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3 results for “biometeorology”
Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020
<p>The presented human thermal bioclimate dataset was created in the frame of the <a href="https://theheatalarm.wordpress.com/">HEAT-ALARM</a> ("Development of a heat-health warning system in Greece") research project.</p> <p><strong>Initially developed for Greece</strong>, it consists of hourly values of population-weighted mPET (modified physiologically equivalent temperature), simulated by the RayMan Pro model for the period 1991-2020 and for 10 population subsets in 72 regional units and combinations thereof, which are based on the NUTS-3 (Nomenclature of Territorial Units for Statistics-3) classification in Greece, using the Copernicus European Regional Reanalysis (CERRA) at 5.5 km spatial resolution. The dataset also includes the main environmental drivers of mPET (e.g. temperature) at the same spatiotemporal resolution.</p> <p>In the framework of <strong>replicating</strong> the original dataset, the current version includes population-weighted values of mPET and its environmental drivers for six populations in five districts of <strong>Cyprus</strong> at the LAU-1 (Local Administrative Units-1) level, covering the period from 1991 to 2020. </p> <p>The code used to produce the presented data is available at: <a href="https://doi.org/10.5281/zenodo.10793067">https://doi.org/10.5281/zenodo.10793067</a>. It can be used to replicate the dataset not only directly in Greece, but also in any other country included in the CERRA domain after appropriate adjustments, as in the case of Cyprus above.</p> <p><em>Compared to the previous version of the dataset for Greece, this version includes vapor pressure (VP) instead of relative humidity (see README.txt for more details), as VP is more relevant for human-biometerological and health-related studies.</em><em> </em></p> <p><strong>References</strong></p> <p>Giannaros, C., Agathangelidis, I., Galanaki, E. <em>et al.</em> Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece. <em>Sci Data</em> <strong>11</strong>, 76 (2024). <a href="https://doi.org/10.1038/s41597-024-02923-y">https://doi.org/10.1038/s41597-024-02923-y</a> </p>
Biometeorological Dataset for 'Novel algorithms for high resolution prediction of canopy evapotranspiration in grapevine'
<p>A head trained <strong><em>Vitis vinifera</em></strong> L. cv. Zinfandel vine was grafted on St. George rootstock (<em>V. rupestris</em>) then planted in a 1.1 m<sup>3</sup> plastic container filled with Yolo County, CA sourced sandy loam.<br> <br> To estimate evapotranspiration, we measured the wind speed, air temperature and relative humidity in vine canopies by mounting each vine with a suite of research grade sensors. We measured wind speed (units m ᐧ s<sup>-1</sup>) inside the vine canopy using a single needle anemometer (<em>East 30 Sensors</em>; Pullman, WA) that took instantaneous wind speed measurements every 10 seconds and recorded the average of the previous 12 instantaneous measurements for every 2-minute interval.</p> <p>We measured temperature (units <sup>o</sup>C) and relative humidity (units %) using HMP60L sensors (Campbell Scientific; Logan, UT) mounted both inside and outside of each vine canopy and recorded instantaneous measurements at each 2-minute interval. We filtered all biometeorological data using a 3-hour moving average to remove noise without causing any significant over or under-approximation of daily maxima and minima.</p> <p>We automated all data collection using two CR1000 data loggers (<em>Campbell Scientific</em>; Logan, UT), with 1 or 2 vines and associated sensors per logger, using custom CR1 programs. A single 30W solar cell and 12V lead acid battery powered the entire vine-sensor system.</p> <p>This dataset represents all sensor data from a single vine, as measured in August 2020. Columns are named accordingly and include units.</p> <p><strong>Please Note</strong>: The column named 'load_cell_kg' is not named accurately. The values given are in units of millivolts, and need to be translated from millivolts to kilograms. The 2020 calibration coefficient is 0.00330693663 millivolts per kilogram.</p>
Analyzing coastal fog effects on carbon and water fluxes in a California agricultural system using approaches in biometeorology, remote sensing, and plant physiology
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OpenNeuro
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