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https://doi.org/10.5281/zenodo...
Dataset . 2022
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Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim

Authors: Hengl, T.; Parente, L.;

Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim

Abstract

Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2021 (https://doi.org/10.5281/zenodo.2615278). Downscaled to 1 km resolution using gdalwarp (cubic splines) and combined with WorldClim (https://worldclim.org/data/worldclim21.html) and CHELSA Climate (https://chelsa-climate.org/downloads/) monthly values. Final values are estimated as a simple average between the three precipitation data sources; a more objective approach would be to use training points e.g. meteo-station monthly values, then train an ensemble model using the 3 data sources as independent variables. Another global data source of precipitation images is the monthly IMERGE dataset, however this requires transformation and is available only for limited span of years. Processing steps are available here. Antarctica is not included. Standard deviation (sd) indicates a difference between the 3 data sources. To access and visualize maps use: https://openlandmap.org. If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, precipitation = variable: precipitation, wc.v2.1.chelsa.v2.1.sm2rain.oct = determination method: long-term average values for October, m = mean value, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 1980..2020 = time reference: from 1980 to 2020, v0.3 = version number: 0.3,

{"references": ["Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Sch\u00fcller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. submitted to Earth System Science Data.", "Karger, D. N., Conrad, O., B\u00f6hner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., ... & Kessler, M. (2017). Climatologies at high resolution for the earth's land surface areas. Scientific data, 4, 170122.", "Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, and P. Xie, (2014). NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), Algorithm Theoretical Basis Document (ATBD). https://storm- pps.gsfc.nasa.gov/storm/IMERG_ATBD_V4.pdf", "Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1\u2010km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315."]}

Keywords

OpenLandMap, precipitation, global

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
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impulse
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