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12 results for “meteorological variables”
Raw and processed hydro-meteorological variables of Jucar river basin for feature selection
<p>The dataset Processed data – input WQEISS.csv was employed for the input variable selection step in Zaniolo et al., 2018. It includes monthly values of 28 hydro-meteorological variables and indexes of Jucar river basin, Spain, for the period 1986-2000, namely:</p> <ul> <li>2 temporal features: day and month of the year;</li> <li>12 inputs to the Jucar State Index: average monthly storage and groundwater levels, average three months river runoff, and cumulated areal precipitation over 12 months;</li> <li>8 additional observed variables in the basin: three months average outflows from, and inflows to, the main reservoirs, and mean monthly areal temperatures;</li> <li>6 traditional drought indicators: Standardized Precipitation Index (SPI) and Standardized Precipitation and Evaporation Index (SPEI). SPI and SPEI indicators are computed on mean monthly data over the entire basin for 3, 6, and 12 months time aggregations.</li> </ul> <p>The last column of the dataset reports the target variable, i.e., the monthly nominal shortage of water conveyed to the irrigation districts simulated via AQUATOOL model. For further details on the dataset please consult Zaniolo et al., 2018, or the dedicated website <a href="http://www.nrm.deib.polimi.it/?page_id=2438">http://www.nrm.deib.polimi.it/?page_id=2438</a></p> <p>The unprocessed data used to compute indices and temporal cumulations in Processed data – input WQEISS.csv are reported in table Raw Data.csv. Public observations of rainfall, streamflows and storage levels come from the SAIH (Hydrological Automatic Information System) of the CHJ (Jucar Hydrological Confederation). Users can directly download data for the last 12 months on the dedicated webpage <a href="http://saih.chj.es/chj/saih/?f">http://saih.chj.es/chj/saih/?f</a> while previous data records are provided for free by CHJ upon request. Observations from piezometers are downloadable from the Piezometric Network Information section section of the CHJ <a href="https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx">https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx</a>.</p>
Meteorological variables for Agriculture: a Dataset for the Italian Area (MADIA)
<p> </p> <p>The dataset is the supplementary material for the following journal paper:</p> <p>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="http://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>)</p> <ol> </ol> <p> </p> <p><strong>Abstract</strong></p> <p>The <strong>MADIA gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, across the Italian domain for the period <strong>1981-2022</strong>, and their respective 1981-2010 and 1991-2020 <strong>climate normals</strong>,<strong> </strong>as well as the following statistics on the 30-year dekadal values of each variable: absolute minimum and maximum, 5<sup>th</sup>, 10<sup>th</sup>, 50<sup>th</sup>, 90<sup>th</sup>, 95<sup>th</sup> percentiles. Temporal and spatial resolutions are <strong>10-daily</strong> and <strong>0.25 degrees</strong> respectively. 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. The dataset is provided in both <strong>NetCDF </strong>and <strong>csv </strong>format. In addition, discovery and description metadata are provided. In order to facilitate the data reuse for computing statistics at Italian <strong>NUTS 2 and 3</strong> levels, a complementary vector file is provided which reports the cell weight in terms of fraction covered of each administrative unit considered. Another vector file is included with the <strong>ERA5 cell polygons</strong> covering the Italian country for visualizing and mapping csv data. </p> <p>A <strong>daily version of the MADIA dataset</strong> (only in csv format) is also available on Zenodo at <a href="http://doi.org/10.5281/zenodo.7621453">https://doi.org/10.5281/zenodo.7621453</a>.</p> <p>Both MADIA datasets will be periodically updated.</p> <p><strong>Attached content</strong></p> <p>A ZIP archive composed by the following folders</p> <ol> <li>nc_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in NetCDF format</li> <li>csv_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in csv format</li> <li>metadata: discovery and description metadata </li> <li>shp_data: two complementary vector layers with the NUTS2-3 cover fractions and 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>
Associations between Dictyocaulus viviparus bulk tank milk seropositivity and meteorological variables
<p>This dataset belongs to the scientific article with the same title "Associations between <em>Dictyocaulus viviparus</em> bulk tank milk seropositivity and meteorological variables" to be published in International Journal for Parasitology.</p>
Country averages of Copernicus ERA5 hourly meteorological variables
<p><strong>Note: a new <a href="https://zenodo.org/record/2650191#.XMCUEBMzY3E">time-series dataset from ERA5</a> has been published — this one won't be updated/maintained anymore</strong></p> <p>Country averages of meteorological variables generated using the R routines available in the package <a href="https://github.com/matteodefelice/panas">panas</a> based on the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">Copernicus Climate Change ERA5 reanalyses</a>. The time-series are at hourly resolution and the included variables are:</p> <ul> <li>2-meter temperature (t2m),</li> <li>snow depth (snow_depth),</li> <li>mean sea-level pressure (mslp),</li> <li>runoff,</li> <li>surface solar radiation (ssrd),</li> <li>surface solar radiation with clear-sky (ssrdc),</li> <li>temperature at 850hPa (t850),</li> <li>total precipitation (total_prec),</li> <li>zonal (west-east direction) wind speed at 10m (u10) and 100m (u100),</li> <li>meridional (north-sud) wind speed at 10m (v10) and 100m (v100),</li> <li>dew point temperature (dew)</li> </ul> <p>The original gridded data has been averaged considered the national borders of the following countries (European 2-letter country codes are used, i.e. ISO 3166 alpha-2 codes with the exception of GB->UK and GR->EL): AL, AT, BA, BE, BG, BY, CH, CY, CZ, DE, DK, DZ, EE, EL, ES, FI, FR, HR, HU, IE, IS, IT, LT, LU, LV, MD, ME, MK, NL, NO, PL, PT, RO, RS, SE, SI, SK, UA, UK.</p> <p>The unit measures here used are listed in the official page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/era5-hourly-data-on-single-levels-from-2000-to-2017?tab=overview</p> <p>The script used to generate the files is available on github <a href="https://github.com/matteodefelice/era5-country-averages">here</a></p> <p> </p> <p> </p>
Spatial interpolation of air pollutant and meteorological variables in Central Amazonia
<p>This dataset presents data of aerosol, trace-gases and meteorological variables from the Amazon Rainforest region, resulting from an interpolation process. The original data were collected from the GOAmazon 2014/15 project, from the Atmospheric Radiation Measurement (ARM) repository. </p>
High-resolution large weighing lysimeter measurements with meteorological and soil-hydrological variables from a Mediterranean Savanna
<p>Raw and processed lysimeter weighing and flux data at the instrumental site ES-LMa of six large high-precision weighing lysimeters in a Mediterranean Savanna ecosystem for the period from 2019-06-01 to 2020-05-31. Additionally, meteorological and radiometric data are provided. Additionally, code for the lysimeter processing is provided.</p> <p>Reproducible workflow of the article <strong>Paulus et al. 2022: Resolving seasonal and diel dynamics of non-rainfall water inputs in a Mediterranean ecosystem using lysimeters. HESS, https://doi.org/10.5194/hess-2021-519</strong></p> <p>Variables, units and detailed description are found in the README.html file.</p>
Observations of snowpack distribution and meteorological variables at the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017
<p>We present a climatic dataset acquired at Izas Experimental Catchment, in the Central Spanish Pyrenees, from 2011 to 2017 snow seasons. The dataset includes information on different meteorological variables acquired with an Automatic Weather Station including precipitation, air temperature, incoming and reflected short and long-wave radiation, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface) and soil temperature; all of them at 10 minute intervals. Snow depth distribution was measured during 23 field campaigns using a Terrestrial Laser Scanner (TLS), and there are also available time-lapse photographs from which can be derived daily information of different variables such as the Snow Covered Area. The experimental site is located in the southern side of the Pyrenees between 2000 and 2300 m above sea level with an extension of 55 ha. The site is a good example of sub-alpine ambient of mid-latitude mountain ranges. Thus, the dataset has a great potential for understanding environmental processes from a hydrometerological or ecological perspective in which snow dynamics play a determinant role.</p>
Effect of meteorological variability on fine particulate matter simulations over the contiguous United States
<p>Supporting data files for the paper titled "Effect of meteorological variability on fine particulate matter simulations over the contiguous United States" submitted to JGR-Atmosphere for publication.</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>
Airborne measurements of aerosols, trace gases and meteorological variables on board a Cessna 172 aircraft (2015 and 2017): dataset
<p>The dataset contains airborne measurements of aerosols, trace gases and meteorological variables on board an instrumented Cessna 172 aircraft (see the included readme files). The measurements were done in May–June 2015, August 2015 and April–May 2017. The flights took off from Tampere-Pirkkala airport (IATA: TMP, ICAO: EFTP) and the measurement profiles profiles were flown over Hyytiälä (61.85N, 24.28E) in southern Finland.</p>
CP_OdU (Climate Projections for Odesa, Ukraine): Climate indices and daily meteorological variables for Odesa (Ukraine) in 2021-2050 by different RCM simulations from Euro-CORDEX
<ol> <li>ODS-UA_RCM_outputs_day_20210101-20501231.zip file contains outputs from Euro-CORDEX RCM’s simulation for a land-located point closest to the Odesa meteorological site (46.44N, 30.77E).</li> <li>The RCM grids define the coordinates for this point (the gridpoint is mostly located in the city center (Kateryninska^Troitska) or near the 7-km market.</li> <li>The nomenclature of files and variables in these files are defined in http://is-enes-data.github.io/cordex_archive_specifications.pdf.</li> <li>Other files contain the so-called climate indices as described in <a href="https://knmi-ecad-assets-prd.s3.amazonaws.com/documents/atbd.pdf">https://knmi-ecad-assets-prd.s3.amazonaws.com/documents/atbd.pdf</a> and table in 0readme.pdf.</li> </ol>
Processed Data, Code, & Supplementary Material for de la Torre Cerro et al. (2024) Modelling asynchrony in phenology using a dynamic representation of meteorological variables
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