Addressing up-scaling methodologies for convection-permitting EPSs using statistical and machine learning tools
In one sentence. Two machine-learning and statistical up-scaling methods for convection-permitting ensemble rainfall forecasts reduce the double-penalty problem and improve severe-weather forecast skill.
Abstract
Convection-permitting weather forecasting models allow for prediction of rainfall events with increasing levels of detail. However, the high resolutions used can create problems and introduce the so-called “double penalty” problem when attempting to verify the forecast accuracy. Post-processing within an ensemble prediction system can help to overcome these issues. In this paper, two new up-scaling algorithms based on Machine Learning and Statistical approaches are proposed and tested. The aim of these tools is to enhance the skill and value of the forecasts and to provide a better tool for forecasters to predict severe weather.
Labels
- Topic
- Post-processing & calibration, Regional & km-scale models
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.5194/asr-18-145-2021 OpenAlex:W3200001725
- Added
- 2026-10-22
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