Summary of Data-driven Rainfall Prediction at a Regional Scale: a Case Study with Ghana, by Indrajit Kalita et al.
Data-driven rainfall prediction at a regional scale: a case study with Ghana
by Indrajit Kalita, Lucia Vilallonga, Yves Atchade
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers aim to improve rainfall forecasting in tropical regions of Africa, particularly Ghana. They develop two machine learning models, U-Net convolutional neural networks (CNNs), to predict 24h rainfall at 12h and 30h lead-time using the ERA5 reanalysis dataset and the GPM-IMERG dataset. The study focuses on interpretability by developing a novel statistical methodology to probe the relative importance of meteorological variables, offering insights into precipitation drivers in Ghana. The results show that the 12h lead-time model matches or outperforms the 18h lead-time forecasts produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) as available in the TIGGE dataset. Furthermore, combining the data-driven model with classical numerical weather prediction (NWP) improves forecast accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to make better predictions about when it will rain in tropical Africa, especially Ghana. The researchers use special computer models and big datasets to make these predictions. They want to know what makes it rain or not rain in Ghana, so they develop a new way to understand this information. The results show that their model is good at predicting rainfall 12 hours before it happens, and even better than some other models when combined with traditional weather forecasting methods. |
Keywords
* Artificial intelligence * Machine learning
