Accurate and timely rainfall nowcasting is crucial for disaster mitigation and water resource management. Despite recent advances in deep learning, precipitation prediction remains challenging due to limitations in effectively leveraging diverse multimedia data sources. We introduce M3R, a Meteorology-informed MultiModal attention-based architecture for direct Rainfall prediction that synergistically combines visual NEXRAD radar imagery with numerical Personal Weather Station (PWS) measurements, using a comprehensive pipeline for temporal alignment of heterogeneous meteorological data. With specialized multimodal attention mechanisms, M3R novelly leverages weather station time series as queries to selectively attend to spatial radar features, enabling focused extraction of precipitation signatures. Experimental results for three spatial areas of 100 km * 100 km centered at NEXRAD radar stations demonstrate that M3R outperforms existing approaches, achieving substantial improvements in accuracy, efficiency, and precipitation detection capabilities. Our work establishes new benchmarks for multimedia-based precipitation nowcasting and provides practical tools for operational weather prediction systems. The source code is available at https://github.com/Sanjeev97/M3Rain
Figures & tables
Fig. 1: Rainfall Event Sample (Sequence 5655) of the LA dataset.
Fig. 2: Illustration of Uniform Frame Sampling for Visual Embedding.
Fig. 3: Illustration of M3R Model with Vision Encoder, TS Encoder, MultiModal Attention Block, TS Decoder and MLP Head.
Methods
RMSE ( ↓ )
MAE ( ↓ )
R 2 ( ↑ )
CC ( ↑ )
CSI 0.1 ( ↑ )
CSI 5 ( ↑ )
CSI 10 ( ↑ )
LA
AL
MS
LA
AL
MS
LA
AL
MS
LA
AL
MS
LA
AL
MS
LA
AL
MS
LA
AL
MS
Transformer [ 19 ]
3.79
4.06
3.68
0.74
1.03
0.64
0.01
0.004
0.09
0.12
0.06
0.30
0.060
0.071
0.070
0.174
0.029
0.221
0.022
0.024
0.172
DLinear [ 22 ]
3.27
3.10
3.49
0.81
0.83
0.77
0.06
0.08
0.12
0.25
0.28
0.35
0.047
0.051
0.042
0.211
0.125
0.234
0.114
0.037
0.154
PatchTST [ 14 ]
3.63
3.14
3.56
0.45
0.36
0.41
0.07
0.06
0.10
0.26
0.24
0.32
0.366
0.359
0.372
0.226
0.145
0.340
0.133
0.071
0.250
iTransformer [ 12 ]
3.43
3.41
3.54
0.49
0.41
0.41
0.08
0.02
0.13
0.28
0.15
0.36
0.365
0.349
0.355
0.233
0.074
0.339
0.125
0.066
0.213
Diffcast-SimVP [ 21 ]
4.70
3.13
4.14
0.97
0.60
0.65
0.05
0.09
0.08
0.21
0.31
0.29
0.074
0.092
0.073
0.218
0.114
0.377
0.105
0.100
0.284
TABLE I: Comparison showing our model is best or second best in both time series forecasting and binary rain classification over three spatial domains of interest (denoted by LA, AL and MS).
Fig. 4: Comparison: (a) Zero (b) High-intensity and (c) Medium-intensity precipitation showing M3R’s consistent performance across all scenarios for LA.
Method
Size
FLOPs
Training
Inference
Memory
Time
Memory
Time
AlphaPre
4.06 M
133.6 G
6111 MB
13 hr
1415 MB
35 s
Diffcast-PhyDnet
3.09 M
18.3 T
1154 MB
12 hr
426 MB
34 s
Diffcast-SimVP
10.85 M
18.3 T
4848 MB
33 hr
2238 MB
68 s
M3R (Ours)
3.22 M
0.14 G
770 MB
1 hr
269 MB
5 s
TABLE II: Efficiency Analysis on Jackson (MS) Station
Methods
RMSE
MAE
R2
CC
CSI 0.1
CSI 10
TS Encoder only
3.79
0.74
0.01
0.12
0.060
0.022
M3R w/o TS Decoder
3.08
0.36
0.13
0.36
0.340
0.091
M3R (Ours)
2.95
0.33
0.21
0.45
0.395
0.150
TABLE III: Ablation study results on Lake Charles (LA) Station
School of Computer Science and Artificial Intelligence, Wuhan University of Technology · School of Earth and Space Science and Technology, Wuhan University