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)
Creators
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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md5:0b148e18f4b44fe914c3bd1d73399379
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md5:5bfd98678c2da23a9ab9beed4b2631ba
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md5:8256120556658019f5071cd50f8b4a48
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md5:1dab1c9707a2a2939bc97e03fb459b37
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md5:172c940b48113f784ecd844b412d28be
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md5:43dfd16ee184bc33874141238252f548
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331.7 MB | Download |
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.