A machine learning bias correction method for precipitation corresponding to weather conditions using simple input data
In one sentence. A simple machine learning bias correction conditioned on weather regimes improves precipitation frequency and local distributions in forecasts beyond 24 hours.
Abstract
Various bias correction methods have recently been proposed using machine learning techniques. Generally, machine learning methods are fairly complicated, and it is extremely difficult to explain how machine learning corrects model biases. Accordingly, researchers perpetually seek to apply machine learning methods to diverse cases and to determine whether these methods are reliable. Here, we developed a machine learning method using simple input data by assuming a relation between observed and simulated precipitation corresponding to weather conditions. This simple method can find the optimal relation without employing dimension reduction and can facilitate the comprehension of precipitation characteristics. According to a validation experiment, this simple method can correct the precipitation frequency corresponding to the orography and estimate the local precipitation distribution characteristics, resulting in values similar to the observed data even when data are forecasted more than 24 hours from the initial time.
Labels
- Topic
- Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1002/essoar.10507695.1 OpenAlex:W3191773775
- Added
- 2026-10-22
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