Downscaling & super-resolution
An Application of ANN Ensemble for Estimating of Precipitation Using Regional Climate Models
In one sentence. ANN ensemble combines five regional climate models to correct Korean precipitation against observations, improving on individual RCMs, most strongly inland.
Abstract
Climate change scenarios are used for predicting future precipitation. More detailed regional climate change scenarios are being used through dynamic downscale based on global circulation model results. There is a global tendency to utilize simulated precipitation data from downscaled regional climate models (RCMs) suitable for each country. In Korea, there are studies for improving the accuracy of climate change scenario precipitation forecasts compared with observed precipitation. In this study, the precipitation of five regional climate models and actual observed precipitation provided in Korea are applied to ANN (artificial neural network), which suggests ways to improve prediction accuracy for precipitation. The ANN ensemble of RCMs simulates the actual observed precipitation more accurately than the individual RCM. In particular, it is more effective inland than in coastal areas, where precipitation patterns are complex. Pearson correlation coefficient of ANN is high as 0.04 compared with MRA. It is expected that more detailed analysis will be possible if it is applied not only to four cities but also to other regions in Korea. If observed precipitation data are collected in sufficient quantity, the applicability of the ANN model will widen.
Labels
- Topic
- Downscaling & super-resolution, Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1155/2021/7363471 OpenAlex:W3133716199
- Added
- 2026-10-22
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