Multi-Layer Networks for Ensemble Precipitation Forecasts Postprocessing

Authors

  • Fengyang Xu Sun Yat-sen University
  • Guanbin Li Sun Yat-sen University
  • Yunfei Du SYSU
  • Zhiguang Chen Sun Yat-sen University
  • Yutong Lu Sun Yat-sen University

DOI:

https://doi.org/10.1609/aaai.v35i17.17756

Keywords:

Other Social Impact, Environmental Sustainability

Abstract

The postprocessing method of ensemble forecasts is usually used to find a more precise estimate of future precipitation, because dynamic meteorology models have limitations in fitting fine-grained atmospheric processes and precipitation is driven more often by smaller-scale processes, while ensemble forecasts can hit this precipitation at times. However, the pattern of these hits cannot be easily summarized. The existing objective postprocessing methods tend to extend the rain area or false alarm the precipitation intensity categories. In this work, we introduce a multi-layer structure to simultaneously reduce the bias in forecast ensembles output by meteorology models and merge them to a quality deterministic (single-valued) forecast using cross-grid information, which differs quite dramatically from the previous statistical postprocessing method. The multi-layer network is designed to model the spatial distribution of future precipitation of different intensity categories(IC-MLNet). We provide a comparison of IC-MLNet to simple average as well as another two state-of-the-art ensemble quantitative precipitation forecasts (QPFs) postprocessing approaches over both single-model and multi-model ensemble forecasts datasets from TIGGE. The experimental results indicate that our model achieves superior performance over the compared baselines in precipitation amount prediction as well as precipitation intensities categories prediction.

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Published

2021-05-18

How to Cite

Xu, F., Li, G., Du, Y., Chen, Z., & Lu, Y. (2021). Multi-Layer Networks for Ensemble Precipitation Forecasts Postprocessing. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14966-14973. https://doi.org/10.1609/aaai.v35i17.17756

Issue

Section

AAAI Special Track on AI for Social Impact