Organizations: School of Computer Science and Artificial Intelligence, Wuhan University of Technology · School of Earth and Space Science and Technology, Wuhan University
Deep learning has significantly improved the accuracy of precipitation nowcasting. However, most existing multimodal models typically use simple channel concatenation or interpolation methods for data fusion, which often overlook the feature differences between different modalities. This paper therefore proposes a novel precipitation nowcasting optimisation framework called FusionCast. This framework incorporates three types of data: historical precipitable water vapour (PWV) data derived from global navigation satellite system (GNSS) inversions, historical radar based quantitative precipitation estimation (QPE), and forecasted radar QPE serving as a future prior. The FusionCast model comprises two core modules: the future prior radar QPE processing Module, which forecasts future radar data; and the Radar PWV Fusion (RPF) module, which uses a gate mechanism to efficiently combine features from various sources. Experimental results show that FusionCast significantly improves nowcasting performance.
Figures & tables
Figure 1: (a) Incorporates sparse GNSS PWV stations, historical radar QPE data, and future radar QPE data as model inputs. (b) Different data types are encoded separately through a multi-branch architecture. (c) Information from diverse data sources is fused to enhance forecasting performance. (d) The final decoder generates forecast outputs, providing projections of future precipitation.
Figure 2: (a) A spatial attention mechanism is applied to PWV features to emphasise critical spatial information. (b) The integration of GNSS and radar data through feature fusion via a gating mechanism is demonstrated. (c) Channel attention mechanisms extract key features from radar data to optimise information representation.
Figure 3: (a) The geographic location of the study area (red bounding box) within the Contiguous United States. (b) Zoomed-in view showing the spatial distribution of the GNSS stations (red triangles) used in this work.
Figure 4: Comparison: Interpolated Image vs. Ground Truth in the PWV Dataset at 2023-02-01 00:00:00 with 496 Valid Stations. (left) Interpolated Image, (right) Ground Truth.
Threshold
Model
CSI
T=10 min
T=40 min
T=80 min
T=120 min
τ=0.1
OpticalFlow
0.680
0.493
0.397
0.340
ConvLSTM
0.602
0.460
0.383
0.336
PredRNN
0.365
0.136
0.130
0.109
NowcastNet
0.478
0.477
0.477
0.477
FusionCast
0.784
0.661
0.614
0.534
Table 1: Performance comparison of CSI(↑ ) scores across different precipitation thresholds ( τ ) and forecasting horizons. The best results for each column are highlighted in bold .
Model
RMSE (mm/h)
MAE (mm/h)
OpticalFlow
1.598
0.214
ConvLSTM
1.181
0.356
PredRNN
1.835
0.743
NowcastNet
1.500
0.179
Ours
1.004
0.103
Table 2: Comparison of continuous metrics (RMSE, MAE) ( ↓ ) comparison of precipitation nowcasting performance across the test dataset. The best results are highlighted in bold .
Figure 5: Visualisation comparison of precipitation intensity forecast results for the Mississippi River basin over the next two hours, at 12:40 UTC on 22 February 2023. From top to bottom: Ground Truth (GT), FusionCast (Ours), NowcastNet, Optical Flow, ConvLSTM and PredRNN. The highlighted area indicates where our model demonstrates significant improvement over all other models. FusionCast preserves both the spatial structure and intensity of precipitation more consistently, especially at longer lead times.
Figure 6: Ablation study across different thresholds ( τ ) and lead times. The inferior performance of ”w/o PWV” and ”w/o Prior” variants validates that integrating multimodal priors is indispensable for accurate precipitation nowcasting.
Figure 7: The proposed Gating strategy effectively mitigates the feature blurring observed in the Concatenation baseline, leading to superior robustness in forecasting heavy precipitation ( τ=4.0 mm/h) over long lead times.
Key Laboratory of Big Data & Artificial Intelligence in Transportation (Ministry of Education), Beijing Jiaotong University, Beijing, China · CMA Key Laboratory of Transportation Meteorology, Nanjing Innovation Institute for Atmospheric Sciences, Nanjing, China