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1,121 results for “weathering”
FIGURE 1 in Towards sustainable treatments to preserve fossils from weathering, as part of the garden redevelopment project at the Natural History Museum
FIGURE 1. Ammonites from the Portland Stone Formation Tisbury Member can be seen on the facing-left of this image, and fossil tree sections from the Purbeck Group Lulworth Formation on the facing-right.
FIGURE 3. Trackway block samples after 3 in Towards sustainable treatments to preserve fossils from weathering, as part of the garden redevelopment project at the Natural History Museum
FIGURE 3. Trackway block samples after 3 treatments with CaLoSiL® E25 (no TiO2) at x10000 magnification, top left: ornithopod trackway before treatment; bottom left: ornithopod trackway after treatment; top right: theropod trackway before treatment; bottom right: theropod trackway after treatment. EHT 6.00kV, Signal A SE2, WD 8.1 mm, Magnification 10000 x.
Figure 2 in Structural changes in needle epicuticular waxes of Balkan Abies species in relation to natural weathering
Figure 2. SEM micrographs of A. x borisii-regis epicuticular waxes. A-F. Adaxial surface of needles with noticeable granules; G-I. Abaxial surface of needles with two stomatal bends; J-L. Abaxial stomatal rows; M-O. Stomatal pores occluded either by wax tubules or by wax crusts; P-R. Agglomeration, thickening and fusion of tubules with needle aging. Bars: A-I = 500 μm; J-L = 50 μm; D-O = 10 μm; P-R = 1 μm.
Figure 3 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 3. The dependence of the speed wind at a height Figure 4. In-situ MS data breakdown scheme for a
Figure 5 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 5. The dependence of the correlation coefficient between in-situ wind speed at the WS and remote sensing data on the orientation angle of the main quadrants (a) and their position relative to the White Sea coastline (b).
Sensitivity of fire weather indices, fuel sticks and satellite observations to fuel moisture content in Central European forests - Data
<p>This data repository contains datasets for destructively measured fuels of different types (FMC_insitu), meteorological data including 10-hour fuel stick measurements (FWS_30min) and calculated fire weather index components (FWS_FWI_24h) for four different sites in the Tharandt forest and Saxon Switzerland National Park in the Free State of Saxony (Germany) during the years 2022 (only DE-Tha) and 2023 (all four sites).</p> <p>The provided folders contain .csv files for each study site. Meteorological data in 30min for DE-Tha can be derived from the ICOS data portal (https://data.icos-cp.eu/portal/). For the remaining three sites (DE-BLB, DE-BWB, DE-SHW), past and recent data can be viewed via EMS Brno (e.g., http://www.emsbrno.cz/p.axd/en/Beech__Landberg.TU__DRESDEN.html). Upon request, the authors can share the data. </p> <p><strong>FMC_insitu</strong>: Destructively sampled fuel moisture content of different fuel types.</p> <p><strong>FWS_30min</strong>: Original measurements from the fire weather stations in 30 min time steps</p> <p><strong>FWS_FWI_24h</strong>: Measurements from fire weather stations in 24h time steps and the calculated fire weather index and its components. As requested for calculation of the FWI, meteorological variables are used at 13:00 (UTC), while PREC and PBC are the 24h sum prior to 13:00. </p> <p><strong>readme.txt</strong>: Description of repository content and the variables provided within the .csv files.</p> <p><strong>stations.csv</strong>: Contains the coordinates and a short description of the study sites. </p>
Data on Crop Yield, Nutrient Content of Barley (Hordeum vulgare L.) and Weather of a Vertical Agrivoltaic System in Sweden
<p>The dataset location is latitude 59.55° N and longitude 16.76° E in Kärrbo Prästgård, Sweden. </p> <p>Crop data:</p> <p>Raw data of barley related to yield kernels and straws (kg DM/ha), nitrogen content in kernels (%), crude protein in kernels (%), kernels yield (kg DM/ha), straws yield (kg DM/ha), starch content in kernels (%), and thousand kernel weight (%) from the harvest on September 12<sup>th</sup>, 2023, at the agrivoltaics research site in Kärrbo Prästgård, Sweden. Fifty squared samples (each 0.25 m<sup>2</sup>) distributed in 5 groups (A, B, C, D, E) were collected according to the layout presented in the corresponding publication. Groups A, B and C are based on the spatial location in the crop area between the three vertical rows of PV modules: west side (A), center side (B) and east side (C). Group R corresponds to the reference control plot conditions. Group D represent the crops that are growing in the space between the rows of the conventional ground-mounted PV system with 30° tilt. </p> <p> </p> <p>Weather data:</p> <p>1-hour timeseries averaged data measurements at local time, raw data, not quality controlled from the barley growing season at Kärrbo Prästgård, Sweden from May 7<sup>th</sup> to September 12<sup>th</sup>,2023.</p> <p>Temperature of air (°C), relative humidity (%), relative air pressure (hPa), wind speed (m/s), and precipitation (mm/h) are measured with a Lufft WS600-UMB Smart Weather Sensor located on-site on a 5 m height mast.</p> <p>Global and diffuse horizontal irradiance (W/m<sup>2</sup>) are measured with a Delta-T SPN1 Sunshine Pyranometer.</p> <p>Photosynthetically active radiation (µmol/m<sup>2</sup>/s) is measured with an Apogee PAR Quantum sensor SQ-500.</p>
UK Climate Projections (UKCP18): Weather Patterns
<p>Provided here are the data files for the weather patterns generated for the <a href="https://www.metoffice.gov.uk/research/approach/collaboration/ukcp">2018 UK Climate Projections</a> (UKCP or UKCP18) Global ensemble (sometimes referred to as the land-gcm). The weather patterns are the same weather patterns as used in <a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/met.1563">Neal et al. (2016)</a> and used the same code to generate the weather patterns for the ensemble members. A historical equivalent of this dataset based on ERA5 can be found in <a href="https://doi.pangaea.de/10.1594/PANGAEA.942896">Neal, (2022)</a>.</p> <p>The UKCP Global model dataset features two ensembles:<br>The PPE-15 : A 15 member perturbed parameter ensemble of the HadGEM3.05 model<br>The CMIP5-13: A sub-selection of 13 members from the CMIP5 ensemble.</p> <p>The PPE-15 data is available using the RCP 2.6 and RCP 8.5 scenarios and the CMIP5-13 is available only using the RCP 8.5 scenario.</p> <p>When using these weather patterns please cite: <a href="https://doi.org/10.1007/s00382-021-06031-0">Pope et al., 2022</a>, Investigation of future climate change over the British Isles using weather patterns. Climate Dynamics. </p>
CM-RWL1-O15S16 Daily refugee arrivals and weather data, Italy/Central Med., Oct.2015 - Sept.2016
<p>========================================================</p> <p> <strong>Dataset: CM-RWL1-O15S16</strong></p> <p><strong> Daily refugee arrivals and weather data<br> Italy/Central Med., Oct.2015 - Sept.2016</strong><br> <br> Release Notes</p> <p> Copyright (c) 2018 by Harris V. Georgiou</p> <p>========================================================<br> Release: Apr 14, 2018</p> <p> - Version: 1.1a<br> - Format: .xlsx/.csv/.txt<br> ========================================================</p> <p><br> This file contains important information about the<br> current version of the dataset package.</p> <p>Downloading and using this material hints that you<br> accept the EULA/Terms-of-Use (please read carefully).</p> <p>We welcome your comments and suggestions.</p> <p>_______________________________________________<br> WHAT'S IN THIS PACKAGE?</p> <p>- Overview<br> - Available file formats<br> - Files and Datasets<br> - License Agreement</p> <p>_______________________________________________<br> OVERVIEW</p> <p>Since early January 2015, Europe has witnessed an unprecedented influx of refugees<br> from regions of war and conflict in the Middle East, primarily Syria, Afghanistan<br> and Iraq. The rapid allocation of proper resources is the most critical factor in<br> the success or failure of any rescue and relief operations, especially in the "hot"<br> zones. In order to do so, proper tools of predictive analytics mus be available,<br> specifically for forecasting the intensity and, if possible, the location of the<br> next refugee influx waves, so that the rescue elements and the logistical support<br> is properly prepared beforehand.</p> <p>This package contains a set of data regarding daily refugee arrivals at the general<br> area of the Central Mediterranean Sea, more specifically towards Italy, for the most<br> intense period of influx waves, from the beginning of October 2015 until the end of<br> September 2016 (one full year).</p> <p>The sources of the data are:<br> 1) For daily arrivals (Italy):<br> UNHCR Refugees/Migrants Emergency Response (Data mashups)<br> http://data2.unhcr.org/en/situations/mediterranean/location/5205<br> 2) For weather:<br> Weather Underground Database (mashup of NOAA, aviation, local)<br> https://www.wunderground.com/about/data</p> <p>The datasets from (1) have already been used in various publications describing<br> such predictive analytics models. Detailed description and related conclusions<br> can be found at:</p> <p>* Harris V. Georgiou, "Identification of refugee influx patterns in Greece via<br> model-theoretic analysis of daily arrivals" (9-May-2016),<br> https://arxiv.org/abs/1605.02784</p> <p>_______________________________________________<br> AVAILABLE FILE FORMATS</p> <p>The datasets are available in the following formats (included):</p> <p>*.xlsx : MS-Excel/LibreOffice native spreadsheets<br> *.csv : comma-separated plaintext spreadsheets<br> *.txt : raw plaintext files with full column headers</p> <p>These data formats are equivalent, i.e., they contain the exact same<br> sets of data. Normally, at least one of them should be compatible<br> with any major programming platform (e.g. Matlab, Octave, R) or any<br> native programming language for arbitrary handling (e.g. C, Java).</p> <p>_______________________________________________<br> FILES AND DATASETS</p> <p>Root folder: CM-RWL1-O15S16\</p> <p>Dataset 1: Arrivals\(csv,xlsx)<br> "Italy-DailyArrivals-Oct2015Sept2016.*"<br> : Complete data series for refugee influx arrivals for Italy</p> <p>Dataset 2: Weather\(xlsx,txt)<br> Habib Bourguiba, Tunisia<br> Sfax El-Maou, Tunisia<br> Lampedusa, Italy<br> Luqa, Malta<br> Tripoli Mitiga, Libya<br> : Weather data (temp,wind,gust,w.dir,...) at local airports</p> <p>The Mitiga airport station at Tripoli, Libya, is the most critical regarding<br> the construction of analytics and predictive modeling of the daily influx series<br> towards Italy. However, due to adverse conditions and lack of maintenance,<br> there are several blocks of consecutive days with missing weather data. Thus,<br> the other four reliable weather stations in the area should be used to build<br> regression models for filling-in these gaps.</p> <p>The satellite map(*) in the \Suppl folder shows the situation of Search & Rescue<br> (SAR) operations, density of shipwrecks by the end of Sept. 2015, as well as<br> the location of these weather stations and how these relate spatially to the<br> target area of Tripoli, which is still the departing spot with the highest<br> density of boats.</p> <p>(*) SAR map source: https://blamingtherescuers.org/report/</p> <p><br> _______________________________________________<br> LICENSE AGREEMENT</p> <p>This program was produced primarily for academic research and educational purposes.<br> Downloading and using this material implies acceptance of the Creative Commons<br> License: Attribution-NonCommercial-ShareAlike 4.0 International (BY-NC-SA), 2016.<br> * http://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>Copyright (c) 2018 by Harris V. Georgiou (MSc,PhD) -- http://xgeorgio.info</p> <p><br> --</p> <p> </p>
Supplemental dataset for "Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model"
<p>This dataset contains the source data used for the experiments of the paper titled "Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model".</p>
National Weather Service Coded Surface Bulletins, 2003- (JSON format)
<p>This dataset contains the Coded Surface Bulletin dataset reformatted as JSON files. The Coded Surface Bulletin dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.</p> <p>Each JSON file contains one top-level object corresponding to one bulletin. The top-level object is composed of name/value pairs with the names bulletinType, createDate, validDate, Highs, Lows, ColdFronts, WarmFronts, OccludedFronts, StationaryFronts, and Troughs. The name/value pairs for bulletinType, createDate, and validDate are always present. The other name/value pairs are only present if there is corresponding data. The value for bulletinType is either "LR" or "HR", for low-resolution or high-resolution, respectively. The values for createDate and validDate are UTC timestamp strings. If the bulletinType value is "LR", the longitudes and latitudes have 1° precision. If the bulletinType value is "HR", the longitudes and latitudes have 0.1° precision.</p> <p>The value associated with the name High in the top-level object is itself an object composed of three name value pairs that describe the geographic locations and surface air pressure levels for one or more high pressure centers. The names of the object elements are lats, lons, and pressures. The values for these are all arrays. For a given object, the arrays will all have the same size. The arrays contain latitudes in degrees, longitudes in degrees, and pressures in millibars. If the arrays contain N elements apiece, the object is describing N pressure centers. The object associated with the name Low in the top-level object is structured in the same way. It describes the geographic locations and surface air pressure levels for one or more low pressure centers.</p> <p>The ColdFronts, WarmFronts, StationaryFronts, OccludedFronts, and Troughs names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front or trough of the given type. Each object is composed of three name/value pairs with the names lats, lons, and strength. The value for the name strength is a string that is one of "weak", "moderate", "strong", or "unstated". The values associated with the names lats and lons are arrays. This pair of arrays represent the vertices of a polyline describing the location of a frontal boundary or trough.</p> <p>The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the <a href="https://mesonet.agron.iastate.edu/">Iowa Environmental Mesonet</a>.</p>
National Weather Service Coded Surface Bulletins, 2003- (netCDF format)
<p>This dataset contains the Coded Surface Bulletin (CSB) dataset reformatted as <a href="https://www.unidata.ucar.edu/software/netcdf/docs/">netCDF-4</a> files. The CSB dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.</p> <p>Each netCDF file contains one year of CSB fronts data represented as spatial map data grids. The times and geospatial locations for the data grid cells are also included. The front data is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded. The front polylines from the CSB dataset are rasterized into the appropriate data grids. Each file conforms to the <a href="http://cfconventions.org/">Climate and Forecast Metadata Conventions</a>.</p> <p>There are two large groupings of the CSB netCDF files. One group uses a data grid based on the <a href="https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr">North American Regional Reanalysis</a> (NARR) <a href="https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID221">grid</a>, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.</p> <p>The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.</p> <p>There are a number of files within each group. The files all follow the naming convention codsus_[masked]_<grid>_<subproduct>.nc, where [masked] indicates that the presence of the word <em>masked</em> is optional and <grid> is either <em>merra2-1deg</em> or <em>narr-96km</em>. The <subproduct> element is either the word <em>mask</em> or the sequence <n>wide_<year>, where <n> is the front width and <year> is the year for the data stored in the file.</p> <p>The codsus_<grid>_mask.nc file is a file containing a single data grid that delineates the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year. The WPC meteorologists don't attempt to provide equal levels of attention to every grid cell displayed on their workstations. The files of the form codsus_masked_<grid>_<n>wide_<year>.nc have all had the mask described above applied to exclude parts of fronts that extend past the envelope. The files of the form codsus_<grid>_<n>wide_<year>.nc have no masking applied.</p> <p>The <n>wide portion of the file names takes two forms—<em>1wide</em> and <em>3wide</em>. The fronts in the<em>1wide</em> files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the <em>3wide</em> files were rasterized by drawing the front polylines with a width of 3 grid cells.</p> <p>Within each grid group, there are five subsets of files:</p> <ul> <li>codsus_masked_<grid>_1wide_<year>.nc</li> <li>codsus_masked_<grid>_3wide_<year>.nc</li> <li>codsus_<grid>_1wide_<year>.nc</li> <li>codsus_<grid>_3wide_<year>.nc</li> <li>codsus_<grid>_mask.nc</li> </ul> <p>The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the <a href="https://mesonet.agron.iastate.edu/">Iowa Environmental Mesonet</a>.</p>
DL-FRONT MERRA-2 vectorized weather fronts over North America, 1980-2018 (netCDF format)
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of <a href="http://www.unidata.ucar.edu/software/netcdf/docs/">netCDF-4</a> files. Each file contains one year of hourly geospatial data grids describing the locations of four types of weather fronts—cold front, warm front, stationary front, and occluded front, over the time span 1980-2018.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>Each weather front probability map was then processed to obtain polyline skeletons of the weather front boundary regions found by DL-FRONT. These vector representations of the fronts were then written to JSON files—one file for each hour. These front polylines were then rasterized into geospatial data grids and stored by year into netCDF-4 files that conform to the <a href="http://cfconventions.org/">Climate and Forecast Metadata Conventions</a>. The front data in each file is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded.</p> <p>There are two large groupings of the netCDF files. One group uses a data grid based on the <a href="https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr">North American Regional Reanalysis</a> (NARR) <a href="https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID221">grid</a>, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.</p> <p>The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.</p> <p>There are a number of files within each group. The files all follow the naming convention merra2_[masked]_<grid>_<n>wide_<year>.nc, where [masked] indicates that the presence of the word <em>masked</em> is optional and <grid> is either <em>merra2-1deg</em> or <em>narr-96km</em>. The the sequence <n>wide indicates the width with which the fronts were drawn, and <year> is the year for the data stored in the file.</p> <p>The files marked as masked had a mask applied to the data grids that corresponded to the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year, as determined using the <a href="https://dx.doi.org/10.5281/zenodo.2651361">Coded Surface Bulletin </a>dataset.</p> <p>The <n>wide portion of the file names takes two forms—<em>1wide</em> and <em>3wide</em>. The fronts in the <em>1wide</em> files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the <em>3wide</em> files were rasterized by drawing the front polylines with a width of 3 grid cells.</p> <p>Within each grid group, there are four subsets of files:</p> <ul> <li>merra2_masked_<grid>_1wide_<year>.nc</li> <li>merra2_masked_<grid>_3wide_<year>.nc</li> <li>merra2_<grid>_1wide_<year>.nc</li> <li>merra2_<grid>_3wide_<year>.nc</li> </ul>
ScintPi: A low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives
<p>These data sets contain ionospheric scintillation (GPS L1) observations (S4 indices) collected by a ScintPi prototype during 2018 at a low magnetic latitude station (Presidente Prudente). ScintPi is a low-cost, easy-to-build GPS ionospheric scintillation monitor for DASI studies of space weather, education, and citizen science initiatives.</p> <p>The file named "ScintPi_PPR_S4_2018.mat" contains S4 values (s4mat) for 2018 as a function of universal time (utmat), day-of-year (doymat), GPS satellite identifier number (prnmat), GPS satellite elevation (elmat) and azimuth (azmat) angles.</p> <p>The file named "ScintPi_PPR_20180211.mat" contains example raw (10 Hz) measurements made by ScintPi on February 11, 2018. The file contains values of receiver's altitude, latitude and longitude (variables alt,lat, and lon), GPS satellite azimuth and elevation (variables el and az), GPS identifier number (prn), signal-to-noise ratio (snr), and day-of-year (doy).</p>
Tutorial Weather Data Cutouts for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>The provided lightweight <strong>cutouts </strong>are spatiotemporal subsets of the German weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset for March 2013 to be used for the <a href="https://pypsa-eur.readthedocs.io/en/latest/tutorial.html">PyPSA-Eur tutorial</a>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Weather Data from Hull Springs Farm: 2019-09-29 to 2019-10-15
<p># General Metadata for Hull Springs Farm Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> HSF_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS)<br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
Weather Data from Env. Education Center at Lancer Park 2018-10-30 to 2019-02-14
<p># General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> LP_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## Metadata File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS)<br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
Weather Data from Env. Education Center at Lancer Park 2019-07-15 to 2019-08-28
<p># General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> LP_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## Metadata File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS)<br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
Weather Data from Env. Education Center at Lancer Park 2019-08-28 to 2019-10-10
<p># General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> LP_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## Metadata File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS)<br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
Weather Data from Env. Education Center at Lancer Park 2018-08-27 t0 2018-09-14
<p># General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> LP_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## Metadata File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS)<br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
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