Downscaling & super-resolution
Subdaily Rainfall Estimation through Daily Rainfall Downscaling Using Random Forests in Spain
In one sentence. Random forests downscale daily rainfall to subdaily statistics at over 700 Spanish gauges, improving skill and the Poisson cluster rainfall simulations built on it.
Abstract
Subdaily rainfall data, though essential for applications in many fields, is not as readily available as daily rainfall data. In this work, regression approaches that use atmospheric data and daily rainfall statistics as predictors are evaluated to downscale daily-to-subdaily rainfall statistics on more than 700 hourly rain gauges in Spain. We propose a new approach based on machine learning techniques that improves the downscaling skill of previous methodologies. Results are grouped by climate types (following the Köppen–Geiger classification) to investigate possible missing explanatory variables in the analysis. The methodology is then used to improve the ability of Poisson cluster models to simulate hourly rainfall series that mimic the statistical behavior of the observed ones. This approach can be applied for the study of extreme events and for daily-to-subdaily precipitation disaggregation in any location of Spain where daily rainfall data are available.
Labels
- Topic
- Downscaling & super-resolution
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.3390/w11010125 OpenAlex:W2910760077
- Added
- 2026-10-22
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