Summary of Using Long Short-term Memory (lstm) to Merge Precipitation Data Over Mountainous Area in Sierra Nevada, by Yihan Wang et al.
Using Long Short-term Memory (LSTM) to merge precipitation data over mountainous area in Sierra Nevada
by Yihan Wang, Lujun Zhang
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Atmospheric and Oceanic Physics (physics.ao-ph)
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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 A deep learning technique, Long Short-term Memory (LSTM), is employed to merge radar-based and satellite-based precipitation products at hourly scale, improving reliability and decreasing detection error probability over complex terrain. The study assesses the merged results against gauge observations from California Data Exchange Center (CDEC) and reanalysis precipitation product Multi-Radar Multi-Sensor (MRMS). While the merged product underestimates gauge observations and fails to provide meaningful estimates at times, it effectively captures temporal trends, outperforming MRMS in this aspect. The findings suggest that incorporating bias correction techniques could enhance accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to combine different ways of measuring precipitation is developed. This method uses special computer models called Deep Learning (DL) models and a technique called Long Short-term Memory (LSTM). It combines radar-based and satellite-based measurements to get more accurate results over complex terrain like mountains. The results are compared with other methods that are widely used, and the new method does better at capturing changes in precipitation over time. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Probability
