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SBC LTER: Meteorological and sea surface data from the R/V Pt. Sur Underway Data Acquisition System (UDAS) in the Santa Barbara Channel: LTER16, 2006-04-26 to 2006-05-03
The data described here were collected on LTER16 which took place from 2006-04-26 to 2006-05-03 on the RV Pt. Sur. Cruises have been conducted in the Santa Barbara Channel, California, USA, 3-4 times per year since March, 2001 and are approximately 7 days in length. There are 2 basic types of measurements: Underway measurements: fluorometry, salinity and temperature are collected at a depth of 3m with a flow-through system which also includes continuous atmospheric climate measurements. Underway acoustic doppler current profiles were collected using a RDInstruments Workhorse 300kHz and RDInstruments Ocean Surveyor 75kHz ADCP.
CO2 NEE and ER + air and soil meteorological and climate parameters in Alpine grasslands, Gran Paradiso National Park, 2017-2019
<p>The dataset “fluxes_meteoclimate_nivolet_V0” is a .csv file reporting CO<sub>2</sub> 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 2700 m.a.s.l.) using the flux chamber method, during the 2017, 2018 and 2019 vegetative seasons (July-September), approximately twice a month. NEE is measured with a transparent flux chamber, while ER with a shaded chamber. Data represent the average values and the corresponding standard deviations obtained from four sites at different altitudes and geological substrate of the soil. Each average value is obtained as a mean over a set of more than 20 point-measures for each site and each sampling date. Flux data are complemented by measurements of soil temperature and volumetric water content, air temperature and moisture, and solar radiance. The four sites are characterized by soils developed over carbonates (carb) (45.500212N-7.152213E), glacial deposits (glac) (45.490167N-7.139916E), gneiss rocks (gnei) (45.490256N-7.149253E) and alluvial deposits (allu) (45.492656 N-7.146092 E).</p> <p>Other relevant shortcuts used in the .csv table: Std = Standard deviation; VWC% = Volumetric Water Content %. Meteorological and climate variables recorded during the measurement of NEE and during the measurement of ER bring the suffix NEE and ER respectively (es. Pressure_NEE (hPa) = atmospheric pressure recorded during the measurement of Net Ecosystem Exchange).</p>
MeteoSerbia1km: the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period
<p>MeteoSerbia1km is the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea level pressure, and total precipitation. Besides daily summaries, it contains monthly and annual summaries, daily, monthly, and annual long term means (LTM). Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology based on Random Forest and using nearest observations and distances to them as spatial covariates, together with environmental covariates.</p> <p>Complete script in R and datasets used for modelling, tuning, validation, and prediction of daily meteorological variables are available <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km">here</a>.</p> <p>If you discover a bug, artifact or inconsistency in the MeteoSerbia1km maps, or if you have a question please use <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km/issues">this channel</a>.</p> <p>File naming convention of .zip files and containing MeteoSerbia1km files:</p> <ul> <li>Daily summaries per year: day_<em>yyyy</em>_<em>proj</em>.zip <ul> <li><em>var</em>_day_<em>yyyymmdd</em>_<em>proj</em>.tif</li> </ul> </li> <li>Monthly summaries: mon_<em>proj</em>.zip <ul> <li><em>var</em>_mon_<em>yyyymm</em>_<em>proj</em>.tif</li> </ul> </li> <li>Annual summaries: ann_<em>proj</em>.zip <ul> <li><em>var</em>_ann_<em>yyyy</em>_<em>proj</em>.tif</li> </ul> </li> <li>Daily, monthly and annual LTM: ltm_<em>proj</em>.zip <ul> <li>daily LTM: <em>var</em>_ltm_day_mmdd_<em>proj</em>.tif</li> <li>monthly LTM: <em>var</em>_ltm_mon_mm_<em>proj</em>.tif</li> <li>annual LTM: <em>var</em>_ltm_ann_<em>proj</em>.tif</li> </ul> </li> </ul> <p>where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection - wgs84 or utm34</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> </p>
Summary raw meteorological data 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 meteorological data that have been extracted from the original raw text data files. Data coverage is from 17th November 2016 until 11th April 2017, with gaps where the ship was in port.</p> <p>Air temperature, relative humidity, dew point, solar radiation, ultraviolet radiation, cloud level and sky cover were recorded with a resolution of 30 seconds. Averaged wind parameter data are provided.</p> <p>Date_time should be combined with TIMEDIFF to convert it to UTC. Latitude and longitude recorded are not corrected. Underway seawater measurements were recorded as null values.</p> <p>Data from this dataset have been corrected and quality-checked in another published dataset. We recommend these data for further use (Landwehr et al., 2019; DOI 10.5281/zenodo.3379590).</p> <p><strong>Dataset contents</strong></p> <ul> <li>metdata_all_YYYYMMDD_YYYYMMDD.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>ace_meteorology_raw_summary_change_log.txt</li> </ul> <p>Data files contain data for each leg of the Antarctic Circumnavigation Expedition (ACE). Dates included in the file name are the start and end dates of the legs and therefore the data within the files as well.</p> <p><strong>Change log</strong></p> <p><strong>v1.2</strong> - Added missing data from 2017-02-05 - 2017-02-08 inclusive. Updated this change log file.</p> <p><strong>v1.1</strong> - Added additional data coverage from 2016-11-17 - 2016-11-22 inclusive, into the first data file. Updated README.txt with information about data coverage. Added this change log file.</p> <p><strong>v1.0</strong> - Initial release of raw summary 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>
Simulated severe convective wind events and environments from the Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)
<p>Contacts for further details:</p> <ul> <li>This data record and associated research: Andrew Brown (andrewb1@student.unimelb.edu.au)</li> <li>BARPA data: Chun Hsu Su (chunhsu.su@bom.gov.au), Christian Stassen (Christian.Stassen@bom.gov.au), Harvey Ye (harvey.ye@bom.gov.au)</li> </ul> <h1>Introduction</h1> <p>This record contains data in support of Brown et al. (2024), including post-processed regional climate model data, automatic weather station observations, and post-processed reanalysis data over southeastern Australia for various time periods <strong>over December-Febrary months only</strong> (see descriptions below). This data relates to analysis of severe convective wind gusts in historical and future climate, with analysis scripts in <a href="https://github.com/andrewbrown31/BARPA/tree/main/wind_gust_analysis">this repository</a>. The data are described here according to the directory structure of this record (noting the files and directories have been compressed into <code>barpa_data.tgz</code>), as well as the relevant data sources. </p> <h1>Data sources</h1> <ul> <li><strong>The Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)</strong>. A regional climate model containing a regional (BARPA-R) and convection-permitting (BARPAC-M) configuration, with large-scale forcing from ERA-Interim (1990-2015) and ACCESS1-0 using a historical (1985-2005) and RCP8.5 (2039-2059) forcing. See Brown et al. (2024) and Su et al. (2021) for more details. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology. Note also that the BARPA data used here was produced as part of the <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/">Electricity Sector Climate Information project</a> (with licence and disclaimers <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/risk-assessment/#Disclaimer">here</a>)<em>,</em> with more current BARPA versions (not used here) available at <a href="https://dx.doi.org/10.25914/z1x6-dq28" target="_blank" rel="noopener">https://dx.doi.org/10.25914/z1x6-dq28</a>. </li> <li><strong>Measured wind gusts from automatic weather stations (AWS)</strong>. Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 272 AWS locations over 2005-2015. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</li> <li><strong>The ERA5 reanalysis </strong>from the European Center for Medium Range Weather Forecasting (Hersbach 2020).</li> <li><strong>The ERA-Interim reanalysis</strong> from the European Center for Medium Range Weather Forecasting (Dee 2011).</li> </ul> <h1>/10min_points</h1> <p>This directory contains .csv files, with wind gust and related environmental data at 10-minute intervals at point locations, corresponding to automatic weather station locations. Data is available over 2005-2015. Files are named in the form <code>barpac_m_aws_<state>.csv</code> and <code>barpac_m_aws_<state>_barpa_r_interp.csv</code>. Here, <state> represents different administrative regions in southeast Australia, including New South Wales (nsw), Victoria (vic), South Australia (sa) and Tasmania (tas). See Figure 1 in Brown et al. (2024) for a map of station locations, that is also included in the /meta directory. The <code>barpa_r_interp</code> suffix indicates that the BARPAC-M wind gusts have been interpolated to the BARPA-R grid for comparison.</p> <p>This data is used in Brown et al. (2024) for evaluation and analysis of BARPA wind gusts in the historical climate (forced by ERA-Interim). The user is directed to that paper for more information on data processing. For the .csv files here, column descriptions are provided in Table 1, below.</p> <h1>/daily_points</h1> <p>This directory contains .csv files, with data associated with daily maximum wind gusts at point locations. This data is derived from the 10min_points data described above, with the same column descriptions in Table 1, below. The different files in this directory are as follows:</p> <ul> <li> <p><code>barpac_m_aws_dmax_obs.csv</code><br>Daily maximum observed wind gust from AWS measurements, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km.csv</code><br>Daily maximum simulated wind gust from BARPAC-M at closest grid point to AWS location, with associated wind gust ratio, lighting flash count, and environmental conditions from BARPA-R.</p> </li> <li> <p><code>barpac_m_aws_dmax_12km.csv</code><br>Daily maximum simulated wind gust from BARPA-R at closest grid point to AWS location, with associated wind gust ratio, lightning flash count, and environmental conditions.</p> </li> <li> <p><code>barpac_m_aws_dmax_erai.csv</code><br>Daily maximum simulated wind gust from ERA-Interim at closest grid point to AWS location, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km_barpa_r_interp.csv</code><br>As in <code>barpac_m_aws_dmax_2p2km.csv</code>, but wind gusts are interpolated to the BARPA-R grid prior to calculating the daily maximum.</p> </li> </ul> <h1>/monthly_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with post-processed BARPA data, relating to simulated severe convective wind gusts (from BARPAC-M), and their associated large-scale environments (from BARPA-R). This includes BARPAC-M and BARPA-R data that has been forced by the ACCESS1-0 global climate model, that is intended for analysis of future changes in severe convective wind events and environments. For further information, the user can refer to the internal file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <experiment> is either <code>hist</code> for historical climate forcing (1985-2005) or <code>rcp</code> for RCP8.5 climate forcing (2039-2059)):</p> <ul> <li> <p><code>barpac_scws_<experiment>_monthly.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpar_<experiment>_monthly.nc</code><br>Monthly counts of favouable severe convective wind environments from BARPA-R (using <code>bdsd</code>, see Table 1), for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_scws_bdsd_<experiment>.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, that occur under favourable environmental conditions from BARPA-R. For each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_max_<experiment>_monthly.nc</code><br>Monthly maximum simulated severe convective wind gust, from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> </ul> <h1>/daily_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with daily maximum wind gusts from BARPAC-M, as well as the wind gust ratio (see <code>wgr_4</code> in Table 1) and the type of convective environment (from BARPA-R, see <code>cluster</code> in Table 1). This includes BARPA data that has been forced by ERA-Interim and by ACCESS1-0. For further information, the user can refer to the file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <code><experiment></code> is either <code>historical</code> for historical climate forcing or <code>rcp85</code> for RCP8.5 climate forcing, <code><forcing_model></code> is either <code>erai</code> for ERA-Interim or <code>ACCESS1-0</code>, <<code>date1></code> is the file start date and <code><date2></code> is the file end date):</p> <ul> <li><code>barpa_scw_<forcing_model>_<experiment>_0_<date1>_<date2>.nc</code></li> </ul> <h1>/meta</h1> <p>Lists of AWS stations, for each administrative state, with a file containing column descriptions. Note that not all of the stations listed in these files are used for analysis. Fig1.jpeg is from Brown et al. (2024), showing the BARPAC-M domain (also defines the netcdf file spatial extents), and the location of AWS.</p> <h3>Table 1</h3> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Name</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td>stn_id</td> <td>Automatic weather station (AWS) identifier</td> <td> </td> </tr> <tr> <td>time</td> <td>Wind gust time (UTC)</td> <td> </td> </tr> <tr> <td>gust</td> <td>Observed wind gust speed from AWS (m/s)</td> <td>Observed wind gusts are measured at a height of 10 m, and represent a 3-second average. Data is provided as a one-minute maximum, and is resampled to a 10-minute maximum here for comparison with BARPA</td> </tr> <tr> <td>wgr_4</td> <td>Wind gust ratio</td> <td>The observed wind gust ratio, defined as the ratio between <code>gust</code>, and the 4-hour mean from the 10-minute data here.</td> </tr> <tr> <td>time_6hr</td> <td>6-hourly time (UTC)</td> <td>The most recent 6-hourly time step prior to <code>time</code>, associated with environmental diagnostics.</td> </tr> <tr> <td>mu_cape</td> <td>Most unstable convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>s06</td> <td>Bulk vertical wind shear from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape</td> <td>Downdraft convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd</td> <td>Brown and Dowdy (2021) Statistical Diagnostic (BDSD) for identifying favourable severe convective wind environments</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01</td> <td>Mass-weighted mean mixing ratio from the surface to 1 km (g/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06</td> <td>Mass-weighted mean wind speed from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13</td> <td>Temperature lapse rate from 1 km above ground level to 3 km above ground level (◦C/km)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster</td> <td>Environment type</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from BARPA-R</p> </td> </tr> <tr> <td>s06_era5</td> <td>See s06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01_era5</td> <td>See qmean01</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06_era5</td> <td>See Umea06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13_era5</td> <td>See lr13</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd_era5</td> <td>See bdsd</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape_era5</td> <td>See dcape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>mu_cape_era5</td> <td>See mu_cape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster_era5</td> <td>See cluster</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from ERA5</p> </td> </tr> <tr> <td>wg10_12km_point</td> <td>Simulated wind gust from BARPA-R (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_12km_point</td> <td>Wind gust ratio from BARPA-R </td> <td>See wgr_4</td> </tr> <tr> <td>wg10_2p2km_point</td> <td>Simulated wind gust from BARPC-M (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_2p2km_point</td> <td>Wind gust ratio from BARPAC-M (see wgr_4)</td> <td>See wgr_4</td> </tr> <tr> <td>n_lightning_fl</td> <td>Number of daily lightning flashes from BARPAC-M</td> <td>See Brown et al. (2024) for more information.</td> </tr> <tr> <td>erai_wg10</td> <td>Simulated wind gust from ERA-Interim (m/s).</td> <td>Intended to represent a 10 meter wind gust. Note that ERA-Interim is provided in 3-hourly intervals, rather than 10-minute intervals for BARPA.</td> </tr> </tbody> </table> <p> </p>
Observed and WRF-simulated near-surface meteorological parameters on selected James Ross Island glaciers during heatwaves in summer 2022/23
<p>The files contain time series of near-surface meteorological conditions observed on Triangular Glacier and Davies Dome on James Ross Island, Antarctica and simulated time series for these glaciers based on the Weather Research and Forecasting (WRF) model output. Observations of 2-m air temperature, 2-m wind speed, net radiation and glacier surface height are available from 01 November 2022 to 16 January 2023 (net radiation is available only on Triangular Glacier). Simulated values of 2-m air temperature, 2-m wind speed, net radiation, sensible and latent heat fluxes are available from 08 November 2022 to 16 January 2023.</p>
CAMELS-LUX: Highly Resolved Hydro-Meteorological and Atmospheric Data for Physiographically Characterized Catchments around Luxembourg
<p>The CAMELS-LUX dataset encompasses hydro-meteorological time series and catchment attributes for 56 partly nested stream gauges feeding into the Luxembourgish stream network. The data is available at three temporal resolutions: daily, hourly and at a 15-minute resolution and spans the hydrological years from 2004-11-01 to 2021-10-31. The static catchment attributes cover parameters classifying the topography, geology and land use as well as climatic and hydrologic annual statistics of the 17-year time period.</p> <p>While an in depth description of the dataset as well as background information on catchments, the environment and exact calculation methods is provided in the accompanying publication in ESSD, the dataset description below isolates information on the available parameters and data structure contained in the provided files.</p> <p>Please note that the dataset might not include data corrections or validations that are subject to a date later than the date of the retrieval of the data for the processing of this dataset. This dates back to 2022 for most hydrologic time series, and to 2023 for the reanalysis data or the precipitation data. We are aware of duplicate rows in the time series file with a resolution of 15 minutes for catchment 16 as well as time stamp shifts in the precipitation data. We are working on correcting these data to update this dataset.</p> <p><strong>Data structure</strong></p> <p><strong>Time series data</strong></p> <ol> <li>Hydrologic parameters</li> <li>Precipitation parameters</li> <li>Air temperature and potential evapotranspiration parameters</li> <li>Thunderstorm relevant atmospheric parameters</li> <li> Soil Moisture parameters</li> </ol> <p><strong>Static catchment attributes</strong></p> <ol> <li>Basin IDs</li> <li>Meta catchment attributes</li> <li>Climatic catchment attributes</li> <li>Geologic catchment attributes</li> <li>Land use catchment attributes</li> <li>Topographic catchment attributes</li> </ol> <p><strong>Spatial data - shapefiles</strong></p>
Navigation and meteorological data collected during the Tara Pacific Expedition 2016-2019
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from navigation and meteorological instruments acquiring continuously during the full course of the campaign.</p> <p> </p> <p>Variables/ descriptions and units:</p> <table> <tbody> <tr> <td>variable</td> <td>description</td> <td>units</td> </tr> <tr> <td>'dt'</td> <td>date-time stamp</td> <td>iso UTC</td> </tr> <tr> <td>'lat'</td> <td>latitude</td> <td>decimal degree</td> </tr> <tr> <td>'lon'</td> <td>longitude</td> <td>decimal degree</td> </tr> <tr> <td>'flag_origin_latlon'</td> <td>origin of the latitude and longitude</td> </tr> <tr> <td>'cog'</td> <td>course over ground</td> <td>degree</td> </tr> <tr> <td>'sog'</td> <td>speed over ground</td> <td>knots</td> </tr> <tr> <td>'sst_batos'</td> <td>Sea surface temperature measured by the navigation station</td> <td>°C</td> </tr> <tr> <td>'temperature_atm'</td> <td>Atmospheric temperature</td> <td>°C</td> </tr> <tr> <td>'pressure_sealevel'</td> <td>Atmospheric presure</td> <td>hp</td> </tr> <tr> <td>'relative_humidity'</td> <td>relative humidity </td> <td>%</td> </tr> <tr> <td>'apparent_windspeed_bow'</td> <td>apparent wind speed</td> <td>knots</td> </tr> <tr> <td>'apparent_winddir_bow'</td> <td>wind direction from the bow</td> <td>degree</td> </tr> <tr> <td>'apparent_wind_trueN'</td> <td>wind direction from north</td> <td>degree</td> </tr> <tr> <td>'true_wind_speed'</td> <td>knots</td> </tr> <tr> <td>'true_wind_dir'</td> <td>wind direction from north</td> <td>degree</td> </tr> <tr> <td>'sunzenith'</td> <td>sun position relative to zenith</td> <td>radian</td> </tr> <tr> <td>'sunazimuth'</td> <td>sun position relative to north</td> <td>radian</td> </tr> </tbody> </table>
diFUME In-situ meteorological dataset
<p>Description</p> <p>Air temperature and vapor pressure are measured on a micrometeorological tower located near the centre of Basel (BKLI). Vapor Pressure Deficit (VPD) is then calculated based on saturated vapor pressure estimation. Direct and diffuse incoming radiation are measured with a 2-axis sun tracker (INTRA, BRUSAG) at the roof-level in an unobscured location next to the micrometeorological tower. Measured shortwave radiant flux densities (W m-2) are converted to photon flux densities (μmol m-2 s-1) within PAR (i.e. photosynthetic active radiation, solar shortwave radiation between 400 – 700 nm) using a standard conversion factor. Soil temperature (oC) and volumetric water content (m3 m-3) are continuously measured at three different locations of the study area at 10 cm below surface. The locations have different characteristics, BKLI is at a street canyon, BKFP is at a non-irrigated park location and BSMP is at an irrigated park location. Soil temperature is also continuously measured at a station outside the city center (BLER) and soil volumetric water content is estimated from lysimeter measurements from another station outside the city centre (BIN).</p> <p>Data format: comma separated values (csv)</p> <p>Time step: 60 min (aggregated)</p> <table> <tbody> <tr> <td> <p><strong>Station acronym</strong></p> </td> <td> <p><strong>Geographic location</strong></p> </td> <td> <p><strong>Variables</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Sensor type - model</strong></p> </td> <td> <p><strong>Sensor height (m a.g.l.)</strong></p> </td> </tr> <tr> <td> <p>BKLI</p> </td> <td> <p>47.56173 °N, 7.58049 °E</p> </td> <td> <p>Air temperature (Tair)</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>Thermo-HYGrometer (Thygan)</p> </td> <td> <p>38</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>Vapor pressure (WVP)</p> </td> <td> <p>kPa</p> </td> <td> <p>Thermo-HYGrometer (Thygan)</p> </td> <td> <p>38</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>Saturation vapor pressure (es)</p> </td> <td> <p>kPa</p> </td> <td> <p>Estimated by Thygan measurements</p> </td> <td> <p>38</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>Vapor pressure deficit (VPD)</p> </td> <td> <p>kPa</p> </td> <td> <p>Estimated by Thygan measurements</p> </td> <td> <p>38</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>Direct radiation</p> </td> <td> <p>W / m2</p> </td> <td> <p>Pyrheliometer (CHP1, Kipp & Zonen)</p> </td> <td> <p>21</p> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>Diffuse radiation</p> </td> <td> <p>W / m2</p> </td> <td> <p>Pyranometer (CM21, Kipp & Zonen)</p> <p> </p> </td> <td> <p>21</p> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>PAR direct</p> </td> <td> <p>μmol / m2 / s</p> </td> <td> <p>Estimated by Pyrheliometer measurements</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>PAR diffuse</p> </td> <td> <p>μmol / m2 / s</p> </td> <td> <p>Estimated by Pyranometer measurements</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>PAR global</p> </td> <td> <p>μmol / m2 / s</p> </td> <td> <p>Estimated by Pyrheliometer and Pyranometer measurements</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>Soil temperature (Tsoil)</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>Thermistor (CS655, Campbell Scientific Inc.)</p> </td> <td> <p>-0.1</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>Soil moisture (theta)</p> </td> <td> <p>m3/m3</p> </td> <td> <p>Water content reflectometer (CS655, Campbell Scientific Inc.)</p> </td> <td> <p>-0.1</p> </td> </tr> <tr> <td> <p>BKFP</p> </td> <td> <p>47.56595 °N, </p> <p>7.56921 °E</p> </td> <td> <p>Soil temperature (Tsoil)</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>Thermistor (CS655, Campbell Scientific Inc.)</p> </td> <td> <p>-0.1</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>Soil moisture (theta)</p> </td> <td> <p>m3/m3</p> </td> <td> <p>Water content reflectometer (CS655, Campbell Scientific Inc.)</p> </td> <td> <p>-0.1</p> </td> </tr> <tr> <td> <p>BSMP</p> </td> <td> <p>47.5529 °N, </p> <p>7.57456 °E</p> </td> <td> <p>Soil temperature (Tsoil)</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>Thermistor (CS655, Campbell Scientific Inc.)</p> </td> <td> <p>-0.1</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>Soil moisture (theta)</p> </td> <td> <p>m3/m3</p> </td> <td> <p>Water content reflectometer (CS655, Campbell Scientific Inc.)</p> </td> <td> <p>-0.1</p> </td> </tr> <tr> <td> <p>BLER</p> </td> <td> <p>47.5923 °N, 7.6493 °E</p> </td> <td> <p>Soil temperature (Tsoil)</p> </td> <td> <p>Degrees Celsius</p> </td> <td> <p>Thermistor</p> </td> <td> <p>-0.1</p> </td> </tr> <tr> <td> <p>BIN</p> </td> <td> <p>47.5411 °N, 7.5835 °E</p> </td> <td> <p>Soil moisture (theta)</p> </td> <td> <p>m3/m3</p> </td> <td> <p>Estimated by weighable lysimeter measurements</p> </td> <td> <p>-2</p> </td> </tr> </tbody> </table>
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 drought lacunarity around the world and its classification
<p>Drought duration strongly depends on the definition thereof. In meteorology, dryness is habitually measured by means of fixed thresholds (e.g. 0.1 or 1 mm usually define dry spells) or climatic mean values (as is the case of the Standardised Precipitation Index), but this also depends on the aggregation time interval considered. However, robust measurements of drought duration are required for analysing the statistical significance of possible changes. Herein we have climatically classified the drought duration around the world according to their similarity to the voids of the Cantor set. Dryness time structure can be concisely measured by the n-index (from the regular/irregular alternation of dry/wet spells), which is closely related to the Gini index and to a Cantor-based exponent. This enables the world’s climates to be classified into six large types based upon a new measure of drought duration. We performed the dry-spell analysis using the full global gridded daily Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. The MSWEP combines gauge-, satellite-, and reanalysis-based data to provide reliable precipitation estimates. The study period comprises the years 1979-2016 (total of 45165 days), and a spatial resolution of 0.5º, with a total of 259,197 grid points.</p> <p>FILES </p> <p>1. "drought_class" (geotiff)</p> <p>2. "legend_drought_class" (csv): legend values for drought classification. </p> <p>3. "rasterbrick_index_HurstCantorGini" (geotiff): raster with three layers (Hurst, Cantor and Gini Index applied to dry spells). </p> <p>4. "rasterbrick_nindex_spells" (geotiff): raster with four layers (Dry Spell Spells n-index, maximum expected dry spell<em>Y</em><sub>1 </sub>, mean dry spell and mean wet spell).</p> <p> </p> <p>Projection: "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs" (EPSG.4326)</p>
Quality-checked meteorological data from the Southern Ocean collected during the Antarctic Circumnavigation Expedition from December 2016 to April 2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains quality-checked meteorological observations of air temperature, relative humidity, dew point, barometric pressure and observations of downwelling solar radiation and ultraviolet radiation. Further it contains the wind speed and direction relative to the ship but not corrected for air-flow distortion, and translated into the earth reference frame. For each of these variables observations are available from a portside and starboard side sensor. The dataset also contains, cloud base height and sky cover at three levels measured with a Ceilometer.</p> <p>As additional information the solar azimuth and altitude angle have been calculated for the ship’s position every five minutes and have been added as a one-minute time series using the nearest value. The ship’s position, heading, course and speed over ground are also provided.</p> <p>The wind speed measurements were made at a height of approximately 30.5 meters above sea level. The measurement height of the temperature and humidity probes is 23.7 meters above sea level. The barometric pressure was measured at 20 meters above sea level.</p> <p>The observations have been screened for implausible values and on some occasions despiking based on visual inspection and a rolling interquartile range filter have been applied. Solar radiation measurements are affected by shadowing of the ship, and the air temperature and humidity by the heating of air that passes over the ship. Masks are provided to flag affected observations. The wind speed readings are affected by airflow distortion and should be used with consideration until a dataset of corrected wind speeds is published. More details on airflow distortion can be requested from the contact person.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_filtered_meteorological_data_1min.csv, data file, comma-separated values</li> <li>diff_TA1_TA3_WDR2_5min_1.png, metadata, portable network graphics</li> <li>ratio_SR1_SR3_solangle_5min_1.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>ace_filtered_meteorological_data_change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - The range check for skycover (SC) and cloudlevel (CL) was added to the quality-checking routines. 53 data points violated the range check for these variables: these have now been marked as NaN.</p> <p><strong>v1.0</strong> - Initial release of verified meteorological data.</p> <p><strong>Dataset license</strong></p> <p>This meteorological dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/</p>
Meteorological warnings issued by INMET for the Brazilian cities of Belém, Belo Horizonte, Porto Alegre, Rio de Janeiro, and São Paulo between 2021 and 2022
<p>The Brazilian National Institute of Meteorology (INMET, from the Portuguese "Instituto Nacional de Meteorologia'') is the Brazilian government agency responsible for monitoring, analysing and forecasting weather and climate. It provides meteorological warnings to be used by the local-level municipal authorities. </p> <p><strong>Data Content</strong></p> <p>INMET periodically publishes data on its website and provides them via XML RSS Feed. This dataset was collected from the RSS feeds mentioning the Brazilian cities of Belém located in the state of Pará, Belo Horizonte in Minas Gerais state, Porto Alegre in Rio Grande do Sul state, Rio de Janeiro in Rio de Janeiro state and São Paulo in São Paulo state from July/2021 to July/2022.</p> <p><strong>Data Structure</strong></p> <p>The description of columns collected from INMET warnings and stored in the warnings file (<em>inmet-meteorological-warnings-1658070001.csv</em>) is presented below. The warnings issued by INMET follow the Common Alerting Protocol (CAP). CAP provides an open, non-proprietary digital message format for all types of alerts and notifications [<em>Standard, OASIS (2010). Common Alerting Protocol Version 1.2. Jul, 1, pp. 1-47. <a href="http://docs.oasis-open.org/emergency/cap/v1.2/CAP-v1.2-os.html">http://docs.oasis-open.org/emergency/cap/v1.2/CAP-v1.2-os.html</a> </em>]. </p> <p>Columns:</p> <ul> <li><em>CITY</em>: Name of the city for which the warning was issued.</li> <li><em>STATE</em>: The Brazilian acronym for the state in which the city is located, for example, MG for Minas Gerais.</li> <li><em>CITYCODE</em>: Unique numeric code for the city for which the warning was issued.</li> <li><em>IDENTIFIER:</em> Unique identifier to INMET warning. </li> <li><em>RESPONSETYPE</em>: Reaction to the warning.</li> <li><em>URGENCY</em>: Urgency for taking action. For example, “Prepare”.</li> <li><em>SEVERITY</em>: Severity of the meteorological event. For example, “Future”</li> <li><em>CERTAINTY</em>: How likely is the event to happen? For example, “Observed” - Determined to have occurred or to be ongoing; “Likely” - (p > ~50%); “Possible” - Possible but not likely (p <= ~50%).</li> <li><em>WARNING</em>: Standardized type of warning. For example, "Aviso de Acumulado de Chuva", "Aviso de Tempestade", "Aviso de Declínio de Temperatura".</li> <li><em>TIMESTAMPDATEONSE</em>: Unix timestamp of the minimum time at which the event is expected to start.</li> <li><em>TIMESTAMPDATEEXPIRES</em>: Unix timestamp of the maximum at which the event is expected to occur or the warning expires.</li> <li><em>COLORRISK</em>: Event colour in hexadecimal following the INMET nomenclature, with yellow meaning potential danger, orange indicating danger, and red indicating great danger.</li> <li><em>BASESOURCE</em>: INMET RSS XML file from which the warning was extracted.</li> </ul>
Soil and meteorological data, and finite element simulation framework for heat transfer through shrubs in winter near Lautaret pass, French Alps
<p>The data allow the calculation using finite element modeling of heat transfer through shrub branches and snow between the atmosphere and the soil. The shrubs are green alders (Alnus viridis). The site where they are found is called Alnus-Nivus (45.034750°N, 6.413630°E, 2034 m asl) near Col du Lautaret, French Alps. The soil data consist in temperature and volumetric liquid water content at 5 and 15 cm depths. One spot is near the alder collar (ALNUS), the other spot is 6 m away, under grass (GRASS).</p> <p>The meteorological data were obtained from the FR-Clt station, 750 m away (45.041278°N, 6.410611°E, 2046 m asl). See (Gupta et al., 2023) for details. Only the data relevant for heat transfer simulations are given.</p> <p>The simulation framework gives the alder mesh used in the heat transfer simulations. Typical simulations use a wood thermal conductivity of 1 W m<sup>-1</sup> K<sup>-1</sup> and a snow thermal conductivity of 0.1 W m<sup>-1</sup> K<sup>-1</sup>. Based on observations, the snow height at Alnus-Nivus is likely to be at least twice the value at FR-Clt. Forcing uses the snow surface temperature, derived from upwelling longwave radiation using an emissivity of 1. The data allow testing thermal bridging through shrub branches. These data are used in a publication in preparation: Domine, Fourteau, Choler, Exploration of Thermal Bridging Through Shrub Branches in Alpine Snow.</p> <p>Reference</p> <p>Gupta, A., Reverdy, A., Cohard, J. M., Hector, B., Descloitres, M., Vandervaere, J. P., Coulaud, C., Biron, R., Liger, L., Maxwell, R., Valay, J. G., and Voisin, D.: Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment, Hydrol. Earth Syst. Sci., 27, 191-212, 2023.</p>
MeteoEurope1km - TMAX (1991–2000): daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period
<p>MeteoEurope1km is the daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period. The dataset consists of five daily variables:</p> <ul> <li><strong>TMAX - maximum temperature</strong> (<strong>1991–2005 period</strong>, 2006–2020 period)</li> <li>TMIN - minimum temperature (1991–2005 period, 2006–2020 period)</li> <li>TMEAN - mean temperature (1991–2005 period, 2006–2020 period)</li> <li>SLP - mean sea level pressure</li> <li>PRCP - total precipitation</li> </ul> <p>Daily gridded temperature data were interpolated using the Regression Kriging, with digital elevation model (DEM) and topographic wetness index (TWI) as covariates.<br> Daily gridded sea level pressure data were interpolated using Ordinary Kriging.<br> Daily gridded precipitation data were interpolated using Indicator and Ordinary Kriging methodology in two steps:</p> <ol> <li>Indicator Kriging - prediction of precipitation occurence</li> <li>Ordinary Kriging - prediction of total daily precipitation for locations where precipitation occurs (1. step).</li> </ol> <p>File naming convention of the MeteoEurope1km files is <em>var_day_yyyymmdd_proj.tif</em> (e.g. <em>tmax_day_20201231_3035.tif</em>), where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection EPSG code - 3035</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> All dataset files are available as Cloud-Optimized GeoTIFFs (COGs).<br> Use the R <a href="https://github.com/AleksandarSekulic/Rmeteo">meteo</a> package, <em>europe1km</em> function to make a point query and obtain the values for a specific location and a specific period.</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) January 2014 - December 2014
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from January 2014 to December 2014 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 20 m [°C]; Salinity @ 20 m [psu].</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) March 2015 - December 2015
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from March 2015 to December 2015 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu].</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) July 2016 - May 2017
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from July 2016 to May 2017 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu] </p>
Measurement differences between air temperature instruments used at H.J. Andrews meteorological stations
The PRIMET Horizontal Radiation Shield Comparison (PHRSC) experiment compares the difference between the air temperature measurements of a reference temperature sensor inside a fan aspirated radiation shield and temperature sensors located inside passively aspirated radiation shields including a cotton region shelter, Gill multi-plate shield, and a custom-fabricated model. Observed variables include air temperature, wind speed, and incoming and reflected solar radiation. Data was collected in the field between 2010 and 2017 at the Primary Meteorological Station (PRIMET) at H.J. Andrews Experimental Forest, located in Oregon’s Western Cascades (44.21, -122.26, elevation 430m).
Hourly meteorological data gapfilled for sensor downtimes collected near Toolik Field Station, Alaska, summers 2012-2016
This data set includes meteorological parameters collected near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It also includes meteorological data collected by two additional entities that are available on public repositories. Toolik data reflect data collected by the Toolik Envronmental Data Center and Imnavait data reflect data collected by the Arctic Observatory Network (AON). These data have been modified such that that sensor downtimes have been gapfilled by pulling data from the next nearest station. These data are associated with publication DOI: 10.1111/jav.01712
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