Published July 15, 2024 | Version 1.0
Dataset Open

The extrAIM dataset: A merged satellite-based daily precipitation dataset for the Mediterranean region (including an ensemble of 20 synthetic realisations)

  • 1. ROR icon National Technical University of Athens
  • 2. ROR icon Democritus University of Thrace
  • 3. National Research Council of Italy
  • 4. European Space Agency, Frascati, Italy

Description

extrAIM dataset is a new merged daily precipitation product (extraim_merged_data.nc) for the Mediterranean region with the following characteristics:

  • Dataset format: NetCDF
  • Spatial resolution: 25 x 25 km
  • Temporal resolution: 1 day
  • Spatial coverage: Longitude: from -6.25 to 38.25, Latitude: 27.75 to 49
  • Temporal coverage: 01-01-2007 to 30-09-2021
  • Merging approach: Two-step merging (classification and regression)
    • Algorithm: Random Forest for both classification and regression
    • Training strategy: Full training strategy
  • Merged precipitation products: SM2Rain-ASCAT and GPM Late Run
  • Reference precipitation product: EMO5
  • Static covariates: Longitude, Latitude and Elevation, in both classification and regression step
    • Classification step: probability dry and probability dry of the 5 neighboring points around the target locations
    • Regression step: mean, standard deviation and skewness of daily precipitation, of the entire series and non-zero amounts, as well as mean precipitation of the 5 neighboring points around the target locations

In addition, an ensemble of 20 synthetic realizations (equiprobable and bias-adjusted) of the merged dataset is provided (files named: “extraim_realisation_XX.nc”). The synthetic realisations were produced using the extrAIM’s uncertainty-quantification approach and the associated conditional sampling method.

Files

Files (7.0 GB)

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Additional details

Related works

Is described by
Publication: 10.1016/j.jhydrol.2024.131424 (DOI)

Funding

European Space Agency
extrAIM: AI-enhanced uncertainty quantification of satellite-derived hydroclimatic extremes 4000137111/22/I-EF

References

  • Kossieris, P., Tsoukalas, I., Brocca, L., Mosaffa, H., Makropoulos, C., & Anghelea, A. (2024). Precipitation data merging via machine learning: Revisiting conceptual and technical aspects. Journal of Hydrology, 131424.