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1,118 results for “Time series”
Observed runoff time series from a green roof field campaign in Hannover-Herrenhausen
<p>This dataset includes a csv file comprising observed runoff time series from a green roof field campaign, which was conducted by Prof. Dr.-Ing. Hans-Joachim Liesecke. The csv file provides runoff from 11 green roof variants (10 was excluded, since it has a different design). Rows include daily runoff totals (collected each morning, excluding weekends). Please refer to this article, which describes the dataset in more detail: </p> <p><strong>Iffland, R., Förster, K., Westerholt, D., Pesci, M. H., & Lösken, G. Robust vegetation parameterization for green roofs in EPA SWMM. Hydrology. </strong><a href="https://doi.org/10.3390/hydrology8010012">https://doi.org/10.3390/hydrology8010012</a></p> <p>The field campaign involved a total of 11 superstructures in triple repetition. In the csv file, each column represents average values computed out of three independent measurements (in mm*d<sup>-1</sup>)</p> <p>The individual test plots were 2 m x 2 m with a slope of 2 % and a drainage opening in the middle of the lowest point of the slope. The outflowing water was collected in non-weighable lysimeters (rain barrels) that were read and emptied at 8 A.M. every day. On weekends, readings were taken the following workday.</p> <p> </p>
Dataset of Sentinel-1 surface soil moisture time series at 1 km resolution over Southern Italy
<p>The dataset consists of a time series of the Sentinel-1 (S-1) surface soil moisture (SSM) product at 1 km spatial resolution validated in Balenzano et al. (2021 a) over the Southern Italy. The specifications of the S-1 SSM product are provided in Balenzano et al. (2021 b). The SSM time series was obtained in correspondence of the ascending (RON A146) S-1 Interferometric Wide swath (IW) acquisition dates from January 2015 to December 2018 with a temporal gap between consecutive of 6 days (when both S-1A and S-1B data are available) or 12 days. On each date (183 in total), two co-registered layers are provided: mean SSM [m3/m3] and its standard deviation [m3/m3], which provides the SSM uncertainty. The retrieval algorithm is a time series short term change detection (STCD) that is implemented in the “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR) code (Balenzano et al. 2013).</p>
1988-2009 time-series of land-use/land-cover maps for the Mar Menor / Campo de Cartagena watershed by means of supervised classification of Landsat images.
<p>Serie de mapas de usos y coberturas de la cuenca del Mar Menor (SE España): 2009, 2000, 1997 y 1998. Así como el documento completo de tesis en las que se generaron y analizaron.</p> <p>Time-series of land-use / land-cover maps of Mar Menor watershed (SE Spain): 2009, 2000, 1997 y 1998. As well as the complete thesis document in which they were generated and analyzed.</p>
Modelled time series of CO2 in the vicinity of a seep in the North Sea.
<p>The Bergen Ocean Model (BOM) is used to simulate dispersion of CO2 leaking from nine different locations in the North Sea, focusing on temporal and spatial variability of the CO2 concentration. </p> <p>For details see: Ali, A., Frøysa, H. G., Avlesen, H., & Alendal, G. (2016). Simulating spatial and temporal varying CO 2signals from sources at the seafloor to help designing risk-based monitoring programs. <em>Journal of Geophysical Research-Oceans</em>, <em>121</em>(1), 745–757. http://doi.org/10.1002/2015JC011198.</p> <p>The data files contains time series of excess CO2 concentration, velocity components and time, and position in longitude and latitude. One file for each grid position, a total of 53x51 grid points with the seep in location (I,j)=(28,27). </p>
Multi-year temperature time series from cinder cones on the Mauna Kea summit plateau, Hawaii
<p>Multi-year time series from data loggers in several cinder cones on the Mauna Kea summit plateau, Hawaii. The variable measured is primarily ground temperature, but several time series for air temperature, relative humidity, and light intensity have also been obtained. </p> <p>Study sites, time period:<br>Puu Wekiu (Summit Cone Crater), 2012-2025<br>Puu Hau Kea (Goodrich Cone), 2012-2025<br>Puu Waiau, 2013-2016<br>Puu Pohaku (Douglas Cone), 2014-2016<br>Puu Makanaka, 2016</p> <p>The raw data are downloads from HOBO data loggers. The reduced data are merged time series without time gaps and corrected for known issues. A list of data loggers with time periods and sensor depths is provided in loggers_summary_mk.csv. For file formats and further information, see README.TXT. For context see references.</p>
Synthetic mobile service traffic time series
<p>This dataset contains synthetic mobile service traffic time series used in the paper titled "kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices", presented at ACM MobiHoc 2023 in Washington, USA. It is composed of 20 time series representing the fluctuations of demands for diverse services categorized under 5G types, including enhanced Mobile Broadband (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC). The time series cover a period of XXX days, and were shown to yield similar properties as those observed in real-world traffic collected in a production mobile network.</p>
Perodiogram NDVI Time Series From AVHRR
<p>Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.</p>
The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series
<p><strong>The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series</strong></p> <p>This repository contains global lightning stroke density and stroke power calculated from georeferenced stroke count data from the World Wide Lightning Location Network <a href="http://wwlln.net">WWLLN</a>. The real-time raw stroke count data were reprocessed by WWLLN to remove artifacts and improve geolocation, which resulted in the "AE" georeferenced and timestamped stroke count data. These data were then gridded at 0.5 degree 5 arc-minute and hourly resolution, converted into density, and corrected for detection efficiency using the WWLLN global gridded detection efficiency maps. Mean, median, and standard deviation of stroke power are also provided at 30-minute resolution. The corrected hourly rasters were then aggregated into daily and monthly totals and into a multi-year monthly mean climatology. The data cover the period 2010-2024 and will be updated in the coming years.</p> <p>For a complete description of the data see:</p> <p>Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. <em>Earth System Science Data, 13</em>(7), 3219-3237. <a href="dx.doi.org/10.5194/essd-13-3219-2021">doi:10.5194/essd-13-3219-2021</a></p> <p>Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. <em>Earth System Science Data, 14</em>(12), 5665-5670. <a href="dx.doi.org/10.5194/essd-14-5665-2022">doi:10.5194/essd-14-5665-2022</a></p> <p>The data are stored in a <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> (version 4) files and have the following attributes:</p> <ul> <li>Spatial extent: Entire Earth</li> <li>Spatial reference system (SRS): Unprojected (geographic, WGS84)</li> <li>Spatial resolution: half-degree and 5 arc-minute</li> <li>Temporal extent: 2010-2024</li> <li>Temporal resolution: daily and monthly*1,2</li> </ul> <p><strong>Variables included in this release</strong></p> <ul> <li>Lightning density (strokes km-2 day-1)</li> <li>Lightning mean, median, and standard deviation of stroke power (MW, 30 arc-minute version only)</li> </ul> <p>For further details, see <a href="https://github.com/ARVE-Research/WGLC">https://github.com/ARVE-Research/WGLC</a></p> <p>1*5479 elements in the time dimension for daily data; 180 for monthly data; 12 for the climatology.</p> <p>2*Daily fields currently available at 30-minute resolution only.</p> <p><a href="../doi/10.5281/zenodo.4774528">The WWLLN Global Lightning Climatology and timeseries (WGLC) </a>© 2025 by Jed O. Kaplan is licensed under <a href="http://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1">CC BY-SA 4.0</a></p>
Long time-series ecological niche modelling using archaeological settlement data.
<p><strong>CR_settlement_niche_[N]_[Yr]_[BC/AD].tif</strong></p> <p>Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement.<br> <strong>N</strong> - chronological ordering<br> <strong>Yr, BC/AD</strong> - calendar years BC or AD</p> <p> </p> <p><strong>CR_settlement_niche_combined.tif</strong></p> <p>All models combined by averaging.</p> <p> </p> <p><strong>CR_settlement_archeo.zip</strong></p> <p>Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields:</p> <p><strong>Site_Type:</strong> Cemetery or Settlement</p> <p><strong>Archeo_Dat:</strong> Archaeological dating (culture or period)</p> <p><strong>Source:</strong> Source dataset (AMCR or LONGWOOD)</p> <p>AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/.</p> <p>LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016). Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554.</p> <p><strong>Yrs_BP_Avg:</strong> Average dating in calendar years BP (based on the archaeological dating)</p> <p><strong>Yrs_BP_Unc:</strong> Temporal uncertainty of the dating (half of the culture or period's duration)</p> <p><strong>Loc_Accur:</strong> Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)</p>
GeoERA RESOURCE CHAKA data set which contains time series of precipitation and discharge of springs in the CHAKA pilot areas (D5.5)
<p>Dataset which contains time series of precipitation and discharge of springs in the pilot areas of the CHAKA work package of the GeoERA RESOURCE project. The file contains precipitation and spring discharge data of 16 pilot areas in the Karst & Chalk work package. A description of the application of the dataset for the characterisation of the typology of karst systems in given in the D5.3 deliverable of GeoERA RESOURCE of which the pdf is provided. Further information about the CHAKA results can be assessed though the webservices of the European Geological Data Infrastructure (EGDI). </p>
Data for time series tutorial
<p>These are sample data files to be used in the time series tutorial found here: <a href="https://github.com/abigailStev/timeseries-tutorial">https://github.com/abigailStev/timeseries-tutorial </a></p> <p>They are public datasets from the NICER X-ray Timing Instrument of a black hole, MAXI J1535-571, and a neutron star, Swift J0243.6+6124. There are also Good Time Intervals I created for each of the photon event lists.</p>
Twitter hashtags time series used in the paper "Universality, criticality and complexity of information propagation in social media"
<pre>These files contain the time series and the associated hashtags we obtained by sampling Twitter for our paper "Universality, criticality and complexity of information propagation on social media". The analysis is reported in <a href="https://arxiv.org/abs/2109.00116">https://www.nature.com/articles/s41467-022-28964-8</a> Please acknowledge the use of these data by citing the paper above. ################################# ################################# DATA ORGANIZATION We created a single zip file with all the time series and a single zip file with all the hashtags. There is a one-to-one correspondence between lines in the two files. ################################# ################################# FILES CONTENT As stated, here is a one-to-one correspondence between lines in the time series file and lines in the hashtags file, i.e., the hashtag stored in line X is the hashtag of the time series stored in line X. Time series are stored as follows: Ka t1 t2 t3 \n Kb t1 t2 t3 t4 t5 \n . . . Kn t1 t2 \n where: Ka, Kb,..., Kn is an integer specifying the number of events that compose the time series a, b,..., n respectively. In the example above we would have Ka=3, Kb=5, Kn=2. t1 t2 ... is the time series, i.e., a sequence of chronologically ordered interevent times. The last interevent time, in our implementation, represents the distance between the end of the temporal window and the last event time. It thus does not represent an event. As stated in the Supplemental Material of our paper, the temporal window ranges from 2019, October 1st to 2019, November 30th. </pre>
A DETAILED TIME SERIES OF HOURLY CIRCUMFERENCE VARIATIONS IN PINUS PINEA L. IN CHILE
<ul> <li>The dataset provides digital dendrometer measurements on stem circumference of irrigated and non-irrigated<em> Pinus pinea</em> trees. Data were obtained in a xeric non-native habitat of central Chile. Forest mensuration were hourly collected from six adult trees during a growth year.</li> </ul>
Adriatic Sea wind and wave time series years 1981-2010 and 2021-2050 (RCP4.5 and RCP8.5)
<p>Wind and wave time series for 27 stations in the Adriatic Sea.</p> <p>Variables:</p> <p>- 10-m height wind speed (wnd) and wind direction (wnddir) from 1-hour ERA5 fields (25 km) statistically scaled to QQ-match COSMO-CLM fields (8 km)</p> <p>- significant wave height (hs) and peak wave period (tp) from WAVEWATCH III v6.07 (2 km) forced with the scaled ERA5 wind fields</p> <p>Reference periods:</p> <p>- Historical climate: years 1981-2010</p> <p>- Future climate: years 2021-2050 (IPCC scenario RCP4.5 and RCP8.5)</p>
Pacific salmon population time-series dataset to support Appendix S1: Data and additional information on declines of Pacific Salmon
<p>Dataset used to support the main paper 'Protecting our coast for everyone’s future: Indigenous and scientific knowledge support marine spatial protections proposed by Central Coast First Nations in Pacific Canada' by Reid et al. 2022. Dataset cited in Appendix S1 regarding trends in adult salmon abundances in the Central Coast. The data were as compiled by Will Atlas from the <a href="https://wildsalmoncenter.org/">Wild Salmon Center</a> to describe trends in the abundance of adult salmon returning to the Central Coast, which is the sum of escapement and harvest, as derived from the following sources:</p> <ol> <li>Escapement data from DFO: <a href="https://open.canada.ca/data/en/dataset/c48669a3-045b-400d-b730-48aafe8c5ee6">NuSEDS-New Salmon Escapement Database System - Open Government Portal (canada.ca)</a></li> <li>Harvest rates estimated by Karl English and colleagues and available at: <a href="https://data.salmonwatersheds.ca/data-library/">Salmon Watersheds Program - Data Library</a>.</li> <li>Information on total harvest that is reported in the DFO post season review (DFO 2020).</li> </ol>
Decadal time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds.<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to decadal averages. Each month is divided into three decades: the first decade of a month covers days 1-10, the second decade covers days 11-20, and the third decade covers days 21-last day of the month.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month; dD = number of decade):<br> <code>ERA5_land_rh2m_avg_decadal_YYYY_MM_dD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Decadal</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7427010">https://zenodo.org/record/7427010</a></p>
Time series measurements of nitrogen fixation in the subtropical North Pacific (extended through 2019)
<p>Rates of N<sub>2</sub> fixation were measured using the <sup>15</sup>N<sub>2</sub> isotopic tracer technique. Sampling occurred during near-monthly Hawaii Ocean Time-series cruises. Whole seawater samples from six discrete depths (5, 25, 45, 75, 100, and 125 m) were subsampled into acid-washed 4.3 L polycarbonate bottles. The <sup>15</sup>N<sub>2</sub> gas was first dissolved into seawater and 100 mL of the resulting <sup>15</sup>N<sub>2</sub>-enriched water was added to 4.3 L polycarbonate sampling bottles. The resulting atom % enrichment of stocks of <sup>15</sup>N<sub>2</sub>-enriched seawater was measured using a membrane inlet mass spectrometer. Incubation bottles amended with the <sup>15</sup>N<sub>2</sub> tracer were attached to a free-drifting array and incubated at the discrete depths from which samples had been collected. The array was deployed before dawn and samples were incubated at in situ light and temperature for 24 h. After recovery of the array, the entire volume from each bottle was filtered onto a pre-combusted glass microfiber filter (Whatman 25 mm GF/F) and filters were placed onto pre-combusted pieces of foil in Petri dishes and stored frozen at -20°C. Filters were dried for 24 h at 60°C, pelleted, and the total mass of N and its isotopic signature on each filter were analyzed on an elemental analyzer-isotope ratio mass spectrometer (Carlo-Erba EA NC2500 coupled with ThermoFinnigan Delta S). </p>
Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (Set 5: 2020 - 2021) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7434396">https://zenodo.org/record/7434396</a></p>
Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (Set 3: 2010 - 2014) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7432432">https://zenodo.org/record/7432432</a></p>
Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (Set 4: 2015 - 2019) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7434447">https://zenodo.org/record/7434447</a></p>
ScienceDex guides
Understand access before you commit
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