Published April 13, 2022 | Version 0.3
Dataset Open

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

  • 1. OpenGeoHub

Description

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:

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,

Files

001_preview_monthly_precipitation.png

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Additional details

Related works

Is supplement to
Dataset: 10.5281/zenodo.1420114 (DOI)
Is supplemented by
Dataset: 10.5281/zenodo.2615278 (DOI)

Funding

MOOD – MOnitoring Outbreak events for Disease surveillance in a data science context 874850
European Commission

References

  • Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, 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., Böhner, 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‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315.