MSWEP Multi-Source Weighted-Ensemble Precipitation
High-resolution precipitation dataset merging gauge, satellite, and reanalysis data using machine learning at 0.1-degree 3-hourly resolution from 1979 to near-real-time incorporating 70,000+ gauge stations
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At a glance
- Region
- Global
- Coverage
- Full global (land + ocean), 0.1deg, 1979-present
- Access
- Registration Required
- Category
- Research Institute
What's inside
- Merged Precipitation
- Sub-Daily Rainfall
- Gauge-Satellite Fusion
- Near-Real-Time Updates
What you can do with it
Typical work that data of this type supports.
- 01
Design storms
Build rainfall intensity-duration-frequency curves for drainage design.
Because it has: Merged Precipitation
- 02
Model input
Drive hydrological, crop and urban drainage models.
- 03
Design flows
Estimate 1-in-50 and 1-in-100 year floods for bridges, culverts and spillways.
Because it has: Gauge-Satellite Fusion
Where AI adds value
Ideas for machine learning and AI agents built on this kind of data.
- 01
Downscaling
Turn coarse rainfall grids into field or ward scale estimates.
- 02
Gauge + satellite fusion
Correct satellite rainfall with gauges using machine learning.
- 03
Flow forecasting
Train LSTM-style models on past records to forecast river levels and flow days ahead.
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