A sieve analysis (or gradation test) is a practice or procedure commonly used in civil engineering to assess the particle size distribution (also called gradation) of a granular material.

As part of the Sediment Analysis and Geo-App (SAGA) a series of data processing web services are available to assist in computing sediment statistics based on results of sieve analysis. The Calculate Percentile service returns one of the following percentiles: D5, D10, D16, D35, D50, D84, D90, D95.

Percentiles can also be computed for classification sub-groups: Overall (OVERALL), <62.5 um (DS_FINE), 62.5-250um (DS_MED), and > 250um (DS_COARSE)

Parameter #1: Input Sieve Size, Percent Passing, Sieve Units.

• Semi-colon separated. ex: 75000, 100, um; 50000, 100, um; 37500, 100, um; 25000,100,um; 19000,100,um
• A minimum of 4 sieve sizes must be used. Units supported: um, mm, inches, #, Mesh, phi
• All sieve sizes must be numeric

Parameter #2: Percentile

• Options: D5, D10, D16, D35, D50, D84, D90, D95

Parameter #3: Subgroup

• Options: OVERALL, DS_COARSE, DS_MED, DS_FINE
• The statistics are computed for the overall sample and into Coarse, Medium, and Fine sub-classes
• Coarse (> 250 um) DS_COARSE
• Medium (62.5 – 250 um) DS_MED
• Fine (< 62.5 um) DS_FINE
• OVERALL (all records)

Parameter #4: Outunits

• Options: phi, m, um

This service is part of the Sediment Analysis and Geo-App (SAGA) Toolkit.

Looking for a comprehensive user interface to run this tool?
Go to SAGA Online to view this geoprocessing service with data already stored in the SAGA database.

This service can be used independently of the SAGA application and user interface, or the tool can be directly accessed through http://navigation.usace.army.mil/SEM/Analysis/GSD

Abstract

This dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Groundwater modelling in Bioregional Assessments was undertaken in a probabilistic manner. Multiple runs (200 runs) of the model using calibration constrained parameter sets were undertaken to predict drawdown impacts caused by the MBC BA baseline coal resource development. This resulted in 200 different sets of predicted drawdown impacts. This dataset gives different percentiles of the drawdown corresponding to the baseline in ASCII grid format. Percentiles from 5th to 95th percentile are registered in this dataset.

Purpose

The purpose of this data set is to provide the base files in required format that was used for producing some figures/maps in MBC 2.6.2

Dataset History

This a derived data set. All the inputs for this data set were obtained from the groundwater model data set. The outputs have been derived from Monte Carlo runs to produce the percentile drawdowns for uncertainty analysis.

200 runs of the groundwater model corresponding to the OGIA base and BA baseline resulted in 200 (each) model output files that stores the groundwater head (registered as groundwater model dataset). The maximum drawdown simulated for each of the model runs (over the entire simulation time period) were extracted from these files. These outputs were then used together with custom made scripts (all registered in this dataset) to identify different percentiles of drawdown among these 200 runs.

Dataset Citation

Bioregional Assessment Programme (2016) MBC Groundwater model baseline 5th to 95th percentile drawdown. Bioregional Assessment Derived Dataset. Viewed 25 October 2017, http://data.bioregionalassessments.gov.au/dataset/6ca506e1-0a2e-464d-a8de-8e931c8f01e8.

Dataset Ancestors

• Derived From MBC Groundwater model

• Derived From MBC Groundwater model mine footprints

• Derived From MBC Groundwater model layer boundaries

List of Subdatasets: Long-term data: 2000-2021 5th percentile (p05) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 50th percentile (p50) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 95th percentile (p95) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 General Description The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from 250m 8d GLASS V6 FAPAR. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes: Long-term: Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R2 (p95.r2_m). Monthly time-series: Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month. Data Details Time period: March 2000 – December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the Scikit-map Python package. Statistical methods used: for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95. Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues Reference Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: https://doi.org/10.21203/rs.3.rs-3415685/v1 Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM) Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile Spatial support: 250m Depth reference: s = surface Time reference begin time: 20000301 = 2000-03-01 Time reference end time: 20211231 = 2022-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230628 = 2023-06-28 (creation date)

List of Subdatasets: Long-term data: 2000-2021 5th percentile (p05) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 50th percentile (p50) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 95th percentile (p95) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 General Description The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from 250m 8d GLASS V6 FAPAR. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes: Long-term: Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R2 (p95.r2_m). Monthly time-series: Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month. Data Details Time period: March 2000 – December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the Scikit-map Python package. Statistical methods used: for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95. Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues Reference Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: https://doi.org/10.21203/rs.3.rs-3415685/v1 Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM) Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile Spatial support: 250m Depth reference: s = surface Time reference begin time: 20000301 = 2000-03-01 Time reference end time: 20211231 = 2022-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230628 = 2023-06-28 (creation date)

M2SMNXPCT (or statM_2d_pct_Nx) is a 2-dimensional monthly data collection for percentile statistics derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V1, the original version of this percentile data collection, is computed based on the 1981-2010 climatology, covering the period from January 1980 to December 2022. In contrast, V2, the second version, is calculated based on a 30-year climatology (1991-2020), covering the period from January 1980 to the present. This collection consists of percentiles used to identify or characterize extreme weather events associated with temperature (maximum, mean, and minimum 2-m air temperature), as well as with precipitation (total precipitation).MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes”, linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original filename.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read the "MERRA-2 File Specification Document'', “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

This dataset contains the geographic data used to create maps for the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt

Demographic data from the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Demographics/q9ix-kfws

Filter by the Indicator column to select data for a particular indicator map.

Export notes: Dataset may not automatically open correctly in Excel due to geospatial data. To export the data for geospatial analysis, select Shapefile or GEOJSON as the file type. To view the data in Excel, export as a CSV but do not open the file. Then, open a blank Excel workbook, go to the Data tab, select “From Text/CSV,” and follow the prompts to import the CSV file into Excel. Alternatively, use the exploration options in "View Data" to hide the geographic column prior to exporting the data.

USER NOTES: 4/7/2025 - The maps and data have been removed for the Health Professional Shortage Areas indicator due to inconsistencies with the data source leading to some missing health professional shortage areas. We are working to fix this issue, including exploring possible alternative data sources.

5/21/2025 - The following changes were made to the 2023 report data (Equity Report Year = 2023). Self-Sufficiency Wage - a typo in the indicator name was fixed (changed sufficienct to sufficient) and the percent for one PUMA corrected from 56.9 to 59.9 (PUMA = San Diego County (Northwest)--Oceanside City & Camp Pendleton). Notes were made consistent for all rows where geography = ZCTA. A note was added to all rows where geography = PUMA. Voter registration - label "92054, 92051" was renamed to be in numerical order and is now "92051, 92054". Removed data from the percentile column because the categories are not true percentiles. Employment - Data was corrected to show the percent of the labor force that are employed (ages 16 and older). Previously, the data was the percent of the population 16 years and older that are in the labor force. 3- and 4-Year-Olds Enrolled in School - percents are now rounded to one decimal place. Poverty - the last two categories/percentiles changed because the 80th percentile cutoff was corrected by 0.01 and one ZCTA was reassigned to a different percentile as a result. Low Birthweight - the 33th percentile label was corrected to be written as the 33rd percentile. Life Expectancy - Corrected the category and percentile assignment for SRA CENTRAL SAN DIEGO. Parks and Community Spaces - corrected the category assignment for six SRAs.

5/21/2025 - Data was uploaded for Equity Report Year 2025. The following changes were made relative to the 2023 report year. Adverse Childhood Experiences - added geographic data for 2025 report. No calculation of bins nor corresponding percentiles due to small number of geographic areas. Low Birthweight - no calculation of bins nor corresponding percentiles due to small number of geographic areas.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.03 degree (2.5-3.75 km, depending on latitude) resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

Data for Figure 3.41 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.41 is a summary figure showing simulated and observed changes in key large-scale indicators of climate change across the climate system, for continental, ocean basin and larger scales. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The data of each panel is provided in a single file. --------------------------------------------------- List of data provided --------------------------------------------------- This datasets contains global and regional anomaly time-series for: - near-surface air temperature (1850-2020) - precipitation (1950-2014) - sea ice extent (1979-2014) - ocean heat content (1850-2014) --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- near-surface air temperature (tas) -fig_3_41_tas_global.nc, fig_3_41_tas_land.nc, fig_3_41_tas_north_america.nc, fig_3_41_tas_central_south_america.nc, fig_3_41_tas_europe_north_africa.nc, fig_3_41_tas_africa.nc, fig_3_41_tas_asia.nc, fig_3_41_tas_australasia.nc, fig_3_41_tas_antarctic.nc: brown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) green line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) black line: exp = 4, stat = 0 (mean) ocean heat content (ohc) -fig_3_41_ohc_global.nc: brown line: ncl5 = 0, ncl6 = 0 (mean); shaded region: ncl6 = 1 (5th percentile) and 2 (95th percentile) green line: ncl5 = 1, ncl6 = 0 (mean); shaded region: ncl6 = 1 (5th percentile) and 2 (95th percentile) black line: ncl5 = 2, ncl6 = 0 (mean) precipitation (pr) -fig_3_41_pr_60N_90N.nc: brown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) green line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) black line: exp = 2, stat = 0 (mean) sea ice extent (siconc) -fig_3_41_siconc_nh.nc, fig_3_41_siconc_sh.nc: brown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) green line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) black line: exp = 2, stat = 0 (mean) The ensemble spread (shaded regions) of CMIP6 data shown in figure 3.41 are the mean, 5th and 95th percentiles. The in-file metadata labels the same ensemble spread with mean, min and max. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 0.04-0.06 degree (5-7 km, depending on latitude) resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, ti ...

The Maximum Considered Earthquake Geometric Mean (MCEG) peak ground acceleration (PGA) values of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each site class, the MCEG peak ground acceleration (PGA_M) is calculated via the following equation: PGA_M = min[ PGA_MUH, max( PGA_M84th , PGA_MDLL ) ] where PGA_MUH = uniform-hazard peak ground acceleration PGA_M84th = 84th-percentile peak ground acceleration PGA_MDLL = deterministic lower limit spectral acceleration

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

List of Subdatasets: Long-term data: 2000-2021 5th percentile (p05) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 50th percentile (p50) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 95th percentile (p95) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 General Description The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from 250m 8d GLASS V6 FAPAR. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes: Long-term: Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R2 (p95.r2_m). Monthly time-series: Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month. Data Details Time period: March 2000 – December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the Scikit-map Python package. Statistical methods used: for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95. Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues Reference Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: https://doi.org/10.21203/rs.3.rs-3415685/v1 Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM) Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile Spatial support: 250m Depth reference: s = surface Time reference begin time: 20000301 = 2000-03-01 Time reference end time: 20211231 = 2022-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230628 = 2023-06-28 (creation date)

In the analysis ensembles of 7-year SEM simulations were performed for 100 assessments for different scenarios and substances. For each assessment, an ensemble of 365 simulations was performed with varying dates of substance application, covering every day of the year. For each simulation the following postprocessing was performed on the daily substance emission (g.m-2.d-1) from the greenhouse and its 10-day moving average:

* Determine the of the annual maximum for each of the 7 simulation years.

* Calculate the 50th and 90th percentiles over the 7 annual maxima (referred to as PEC50 and the PEC90, respectively).

This results in four PEC values (PEC50--daily, PEC90--daily, PEC50--10-day-average, PEC90--10-day-average) for each of the 100x365 simulations. Next, for each of the 100 assessments, the results of the 365 simulations were processed as follows:

* Calculate the 90th percentile over 365 values for the four PEC values--this is referred to as the "true" 90th percentile.

* Remove 5 simulations for application dates 7-Feb, 21-Apr, 3-Jul, 14-Sep and 26-Nov, resulting in a set of 360 simulations. This is done because 360 has more divisors than 365.

Subsequently, processing was performed on subsamples of different sizes N, taken from the 360 simulations. The following subsample sizes were considered: 12, 15, 18, 20, 24, 30, 36, 40, 45, and 60. For each subsample size N, M_N = 360/N sets of subsamples were taken with application date evenly spread over the year. For example, for N=12, M_12=30 sets of application dates were selected, with each set one day offset to the next. This results in 10 sets of subsamples of varying size. For each set N, the following processing was performed:

* For each M_N values for the four PECs, calculate the relative difference compared to the true 90th percentile (based on the full 365 set of simulations; see above) as follows: RD = (PEC_est-PEC_365)/PEC_365.

* Calculate the 10th percentile over the M_N relative differences for each of the four PECs; this is referred to as the 90th percentile underestimation

* For each M_N values for the four PECs, calculate the multiplication factor relative to the true 90th percentile as follows: MF = PEC_est/PEC_365.

* Calculate the 90th percentile over the M_N multiplication factors for each of the four PECs.

This results in 4000 values for the relative difference and multiplication factor for each combination of assessment (100), subsample size N (10), and PEC quantity (4). The relative underestimations form the data underlying Figure 13.3 in Braakhekke et al. (2024). The multiplication factors for N=12 form the data underlying table 13.1 in Braakhekke et al. (2024).

IntroductionThis dataset contains data captured from remote Power Quality logging devices currently available across 450 UK Power Network sites*. A weekly 95th percentile value per harmonic is calculated and the highest value of each harmonic amongst all weeks, over a period of 12 months (also applicable to THD) is shown.

Methodological Approach Power Quality data is collected from meters on a 10-minute basis and stored in a database. 95th percentile statistics are calculated on a weekly basis and used to generate the harmonics report.Year-week is the ISO 8601 year and week number.

Quality Control Statement The data is provided "as is".

Assurance Statement Harmonic data is periodically extracted and reviewed prior to publication.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/To view this data please register and login.

Please note this dataset supersedes previous versions on the Climate Data Portal. It has been uploaded following an update to the dataset in March 2023. This means sea level rise is approximately 1cm higher (larger) compared to the original data release (i.e. the previous version available on this portal) for all UKCP18 site-specific sea level projections at all timescales. For more details please refer to the technical note.What does the data show?The time-mean sea-level projections to 2100 show the amount of sea-level change (in cm) for each coastal location (grid-box) around the British Isles for several emission scenarios. Sea-level rise is the primary mechanism by which we expect coastal flood hazard to change in the UK in the future. The amount of sea-level rise depends on the location around the British Isles and increases with higher emission scenarios. Here, we provide the relative time-mean sea-level projections to 2100, i.e. the local sea-level change experienced at a particular location compared to the 1981-2000 average, produced as part of UKCP18.For each grid box the time-mean sea-level change projections are provided for the end of each decade (e.g. 2010, 2020, 2030 etc) for three emission scenarios known as Representative Concentration Pathways (RCP) and for three percentiles.The emission scenarios are:RCP2.6RCP4.5RCP8.5The percentiles are:5th percentile50th percentile95th percentileImportant limitations of the dataWe cannot rule out substantial additional sea-level rise associated with ice sheet instability processes that are not represented in the UKCP18 projections, as discussed in the recent IPCC Sixth Assessment Report (AR6). Although the time-mean sea-level projections presented here are to 2100, past greenhouse gas emissions have already committed us to substantial additional sea level rise beyond 2100. This is because the ocean and cryosphere (i.e. the frozen parts of our planet) are very slow to respond to global warming. So, even if global average air temperature stops rising, as global emissions are reduced, sea level will continue to rise well beyond the time changes in global average air temperature level off or decline. This is illustrated by the extended exploratory time-mean sea level projections and discussed further in AR6 (Fox-Kemper et al, 2021).What are the naming conventions and how do I explore the data?The data is supplied so that each row corresponds to the combination of a RCP emissions scenario and percentile value e.g. 'RCP45_50' is the RCP4.5 scenario and the 50th percentile. This can be viewed and filtered by the field 'RCP and Percentile'. The columns (fields) correspond to the end of each decade and the fields are named by sea level anomaly at year x, e.g. '2050 seaLevelAnom' is the sea level anomaly at 2050 compared to the 1981-2000 average.Please note that the styling and filtering options are independent of each other and the attribute you wish to style the data by can be set differently to the one you filter by. Please ensure that you have selected the RCP/percentile and decade you want to both filter and style the data by. Select the cell you are interested in to view all values. To understand how to explore the data please refer to the New Users ESRI Storymap.What are the emission scenarios?The 21st Century time-mean sea level projections were produced using some of the future emission scenarios used in the IPCC Fifth Assessment Report (AR5). These are RCP2.6, RCP4.5 and RCP8.5, which are based on the concentration of greenhouse gases and aerosols in the atmosphere. RCP2.6 is an aggressive mitigation pathway, where greenhouse gas emissions are strongly reduced. RCP4.5 is an intermediate ‘stabilisation’ pathway, where greenhouse gas emissions are reduced by varying levels. RCP8.5 is a high emission pathway, where greenhouse gas emissions continue to grow unmitigated. Further information is available in the Understanding Climate Data ESRI Storymap and the RCP Guidance on the UKCP18 website.What are the percentiles?The UKCP18 sea-level projections are based on a large Monte Carlo simulation that represents 450,000 possible outcomes in terms of global mean sea-level change. The Monte Carlo simulation is designed to sample the uncertainties across the different components of sea-level rise, and the amount of warming we see for a given emissions scenario across CMIP5 climate models. The percentiles are used to characterise the uncertainty in the Monte Carlo projections based on the statistical distribution of the 450,000 individual simulation members. For example, the 50th percentile represents the central estimate (median) amongst the model projections. Whilst the 95th percentile value means 95% of the model distribution is below that value and similarly the 5th percentile value means 5% of the model distribution is below that value. The range between the 5th to 95th percentiles represent the projection range amongst models and corresponds to the IPCC AR5 “likely range”. It should be noted that, there may be a greater than 10% chance that the real-world sea level rise lies outside this range. Data sourceThis data is an extract of a larger dataset (every year and more percentiles) which is available on CEDA at https://catalogue.ceda.ac.uk/uuid/0f8d27b1192f41088cd6983e98faa46eData has been extracted from the v20221219 version (downloaded 17/04/2023) of three files:seaLevelAnom_marine-sim_rcp26_ann_2007-2100.ncseaLevelAnom_marine-sim_rcp45_ann_2007-2100.ncseaLevelAnom_marine-sim_rcp85_ann_2007-2100.ncUseful links to find out moreFor a comprehensive description of the underpinning science, evaluation and results see the UKCP18 Marine Projections Report (Palmer et al, 2018).For a discussion on ice sheet instability processes in the latest IPCC assessment report, see Fox-Kemper et al (2021). Technical note for the update to the underpinning data: https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/ukcp/ukcp_tech_note_sea_level_mar23.pdfFurther information in the Met Office Climate Data Portal Understanding Climate Data ESRI Storymap.

Roads and bridges are vulnerable to a range of stressors, such as flooding, heat waves, and other extreme events. The probability of these stressors impacting roads and bridges cannot be exactly calculated due to various uncertainties related to the scientific understanding of future environmental conditions. Resilient design methods find ways to account for the uncertainty in various stressors. This data set provides temperature and precipitation variables that can be used to help transportation professionals better characterize risks to transportation assets and provide more resilient designs. We applied daily climate projections to calculate 19 variables related to resilient roadway design. The source data set is the statistically downscaled CMIP6-LOCA2 (Localized Constructed Analogs, v20240915 version, Pierce et al. 2023), which includes temperature and precipitation projections from the Climate Model Intercomparison Program Phase 6 (CMIP6) for 27 models under the ssp245, ssp370, and ssp585 scenarios. The “unsplit Livneh” (Pierce et al., 2021) is used as the training data set for LOCA2. We adopt the v20240915 version of CMIP6-LOCA2 as it includes recent changes to the downscaling methodology to improve the representation of precipitation extreme events. The Python xclim (v0.56) library was used to process daily temperature and precipitation from CMIP6-LOCA2. These data are provided as climatology and percentile maps for the 1981-2010, 2025-2049, 2050-2074, and 2075-2099 periods. County-level time series from 1950-2100 are provided, as well as climatology and percentile summaries for 1981-2010, 2025-2049, 2050-2074, and 2075-2099 periods. Users interested in 6 km grids are referred to the home pages of each of the respective sources. The county-level data sets are spatially averaged using the 2023 United States Census Bureau TIGER/Line Shapefiles (https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html). The NetCDF time series files herein can be linked to the shapefile geometry using the “GEOID” field. The variables included are: Minimum daily minimum temperature (TN_min, units=degF, freq=monthly/annual) Minimum 7-day minimum temperature (TN7day_min, units=degF, freq=monthly/annual) Maximum daily maximum temperature (TX_max, units=degF, freq=monthly/annual) Maximum 7-day maximum temperature (TX7day_max, units=degF, freq=monthly/annual) Maximum number of consecutive days with maximum daily temperature above 95 degF (maximum_consecutive_warm_days_95F, units=d, freq=annual) Maximum number of consecutive days with maximum daily temperature above 100 degF (maximum_consecutive_warm_days_100F, units=d, freq=annual) Maximum number of consecutive days with maximum daily temperature above 105 degF (maximum_consecutive_warm_days_105F, units=d, freq=annual) Maximum number of consecutive days with maximum daily temperature above 110 degF (maximum_consecutive_warm_days_110F, units=d, freq=annual) Maximum Near-Surface Air Temperature (95th percentile) (TX95p_per, units=degF, freq=time window) Maximum Near-Surface Air Temperature (99th percentile) (TX99p_per, units=degF, freq=time window) Minimum Near-Surface Air Temperature (1st percentile) (TN01p_per, units=degF, freq=time window) Minimum Near-Surface Air Temperature (5th percentile) (TN05p_per, units=degF, freq=time window) Number of days with daily precipitation at or above 0.01 in/day (wetdays, units=d, freq=monthly/annual) Number of days with daily precipitation at or above 0.5 in/day (intense_wetdays, units=d, freq=monthly/annual) Maximum 1-day total precipitation (rx1day, units=in/d, freq=annual) Maximum 1-day total precipitation (50th percentile) (rx1day_50p_per, units=in/d, freq=time window, notes=See Processing Step 4 for details) Maximum 1-day total precipitation (90th percentile) (rx1day_90p_per, units=in/d, freq=time window, notes=See Processing Step 4 for details) Maximum 1-day total precipitation (estimated 90th percentile) (rx1day_90p_per_est, units=in/d, freq=time window, notes=See Processing Step 4 for details) Maximum 1-day total precipitation (96th percentile) (rx1day_96p_per, units=in/d, freq=time window, notes=See Processing Step 4 for details) The 27 included CMIP6 GCMs are: ACCESS-CM2, ACCESS-ESM1-5, AWI-CM-1-1-MR, BCC-CSM2-MR, CESM2-LENS, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, CanESM5, EC-Earth3, EC-Earth3-Veg, FGOALS-g3, GFDL-CM4, GFDL-ESM4, HadGEM3-GC31-LL, HadGEM3-GC31-MM, INM-CM4-8, INM-CM5-0, IPSL-CM6A-LR, KACE-1-0-G, MIROC6, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, NorESM2-LM, NorESM2-MM, TaiESM1

Please note this dataset supersedes previous versions on the Climate Data Portal. It has been uploaded following an update to the dataset in March 2023. This means sea level rise is approximately 1cm higher (larger) compared to the original data release (i.e. the previous version available on this portal) for all UKCP18 site-specific sea level projections at all timescales. For more details please refer to the technical note.What does the data show?The exploratory extended time-mean sea-level projections to 2300 show the amount of sea-level change (in cm) for each coastal location (grid-box) around the British Isles for several emission scenarios. Sea-level rise is the primary mechanism by which we expect coastal flood risk to change in the UK in the future. The amount of sea-level rise depends on the location around the British Isles and increases with higher emission scenarios. Here, we provide the relative time-mean sea-level projections to 2300, i.e. the local sea-level change experienced at a particular location compared to the 1981-2000 average, produced as part of UKCP18.For each grid box the time-mean sea-level change projections are provided for the end of each decade (e.g. 2010, 2020, 2030 etc) for three emission scenarios known as Representative Concentration Pathways (RCP) and for three percentiles.The emission scenarios are:RCP2.6RCP4.5RCP8.5The percentiles are:5th percentile50th percentile95th percentileImportant limitations of the dataWe cannot rule out substantial additional sea-level rise associated with ice sheet instability processes that are not represented in the UKCP18 projections, as discussed in the recent IPCC Sixth Assessment Report (AR6). These exploratory projections show sea levels continue to increase beyond 2100 even with large reductions in greenhouse gas emissions. It should be noted that these projections have a greater degree of uncertainty than the 21st Century Projections and should therefore be treated as illustrative of the potential future changes. They are designed to be used alongside the 21st Century projections for those interested in exploring post-2100 changes.What are the naming conventions and how do I explore the data?The data is supplied so that each row corresponds to the combination of a RCP emissions scenario and percentile value e.g. 'RCP45_50' is the RCP4.5 scenario and the 50th percentile. This can be viewed and filtered by the field 'RCP and Percentile'. The columns (fields) correspond to the end of each decade and the fields are named by sea level anomaly at year x, e.g. '2050 seaLevelAnom' is the sea level anomaly at 2050 compared to the 1981-2000 average.Please note that the styling and filtering options are independent of each other and the attribute you wish to style the data by can be set differently to the one you filter by. Please ensure that you have selected the RCP/percentile and decade you want to both filter and style the data by. Select the cell you are interested in to view all values.To understand how to explore the data please refer to the New Users ESRI Storymap.What are the emission scenarios?The 21st Century time-mean sea level projections were produced using some of the future emission scenarios used in the IPCC Fifth Assessment Report (AR5). These are RCP2.6, RCP4.5 and RCP8.5, which are based on the concentration of greenhouse gases and aerosols in the atmosphere. RCP2.6 is an aggressive mitigation pathway, where greenhouse gas emissions are strongly reduced. RCP4.5 is an intermediate ‘stabilisation’ pathway, where greenhouse gas emissions are reduced by varying levels. RCP8.5 is a high emission pathway, where greenhouse gas emissions continue to grow unmitigated. Further information is available in the Understanding Climate Data ESRI Storymap and the RCP Guidance on the UKCP18 website.What are the percentiles?The UKCP18 sea-level projections are based on a large Monte Carlo simulation that represents 450,000 possible outcomes in terms of global mean sea-level change. The Monte Carlo simulation is designed to sample the uncertainties across the different components of sea-level rise, and the amount of warming we see for a given emissions scenario across CMIP5 climate models. The percentiles are used to characterise the uncertainty in the Monte Carlo projections based on the statistical distribution of the 450,000 individual simulation members. For example, the 50th percentile represents the central estimate (median) amongst the model projections. Whilst the 95th percentile value means 95% of the model distribution is below that value and similarly the 5th percentile value means 5% of the model distribution is below that value. The range between the 5th to 95th percentiles represent the projection range amongst models and corresponds to the IPCC AR5 “likely range”. It should be noted that, there may be a greater than 10% chance that the real-world sea level rise lies outside this range.Data sourceThis data is an extract of a larger dataset (every year and more percentiles) which is available on CEDA at https://catalogue.ceda.ac.uk/uuid/a077f4058cda4cd4b37ccfbdf1a6bd29Data has been extracted from the v20221219 version (downloaded 17/04/2023) of three files:seaLevelAnom_marine-sim_rcp26_ann_2007-2300.ncseaLevelAnom_marine-sim_rcp45_ann_2007-2300.ncseaLevelAnom_marine-sim_rcp85_ann_2007-2300.ncUseful links to find out moreFor a comprehensive description of the underpinning science, evaluation and results see the UKCP18 Marine Projections Report (Palmer et al, 2018).For a discussion on ice sheet instability processes in the latest IPCC assessment report, see Fox-Kemper et al (2021). Technical note for the update to the underpinning data: https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/ukcp/ukcp_tech_note_sea_level_mar23.pdf.Further information in the Met Office Climate Data Portal Understanding Climate Data ESRI Storymap.

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

General Description

The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from 250m 8d GLASS V6 FAPAR. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping.

The dataset includes:

• Long-term:

Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R² (p95.r2_m).

• Monthly time-series:

Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate images inside the months and one image before and after, about 5 to 6 images for a single month depending on the number of images inside the month.

Data Details

• Time period: March 2000 – December 2021
• Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)
• How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series analysis were computed using the Scikit-map Python package.
• Statistical methods used: for the long-term, trend analysis of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.
• Limitations or exclusions in the data: The dataset does not include data for Antarctica.
• Coordinate reference system: EPSG:4326
• Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000)
• Spatial resolution: 1/480 d.d. = 0.00208333 (250m)
• Image size: 172,800 x 71,698
• File format: Cloud Optimized Geotiff (COG) format.

Support

If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:

• Technical issues and questions about the code: GitLab Issues
• General questions and comments: LandGIS Forum

Name convention

To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:

1. generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation
2. variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)
3. Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile
4. Spatial support: 250m
5. Depth reference: s = surface
6. Time reference begin time: 20000301 = 2000-03-01
7. Time reference end time: 20211231 = 2022-12-31
8. Bounding box: go = global (without Antarctica)
9. EPSG code: epsg.4326 = EPSG:4326
10. Version code: v20230628 = 2023-06-28 (creation date)