Precipitation Nowcasting Based on Deep Learning over Guizhou, China
Problem
Accurate 0-2 hour precipitation nowcasting at high spatiotemporal resolution is needed for weather-dependent social and operational activities.
Why it matters
Short-term rainfall forecasts matter for operational decisions in weather-sensitive activities in Guizhou, China.
What the AI did
Trained ConvLSTM and PredRNN deep learning models on 1-minute-resolution station observations to produce 6-minute-interval precipitation nowcasts, benchmarked against a Lucas-Kanade optical flow extrapolation baseline.
Results
Evaluation used RMSE, ETS, and POD metrics; concrete values not stated in abstract
Impact
Evidence supports a comparative deep learning nowcasting study; measured outcomes are not stated.
Record
| Venue | Atmosphere |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Dexuan Kong, Xiefei Zhi, Yan Ji, Chunyan Yang, Yuhong Wang, Yuntao Tian, Gang Li, Xiaotuan Zeng |
| Citations | 14 (OpenAlex, at import time) |
| References | 59 |
| Model or method | ConvLSTM, PredRNN |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/atmos14050807 |
| Open access | gold |
| Keywords | precipitation nowcasting, deep learning, ConvLSTM, PredRNN, Guizhou |
Importance rating
real but narrower advance
Nowcasting aids weather-dependent operational activities, but abstract gives no scale or quantified societal impact; regional Guizhou study.
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