Convolutional Neural Network for Convective Storm Nowcasting Using 3-D Doppler Weather Radar Data

Lei Han, Juanzhen Sun, Wei Zhang

IEEE Transactions on Geoscience and Remote Sensing 2019 article cited by 95

Source Open-access PDF More in Climate & Environment

importance 6/10 partially solved Meteorological Phenomena and Simulations severe weather forecasting stage: research

Problem

Short-term forecasting (nowcasting) of convective storms is difficult because they are small-scale and short-lived, and extracting information from 3-D radar data was previously problematic.

Why it matters

Convective storms are severe warm-season weather hazards; better nowcasting improves warnings and safety.

What the AI did

Reframed storm nowcasting as a classification problem and trained an end-to-end convolutional neural network on 3-D Doppler weather radar images to make predictions.

Results

not stated in source

Impact

Demonstrates feasibility of GPU-enabled deep learning nowcasting from 3-D radar data.

Record

Venue	IEEE Transactions on Geoscience and Remote Sensing
Published	2019
Work type	article
Authors	Lei Han, Juanzhen Sun, Wei Zhang
Citations	95 (OpenAlex, at import time)
References	28
Model or method	Convolutional neural network (CNN)
Organization	not stated in source
Primary source	https://doi.org/10.1109/tgrs.2019.2948070
Open access	green
Keywords	CNN, nowcasting, convective storms, Doppler radar, weather forecasting

Importance rating

6 / 10

significant — thousands to millions affected

Convective storms are severe warm-season hazards; improved nowcasting supports warnings and public safety, demonstrated on Beijing-Tianjin-Hebei operational radar data.

Basis: stated. The source itself states the scale. Stated scale: Severe warm-season hazard; Beijing-Tianjin-Hebei region, 3,737,332 training samples. Method and limits.

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