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1,118 results for “Time series”

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zenodo44/100

Enron Email Time-Series Network

We use the <a href="https://www.kaggle.com/wcukierski/enron-email-dataset">Enron email dataset</a> to build a network of email addresses. It contains 614586 emails sent over the period from 6 January 1998 until 4 February 2004. During the pre-processing, we remove the periods of low activity and keep the emails from 1 January 1999 until 31 July 2002 which is 1448 days of email records in total. Also, we remove email addresses that sent less than three emails over that period. In total, the Enron email network contains 6 600 nodes and 50 897 edges. To build a graph G = (V, E), we use email addresses as nodes V. Every node vi has an attribute which is a time-varying signal that corresponds to the number of emails sent from this address during a day. We draw an edge eij between two nodes i and j if there is at least one email exchange between the corresponding addresses. Column 'Count' in 'edges.csv'  file is the number of 'From'->'To' email exchanges between the two addresses. This column can be used as an edge weight. The file 'nodes.csv' contains a dictionary that is a compressed representation of time-series. The format of the dictionary is Day->The Number Of Emails Sent By the Address During That Day. The total number of days is 1448. 'id-email.csv' is a file containing the actual email addresses.

opencc-by-4.0Aug 2018View details →

zenodo44/100

Photovoltaic time series for European countries and different system configurations

This repository comprises 38 years-long hourly time series representing the photovoltaic (PV) capacity factors in every European country (EU-28 plus Serbia, Bosnia-Herzegovina, Norway, and Switzerland). The term capacity factor is defined as the ratio between the delivered power and the cumulative installed capacity (DC). 3 letter codes (ISO-3166-3) are used to identify the countries. Time series include years from 1979 to 2017. To obtain PV time series irradiance from Climate Forecast System Reanalysis (CFSR) dataset has been converted into electricity generation and aggregated at country level. The PV model used for the conversion is described in the article linked below. Prior to conversion, reanalysis irradiance is bias corrected using satellite-based SARAH dataset and a globally-applicable methodology, which is also described in the article. For every country, four different time series assuming alternative PV configurations, i.e., rooftop, optimum tilt, 2-axis tracking, and delta are provided. To obtain the PV hourly capacity factors for a country, different assumptions on the shares of the alternative configurations can be made and the weighted time series can be aggregated accordingly. The license for the AU REatlas photovoltaic time series dataset is: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0)</a> When using this data please make sure you include the following citation: M. Victoria and Gorm B. Andresen, Using validated reanalysis data to investigate the impact of the PV system configurations at high penetration levels in European countries, Progress in Photovoltaics: Research and Applications (2019)  <a href="https://doi.org/10.1002/pip.3126">https://doi.org/10.1002/pip.3126</a> More information can be requested from M. Victoria (<a href="mailto:mvp@eng.au.dk">mvp@eng.au.dk</a>) and Gorm B. Andresen (<a href="mailto:gba@eng.au.dk">gba@eng.au.dk</a>). Version 2 assumes tilt angle of 60º for PV panels in delta configuration (in version 1, tilt angle in delta configuration is equal to latitude). The remaining files do not change. Version 3 includes one additional file corresponding to country-wise time series obtained assuming 1 axis-tracker (horizontal axis oriented North-South). In addition, small corrections of the previous time series have been implemented affecting only early hours in the day.

opencc-by-4.0Jul 2018View details →

zenodo44/100

CO2 emissions, water table and temperature time series from an undrained tropical peatland

Supplement to: Hoyt, A. M., Gandois, L. , Eri, J. , Kai, F. M., Harvey, C. F. and Cobb, A. R. (2019), CO2 emissions from an undrained tropical peatland: Interacting influences of temperature, shading and water table depth. Global Change Biology. https://doi.org/10.1111/gcb.14702

opencc-by-4.0May 2019View details →

zenodo44/100

Fed4Fire/CDN-X-ALL network metrics dataset for time series analysis in Media content delivery for 4G/5G networks

The following dataset was generated at VICOMTECH (https://www.vicomtech.org) under project/experiment CDN-X-ALL: "CDN edge-cloud computing for efficient cache and reliable streaming aCROSS Aggregated unicast-multicast LinkS". Project funded by Fed4FIRE+ OC5 (<a href="https://www.fed4fire.eu/">https://www.fed4fire.eu</a>) under grant 732638. The dataset provides network metrics captures across several days employing a GStreamer-based MPEG-DASH player running on an UE connected to a LTE network. Nitos LTE/OpenAirInterface (OAI) testbed (<a href="https://nitlab.inf.uth.gr/NITlab/nitos/lte">https://nitlab.inf.uth.gr/NITlab/nitos/lte</a>) was used to deploy the LTE network. CDN-like server/DASH Dataset -> Internet -> EPC/OAI -> eNodeB/OAI -> UE/DASH player The player downloads MPEG-DASH video files provided by Distributed DASH dataset (<a href="https://dash.itec.aau.at/distributed-dash-datset/">https://dash.itec.aau.at/distributed-dash-datset/</a>), a dataset for CDN-like experiments, and captures the following data: <ol> <li>Date: date when the data is collected</li> <li>Player: type of the player (in this case it is always "GStreamer")</li> <li>Num: identifier of the player</li> <li>URLVid: URL of the MPD file</li> <li>Latency: latency experienced by the player</li> <li>BW: bandwidth experienced by the player</li> <li>Quality: chosen DASH video representation</li> </ol> During the experiments, other players run in order to generate realistic media streaming traffic at the CDN-like servers. These players start playing by following Poisson or Pareto distribution. The dataset was used to train Machine Learning Time Series predictor in order to forecast network capabilities and can be used for further experimentation concerning time series analysis.

opencc-by-4.0Sep 2019View details →

zenodo44/100

Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement"

Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement" submitted to The Cryosphere.

opencc-by-4.0Sep 2019View details →

zenodo44/100

Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification

AcTBeCalf Dataset Description The AcTBeCalf dataset is a comprehensive dataset designed to support the classification of pre-weaned calf behaviors from accelerometer data. It contains detailed accelerometer readings aligned with annotated behaviors, providing a valuable resource for research in multivariate time-series classification and animal behavior analysis. The dataset includes accelerometer data collected from 30 pre-weaned Holstein Friesian and Jersey calves, housed in group pens at the Teagasc Moorepark Research Farm, Ireland. Each calf was equipped with a 3D accelerometer sensor (AX3, Axivity Ltd, Newcastle, UK) sampling at 25 Hz and attached to a neck collar from one week of birth over 13 weeks. This dataset encompasses 27.4 hours of accelerometer data aligned with calf behaviors, including both prominent behaviors like lying, standing, and running, as well as less frequent behaviors such as grooming, social interaction, and abnormal behaviors. The dataset consists of a single CSV file with the following columns: <ul> <li>dateTime: Timestamp of the accelerometer reading, sampled at 25 Hz.</li> <li>calfid: Identification number of the calf (1-30).</li> <li>accX: Accelerometer reading for the X axis (top-bottom direction)*.</li> <li>accY: Accelerometer reading for the Y axis (backward-forward direction)*.</li> <li>accZ: Accelerometer reading for the Z axis (left-right direction)*.</li> <li>behavior: Annotated behavior based on an ethogram of 23 behaviors.</li> <li>segId: Segment identification number associated with each accelerometer reading/row, representing all readings of the same behavior segment.</li> </ul> * the directions are mentioned in relation to the position of the accelerometer sensor on the calf. Code Files Description The dataset is accompanied by several code files to facilitate the preprocessing and analysis of the accelerometer data and to support the development and evaluation of machine learning models. The main code files included in the dataset repository are: <ol> <li>accelerometer_time_correction.ipynb: This script corrects the accelerometer time drift, ensuring the alignment of the accelerometer data with the reference time.</li> <li>shake_pattern_detector.py: This script includes an algorithm to detect shake patterns in the accelerometer signal for aligning the accelerometer time series with reference times.</li> <li>aligning_accelerometer_data_with_annotations.ipynb: This notebook aligns the accelerometer time series with the annotated behaviors based on timestamps.</li> <li>manual_inspection_ts_validation.ipynb: This notebook provides a manual inspection process for ensuring the accurate alignment of the accelerometer data with the annotated behaviors.</li> <li>additional_ts_generation.ipynb: This notebook generates additional time-series data from the original X, Y, and Z accelerometer readings, including Magnitude, ODBA (Overall Dynamic Body Acceleration), VeDBA (Vectorial Dynamic Body Acceleration), pitch, and roll.</li> <li>genSplit.py: This script provides the logic used for the generalized subject separation for machine learning model training, validation and testing.</li> <li>active_inactive_classification.ipynb: This notebook details the process of classifying behaviors into active and inactive categories using a RandomForest model, achieving a balanced accuracy of 92%.</li> <li>four_behv_classification.ipynb: This notebook employs the mini-ROCKET feature derivation mechanism and a RidgeClassifierCV to classify behaviors into four categories: drinking milk, lying, running, and other, achieving a balanced accuracy of 84%.</li> </ol> Kindly cite one of the following papers when using this data: Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models. arXiv preprint arXiv:2404.18159. Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). Development of a digital tool for monitoring the behaviour of pre-weaned calves using accelerometer neck-collars. arXiv preprint arXiv:2406.17352

opencc-by-4.0Aug 2024View details →

zenodo44/100

Time series of turbidity in Northern Patagonia using the Nechad algorithms (v2009 and v2016) at 665 nm. Time series 2016-2020.

Time series of turbidity in Northern Patagonia using the Nechad algorithms (v2009 and v2016) at 665 nm. Time series 2016-2020. Our study aimed to evaluate the spatio-temporal variability of turbidity from Sentinel-2 (S2) images in the Reloncaví sound and fjord, in Northern Patagonia, Chile, a coastal ecosystem that is intensively used by finfish and shellfish aquaculture. To this end, we downloaded 123 S2 images and assembled a five-year time series (2016-2020) covering five study sites (R1 to R5) located along the axis of the fjord and seaward into the sound. We used Acolite to perform the atmospheric correction and estimate turbidity with two algorithms proposed by Nechad et al. (2009, 2016 Nv09 and Nv16, respectively). Columns (R) represent the spatial distribution of study sites (see Figure 2). Link: https://doi.org/10.1016/j.ecoinf.2024.102814 For more information see materials and methods. Nv2009 or Nv09 are the results obtained for the Nechad algorithm version 2009. Similar to Nv2016 or Nv16 are the results obtained for the Nechad algorithm version 2016.

opencc-by-4.0Sep 2024View details →

zenodo44/100

CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting

<h2>CESNET-TimeSeries24: The dataset for network traffic forecasting and anomaly detection</h2> The dataset called CESNET-TimeSeries24 was collected by long-term monitoring of selected statistical metrics for 40 weeks for each IP address on the ISP network CESNET3 (Czech Education and Science Network). The dataset encompasses network traffic from more than 275,000 active IP addresses, assigned to a wide variety of devices, including office computers, NATs, servers, WiFi routers, honeypots, and video-game consoles found in dormitories. Moreover, the dataset is also rich in network anomaly types since it contains all types of anomalies, ensuring a comprehensive evaluation of anomaly detection methods. Last but not least, the CESNET-TimeSeries24 dataset provides traffic time series on institutional and IP subnet levels to cover all possible anomaly detection or forecasting scopes. Overall, the time series dataset was created from the 66 billion IP flows that contain 4 trillion packets that carry approximately 3.7 petabytes of data. The CESNET-TimeSeries24 dataset is a complex real-world dataset that will finally bring insights into the evaluation of forecasting models in real-world environments. Please cite the usage of our dataset as: <blockquote> Koumar, J., Hynek, K., Čejka, T. et al. CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting. Sci Data 12, 338 (2025). https://doi.org/10.1038/s41597-025-04603-x @Article{cesnettimeseries24,     author={Koumar, Josef and Hynek, Karel and {\v{C}}ejka, Tom{\'a}{\v{s}} and {\v{S}}i{\v{s}}ka, Pavel},     title={CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting},     journal={Scientific Data},     year={2025},     month={Feb},     day={26},     volume={12},     number={1},     pages={338},     issn={2052-4463},     doi={10.1038/s41597-025-04603-x},     url={https://doi.org/10.1038/s41597-025-04603-x} } </blockquote>   <h3>Time series</h3> We create evenly spaced time series for each IP address by aggregating IP flow records into time series datapoints. The created datapoints represent the behavior of IP addresses within a defined time window of 10 minutes. The vector of time-series metrics v_{ip, i} describes the IP address ip in the i-th time window. Thus, IP flows for vector v_{ip, i} are captured in time windows starting at t_i and ending at t_{i+1}. The time series are built from these datapoints.   Datapoints created by the aggregation of IP flows contain the following time-series metrics: <ul> <li>Simple volumetric metrics: the number of IP flows, the number of packets, and the transmitted data size (i.e. number of bytes)</li> <li>Unique volumetric metrics: the number of unique destination IP addresses, the number of unique destination Autonomous System Numbers (ASNs), and the number of unique destination transport layer ports. The aggregation of \textit{Unique volumetric metrics} is memory intensive since all unique values must be stored in an array. We used a server with 41 GB of RAM, which was enough for 10-minute aggregation on the ISP network.   </li> <li>Ratios metrics: the ratio of UDP/TCP packets, the ratio of UDP/TCP transmitted data size, the direction ratio of packets, and the direction ratio of transmitted data size</li> <li>Average metrics: the average flow duration, and the average Time To Live (TTL)</li> </ul>   Multiple time aggregation:  The original datapoints in the dataset are aggregated by 10 minutes of network traffic. The size of the aggregation interval influences anomaly detection procedures, mainly the training speed of the detection model. However, the 10-minute intervals can be too short for longitudinal anomaly detection methods. Therefore, we added two more aggregation intervals to the datasets--1 hour and 1 day. Time series of institutions:  We identify 283 institutions inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution's data.  Time series of institutional subnets: We identify 548 institution subnets inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution subnet's data.    <h3>Data Records</h3> The file hierarchy is described below: <blockquote> cesnet-timeseries24/      |- institution_subnets/      |     |- agg_10_minutes/<id_institution>.csv      |     |- agg_1_hour/<id_institution>.csv      |     |- agg_1_day/<id_institution>.csv      |     |- identifiers.csv      |- institutions/      |     |- agg_10_minutes/<id_institution_subnet>.csv      |     |- agg_1_hour/<id_institution_subnet>.csv      |     |- agg_1_day/<id_institution_subnet>.csv      |     |- identifiers.csv      |- ip_addresses_full/      |     |- agg_10_minutes/<id_ip_folder>/<id_ip>.csv      |     |- agg_1_hour/<id_ip_folder>/<id_ip>.csv      |     |- agg_1_day/<id_ip_folder>/<id_ip>.csv      |     |- identifiers.csv      |- ip_addresses_sample/      |      |- agg_10_minutes/<id_ip>.csv      |      |- agg_1_hour/<id_ip>.csv      |      |- agg_1_day/<id_ip>.csv      |      |- identifiers.csv      |- times/      |      |- times_10_minutes.csv      |      |- times_1_hour.csv      |      |- times_1_day.csv      |- ids_relationship.csv      |- weekends_and_holidays.csv </blockquote> The following list describes time series data fields in CSV files: <ul> <li>id_time:  Unique identifier for each aggregation interval within the time series, used to segment the dataset into specific time periods for analysis.</li> <li>n_flows: Total number of flows observed in the aggregation interval, indicating the volume of distinct sessions or connections for the IP address.</li> <li>n_packets: Total number of packets transmitted during the aggregation interval, reflecting the packet-level traffic volume for the IP address.</li> <li>n_bytes: Total number of bytes transmitted during the aggregation interval, representing the data volume for the IP address.</li> <li>n_dest_ip: Number of unique destination IP addresses contacted by the IP address during the aggregation interval, showing the diversity of endpoints reached.</li> <li>n_dest_asn: Number of unique destination Autonomous System Numbers (ASNs) contacted by the IP address during the aggregation interval, indicating the diversity of networks reached.</li> <li>n_dest_port: Number of unique destination transport layer ports contacted by the IP address during the aggregation interval, representing the variety of services accessed.</li> <li>tcp_udp_ratio_packets: Ratio of packets sent using TCP versus UDP by the IP address during the aggregation interval, providing insight into the transport protocol usage pattern. This metric belongs to the interval <0, 1> where 1 is when all packets are sent over TCP, and 0 is when all packets are sent over UDP.</li> <li>tcp_udp_ratio_bytes: Ratio of bytes sent using TCP versus UDP by the IP address during the aggregation interval, highlighting the data volume distribution between protocols. This metric belongs to the interval <0, 1>  with same rule as tcp_udp_ratio_packets.</li> <li>dir_ratio_packets: Ratio of packet directions (inbound versus outbound) for the IP address during the aggregation interval, indicating the balance of traffic flow directions. This metric belongs to the interval <0, 1>, where 1 is when all packets are sent in the outgoing direction from the monitored IP address, and 0 is when all packets are sent in the incoming direction to the monitored IP address.</li> <li>dir_ratio_bytes: Ratio of byte directions (inbound versus outbound) for the IP address during the aggregation interval, showing the data volume distribution in traffic flows. This metric belongs to the interval <0, 1> with the same rule as dir_ratio_packets.</li> <li>avg_duration: Average duration of IP flows for the IP address during the aggregation interval, measuring the typical session length.</li> <li>avg_ttl: Average Time To Live (TTL) of IP flows for the IP address during the aggregation interval, providing insight into the lifespan of packets.</li> </ul> Moreover, the time series created by re-aggregation contains following time series metrics instead of n_dest_ip, n_dest_asn, and n_dest_port: <ul> <li>sum_n_dest_ip: Sum of numbers of unique destination IP addresses.</li> <li>avg_n_dest_ip: The average number of unique destination IP addresses.</li> <li>std_n_dest_ip: Standard deviation of numbers of unique destination IP addresses.</li> <li>sum_n_dest_asn: Sum of numbers of unique destination ASNs.</li> <li>avg_n_dest_asn: The average number of unique destination ASNs.</li> <li>std_n_dest_asn: Standard deviation of numbers of unique destination ASNs)</li> <li>sum_n_dest_port: Sum of numbers of unique destination transport layer ports.</li> <li>avg_n_dest_port:  The average number of unique destination transport layer ports.</li> <li>std_n_dest_port: Standard deviation of numbers of unique destination transport layer ports.</li> </ul>   Moreover, files  identifiers.csv in each dataset type contain IDs of time series that are present in the dataset. Furthermore, the ids_relationship.csv file contains a relationship between IP addresses, Institutions, and institution subnets. The weekends_and_holidays.csv contains information about the non-working days in the Czech Republic.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Daily time series of 12 human thermal stress indices in Greece aggregated at commune level (1998-2022)

The overview table of the dataset containing 12 Human Thermal Stress Indices in Greece (HTSI-GR): <div> <table> <tbody> <tr> <th> Heat indices names </th> <th> Description </th> <th> Units </th> <th> Reference </th> <th> Dataset file names </th> </tr> <tr> <td> AT </td> <td> Apparent Temperature </td> <td> °C </td> <td> Steadman, R. G. Norms of apparent temperature in Australia. Aust. Met. Mag. 43, 1–16 (1994). </td> <td> AT_min_1998-01-01_2022-12-31.csv, AT_mean_1998-01-01_2022-12-31.csv, AT_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> HI </td> <td> Heat Index </td> <td> °C </td> <td> Rothfusz, L.P. Te heat index equation. National Weather Service Technical Attachment. Report No. SR 90–23 (1990). </td> <td> HI_min_1998-01-01_2022-12-31.csv, HI_mean_1998-01-01_2022-12-31.csv, HI_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> Humidex </td> <td> Humidity Index </td> <td> °C </td> <td> Masterson, J. & Richardson, F.A. Humidex: a method of quantifying human discomfort due to excessive heat and humidity (Environment Canada, 1979). </td> <td> Humidex_min_1998-01-01_2022-12-31.csv, Humidex_mean_1998-01-01_2022-12-31.csv, Humidex_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> NET </td> <td> Normal Effective Temperature </td> <td> °C </td> <td> Landsberg HE. The assessment of human bioclimate: a limited review of physical parameters. Technical Note No. 123, WMO-No. 331 (World Meteorological Organization, 1972). </td> <td> NET_min_1998-01-01_2022-12-31.csv, NET_mean_1998-01-01_2022-12-31.csv, NET_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> WBGT </td> <td> Wet Bulb Globe Temperature (simple) </td> <td> °C </td> <td> Australian Bureau of Meteorology. Thermal comfort observations http://bom.gov.au/info/thermal_stress/ (2020). </td> <td> WBGT_min_1998-01-01_2022-12-31.csv, WBGT_mean_1998-01-01_2022-12-31.csv, WBGT_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> thermofeelWBGT </td> <td> Wet Bulb Globe Temperature </td> <td> °C </td> <td> Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011). </td> <td> thermofeelWBGT_min_1998-01-01_2022-12-31.csv, thermofeelWBGT_mean_1998-01-01_2022-12-31.csv, thermofeelWBGT_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> WBT </td> <td> Wet Bulb Temperature </td> <td> °C </td> <td> Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011). </td> <td> WBT_min_1998-01-01_2022-12-31.csv, WBT_mean_1998-01-01_2022-12-31.csv, WBT_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> WCT </td> <td> Wind Chill Temperature </td> <td> °C </td> <td> Office of the Federal Coordinator for Meteorological services and supporting research (OFCM). Report on Wind Chill Temperature and extreme heat indices: evaluation and improvement projects. Report No. FCM-R19-2003 (U.S. Office of the Federal Coordinator for Meteorological Services and Supporting Research, 2003). </td> <td> WCT_min_1998-01-01_2022-12-31.csv, WCT_mean_1998-01-01_2022-12-31.csv, WCT_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> MRT </td> <td> Mean Radiant Temperature </td> <td> °C </td> <td> Weihs, P. et al. The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data. Int. J. Biometeorol. 56, 537–555 (2012). </td> <td> MRT_min_1998-01-01_2022-12-31.csv, MRT_mean_1998-01-01_2022-12-31.csv, MRT_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> UTCI </td> <td> Universal Thermal Climate Index (UTCI) </td> <td> °C </td> <td> Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494 (2012). </td> <td> UTCI_min_1998-01-01_2022-12-31.csv, UTCI_mean_1998-01-01_2022-12-31.csv, UTCI_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> UTCI2 </td> <td> Indoor environment UTCI with 2 parameters (air temperature and humidity) </td> <td> °C </td> <td> Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494 (2012). </td> <td> UTCI2_min_1998-01-01_2022-12-31.csv, UTCI2_mean_1998-01-01_2022-12-31.csv, UTCI2_max_1998-01-01_2022-12-31.csv </td> </tr> <tr> <td> UTCI3 </td> <td> Outdoor shaded space environment UTCI with 3 parameters (air temperature, humidity, and wind speed) </td> <td> °C </td> <td> Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494 (2012). </td> <td> UTCI3_min_1998-01-01_2022-12-31.csv, UTCI3_mean_1998-01-01_2022-12-31.csv, UTCI3_max_1998-01-01_2022-12-31.csv </td> </tr> </tbody> </table> </div>   The overview table of the HTSI-GR additional resources folder containing support files and instructions for dataset replication: <table> <tbody> <tr> <th> File names </th> <th> Description </th> </tr> <tr> <td> 0. Calculate thermofeelWBGT.py </td> <td> A python script that calculates the Wet Bulb Globe Temperature (WBGT) using the Thermofeel library. Processes NetCDF files containing daily meteorological data and outputs WBGT values in new NetCDF files for each day. </td> </tr> <tr> <td> 1. Merge_HI_by_max-mean-min.py </td> <td> A python script that merges daily NetCDF files containing heat index (HI) data into three separate files based on mean, maximum and minimum values for further processing. </td> </tr> <tr> <td> 2. QGIS_zonal_statistics.py </td> <td> A python script that calculates zonal statistics for heat indices using QGIS python console. Uses a shapefile of Greek communes and a raster NetCDF file containing daily index values, and outputs daily CSV files with computed statistics. </td> </tr> <tr> <td> 3. Zonal_format.py </td> <td> A python script that formats the zonal statistics results into a comprehensive dataset. Combines daily CSV files into a single CSV, fills in missing data using nearest neighbour values, and produces a final formatted dataset. </td> </tr> <tr> <td> Greek Communes.ZIP </td> <td> Contains the shapefile of Greek communes derived from the Hellenic Statistical Authority (ELSTAT) required for zonal statistics calculations. KALCODE and Commune names are linked in the .shp. </td> </tr> <tr> <td> Nearest Neighbour data table.csv </td> <td> A support table to script 3.Zonal_format.py that lists communes with missing data and their nearest neighbour with data. </td> </tr> <tr> <td> Read me.txt </td> <td> Provides an overview and instructions for using the scripts. Describes the purpose of each script, lists prerequisites, and provides step-by-step instructions for replicating the dataset. </td> </tr> </tbody> </table>

opencc-by-4.0Nov 2024View details →

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Morphological phytoplankton counts for the SOMLIT-Astan time-series (2007-2017)

The present text file includes morphologic microscopic counts for SOMLIT-Astan station (Western English Channel). Samples (250 mL) of natural seawater intended for the acquisition of microscopic counts were preserved with acid Lugol’s iodine (Sournia, 1978, Guilloux et al. 2013), stored in the dark, and further processed between 15 days and up to 1 year after sampling. The morphological taxa contingency table was carefully examined to detect inconsistencies (e.g., abrupt changes in cell counts over the time series), and taxa for which identification was uncertain were grouped into broader taxonomic categories. For example, Fragilaria and Brockmaniella or Cylindrotheca closterium and Nitzschia longissima which are difficult to distinguished between each other, were considered in association in the same group of microscopic counts. The final morphological dataset consisted of counts of 146 taxonomical entities (taxa larger than 10µm in size) across 185 dates from 2007 to 2017. Raw microscopic counts were regularly stored in a local MS-Access database and uploaded in the RESOMAR PELAGOS (<a href="http://abims.sb-roscoff.fr/pelagos/">http://abims.sb-roscoff.fr/pelagos/</a>) national database.

opencc-by-4.0Jun 2021View details →

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PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids (Dataset)

Abstract The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility.  Data Navigation Please download, unzip and put somewhere for later benchmark results reproduction and data loading and performance evaluation for proposed methods. <pre><code>wget https://zenodo.org/record/5130612/files/PSML.zip?download=1 7z x 'PSML.zip?download=1' -o./ </code></pre> Minute-level Load and Renewable <ul> <li>File Name <ul> <li>ISO_zone_#.csv: `CAISO_zone_1.csv` contains minute-level load, renewable and weather data from 2018 to 2020 in the zone 1 of CAISO.</li> </ul> </li> <li>- Field Description <ul> <li>Field `time`: Time of minute resolution.</li> <li>Field `load_power`: Normalized load power.</li> <li>Field `wind_power`: Normalized wind turbine power.</li> <li>Field `solar_power`: Normalized solar PV power.</li> <li>Field `DHI`: Direct normal irradiance.</li> <li>Field `DNI`: Diffuse horizontal irradiance.</li> <li>Field `GHI`: Global horizontal irradiance.</li> <li>Field `Dew Point`: Dew point in degree Celsius.</li> <li>Field `Solar Zeinth Angle`: The angle between the sun's rays and the vertical direction in degree.</li> <li>Field `Wind Speed`: Wind speed (m/s).</li> <li>Field `Relative Humidity`: Relative humidity (%).</li> <li>Field `Temperature`: Temperature in degree Celsius.</li> </ul> </li> </ul> Minute-level PMU Measurements <ul> <li>File Name <ul> <li>case #: The `case 0` folder contains all data of scenario setting #0. <ul> <li>pf_input_#.txt: Selected load, renewable and solar generation for the simulation.</li> <li>pf_result_#.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>Field `time`: Time of minute resolution.</li> <li>Field `Vm_###`: Voltage magnitude (p.u.) at the bus ### in the simulated model.</li> <li>Field `Va_###`: Voltage angle (rad) at the bus ### in the simulated model.</li> <li>Field `P_#_#_#`: `P_3_4_1` means the active power transferring in the #1 branch from the bus 3 to 4.</li> <li>Field `Q_#_#_#`: `Q_5_20_1` means the reactive power transferring in the #1 branch from the bus 5 to 20.</li> </ul> </li> </ul> Millisecond-level PMU Measurements <ul> <li>File Name <ul> <li>Forced Oscillation: The folder contains all forced oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li> info.csv: This file contains the start time, end time, location and type of the disturbance</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Natural Oscillation: The folder contains all natural oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li>info.csv: This file contains the start time, end time, location and type of the disturbance.</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>trans.csv <ul> <li>  - Field `Time(s)`: Time of millisecond resolution.</li> <li>  - Field `VOLT ###`: Voltage magnitude (p.u.) at the bus ### in the transmission model.</li> <li>  - Field `POWR ### TO ### CKT #`: `POWR 151 TO 152 CKT '1 '` means the active power transferring in the #1 branch from the bus 151 to 152.</li> <li>  - Field `VARS ### TO ### CKT #`: `VARS 151 TO 152 CKT '1 '` means the reactive power transferring in the #1 branch from the bus 151 to 152.</li> </ul> </li> <li>dist.csv <ul> <li>Field `Time(s)`: Time of millisecond resolution.</li> <li>Field `####.###.#`: `3005.633.1` means per-unit voltage magnitude of the phase A at the bus 633 of the distribution grid, the one connecting to the bus 3005 in the transmission system.</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Aug 2021View details →

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Data from: Moth species richness and diversity decline in a 30-year time series in Norway, irrespective of species' latitudinal range extent and habitat

Data from: Burner, R., V. Selås, S. Kobro, R. Jacobsen, A. Sverdrup-Thygeson. 2021. Moth species richness and abundance decline in a 30-year time series, irrespective of species’ latitudinal range extent and habitat. Journal of Insect Conservation   Current contact info for corresponding author: Ryan C. Burner, rburner[at]usgs.gov   These data consist of a 30-year time series (1984 to 2013) of moth captures from a single site in southeast Norway, along with trait data for many of the species and climate data for the site. The moths were collected and identified by Sverre Kobro for the entire 30-year period and we are grateful for his efforts.    Abstract from manuscript: Introduction Insects are reported to be in decline around the globe, but long-term datasets are rare. The causes of these trends are elusive, with land use change and climate change among the top candidates. Yet if species traits can predict rates of population change, this can help identify underlying mechanisms. If climate change is important, for example, northern species may decline as southern species expand. Land use changes, however, may impact species that rely on certain habitats. Aims and Methods We present 30 years of moth captures (comprising 85,149 individuals of 885 species) from a site in southeastern Norway to test for population trends that are correlated with species traits. We use time series analyses and joint species distribution models combined with local climate and habitat data. Results and Discussion Species richness and abundance declined by 10.1% and 13.8% per decade, respectively. Capture rates declined for 19% of species during this time as well, though 6% have increased. Annual summer weather is correlated with annual rates of abundance change for many species. But, opposite to a general expectation, many species in our study responded negatively to increasing summer temperatures. Surprisingly, neither species’ northern range limits nor the habitat in which their primary food plants grow are strong predictors of their rates of change, or their responses to climatic factors. However, species with more southerly distributions are less likely to be declining. Complex and indirect effects of both land use and climate change may play a role in these declines. Implications for insect conservation Our results provide additional evidence for long-term declines in insect abundance. The multifaceted causes of population changes may limit the ability of species traits to reveal which species are most at risk.     ACKNOWLEDGEMENTS Thanks to J. Fjelddalen, who helped with geometrid moth identifications. This project was supported by internal funding from the Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences.

opencc-by-4.0Dec 2020View details →

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Dataset: Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan

Description: This version of the repository holds an updated dataset of the annual forest disturbance agents/stable land cover maps across Japan using Landsat time series data. This version covers the period for 1985-2023 (1985-2019 in the original dataset). Harvest, Conversion, Thinning, and Other disturbances are mapped as different disturbance agents. The maps cover the entire country of Japan, excluding several isolated islands. The maps are available in compressed GeoTIFF format in Albers Equal Area Conic projection. The maps can be browsed in <a href="https://dulvrq3317.users.earthengine.app/view/japan-harvest-year-1985-2023">Google Earth Engine Apps</a>. Citation: Shimizu, K. and Saito, H. (2021) Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan. International Journal of Applied Earth Observation and Geoinformation 104: 102555. <a href="https://doi.org/10.1016/j.jag.2021.102555">https://doi.org/10.1016/j.jag.2021.102555</a> Period: 1985 to 2023 annually Spatial resolution: 30m Projection: Albers Equal Area Conic Projection (Two standard parallels: 33N and 44N, Central Meridian: 135E)

opencc-by-4.0May 2023View details →

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A time series database of available P concentrations and grass growth in soils receiving DPW fertilizers

This data set contains a time-series database of available and exchangeable P concentrations and grass dry matter yield in soils receiving struvites and hydrochar produced from DPS

opencc-by-4.0Nov 2022View details →

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Electrical tomography resistivity (ERT) monitoring time series

Multi-temporal electrical tomography resistivity (ERT) measurements for monitoring the performance of the bio-degradable bentonite mat in OAL-Austria. The first measurement was conducted on 30 July 2020 before the implementation of the mat, afterwards seasonal measurement (except for winter due to snow cover) were obtained: 19 Oct 2020, 27 Arpil 2021, 10 August 2021, 4 October 2021, 13 April 2022. A time-lapse inversion algorithm was used to prepare the final results. See OPERANDUM deliverable 4.6 for more details. Device: Lippmann 4point light 10 W

opencc-by-4.0Jan 2022View details →

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Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (Set 5: 2020 - 2021) derived from ERA5-Land data

Overview: 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. Processing steps: 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: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA 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. 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): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> Data provided is the daily averages of relative humidity. This set provides data for the years 2020 - 2021. For other time periods, please see further linked data sets. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. The data have been reprojected to EU LAEA. File naming scheme (YYYY = year; MM = month; DD = day): <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code> Projection + EPSG code: EU LAEA (EPSG: 3035) Spatial extent: north: 6874000 south: -485000 west: 869000 east: 8712000 Spatial resolution: 1000 m Temporal resolution: Daily Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> CHELSA climatologies (V1.2): 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> 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> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) 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 Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="http://https://doi.org/10.5281/zenodo.6344125">https://doi.org/10.5281/zenodo.6344125</a>

opencc-by-sa-4.0Dec 2022View details →

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Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (Set 3: 2010 - 2014) derived from ERA5-Land data

Overview: 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. Processing steps: 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: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA 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. 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): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> Data provided is the daily averages of relative humidity. This set provides data for the years 2010 - 2014. For other time periods, please see further linked data sets. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. The data have been reprojected to EU LAEA. File naming scheme (YYYY = year; MM = month; DD = day): <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code> Projection + EPSG code: EU LAEA (EPSG: 3035) Spatial extent: north: 6874000 south: -485000 west: 869000 east: 8712000 Spatial resolution: 1000 m Temporal resolution: Daily Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> CHELSA climatologies (V1.2): 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> 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> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) 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 Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="http://https://doi.org/10.5281/zenodo.6344012">https://doi.org/10.5281/zenodo.6344012</a>

opencc-by-sa-4.0Dec 2022View details →

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Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (Set 2: 2005 - 2009) derived from ERA5-Land data

Overview: 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. Processing steps: 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: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA 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. 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): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> Data provided is the daily averages of relative humidity. This set provides data for the years 2005 - 2009. For other time periods, please see further linked data sets. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. The data have been reprojected to EU LAEA. File naming scheme (YYYY = year; MM = month; DD = day): <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code> Projection + EPSG code: EU LAEA (EPSG: 3035) Spatial extent: north: 6874000 south: -485000 west: 869000 east: 8712000 Spatial resolution: 1000 m Temporal resolution: Daily Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> CHELSA climatologies (V1.2): 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> 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> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) 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 Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="http://https://doi.org/10.5281/zenodo.6342822">https://doi.org/10.5281/zenodo.6342822</a>

opencc-by-sa-4.0Dec 2022View details →

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Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (Set 1: 2000 - 2004) derived from ERA5-Land data

Overview: 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. Processing steps: 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: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA 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. 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): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> 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. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. The data have been reprojected to EU LAEA. File naming scheme (YYYY = year; MM = month; DD = day): <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code> Projection + EPSG code: EU LAEA (EPSG: 3035) Spatial extent: north: 6874000 south: -485000 west: 869000 east: 8712000 Spatial resolution: 1000 m Temporal resolution: Daily Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> CHELSA climatologies (V1.2): 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> 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> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) 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 Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="https://doi.org/10.5281/zenodo.6342776">https://doi.org/10.5281/zenodo.6342776</a>

opencc-by-sa-4.0Dec 2022View details →

zenodo44/100

Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (Set 4: 2015 - 2019) derived from ERA5-Land data

Overview: 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. Processing steps: 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: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA 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. 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): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> Data provided is the daily averages of relative humidity. This set provides data for the years 2015 - 2019. For other time periods, please see further linked data sets. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. The data have been reprojected to EU LAEA. File naming scheme (YYYY = year; MM = month; DD = day): <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code> Projection + EPSG code: EU LAEA (EPSG: 3035) Spatial extent: north: 6874000 south: -485000 west: 869000 east: 8712000 Spatial resolution: 1000 m Temporal resolution: Daily Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> CHELSA climatologies (V1.2): 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> 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> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) 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 Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="http://https://doi.org/10.5281/zenodo.6344066">https://doi.org/10.5281/zenodo.6344066</a>

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