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653 results for “rain”
Precipitation measurements from historic and current standard, storage and recording rain gauges at the Andrews Experimental Forest, 1951 to present
Andrews Forest precipitation has been measured continuously using various rain gage types since 1951. Most of these rain gages are standard (non-recording) gages with 7.5 or 8 inch orifices or large capacity storage gages intended for sites with limited access collected irregularly over longer intervals. Recording rain gages have also been established to collect higher temporal resolutions (e.g., 5 minute or 15 minute) and also used as a means of parsing (“prorating”) these periodic interval measurements from these standard and storage gages into daily totals. This data set includes an inventory of all rain gages that have operated within the Andrews as well as one site in the nearby Wildcat RNA and one in the town of Blue River. The inventory includes information regarding the date range of operation, gage location, type of gage, the rain network within which it was established, general availability of data and descriptive notes. A second table includes all of the raw measurement data for these non-recording gages over every interval where data were taken, and additionally includes the corresponding recording gage and its measurement total used to prorate data into a daily record. A third table includes the prorated daily data for all of these standard and storage gages as well as the true daily totals for two recording rain gages. A fourth table includes high temporal resolution for one early recording gage at Forks and the Mack Creek recording gage. Note that while precipitation data associated with the 6 benchmark stations are included in this rain gage inventory (Entity 1), the daily and high temporal resolution data for these sites were available through a separate meteorological data set, database code MS001, until 2025. In 2025, the benchmark station data was migrated here and will be combined with the Forks and Mack Creek data.
Long-term dynamics of tropical rain forests in permanent inventory plots, La Selva, Costa Rica (1969-1995)
Three permanent plots comprising a total of 12.4 ha were established in 1969 in tropical rain forest at La Selva Biological Station, near Puerto Viejo de Sarapiquí, in the Caribbean lowlands of Costa Rica. The plots were established in old-growth forest on three contrasting landforms: Plot 1 (4.4 ha) on old alluvial terrace; Plot 2 (4.0 ha) in swamp forest and rolling hills; and Plot 3 (4.0 ha) on steeply dissected terrain with residual soils. The data archived here include plot inventories carried out at five census dates over a period of 27 years. The inventory starting dates were 1969; 1982; 1985; 1989; and 1995. All stems 10 cm dbh or greater were tagged with a permanent numbered tag; measured in diameter at breast height and above buttresses to the nearest mm; mapped on the ground to the nearest m; and identified to species. At each census, live trees were re-measured, dead trees were recorded along with information on the manner of death, other details on the condition of the tree were noted, and new recruits were tagged, mapped, measured, and identified. The archived data include these five components: (1) The master data file, including comprehensive data on all tagged individuals in the three plots for the five censuses from 1969-1995. Each line in the data set represents an individual tagged tree or liana. The data array comprises 8689 lines (the number of tagged individuals) x 48 columns of data. The lines in the data set are ordered first by Plot number (1, 2, 3); next by subplot within each plot; and then by tag number within each subplot. (2) A list of column identifiers, describing in detail the information represented in each of the 48 columns within the master data file. The list gives a description of the data in each column, the units of measurement, and a guide to the interpretation of zeroes in the data. (3) A key to codes used in the field to describe the condition of individual trees. (4) A taxonomic reference list, including all species found
Seedling composition, growth, and dynamics in tropical rain forest, La Selva, Costa Rica (1983-1996)
Recruitment, growth, and survivorship of the regeneration stages of trees and lianas were studied in old-growth tropical rain forest at La Selva Biological Station of the Organization for Tropical Studies (OTS), near Puerto Viejo de Sarapiquí, Heredia Province, in the Caribbean lowlands of Costa Rica. A total of 48 permanent seedling transects each measuring 10 m x 0.5 m were established at random locations within three La Selva permanent forest inventory plots. The forest plots occupy contrasting landforms: Plot 1 (4.4 ha), old alluvial terrace; Plot 2 (4.0 ha), swamp forest and low hills; and Plot 3 (4.0 ha), steeply dissected terrain with residual volcanic soils. Seedling locations are georeferenced within the grid system of the permanent forest inventory plots, facilitating spatial analysis of seedling populations with respect to adult cohorts. Beginning in June 1983, all seedlings ≤ 0.5 m in height belonging to tree and liana species capable of reaching 10 cm diameter at breast height (dbh) at maturity were tagged, identified to species or morphospecies, mapped to the nearest cm, and measured in height to the nearest cm. Over a period of 18 months, a total of 6403 seedlings belonging to 167 species were tagged. Monitoring and re-measurement of all tagged individuals continued through November 1996. Data include 17 census dates over a period of 13.5 years. At the time of the final census, only 97 individuals (1.52% of the tagged seedlings) were still alive, representing 43 species (25.7% of the initial number). The largest surviving seedling had grown in height from 4 cm to 13 meters during the study period. This dataset on the regeneration stages in old-growth tropical rain forest in the La Selva permanent inventory plots forms a complement to the studies of long-term growth and demography of these species and assemblages at adult stages within the plots. Forest inventory data for trees and lianas ≥ 10 cm dbh in the permanent plots in which the seedling transec
Hubbard Brook Experimental Forest: Daily Precipitation Rain Gage Measurements, 1956 - present
Precipitation has been measured at the Hubbard Brook Experimental Forest using rain gauges located in or around each watershed since 1956. Three types of rain gauges have been used: standard, mechanical weight recording, and electronic weight recording. Between 1956 and 2014, precipitation was measured weekly at standard gages located at 24 stations in or near gauged watersheds and at the headquarters building. Weight-recording gauges were located at 7 of the 24 stations and capture a continuous strip-chart record. Weekly totals were prorated using daily totals from the nearest recording gauges. Beginning in 2011, electronic weighing rain gauges were implemented to measure 15-minute precipitation. The number of precipitation stations was reduced to 10, when each station was fully converted to an electronic gauge for measuring 15-minute and daily precipitation beginning in 2015. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Graduated rain gauge (GRG) precipitation observations from 21 sites at the Jornada Basin LTER site, 1989-ongoing
This dataset contains long-term precipitation measurements from graduated rain gauges (GRGs) at 21 sites in the Jornada Basin of southern New Mexico, USA. Gauges are located on the Jornada Experimental Range (JER) and the Chihuahuan Desert Rangeland Research Center (CDRRC), and this set of gauges includes all 15 net primary production (NPP) study sites monitored by the Jornada Basin LTER program. At each site a 4 inch diameter cylindrical graduated rain gauge (11" x 0.01" capacity) is mounted on a 4x4 inch diameter redwood post or a wooden exclosure post next to gate at or near each site. For NPP sites, the primary collection is made on the day that monthly hydroprobe soil water content measurements are made. This enables correlation of precipitation with belowground soil water content. Additional data collections during the month may be made in coordination with other studies. Observations at each site come primarily from GRGs. However, at some sites in the NPP study, GRGs were not installed until later, and the nearest available rain gauge in the area has been used to gapfill the precipitation record prior to installation (details in methods section). Rain gauge identity and field measurement date is recorded with each observation in the data file. Other gauge types that may be listed are the Standard Can Gauge (DSRG or dipstick rain gauge), Belfort Weigh Bucket Rain Gauge (WBRG), and Qualimetrics Tipping Bucket Rain Gauge (TBRG). Data collection is ongoing for all 21 gauges in this dataset.
Precipitation data from a standard can rain gauge at the LTER weather station, Jornada Basin, southern New Mexico, USA, 1992-ongoing
This data package contains precipitation measurements collected from a "dipstick" rain gauge (NOAA standard can type) at the LTER Weather Station in the Jornada Basin, southern New Mexico, USA. The primary purpose of this data set is to validate the LTER Weather Station tipping bucket rain gauge data. The dipstick rain gauge (DSRG) data is measured at least weekly during scheduled maintenance trips to the LTER Weather Station to maintain the evaporation pan water levels. During the summer months this may be twice a week. Additionally, DSRG data is collected after any rain event that requires the collection of the Wetfall/Dryfall precipitation buckets which are located about 10 meters from the DSRG. This is usually any amount greater than 0.02 inches. DSRG data is also collected after very small events when personnel are in the vicinity. Rain gauge records at this gauge began in 1992 and the study is ongoing.
Radar measurements for the article "Dynamic differential reflectivity calibration using vertical profiles in rain and snow"
<p><strong>Dataset documentation</strong></p> <p>The archives in hdf5 format provided at this link contain the datasets used in the manuscript <em>Dynamic differential reflectivity calibration using vertical profiles in rain and snow</em>, submitted to <em>Remote Sensing</em> (MDPI) by Alfonso Ferrone and Alexis Berne in 2020.</p> <p> </p> <p><strong>File content</strong></p> <p>Each file is structured as a table, with each column referring to a specific variable and each row containing a different realization (in space or time). The set of available variables is campaign dependent, and the possibilities are:</p> <ul> <li> <p><strong>idx</strong>, and integer index that starts at 1 for the first scan of the dataset and increases by 1 for every successive scan;</p> </li> <li> <p><strong>t</strong>, the timestamp of the scan, in seconds since seconds since Jan 01, 1970;</p> </li> <li> <p><strong>r</strong> or <strong>rg</strong> (depending on the file), the distance from the radar in meters;</p> </li> <li> <p><strong>az</strong>, the azimuth angle in degrees;</p> </li> <li> <p><strong>el</strong>, the elevation angle in degrees;</p> </li> <li> <p><strong>zdr</strong>, the uncalibrated differential reflectivity, in dB;</p> </li> <li> <p><strong>zh</strong>, the horizontal reflectivity, in dBZ;</p> </li> <li> <p><strong>rhovh</strong> or <strong>rho</strong>, the co-polar correlation coefficient, unitless;</p> </li> <li> <p><strong>snr_h</strong> or <strong>snr</strong>, the signal to noise ratio for the horizontal channel in dB;</p> </li> <li> <p><strong>snrv</strong>, the signal to noise ratio for the vertical channel, in dB,</p> </li> <li> <p><strong>ngates</strong>, the number of unique range gates.</p> </li> </ul> <p>For the comparison of the data collected by MXPol and DX50 during the PAYERNE campaign, two auxiliary variables were added to the archives:</p> <ul> <li> <p><strong>x</strong> the horizontal distance from the current radar, computed on a line passing through the location of two radars;</p> </li> <li> <p><strong>z</strong> the vertical distance from the current radar.</p> </li> </ul> <p> </p> <p><strong>Usage</strong></p> <p>The dataset are provided in the the Hierarchical Data Format version 5 (HDF5), an open source file format, supported by several programming language.</p> <p>They archives were created using the <em>vaex</em> library for Python 3:</p> <p>https://github.com/vaexio/vaex</p> <p>The function <em>vaex.open</em> from the same library can be used for accessing the archives and converting them to <em>vaex.DataFrame</em>.</p> <p> </p> <p><strong>Campaign-specific information</strong></p> <p>Some of the parameters associated to the variables included in the archives may change depending on the campaign. The following subsection provide a summary of these information.</p> <p> </p> <p><strong>dataframe_HYMEX_2013_from_20130907-040344_to_20131105-175944.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the HYMEX campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 44.61° N</p> </li> <li> <p>Longitude: 4.55° E</p> </li> <li> <p>Altitude: 604 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: Z-PHI method</p> </li> <li> <p>Note on reflectivity calibration: The original manufacturer calibration constant was 7.56 dBZ. The value used here derives from comparison with disdrometers during the HYMEX campaign.</p> </li> </ul> <p> </p> <p><strong>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by MXPol during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.81° N</p> </li> <li> <p>Longitude: 6.94° E</p> </li> <li> <p>Altitude: 496 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DAVOS_2014_from_20140704-090224_to_20141231-105720.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the DAVOS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.82° N</p> </li> <li> <p>Longitude: 9.82° E</p> </li> <li> <p>Altitude: 2220 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 204.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_APRES3_from_20151207-123944_to_20160129-125856.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the APRES3 campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 66.66 S</p> </li> <li> <p>Longitude: 140.00 E</p> </li> <li> <p>Altitude: 40 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.45°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 354.3 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_VALAIS_2016_from_20161104-154312_to_20170306-195912.hdf5</strong></p> <p>Contains PPI scans at 90° elevation for the VALAIS campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.12 N</p> </li> <li> <p>Longitude: 7.10 E</p> </li> <li> <p>Altitude: 460 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.41 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.27°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 30 m</p> </li> <li> <p>Range to the first gate: 226.95 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.6</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</strong></p> <p>Contains PPI scans at 90° elevation performed by DX50 during the PAYERNE campaign.</p> <p>Location information:</p> <ul> <li> <p>Latitude: 46.84° N</p> </li> <li> <p>Longitude: 6.92° E</p> </li> <li> <p>Altitude: 450 m.a.s.l.</p> </li> </ul> <p>Technical information:</p> <ul> <li> <p>Radar Frequency: 9.459 GHz</p> </li> <li> <p>Theoretical 3 dB angular beamwidth of the antenna: 1.273°</p> </li> </ul> <p>Scan setup:</p> <ul> <li> <p>Range resolution: 75 m</p> </li> <li> <p>Range to the first gate: 0.0 m</p> </li> </ul> <p>Processsing parameters:</p> <ul> <li> <p>Clutter filtering: OFF</p> </li> <li> <p>Minimum rhohv: 0.0</p> </li> <li> <p>Attenuation correction: None</p> </li> </ul> <p> </p> <p><strong>dataframe_DX50_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from DX50 the PAYERNE campaign.</p> <p>The remaining information equal to the ones listed for <em>dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5</em>.</p> <p> </p> <p><strong>dataframe_MXPol_RHI_201405.hdf5</strong></p> <p>Contains RHI scans from MXPol the PAYERNE campaign.</p> <p>The remaining information is equal to the ones listed for <em>dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5</em>.</p>
CFD simulation and measurements of effect of wind on non-catching rain gauge
<p>Simulation_dataset file shows the results of a CFD simulation of the measurements of a Thies laser precipitation monitor under different conditions of wind.</p> <p>Wind_tunnel_dataset shows the results of the model validation using an actual wind tunnel.</p>
Data for the article "Typhon: a polar stream from the outer halo raining through the Solar neighborhood"
<p>This data contains the stellar parameters of Typhon stream stars in the context of the "Typhon: a polar stream from the outer halo raining through the Solar neighborhood" (Tenachi et al. 2022) paper, in two formats:.csv and .fits (which also contains a short description of each column).</p> <p>This data includes:</p> <ul> <li>Stellar coordinates and parameters from Gaia DR3 (Gaia Collaboration 2022) with extinction-corrected magnitudes using the (Schlafly and Finkbeiner 2011) corrections to the (Schlegel et al. 1998) extinction maps, assuming the extinction ratios A<sub>G</sub>/A<sub>V</sub> = 0.86117, A<sub>GBP</sub>/A<sub>V</sub> = 1.06126 and A<sub>GRP</sub> /A<sub>V</sub> = 0.64753, as listed on the web interface to the PARSEC isochrones (Bressan et al. 2012) and assuming a solar position (x,y,z) = (−8.2240, 0, 0.0028) kpc (Bovy 2020, Widmark et al. 2021) and a solar velocity (vx,vy,vz) = (11.10, 7.20, 7.25) km/s with a circular velocity = 243 km/s (Schonrich et al. 2010, Bovy 2020).</li> <li>Added dynamical parameters (actions, energy, apocenters and pericenters values) derived in a (McMillan et al 2017) potential.</li> <li>Metallicity parameters from LAMOST DR8 PASTEL column (Wang et al 2022).</li> <li>Independent measurements from the "Chemical Abundances of the Typhon Stellar Stream" follow-up paper (Ji et al 2022).</li> </ul>
Table S27: Target and identified unknown organic micropollutants detected in surface water samples taken during heavy rain events
<p>In the following table, peak intensities of detected organic micropollutants in water samples are displayed.</p> <p>This data table is part of the appendix of Chapter 4 of the PhD thesis “Novel approaches to identify drivers of chemical stress in small rivers” by Liza-Marie Beckers prepared at RWTH Aachen University and at the Helmholtz Centre for Environmental Research-UFZ. In Chapter 4, precipitation-related pollutant patterns and indicator compounds during heavy rain events were identified in the Holtemme River by nontarget screening and cluster analysis. The table contains peak heights of organic micropollutants detected in water samples taken during heavy rain events in the Holtemme River (Saxony – Anhalt, Germany). The table is structured into the following columns: Compound name, use class of compound (e.g., pharmaceutical or pesticide), distinction between target or identified unknown compounds, mass-to-charge ratio (m/z), retention time (RT), assignment to a pattern identified by cluster analysis (i.e., “Base” or “Quick”), the probability of belonging to the assigned pattern as number between 0 and 1 as well as the peak height of the compound in each sample. The samples are indicated by "B" for "bottle" and a number from 1-16. The use class “NA” indicates that now major use class for this compound could be identified.</p> <p>The sampling was triggered by combined sewer overflow at a wastewater treatment plant upstream of the sampling point. Samples were taken by an automated sampler in 30-min composite samples for 8 hours resulting in 16 samples per rain event. In total, 6 heavy rain events from May to September 2016 were sampled during this study. The table is divided into 6 subtables (i.e., Table S27 A-F). Each subtable displays compounds and their peak heights detected in samples from one heavy rain event. The different rain events are abbreviated by the sampling date:</p> <p>Table S27A displays results from the rain event samples May 29<sup>th</sup> 2016 : E2905</p> <p>Table S27B displays results from the rain event samples June 01<sup>st</sup> 2016 : E0106</p> <p>Table S27C displays results from the rain event samples June 24<sup>th</sup> 2016 : E1306</p> <p>Table S27D displays results from the rain event samples June 13<sup>th</sup> 2016 : E2406</p> <p>Table S27E displays results from the rain event samples July 13<sup>th</sup> 2016 : E1307</p> <p>Table S27F displays results from the rain event samples September 17<sup>th</sup> 2016 : E1709</p> <p>Chemical analysis of the water samples was performed by liquid chromatography (UltiMate 3000 LC system (Thermo Scientific)) coupled to high resolution mass spectrometry (Q Exactive Plus, Thermo Scientific) with a heated electrospray ionization (HESI) source. Nontarget screening was performed as it allows for a comprehensive characterization of the chemical exposure during heavy rain events. However, only annotated target compounds and unknown compounds identified by structure elucidation are presented in the table. Details on data evaluation methods are described in Chapter 4 of the PhD thesis.</p> <p>Beckers, L.M. (2019): Novel approaches to identify drivers of chemical stress in small rivers. RWTH Aachen University, Aachen.</p>
Water stable isotope, temperature and electrical conductivity dataset (snow, ice, rain, surface water, groundwater) from a high alpine catchment (2019-2021).
<p>Data collected in the Otemma forefield in Switzerland (45°56’03”N,7°24’42”) from July 2019 to October 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>Description of the dataset</strong></p> <p>This dataset contains water stable isotope (δ<sup>2</sup>H, δ<sup>17</sup>O, δ<sup>18</sup>O), water temperature and water electrical conductivity (EC) measurements collected from the Otemma glacier catchment.</p> <p>All water isotope samples were collected directly from the source and stored in 12 mL amber glass vials with an air-tight caps. River samples were first collected with an automatic ISCO 6712 portable water sampler with 1L open plastic bottles and transferred in 12 mL vials every one to two weeks. All isotope analysis were performed using a Wavelength-Scanned Cavity Ring Down Spectrometer (Picarro 2140-I, Santa Clara, California, USA) and expressed relative to the international Vienna Standard Mean Ocean Water (VSMOW) standards.</p> <p>All EC and water temperature measurements were performed with a WTW Multi 3510 IDS logger with a IDS TetraCon® 925 probe.</p> <p>The dataset contains measurements performed at various locations within the catchment. A total of approximately 1500 measurements are provided. In the dataset each point correspond to a measurement station (column "<strong>Station</strong>") which we classified in specific class of water (column "<strong>Type</strong>") as follows :</p> <ul> <li><strong>Stream </strong>: samples collected at three locations, from the glacier snout, after a small outwash plain and 2km downstream.</li> <li><strong>Tributary </strong>: 5 hillslopes tributaries originating from small seasonal overland flow or small springs at the base of the morainic hillslope. Those tributaries were monitored weekly. In addition, a few other seasonal lateral streams were sampled in various locations (Type: Other tributaries).</li> <li><strong>Bedrock </strong>: A few exfiltrations directly leaking out of the bedrock outcrop were sampled.</li> <li><strong>Ice </strong>: Ice was sampled either as surface ice (small cores 5 cm deep), as deeper cores (5 to 8m deep) or as meltwater from supraglacial gullies. All solid ice samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials.</li> <li><strong>Snow </strong>: The snowpack was sampled either at the surface (0 to 5cm) or at about 20 cm depth. Where possible, meltwater leaking from the snowpack was sampled. At 3 locations in 2021, we dug snowpits from which we sampled snow at different layers with depth. All solid snow samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials</li> <li><strong>Rain </strong>: Rainwater was mostly sampled at our camp site at 2450 m. asl. Rainwater samples represent single rain events which are identified by dry periods of at least one day long.</li> <li><strong>Groundwater </strong>: shallow (2 to 3 meters) fully-screened groundwater wells were installed in the outwash plain and water sampled monthly in the snow-free season.</li> </ul> <p>- GPS coordinates are provided with each point (Swiss coordinate system CH1903+ / LV95<strong> (EPSG: 2056)).</strong></p> <p>- Dates are provided in local timezone (GMT+1 with daylight saving time) and in UTC date format.</p> <p>- Analyitcal error from the Picarro spectrometer is reported as 1 standard deviation.</p> <p>More information can be accessed in the corresponding publication by Müller et al. (to be published in 2023).</p> <p><strong>Data files</strong></p> <ul> <li><em>Otemma_isotope_EC_T_2019_2021.csv</em> : file containing all data with GPS coordinates</li> <li> <p><em>isotope_locations_Otemma.jpg</em> : an overview of the locations of each measurement point</p> </li> <li> <p><em>Otemma_Isotopes_2019-2020.html </em>: interactive plots of all datasets (δ<sup>2</sup>H, EC, temperature), classified by Type.</p> </li> </ul>
Soil water content before and after rain events, May-July 2014-2016, Hainich, Germany, project AquaDiva
<p>This dataset contains soil water content data used for the analysis published in Fischer-Bedtke et al., (2023). It gives spatially distributed soil water contents evaluated at a rain event scale covering the following periods:</p> <p>May 5 - July 26 2014</p> <p>May 13 - July 28 2015</p> <p>May 25 - July 24 2016</p> <p>The enclosed files cover two different soil depth: topsoil (soil depth 7 cm) and subsoil (soil depth 27 cm).</p> <p>Fischer et al. (2023) give details about the included data and should be cited along the with the dataset when using the data.</p> <p><strong>Related datasets</strong></p> <p>Design information on the soil water content measurement points, including position to the next tree, hydraulic soil properties can be found in the follwowing associated dataset</p> <p>Metzger, Johanna Clara, Hildebrandt, Anke, & Filipzik, Janett. (2023). Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8065170</p> <p> </p> <p><strong>References</strong></p> <p>Fischer, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.</p>
Short-term disappearance of foliar litter of three tree species native to rain forest of Puerto Rico
Litter disappearance was examined before (1989) and after (1990) Hurricane Hugo in the Luquillo Experimental Forest, Puerto Rico using mesh litterbags containing abscised Cyrilla racemiflora or Dacryodes excelsa leaves or fresh Prestoea montana leaves. Biomass and nitrogen dynamics were compared among: i) species; ii) mid- and high-elevation forest types; iii) riparian and upland sites; and iv) among pre- and post-hurricane disturbed environments. Biomass disappearance was compared using multiple regression and negative exponential models in which the slopes were estimates of the decomposition rates subsequent to apparent leaching losses and the y-intercepts were indices of initial mass losses (leaching). C. racemiflora leaves with low nitrogen (0.39 %) and high lignin (22.1 %) content decayed at a low rate and immobilized available nitrogen. D. excelsa leaves had moderate nitrogen (0.67 %) and lignin (16.6 %) content, decayed at moderate rates, and maintained the initial nitrogen mass. P. montana foliage had high nitrogen (1.76 %) and moderate lignin (16.7 %) content and rapidly lost both mass and nitrogen. There were not significant differences in litter disappearance and nitrogen dynamics among forest types and slope positions. Initial mass loss of C. racemiflora leaves was lower in 1990 but the subsequent decomposition rate did not change. Initial mass losses and the overall decomposition rates were lower in 1990 than in 1989 for D. excelsa. D. excelsa and C. racemiflora litter immobilized nitrogen in 1990 but released 10-15% of their initial N in 1989, whereas P. montana released nitrogen in both years (25-40 %). Observed differences in litter disappearance rates between years may have been due to differences in the timing of precipitation. Foliar litter inputs during post-hurricane recovery of vegetation in Puerto Rico may serve to immobilize and conserve site nitrogen. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-00
Geospatial data for Luquillo Mountains, Puerto Rico: Mean annual precipitation, elevation, watershed outlines, and rain gage locations
The data archive is here: https://doi.org/10.5066/F74F1PM2 please use this DOI when citing this data set. These geospatial data sets were developed as part of a new analysis of all known current and historical rain gages in the Luquillo Mountains, Puerto Rico published in the journal article Murphy, S.F., Stallard, R.F., Scholl, M.A., Gonzalez, G., and Torres-Sanchez, A.J., 2017, Reassessing rainfall in the Luquillo Mountains, Puerto Rico: Local and global ecohydrological implications: PLOS One 12(7): e0180987, p. 1-26, https://doi.org/10.1371/journal.pone.0180987. That article provides a revised map of mean annual precipitation developed using elevation regression functions and residual interpolation, and that map is presented here in a raster file. Most previous forest- and watershed-wide estimates of precipitation (and evapotranspiration, as inferred by a water balance) have assumed that precipitation increases consistently with elevation in the Luquillo Mountains; therefore, precipitation in leeward Luquillo watersheds has been overestimated by up to 40%.Because the Luquillo Mountains often serve as a wet tropical archetype in global assessments of basic ecohydrological processes, these revised estimates are relevant to regional and global assessments of runoff efficiency, hydrologic effects of reforestation, geomorphic processes, and climate change. \<para\> Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.\</para\>
Microwave single scattering properties of non-spheroidal rain drops
<p>The database contains single scattering properties (SSP) of non-spheroidal droplets. They were modeled using the parameterization by Chuang and Beard (1990) which make use of Chebyshev polynomials. Spherical and spheroidal drop are also included for reference. The spheroidal drops are set to have the same aspect ratio as the Chebyshev drops.</p> <p>The frequency and temperature grid is identical to the one used for the SSP database presented in Eriksson et al. (2018). Frequencies range from 1 to 886.4 GHz and 5 temperatures from 230 to 310 K are included. Sizes range from 10 μm to 5.75 mm, with logarithmic spacing up to 1 mm and linear spacing above 1 mm in steps of 0.25 mm. Note that below 788 μm the Chebyshev drops are essentially spherical, and are therefore not included. At sizes below 788 μm the spheroidal drop SSP can be used instead.</p> <p>A manuscript (Ekelund et al., 2020) has been submitted to Atmospheric Measurement Techniques, which will serve as the main documentation of the data.</p> <p>Database Specifications:</p> <p>Format:<br> NetCDF4</p> <p>Version:<br> 1.0.0</p> <p>Shapes:<br> Chebyshev (non-spheroidal), spheroidal, sphere.</p> <p>Diameter grid (um):<br> 1.00, 1.27, 1.61, 2.04, 2.59, 3.29, 4.18, 5.30, 6.72, 8.53, 10.83, 13.74, 17.43, 22.12, 28.07, 35.62, 45.20, 57.36, 72.79, 92.37, 117.21, 148.74, 188.74, 239.50, 303.92, 385.66, 489.39, 621.02, 788.05, 1000.00, 1250.00, 1500.00, 1750.00, 2000.00, 2250.00, 2500.00, 2750.00, 3000.00, 3250.00, 3500.00, 3750.00, 4000.00, 4250.00, 4500.00, 4750.00, 5000.00, 5250.00, 5500.00, 5750.00.</p> <p>Frequency grid (GHz):<br> 1.00, 1.40, 3.00, 5.00, 7.00, 9.00, 10.00, 13.40, 15.00, 18.60, 24.00, 31.30, 31.50, 35.60, 50.10, 57.60, 88.80, 94.10, 115.30, 122.20, 164.10, 166.90, 175.30, 191.30, 228.00, 247.20, 314.20, 336.10, 439.30, 456.70, 657.30, 670.70, 862.40, 886.40.</p> <p>Temperature grid (K):<br> 230, 250, 270, 290, 310.</p> <p>References:</p> <p>Ekelund, R., Eriksson, P., and Kahnert, M.: Microwave single scattering properties of non-spheroidal rain drops, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-85, in review, 2020.</p> <p>Eriksson, P., Ekelund, R., Mendrok, J., Brath, M., Lemke, O., and Buehler, S. A.: A general database of hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Syst. Sci. Data, 10, 1301–1326, https://doi.org/10.5194/essd-10-1301-2018, 2018.</p>
Data for the Manuscripts of "Variability of Jakarta Rain-Rate Characteristics Associated with the Madden-Julian Oscillation and Topography" and "Subdaily Rain-Rate Properties in Western Java Analyzed Using C-Band Doppler Radar"
<p>This archive consists of the post-processed data of C-Band Doppler Radar (CDR) over Jakarta and surrounding regions for the studies of "Variability of Jakarta Rain-Rate Characteristics Associated with the Madden-Julian Oscillation and Topography" and "Subdaily Rain-Rate Properties in Western Java Analyzed Using C-Band Doppler Radar".</p> <p>The dataset is a gridded rainfall data derived from the local relationship of Z (reflectivity) from the CDR and rainfall (R) from stations. The derived rainfall data are in daily estimates from 2009 to 2012 with the format in NetCDF files.</p> <p>The CDR data were obtained from the projects “Hydrometeorological Array for Intraseasonal Variation-Monsoon Automonitoring (HARIMAU)” (JFY 2005-2009), and the Science Technology Research Partnership for Sustainable Development (SATREPS) “Maritime Continent Center of Excellence (MCCOE) (JFY 2009-2013) of the Japan Science and Technology Agency (JST)/Japan International Cooperation Agency(JICA) under a collaboration of the Agency for the Assessment and Application of Technology (BPPT)-Indonesia and Japan Agency for Marine-earth Science and Technology (JAMSTEC)-Japan.</p>
Annual rain erosion (R) in Brazil
<p>The erosivity data in Brazil. It has a spatial resolution of <strong>30 seconds (~ 1 km²)</strong>. The data set grid is in <strong>GeoTIFF</strong> <strong>format </strong>and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection (EPSG: 4326)</strong>.</p> <p>Soil is a most important non-renewable natural resource for sustaining life. The rates of soil loss have been increasing. The strength of storms can become a disturbing factor, this water energy is known as rain erosivity, and is a major cause of the loss of sediment and nutrients worldwide. The method of obtaining these values is not simple and is usually one-off and uses the USLE or RUSLE equation. Point values cannot be applied in areas that need to estimate soil losses. And traditional spatialization techniques like kriging, IDW or Thiessen polygons do not represent the variability that actually occurs. Thus, the objective of this article was to model a map of rainfall erosivity for Brazil, with spatial resolution of 30 seconds of arc (~ 1 km²). Using products made available by other articles, GIS techniques and machine learning modeling. Of the 31 pre-selected covariates 8 were used in the modeling, in order of importance, they were: Longitude, Solar Radiation, Annual precipitation (BIO12), Precipitation of the coldest quarter (BIO19), Wind speed, Precipitation of the warmest quarter (BIO18 ) and the annual reference evapotranspiration. After 400 trainings and validations, the model with the best performance indicators was the Random Forest, using the medians, the indices were: NSE of 0.5823, RMSE of 1567.17 MJ.mm/ha.h.ano, MAE of 1135.90 MJ.mm / ha.h.year, nRMSE of 58.50%, ME of -17.76 MJ.mm/ha.h.year and D of 0.8487.</p> <p>The article was submitted for publication.</p> <p>Dados_Erosividade_BR.csv - Data used to model the models.<br> eros_cubist.tif - Erosivity image generated by the cubist model<br> eros_gbm.tif - Image of erosivity generated by the gbm model<br> eros_lm.tif - Erosivity image generated by the linear model<br> eros_rf.tif - Erosivity image generated by the random forest model</p>
Spatially Aggregated Rain Radar Forecast for the Koeln Weiden, Germany
<p>radar_forecast.csv contains time series data generated by spatial aggregation of rain radar forecasts constructed using robust local optical flow extrapolation.</p>
Wastewater Treatment Plant Inflow Rate, Network Water Levels and Rain Rate for Koeln Weiden, Germany
<p>wtp_network_rain.csv contains time series data of measured wastewater treatment plant inflow rates, water levels from multiple locations within the wastewater sewer network and rain rate measured from a rain recorder located at the wastewater treatment plant.</p>
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models
<p>This Zenodo repository contains the runs data for the paper <strong>RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models </strong>[<a href="https://arxiv.org/abs/2408.16118" target="_blank" rel="noopener">https://arxiv.org/abs/2408.16118</a>] presented at the NeurIPS 2024 workshop on Tackling Climate Change with Machine Learning, as well as the Master of Research (MRes) report <strong>Towards improving weather and climate models using reinforcement learning</strong> at the University of Cambridge<strong>.</strong> For questions, please contact Pritthijit Nath, <a href="mailto:pn341@cam.ac.uk" target="_blank" rel="noopener">pn341@cam.ac.uk</a>. Full documentation is available in the README.md file of the associated <a href="https://github.com/nathzi1505/climate-rl" target="_blank" rel="noopener">GitHub repo</a>.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.