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9 results for “Meteorological time series”
Time series of high-frequency meteorological data at Carvins Cove Reservoir, Virginia, USA 2021-2025
This dataset consists of variables measured by a research-grade Campbell Scientific meteorological station deployed on the dam of Carvins Cove Reservoir. Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.36944, -79.95778), is owned and operated by the Western Virginia Water Authority as a primary water source. The meteorological variables include photosynthetically active radiation, barometric pressure, ambient air temperature, relative humidity, rainfall, wind speed and direction, shortwave radiation, infrared radiation, and albedo. All variables were measured every minute from 2021-03-29 19:00:00 to the end of the dataset at 2025-12-31 23:59:00, except for periods of maintenance. We applied extensive quality assurance/quality control protocols to the observations, as described in the methods. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
Time series of high-frequency meteorological data at Falling Creek Reservoir, Virginia, USA 2015-2025
This dataset consists of meteorological variables measured by a research-grade Campbell Scientific meteorological station deployed on the dam of Falling Creek Reservoir (37.3025, -79.83667). Falling Creek Reservoir (Vinton, Virginia, USA), is owned and operated by the Western Virginia Water Authority as a primary water source. The meteorological variables include photosynthetic active radiation, barometric pressure, ambient air temperature, relative humidity, rainfall, wind speed and direction, shortwave radiation, infrared radiation, and albedo. All variables were measured every 5 minutes from 2015-07-07 15:45:00 to 2015-07-13 11:28:00 (YYYY-MM-DD hh:mm:ss) and every minute thereafter to the end of the dataset at 2025-12-31 23:59:00. We applied substantial Quality Assurance/Quality Control (QA/QC) protocols to the raw observations, as described in the methods. The dataset is accompanied by a sensor maintenance log and QA/QC analysis scripts.
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>
Association between meteorological factors and the number of tuberculosis notifications: a time-series study in Hong Kong
<p> Using a 22-year consecutive surveillance data in Hong Kong, including monthly averages of meteorological factors, air pollution concentrations , total number of TB cases notified, to analyze the association of monthly average temperature and relative humidity with temporal dynamics of monthly total number of TB cases notified. </p>
Catchment attributes and hydro-meteorological time series for large-sample studies across hydrologic Switzerland (CAMELS-CH)
<p>CAMELS-CH (Catchment Attributes and MEteorology for large-sample Studies - Switzerland) is a large-sample hydro-meteorological data set for hydrological Switzerland in Central Europe that covers 331 basins within Switzerland and neighboring countries (Austria, France, Germany and Italy). CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes.</p> <p>The data set covers 40 years of data between 1st January 1981 and 31st December 2020 for each catchment: daily time series of stream flow and water levels, of meteorological data such as precipitation and air temperature and of daily snow water equivalent data. Additionally, CAMELS-CH encompasses annual time series of land cover change and glacier evolution per catchment. The static catchment attributes comprise the following categories: location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers.</p> <p>The corresponding manuscript is published at the journal "Earth System Science Data" (ESSD) and available <a href="https://essd.copernicus.org/articles/15/5755/2023/">here</a>. The code used to generate the dataset is available on <a href="https://github.com/camels-ch">Github</a>.</p> <p>The data description file below contains a comprehensive list of all time series and attribute variables covered by the dataset and references to the original data sources. Further, this repository contains the "Caravan extension CH" for the "Caravan - A global community dataset for large-sample hydrology" <a href="../records/7944025">Caravan dataset</a> (see the <a href="https://github.com/kratzert/Caravan/discussions/10">list of extensions</a>). This extension has the same format like other Caravan parts and is based on the same data sources. Note that some features like the annual glacier time series, etc. are therefore only available in the original CAMELS-CH dataset.</p> <p> </p> <h2>Updates:</h2> <p>- Update version 0.9: affects "Caravan_extension_CH" - In version 1.5 of the Caravan dataset, Penman-Monteith PET was added as an additional time series feature. Additional to the new time series feature, also all pet-related climate indices were recomputed using the new Penman-Monteith PET. For consistency, the old ERA5-Land potential_evaporation time series and climate indices were kept, but renamed for a better identification of the differences. </p> <p>- Update version 0.8: resolving projection issue for shapefiles in "Caravan_extension_CH" using EPSG:4326 (WGS84); updating readme file of "camels_ch" regarding the <a href="../communities/dischma/">Dischma</a> catchment</p> <p>- Update version 0.7: update corresponding to the revision of the manuscript at "Earth System Science Data" (ESSD)</p> <ul> <li>dataset file delimiters have been changed to commas from semicolons</li> <li>the "time_series" folder was renamed to "timeseries"</li> <li>in the simulation-based data, there was an error in the previous aggregation of precipitation and evapotranspiration. The corresponding time series, affected hydrologic signatures and climatic indices were corrected</li> <li>the order of simulation-based variables in the timeseries files was changed to resemble the order shown in the tables of the corresponding publication in ESSD</li> <li>blank values that were masked by "NA" are now consistently indicated by "NaN"</li> <li>the readme file has been extended</li> </ul> <p>- Update version 0.6: updating links to related material (all links and references are available in the preprint/manuscript) and abstract</p> <p>- Update version 0.5: adding the "camels_ch_data_description.pdf" file</p> <p>- Update version 0.4: update of several static attributes in "Caravan_extension_CH" following a general update in Caravan and all its extensions + adopting the geographic coordinate system to Caravan-standard EPSG:4326</p> <p>- Update version 0.3: renaming single files/entries in "Caravan_extension_CH" to start with "camelsch" as unique Caravan extension identifier</p> <p>- Update version 0.2: CH extension to <a href="../records/7944025">Caravan</a> added</p>
Meteorological time series recorded at the station implemented in OAL-Austria
<p>Time series of meteorological variables recorded at the station implemented in OAL-Austria with the following senors:</p> <p>Variable | Unit | Sensors | Measurement<br> AT | degC | Judd, HMP45 | 60 min Avg<br> RH | % | HMP45 | 60 min Avg<br> SR | W/m2 | Pyranometer SP-Lite | 60 min Avg<br> WS | m/s | Wind wheel A100R | 60 min Avg + Max<br> JD | cm | Judd Ultraschall | 60 min burst<br> VSWD | cm | Yard stick+DCIM | daily<br> SLT | degC | TDR-315L in 15, 30 and 50cm depth | 60 min burst - Avg<br> SLVW | % | TDR-315L in 15, 30 and 50cm depth | 60 min burst - Avg<br> P | mm/m2 | ARG100 (during summer season) | 60 min Total</p> <p> </p> <p>Location: 11.596024 E, 47.267723 N, elevation: 1090m a.s.l.</p>
HydroPenIndia: A catalogue of streamflow metrics, meteorological time series and catchment attributes of Peninsular India
<p><em>HydroPenIndia, </em>a catalogue of streamflow metrics, hydro-meteorological time series and landscape attributes of 204 catchments of Peninsular India is introduced. This catalogue consists of daily hydro-meteorological time series (rainfall, soil moisture, potential evapotranspiration, actual evapotranspiration, maximum temperature, minimum temperature, longwave radiation, shortwave radiation, wind speed and humidity) for a period of 36 years from 1980-2015. The time series of 26 streamflow metrics, 13 topographic metrics, 12 climate indices, 15 hydrologic signatures, 8 land cover descriptors, 6 geologic characteristics, 6 soil characteristics (see table 8) and 13 human intervention indices are also included in this dataset. <em>HydroPenIndia</em> is an initiative to encourage hydrologists to advance knowledge of hydrological processes by contributing to fundamental research questions on Indian catchments. Free availability of the dataset will provide access to global users to represent India in large-sample hydrology studies. <em>HydroPenIndia</em> is derived from multiple databases to help researchers start their research without wasting time on collecting and processing datasets. This catalogue will motivate researchers to solve pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unravelling regional scale hydrologic functioning and climate change impact assessment over Peninsular India.</p>
Meteorological variables for Agriculture: daily time series for the Italian Area (MADIA daily)
<p> </p> <p><strong>Abstract</strong></p> <p>The <strong>MADIA daily gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, with a spatial resolution of 0.25 degrees, across the Italian domain for the period <strong>1981-2022</strong>. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. Data is provided at daily temporal resolution and in <strong>csv </strong>format (every cell is identified by the latitude/longitude coordinates of its centre). The dataset is annotated with discovery and description metadata. A vector file is included with the <strong>ERA5 cell polygons </strong>covering the Italian country for visualizing and mapping csv data. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file which reports the cell weight in terms of fraction covered of each administrative unit considered, as well as its altitude, is provided in:</p> <ul> <li>Parisse Barbara, Alilla Roberta, Pepe Antonio Gerardo, & De Natale Flora. (2022). <em>Meteorological variables for Agriculture: a dataset for the Italian Area (MADIA)</em> [Data set]. Zenodo. <a href="http://10.5281/zenodo.6868944">https://doi.org/10.5281/zenodo.6868944</a> </li> </ul> <p>Further details on methods applied for data processing are available in:</p> <ul> <li>Parisse B., Alilla R., Pepe A.G., De Natale F., <em>MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area</em>, Data in Brief, 46 (2023), 108843, <a href="https://doi.org/10.1016/j.dib.2022.108843">10.1016/j.dib.2022.108843</a>, (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922010460">https://www.sciencedirect.com/science/article/pii/S2352340922010460</a>)</li> </ul> <p>The MADIA daily dataset will be periodically updated.</p> <p><strong>Attached content</strong></p> <p>A ZIP archive composed by the following folders:</p> <ol> <li>csv_data: daily time series for each year from 1981 to 2022 in csv format</li> <li>metadata: discovery and description metadata</li> <li>shp_data: a complementary vector layer with the ERA5 cell polygons for Italy</li> </ol> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)</p>
meteorological time series from OAL-UK
<p>Raw meteorological time series from OAL-UK. Temporal resolution 1 sec. Variables information can be found https://cumuluswiki.org/a/Standard_log_files </p>
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