Summary of How Far Are Today’s Time-series Models From Real-world Weather Forecasting Applications?, by Tao Han et al.
How far are today’s time-series models from real-world weather forecasting applications?
by Tao Han, Song Guo, Zhenghao Chen, Wanghan Xu, Lei Bai
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (stat.ML)
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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 The paper introduces the WEATHER-5K dataset, a comprehensive collection of observational weather data designed to better reflect real-world scenarios. This addresses limitations in current datasets, which hinder the optimization and evaluation of Time-Series Forecasting (TSF) models for operational weather forecasting. The authors provide benchmarks against Numerical Weather Prediction (NWP) models, highlighting performance disparities between TSF and NWP models across various weather variables, extreme event prediction, and model complexity. This research enables a more accurate assessment of real-world forecasting capabilities and pushes TSF models closer to in-situ applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset for weather forecasting called WEATHER-5K. This helps train better models and test them correctly. Right now, the datasets used for this type of forecasting are limited and don’t match what really happens in the world. The authors compared their new dataset to how real-world forecasters do things, and showed that current models are not as good as they think they are. |
Keywords
» Artificial intelligence » Optimization » Time series
