arXiv:2606.30647v1 Announce Type: cross Abstract: Dense volumetric reconstruction of cloud microphysical fields from sparse ground-based instruments remains an open problem, largely because the available measurements are heterogeneous in both modality and spatial coverage. We present AtmoFuseNet, a framework that fuses multi-view sky camera imagery with millimeter-wave cloud radar and ceilometer observations to produce 4D (three spatial dimensions plus time) estimates of cloud state and wind. The method operates in three stages: a cross-modal hierarchical aggregation module that combines image
The continuous advancements in AI and sensor technology enable more sophisticated fusion techniques for environmental monitoring, moving towards real-time, comprehensive atmospheric data.
This development improves the accuracy and resolution of environmental forecasting, crucial for climate modeling, disaster prediction, and resource management.
We can now achieve much denser and more accurate volumetric reconstructions of complex atmospheric phenomena like cloud microphysics from disparate ground-based sensors.
- · Meteorological services
- · Climate research institutes
- · Environmental monitoring agencies
- · Smart agriculture
- · Traditional satellite-only forecasting models
- · Sectors reliant on less precise weather predictions
Improved local and regional weather forecasting capabilities.
Better understanding of climate change impacts on atmospheric processes, leading to more informed policy decisions.
Potential for hyper-localized, real-time weather control or modification (e.g., fog dispersal, rain enhancement) through deeper atmospheric understanding.
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Read at arXiv cs.AI