# A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-deep-learning-method-for-bias-correction-of-ecmwf-24-240-h-forecasts/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Lei Han,Mingxuan Chen,Kangkai Chen,Haonan Chen,Yanbiao Zhang,Bing Lu,Linye Song,Rui Qin |
| Citations | 158 |
| DOI | 10.1007/s00376-021-0215-y |
| Fields | Earth and Planetary Sciences,Environmental Science |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://link.springer.com/content/pdf/10.1007/s00376-021-0215-y.pdf |
| OpenAlex ID | https://openalex.org/W3171394698 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Bing Lu](https://scholariq.org/researchers/bing-lu/)

## Paper primary topic

- [Meteorological Phenomena and Simulations](https://scholariq.org/topics/meteorological-phenomena-and-simulations/)

## Paper topics

- [Meteorological Phenomena and Simulations](https://scholariq.org/topics/meteorological-phenomena-and-simulations/)
- [Climate variability and models](https://scholariq.org/topics/climate-variability-and-models/)
- [Flood Risk Assessment and Management](https://scholariq.org/topics/flood-risk-assessment-and-management/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.