Deep learning model based on multi-scale feature fusion for precipitation nowcasting

Jinkai Tan, Qiqiao Huang, Sheng Chen

Geoscientific model development 2024 article cited by 27

Source Open-access PDF More in Climate & Environment

importance 6/10 partially solved Precipitation Measurement and Analysis weather forecasting stage: research

Problem

Deep learning models struggle to accurately forecast heavy precipitation, especially at longer lead times.

Why it matters

Accurate short-term heavy rain forecasts protect lives and enable timely warnings.

What the AI did

Built a multi-scale feature fusion (MFF) deep learning architecture using varied convolution kernels and discrete probability to nowcast precipitation up to 3 hours ahead.

Results

Superior forecast skills vs TSRC, optical flow, and UNet baselines; trained on 2018-2021 radar data, tested on 2022; exact metrics truncated

Impact

Outperformed three existing nowcasting models on radar echo test data.

Record

Venue	Geoscientific model development
Published	2024
Work type	article
Authors	Jinkai Tan, Qiqiao Huang, Sheng Chen
Citations	27 (OpenAlex, at import time)
References	48
Model or method	MFF (multi-scale feature fusion) deep learning model
Organization	not stated in source
Primary source	https://doi.org/10.5194/gmd-17-53-2024
Open access	gold
Keywords	precipitation nowcasting, deep learning, multi-scale feature fusion, radar echo, benchmark

Importance rating

6 / 10

significant — thousands to millions affected

Accurate heavy-precipitation nowcasting up to 3h lead time, where most DL models struggle; severe-weather forecasting is broadly consequential, but abstract states no population or operational scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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