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1,121 results for “weathering”
Weather Data from Hull Springs Farm: 2019-09-10 to 2019-09-29
<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 datalogger assigned to the record that was collected (YYYY-MM-DD HH:MM:SS)</p> <p> * TIMESTAMP.corr - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS). The date and time on the data logger was set incorrectly when the data collection began so the time stamps from the logger are incorrect. We used the file download time to identify the last actual time that data logger collected a sample and then offset the TIMESTAMP values by the difference between the recorded and actual time of the last sample collection. <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>
Figure 2 in Trapping Sweetpotato Weevil, Cylas formicarius (Coleoptera: Brentidae), with High Doses of Sex Pheromone: Catch Enhancement and Weathering Rate in Hawaii
Figure 2. Effect of lure loading on trap catch over one week of weathering. Average (± SEM) male sweetpotato weevil catch per trap per week in sweetpotato fields in the vicinity of Pepeekeo, Hawaii, in traps baited with one of three different loadings of male sweetpotato weevil attractant. Catch results are from the first week following initial trap deployment with five traps for each loading, deployed in a randomized complete block design (average of three separate trials). Bars labeled with the same letter are not significantly different at the α = 0.05 level.
Figure 1 in Trapping Sweetpotato Weevil, Cylas formicarius (Coleoptera: Brentidae), with High Doses of Sex Pheromone: Catch Enhancement and Weathering Rate in Hawaii
Figure 1. Map of weathering trial showing locations of fields where traps with lures were placed (developed using ArcGIS [ESRI 2012]). Traps were initially deployed at Site 1 on 14 February, 2012, and moved on to Sites 2, 3, 4, and 5 over the course of the weathering trial. The weathering time of the traps at each site was as follows: (Site 1) first 8 weeks; (Site 2) weeks 9–16; (Site 3) weeks 17–24; (Site 4) weeks 25–40; and (Site 5) week 41 (assessment). A weather station was maintained over the course of the weathering trial and was located at Site 2 for the first 16 weeks and then located at the location of the filled circle on the map for the remaining weeks of the trial.
Figure 4. Trial 4 in Trapping Sweetpotato Weevil, Cylas formicarius (Coleoptera: Brentidae), with High Doses of Sex Pheromone: Catch Enhancement and Weathering Rate in Hawaii
Figure 4. Trial 4 results: Effect of weathering over 40 weeks on trap catch. Decline in sweetpotato weevil catch/trap/week over 40 weeks in traps baited with (A) septum holding 1.0 mg male lure (see text for calculated exponential decay curve), and (B) septum holding 120 μg male lure (see text for calculated exponential decay curve). Calculated septum age where catch is 50% of fresh catch is presented for each curve.
Figure 1 in Impact of warm weather events on prolongation of the life cycle of Stomaphis Walker (Hemiptera, Aphididae, Lachninae)
Figure 1. Apterous female of Stomaphis sp. collected on 05.01.2014 (a); oviparous female of S. graffii collected on 01.03.2014 (b) (arrow indicates large subgenital plate, typical of oviparous females).
Figure 1 in Possible implications of weather variation on reproductive phenology of European common toad in southeastern Europe
Figure 1. Modeled relation between (a) mean daily humidity (%) and (b) mean daily cloud cover (oktas) and breeding duration (days) for the study population of common toads in Serbia. The black line and shaded region represent model estimates and 95% confidence intervals, respectively. Points are representative of raw data points.
Fig. 4 in The weathering-modified iridium record of a new Cretaceous-Palaeogene site at Lechówka near Chełm, SE Poland, and its palaeobiologic implications
Fig. 4. Two−step interpretation of weathering−modified iridium anomaly in the K–Pg succession at Lechówka (see Fig. 3), showing a significant original iridium anomaly (A) altered by secondary redistribution/participation processes (B), resulting in a substantial extension of the iridium enrichment and a lower position of the diminished iridium spike relative to the K–Pg boundary clay, perhaps controlled by a precipitation front at an assumed redox barrier (cf. Sawłowicz 1993; Gawrilov 2010). Observed and expected weathering−controlled iridium chemostratigraphic profiles are shown (the iridiumr baseline is carefully taken as 0.1 ppb; see Table 2), as well as the comparative placement of the recognised iridium enrichment against a diversity of K–Pg reference levels and key localities (compiled from Crocket et al. 1988, Hansen et al. 1989, Koeberl et al. 2007, and Schulte et al. 2010).
Fig. 1 in The weathering-modified iridium record of a new Cretaceous-Palaeogene site at Lechówka near Chełm, SE Poland, and its palaeobiologic implications
Fig. 1. Location of the Lechówka section in Poland (A), and general view of this outcrop (B). K, Cretaceous; Pg, Palaeogene.
Fig. 2 in The weathering-modified iridium record of a new Cretaceous-Palaeogene site at Lechówka near Chełm, SE Poland, and its palaeobiologic implications
Fig. 2. Lithologic column, field photo and two close−ups of the K–Pg passage at Lechówka. Note a local abundance of burrows (b) and Fe oxy−hydroxides, especially in the boundary clay.
Weather Station-Scale Photosynthetic Phenology Dataset in the Middle and High Latitudes of the Northern Hemisphere
<p>This dataset includes the start, peak, and end times of the growing season (SOS, POS and EOS), extracted from GPP time series data estimated at weather stations. It covers a total of 57,829 site-years. The dataset provides valuable information for large-scale phenology analysis, ecosystem model validation, and other studies in the carbon cycle and ecology fields.</p>
Brightness temperature data and weather station data from general scans of the microwave radiometer HATPRO
<p>This set contains brightness temperature observations and associated weather station data from microwave radiometer HATPRO for three events on Jul 27 and Aug 1, 2023.</p>
Exploring the Synergy of Enhanced Weathering and Bacillus subtilis: A Promising Strategy for Sustainable Agriculture
<p>Data: Exploring the Synergy of Enhanced Weathering and Bacillus subtilis: A Promising Strategy for Sustainable Agriculture</p>
Brazil - Future weather files for building energy simulation
<p>This dataset contains future weather files for energy simulation of buildings of the state capitals of Brazil plus the Federal District. The weather data is provided in EPW format, commonly used in <a href="https://energyplus.net/">EnergyPlus inputs</a>. The periods of 2010, 2050, and 2090 are considered for generating the Typical Meteorological Years (TMY) for each location. Additionally, different climate model projections were used to enable an analysis of uncertainties in the simulation results. <strong>In this updated version, we have included the complete individual years used to develop the TMYs. This data is provided in CSV format and has been bias-corrected.</strong></p> <p>The future climate data was derived from various regional climate model projections from the <a href="https://cordex.org/">Coordinated Regional Downscaling Experiment (CORDEX) project</a>. These projections are part of the CORDEX-CORE experiment, which includes three GCMs (HadGEM2, MPI-ESM, and NorESM1) as driving models, and two nested RCMs (regcm and remo) for dynamical downscaling, totaling an ensemble of six members at a spatial resolution of approximately 25km. The <a href="https://doi.org/10.1007/s10584-011-0148-z">representative concentration pathways </a>RCP 8.5 and RCP 2.6 were the available scenarios for the region, and both were considered for developing the weather files. The future climatic variable values were interpolated and formatted to an hourly resolution, commonly used in building energy simulation tools. Subsequently, different bias correction methods were applied to specific climate variables based on historical weather series. These data series consist of hourly data measured at weather stations located in each city between the years 2001 and 2021. The historical series files were made available by Dru Crawley and Linda Lawrie and served as the basis for developing the TMYx weather files available on the <a href="https://climate.onebuilding.org/">OneClimate Building website</a>.</p> <p>It is crucial to recognize that the historical data is based on measurements taken at airports, which are often situated far from urban centers. As a result, urban overheating was not factored into these developed weather files. It is also important to note that the developed weather files represent a typical meteorological year for each period, and do not include the most extreme periods, such as heatwaves and atypical summers.</p> <p>The weather files were developed specifically for use with the EnergyPlus engine. Therefore, climatic variables not used by the engine (e.g., precipitation and ceiling height), even if available in EPW format, should not be considered for other studies.</p> <p>It is important to emphasize the higher internal operative temperature results obtained when using the REGCM model compared to the REMO model in Building Energy Simulations. <strong>Caution is recommended when using files developed using a single combination of climate models, especially with weather files based on the REGCM model.</strong></p> <p><strong>Suggestions for corrections can be sent to matheus.bracht@posgrad.ufsc.br</strong></p>
Data Access Made Easy: flexible, on the fly data standardization and processing (for research automatic weather stations)
<pre>Automatic Weather Stations (AWS) deployed in the context of research projects provide very valuable data thanks to the flexibility they offer in term of measured meteorological parameters, choice of sensors and quick deployment and redeployment. However this flexibility is a challenge in terms of metadata and data management. Traditional approaches based on networks of standard stations can not accommodate these needs and often no tools are available to manage these research AWS, leading to wasted data periods because of difficult data reuse, low reactivity in identifying potential measurement problems, and lack of metadata to document what happened. The Data Access Made Easy (DAME) effort is our answer to these challenges. At its core, it relies on the mature and flexible open source MeteoIO meteorological pre-processing library. It was originally developed as a flexible data processing engine for the needs of numerical models consuming meteorological data and further developed as a data standardization engine for the Global Cryosphere Watch (GCW) of the World Meteorological Organization (WMO). For each AWS, a single configuration file describes how to read and parse the data, defines a mapping between the available fields and a set of standardized names and provides relevant Attribute Conventions Dataset Discovery (ACDD) metadata fields, if necessary on a per input file basis. Low level data editing is also available, such as excluding a given sensor, swapping sensors or merging data from another AWS, for any given time period. Moreover an arbitrary number of filters can be applied on each meteorological parameter, restricted to specific time periods if required. This allows to describe the whole history of an AWS within a single configuration file and to deliver a single, consistent, standardized output file possibly spanning many years, many input data files and many changes both in format and available sensors. Finally, all configuration files are versionned in order to document their history. A web interface has been developed that allows data owners to manage the configuration files for their stations, refresh their data at regular intervals, inspect the data QA log files and allow on-demand data generation. The same interface allows other users to request data on-demand for any time period.<br><br>This presentation and software has received funding from the World Meteorological Organization under grant agreement No. 29539/2022-1.9 as well as the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101003472 (Arctic Passion). It has also been supported by the WSL/SLF over many years and projects.</pre>
Data for the publication "Repurposing weather modification for cloud research showcased by ice crystal growth"
<p><span>This repository contains the data of the publication:</span></p> <p><span>"Repurposing weather modification for cloud research showcased by ice crystal growth"</span></p> <p><span>Ramelli et al., 2024, PNAS Nexus</span></p>
Dataset for "GPTCast: a weather language model for precipitation nowcasting"
<p>Dataset for "<em><strong>GPTCast: a weather language model for precipitation nowcasting</strong></em>"</p> <ul> <li>Preprint: <a href="https://arxiv.org/abs/2407.02089">https://arxiv.org/abs/2407.02089</a></li> <li>Code: <a href="https://github.com/DSIP-FBK/GPTCast">https://github.com/DSIP-FBK/GPTCast</a></li> <li>Pretrained models: <a href="https://doi.org/10.5281/zenodo.13594332">https://doi.org/10.5281/zenodo.13594332</a></li> </ul> <p>Version 2 of this dataset contains also the "Forecaster Test Set" (fts.tar) which includes all generated forecasts for GPTCast8x8, GPTCast16x16, and Linda, to ensure reproducibility of the results.</p>
Data from public granaries as a source of proxy data on grain harvests and weather extremes (Sušice region, Czech Republic)
<p><span>This deposit contains eight .xlsx files related to the study of public granary data and their relation to grain harvests and weather extremes in the Sušice region (Czech Republic). In the study, annual values of grain borrowed by serfs, their grain depositions, the total grain storage and the total debt of serfs at the end of year were used to calculation of weighted grain indices considering the balance between borrowed and returned grain: a weighted bad harvest index (WBHI), a weighted good harvest index (WGHI), a weighted stored grain index (WSGI: WSGI-, more borrowed than returned; WSGI+, more returned than borrowed) and a weighted serf debt index (WSDI: WSDI+, more borrowed than returned grain; WSDI-, more returned than borrowed grain). WBHI, WSGI- and WSDI+ were used to select years of extreme bad harvest and WGHI, WSGI+ and WSDI- years of extreme good harvest. Selected extreme harvest years were tested against documentary weather data and reconstructed temperature, precipitation and drought series of the Czech Lands.</span></p> <p><span>First six files (01a_borrowed grain_plus, 01b_returned grain_plus, 01c_stored grain_minus, 01d_stored grain_plus, 01e_serfs debt_plus, 01f_serfs debt_minus) contain three sheets representing individual cereals (rye, oats and barley). For each cereal, there are series of multiples of standard deviation used for calculation of individual indices mentioned above. These multiples were obtained from detrended series (using high-pass filter) of every grain characteristic as arithmetic mean plus/minus corresponding multiple of standard deviation.</span></p> <p><span>The file „</span> <span>02_indices“ contains three sheets with weighted grain indices: WBHI, WGHI, WSGI-, WSGI+, WSDI+, WSDI-. Sheets represent individual cereals – rye, oats and barley.</span></p> <p><span>The file „</span> <span>03_clima factors“ contains mean seasonal (DJF, MAM, JJA, and SON) temperature, precipitation and scPDSI for the period 1789–1849, expressed in deviations relative to the 1961–1990 reference period. The series are reconstructed temperatures for central Europe (Dobrovolný et al., 2010), reconstructed precipitation for the Czech Lands (Dobrovolný et al., 2015) and both of them were used for creation of scPDSI series (Brázdil et al., 2016).</span></p>
Precipitation and Temperature data from the on-site weather station at the Rüdersdorf limestone quarry near Berlin, Germany, from January 2023 to February 2024
<p>The weather data from the on site station is provided as Temperature [°C] every 5 minutes and the daily sums of Precipitation [mm]. </p>
Measured weather variables and power generation from vertical agrivoltaic installation in Foulum, Denmark
<p>Measured weather variables and power generation from vertical agrivoltaic installation in Foulum, Denmark.</p> <p>Data is recorded every 5 minutes for the period December 2022 to October 2024. See the figure 'summary_clean_data.jpg' for an overview of data availability.</p> <p>Data is collected and curated in the following Github repository: https://github.com/martavp/agrivoltaic_foulum</p> <p>The Agrivoltaic demonstration system is described in the pre-print <a href="https://www.researchsquare.com/article/rs-5358908/v1" rel="nofollow">"Vertical Agrivoltaics in a Temperate Climate: Exploring Technical, Agricultural, Meteorological, and Social Dimensions"</a></p>
ECOCLIMAP-SG-ML: an ensemble land cover map for numerical weather prediction
<p>This dataset contains ensemble land cover maps for numerical weather prediction at 60 m resolution over Europe. As they were<br>generated thanks to machine learning, the weights and the training data are also provided.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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