background">NUTS 2016 classification</a>. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries).</p> <p>This dataset contains NUTS0/1/2 time-series for the following variables obtained from the <strong>ERA5 reanalysis data</strong> (in brackets the name of the variable on the Copernicus Data Store and its unit measure):</p> <p> - <strong>t2m</strong>: 2-meter temperature (`2m_temperature`, Celsius degrees)<br> - <strong>ssrd</strong>: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter)<br> - <strong>ssrdc</strong>: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter)<br> - <strong>ro</strong>: Runoff (`runoff`, millimeters)<br> - <strong>sd</strong>: Snow depth (`sd`, meters)<br> <br> There are also a set of derived variables:<br> - <strong>ws10</strong>: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second)<br> - <strong>ws100</strong>: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second)<br> - <strong>CS</strong>: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky)<br> - <strong>RH</strong>: Relative Humidity (computed following Lawrence, BAMS 2005 and Alduchov & Eskridge, 1996)<br> - <strong>HDD</strong>/<strong>CDD</strong>: Heating/Cooling Degree days (derived by 2-meter temperature the <a href="https://ec.europa.eu/eurostat/cache/metadata/en/nrg_chdd_esms.htm">EUROSTAT definition</a>.</p> <p>For each variable we have <strong>367 440 hourly samples</strong> (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for <strong>34/115/309 regions</strong> (NUTS 0/1/2).<br> <br> The data is provided in two formats:</p> <p> - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as `int16` type using a `scale_factor` to minimise the size of the files.<br> - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly)<br> <br> All the CSV files are stored in a zipped file for each variable.</p> <p><strong>## Methodology</strong></p> <p>The time-series have been generated using the following workflow:</p> <p> 1. The NetCDF files are downloaded from the Copernicus Data Store from the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form">ERA5 hourly data on single levels from 1979 to present</a> dataset<br> 2. The data is read in R with the <a href="http://www.meteo.unican.es/climate4R">climate4r</a> packages and aggregated using the function `/get_ts_from_shp` from <a href="https://github.com/matteodefelice/panas">panas</a>. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders.<br> 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R<br> 4. The NetCDF are created using `xarray` in Python 3.8.</p> <p><strong>## Example notebooks</strong></p> <p>In the folder `notebooks` on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in `xarray` and how to visualise them in several ways by using matplotlib or the <a href="https://github.com/kavvkon/enlopy">enlopy</a> package.</p> <p>There are currently two notebooks:</p> <p> - <strong>exploring-ERA-NUTS</strong>: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.<br> - <strong>ERA-NUTS-explore-with-widget</strong>: explorer interactively the datasets with [<a href="https://jupyter.org/">jupyter</a>]() and <a href="https://ipywidgets.readthedocs.io/en/stable/">ipywidgets</a>.</p> <p>The notebook `exploring-ERA-NUTS` is also available rendered as HTML.<br> <br> <strong>## Additional files</strong></p> <p>In the folder `additional files`on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region.</p> <p><strong>## License</strong></p> <p>This dataset is released under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0 license</a>.</p> <p><strong>## Changelog</strong></p> <p><strong>2022-04-08 </strong>Added Relative Humidity (RH)<br> <strong>2022-03-07 </strong>Added the missing month in CDD/HDD <br> <strong>2022-02-08 </strong>Updated the wind speed and temperature data due to missing months. </p> <p> </p>
Particle number concentration and meteorological measurements at Berlin-Tegel Airport during its closure
<p>Observation data of particle number concentrations (PNC) on the airfield of Berlin-Tegel Airport (TXL) during its closure. PNC was recorded with a Grimm EDM465 UFPC, including meteorological parameters measured with a Lufft WS600-UMB.</p> <ul> <li>observations between 20. October 2020, 14:44 LT and 3 December 2020, 03:03 LT</li> <li>time interval: 5 seconds</li> <li>air inlet of CPC at 1.4 m above ground</li> <li>weather sensor at 1.3 m above the ground.</li> <li>Location: 52,561 North, 13,320 East</li> <li>Variables: <ul> <li>particle number concentration in particles/cm³ (pnc)</li> <li>wind speed in m/s (ws)</li> <li>wind direction in ° (wdir)</li> <li>air temperature in °C (temp)</li> <li>relative humidity in % (rh)</li> <li>air pressure in hPa (pressure)</li> <li>precipitation in mm (pcpn)</li> <li>date as local time</li> <li>phase: whether the airport was still open ("TXL_open") or already closed ("TXL_closed")</li> <li>no data available between between 15.11.2020 02:15:00 and 18.11.2020 11:14:55 due to hardware issues</li> </ul> </li> </ul>
Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements
<p>The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled “Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.” The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017).</p> <p> </p> <p>Software versions used:</p> <ul> <li>Image Processing Workbench v2.2.0 (Marks et al., 2017)</li> <li>Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019)</li> </ul> <p> </p> <p><strong>NOTE:</strong> Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file.</p> <p> </p> <p><strong>topo.nc</strong> – Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are:</p> <ul> <li>dem – digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017)</li> <li>mask – basin mask for the Boise River Basin</li> <li>veg_height – vegetation height in meters from the National Land Cover Database (Homer et al., 2015)</li> <li>veg_type – vegetation type from the National Land Cover Database</li> <li>veg_tau – vegetation fractional transmissivity derived from the vegetation type</li> <li>veg_k – vegetation emissivity derived from the vegetation type</li> </ul> <p> </p> <p><strong>maxus.nc</strong> – maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002)</p> <p> </p> <p><strong>Station data:</strong></p> <ul> <li>Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF.</li> <li>metadata.csv – metadata for 40 stations</li> <li>air_temp.csv – 38 stations</li> <li>cloud_factor.csv – 7 stations</li> <li>precip.csv – 21 stations</li> <li>vapor_pressure.csv – 19 stations</li> <li>wind_direction.csv – 14 stations</li> <li>wind_speed.csv – 14 stations</li> </ul> <p> </p> <p><strong>smrf_config.ini</strong> – Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.</p>
MERIDA - MEteorological Reanalysis Italian DAtaset
<p>The new <strong>ME</strong>teorological <strong>R</strong>eanalysis <strong>I</strong>talian <strong>DA</strong>taset (<strong>MERIDA</strong>) has been developed to cope with the increasing weather extremes of the last 20 years, which caused several disruptions to the Italian electric system. This work has been developed following the indications emerged from the “Resilience Working Table” set up by the Italian Regulatory Authority for Energy, Networks and the Environment (ARERA). MERIDA is able to respond to the energy stakeholders, who need reliable meteorological data to implement effective adaptation strategies to operate the electric system safely.</p> <p>MERIDA consists of a dynamical downscaling of the ERA5 global reanalysis using the mesoscale model WRF-ARW. ERA5 data are retrieved with a 3-hourly temporal resolution to assure good temporal consistency. Temperature data from the SYNOP Air Force stations are also retrieved to be ingested in the WRF simulations at 3-hourly temporal resolution.</p> <p>The computational domain of MERIDA consists of 2 grids with horizontal resolution of 21 km and 7 km respectively, with the internal grid centered over Italy.</p> <p>The meteorological fields of MERIDA are open access and distributed in NETCDF file format on a regular lat-lon grid of 0.07° resolution.</p> <p>A subset of the dataset is available here for download for the period 2000-2018. The full dataset covering the period 1990-2019, and continuoulsy updated, is available at the following website: <a href="http://merida.rse-web.it">http://merida.rse-web.it/</a> </p> <p>The following subset of meteorological fields is available here for download:</p> <ul> <li>T2 - 2m temperature (K)</li> <li>PREC - Total Precipitation (mm/h)</li> <li>U10 - 10m u-component of wind (m/s)</li> <li>V10 - 10m v-component of wind (m/s)</li> <li>PSFC - Surface pressure (Pa)</li> <li>Q2 - 2m Specific Humidity (Kg/Kg)</li> <li>SWDIR - Direct global short-wave radiation (W/m<sup>2</sup>)</li> <li>SWDIF - Diffuse global short-wave radiation (W/m<sup>2</sup>)</li> <li>MSLP - Mean Sea Level Pressure (Pa)</li> <li>SNEQV - Snow Water Equivalent (mm)</li> <li>SOIL_T - Soil Temperature - Layer 5 cm (K)</li> <li>SOIL_M - Soil Moisture - Layer 5 cm (m<sup>3</sup>/m<sup>3</sup>)</li> </ul> <p>The following variables are available under request:</p> <ul> <li>SOIL_T - Soil Temperature (K, Layers: 5,25,70,150 cm)</li> <li>SOIL_M - Soil Moisture - Layer 5 cm (m<sup>3</sup>/m<sup>3</sup>, Layers: 5,25,70,150 cm)</li> <li>TT - Temperature (K, Pressure levels: 850,700,500 hPa)</li> <li>RH - Relative Humidity (%, Pressure levels: 850,700,500 hPa)</li> <li>GHT - Geopotential Height (gpm, Pressure levels: 850,700,500 hPa)</li> <li>UU - u-component of wind (m/s, Pressure levels: 850,700,500 hPa)</li> <li>VV - v-component of wind (m/s, Pressure levels: 850,700,500 hPa)</li> <li>TG - Ground Temperature (K)</li> <li>HFX - Sensible Heat Flux (W/m<sup>2</sup>)</li> <li>LH - Latent Heat Flux (W/m<sup>2</sup>)</li> <li>GRDFLX - Ground Flux (W/m<sup>2</sup>)</li> <li>TR - Transpiration Flux (W/m<sup>2</sup>)</li> </ul> <p>All the variables not included for download may be downloaded at : <a href="http://merida.rse-web.it">http://merida.rse-web.it/</a></p> <p>or requested at:</p> <ul> <li>riccardo.bonanno@rse-web.it</li> <li>matteo.lacavalla@rse-web.it</li> <li>simone.sperati@rse-web.it</li> </ul> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
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DANDI Archive for NWB datasets
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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.