AI Rebuilds Missing Radar Echoes Without Knowing Where the Gaps Are

When a severe thunderstorm is bearing down on a city, forecasters rely on a seamless mosaic of radar echoes stitched together from multiple radars to track the storm’s every move. But that mosaic is only as good as its weakest link. A single radar’s hardware failure, a delayed file transfer, or a software glitch can carve a silent hole into the composite picture, leaving forecasters blind over an entire region at the worst possible moment. A new study published in Atmospheric Measurement Techniques by Husong Guo, Muyun Du, and colleagues presents a deep learning method that can fill those holes automatically, and it does so without ever being told where the holes are.

The problem is more consequential than it might first appear. A single weather radar can only see effectively out to roughly 200 to 300 kilometers, and terrain, buildings, and vegetation can block parts of its view. To monitor large-scale weather systems continuously, meteorological agencies fuse observations from networks of radars into mosaic products. In China, the operational Severe Weather Automatic Nowcasting system, run by the Hubei Meteorological Observatory, generates composite reflectivity mosaics from nine S-band radars at a spatial resolution of one kilometer and a temporal resolution of six minutes. These products underpin quantitative precipitation estimation and the nowcasting of severe convective weather. When a region of the mosaic goes dark, the accuracy of rainfall estimates and storm warnings degrades immediately.

Existing repair strategies have struggled to keep pace with the variety of ways data can go missing. Physical approaches, such as correcting beam blockage using dual-polarization measurements, address only specific obstruction problems. Optical flow methods estimate the motion field of radar echoes and extrapolate observed information into the gap, which works reasonably well when storms evolve smoothly and the missing region is spatially continuous. But during rapidly developing convection, when echoes can form, dissipate, or intensify abruptly, the assumption that echoes simply translate breaks down and reconstruction accuracy collapses. Meanwhile, deep learning approaches such as CNN-BiConvLSTM and DSA-UNet have shown strong modeling power, yet most of them depend on a missing-data mask, an explicit map of which pixels are absent. In real operational mosaics, especially when the spatial distribution of gaps is highly uncertain, such masks are difficult to obtain, which limits practical deployment.

The new method, called BiConvLSTM-UNet, sidesteps that requirement entirely. It treats missing echo restoration as a sequence reconstruction task: given a corrupted sequence of radar frames, the model learns the inherent spatiotemporal patterns of echo evolution and reconstructs the complete sequence, implicitly identifying and filling the gaps. The architecture follows a U-shaped encoder-decoder design. The encoder stacks depthwise separable convolution modules that progressively reduce spatial resolution while expanding the channel dimension, building hierarchical spatial features. At the bottleneck, a bidirectional ConvLSTM module models the feature sequence in both forward and backward time directions, so the model can draw on context from frames both before and after a gap. The decoder then restores spatial resolution using PixelShuffle upsampling combined with skip connections that merge high-level semantics with fine spatial detail, and a ReLU1 activation confines outputs to the physically valid range between zero and one.

Training the model required careful data engineering. The team used composite reflectivity mosaics from March to September of 2022 and 2023, covering two full rainy seasons over Hubei Province and totaling 102,720 frames. They focused on meteorologically significant echoes between 10 and 75 dBZ, clipped values outside that range, and discarded frames with almost no echo coverage. After testing sequence lengths of 5, 15, and 30 frames, they settled on 15 frames per sample as the best balance between informational richness and computational efficiency, yielding a final dataset of 60,615 frames split into training, validation, and test sets. To simulate realistic data loss, each frame was divided into five sectors, and random subregions of 100 by 100, 150 by 150, or 200 by 200 grid points were blanked out, with one to three frames per sample randomly affected so the model would learn to handle both isolated and consecutive gaps.

The loss function proved equally important. The researchers combined a weighted L1 loss, in which pixel weights increase with echo intensity so the model pays more attention to strong reflectivity, with a multi-scale structural similarity loss that preserves both global morphology and local texture. Ablation experiments confirmed that each component matters. Removing the structural similarity term dropped the peak signal-to-noise ratio from 25.608 to 24.445 and the structural similarity index from 0.765 to 0.741, and for intense echoes above 40 dBZ the critical success index fell to just 0.078. Replacing the weighted L1 term with a standard unweighted version reduced the critical success index at the 40 dBZ threshold from 0.192 to 0.163 and the probability of detection from 0.359 to 0.215, showing that intensity weighting is what enables the model to recover fierce convective cores rather than smoothing them away.

Head-to-head comparisons against ConvLSTM, DSA-UNet, and classical optical flow showed BiConvLSTM-UNet achieving the best overall performance across peak signal-to-noise ratio, structural similarity, critical success index, and probability of detection, while preserving sharper boundaries and more spatially coherent echo structures. ConvLSTM produced robust but locally unsmooth reconstructions, DSA-UNet recovered echoes proactively but tended toward over-smoothed edges and a high false alarm ratio, and optical flow, faithful to echo boundaries when storms moved steadily, failed badly under rapidly evolving convection. One honest caveat emerged: BiConvLSTM-UNet showed a moderately elevated false alarm ratio, reflecting a recall-oriented strategy that prefers to reconstruct plausible echoes rather than miss them, particularly in low-reflectivity or ambiguous areas.

Because the model reconstructs the entire sequence rather than only the gaps, it risks subtly altering pixels that were actually observed. To guard against this, the team added a post-processing step that keeps original observed values everywhere the input data exists and substitutes model predictions only where the input reads zero. The effect was dramatic: in non-missing regions, the root mean square error fell from 0.704 to 0.266 and the mean absolute error from 0.260 to 0.013, while a newly introduced boundary continuity metric showed that the smoothness of intensity transitions across gap edges was preserved rather than degraded. Generalization tests extended the picture further. When the missing length grew from one to three frames to four random frames, performance declined only marginally, but four consecutive missing frames caused consistent deterioration across all metrics, indicating that uninterrupted loss of temporal context fundamentally challenges the model, which then adopts a conservative bias favoring omission over false activation.

The authors also tested the model outside its comfort zone, evaluating it on data from the non-rainy season months of 2023. It maintained moderate performance, with a peak signal-to-noise ratio of 25.838 and a structural similarity of 0.749, but detection of strong echoes weakened as thresholds rose, a consequence of the distributional shift between sparse, dispersed cold-season echoes and the dense rainy-season data it was trained on. The team proposes expanding training data across seasons, incorporating complementary variables such as vertical wind shear and convective available potential energy, and refining loss formulations with spatially adaptive weighting to address these limits. The study does not claim mask-free reconstruction is inherently superior to mask-based methods; rather, it demonstrates for the first time that high-fidelity radar echo restoration is feasible using only the intrinsic spatiotemporal structure of the echo sequences themselves. For operational meteorology, where missing masks are rarely available and every minute of a storm’s evolution counts, that is a meaningful step toward weather radar mosaics that heal themselves in real time.

Subject of Research: Deep learning-based restoration of missing radar echoes in weather radar mosaic data

Article Title: Research on deep learning-based missing echo restoration method for weather radar mosaic data

Article References: Guo, H., Du, M., Fan, X., Wu, C., Lai, A., & Ma, H. (2026). Research on deep learning-based missing echo restoration method for weather radar mosaic data. Atmospheric Measurement Techniques, 19(19), 6341-6356. https://doi.org/10.5194/amt-19-6341-2026

Image Credits: AI Generated

DOI: 10.5194/amt-19-6341-2026

Keywords: weather radar, radar mosaic, deep learning, BiConvLSTM-UNet, data reconstruction, nowcasting, severe convective weather, ConvLSTM, U-Net, optical flow, precipitation estimation, Atmospheric Measurement Techniques