A Sensor-Adaptive Incremental Learning Framework for Artifact Detection in Satellite Precipitation Data
Historically, retrieving rainfall data from satellite imagery has been the domain of space agencies. However, in recent years, the development of cheaper, more compact satellites (SmallSats) capable of detecting rainfall proxies has led to a significant increase in private-sector initiatives for satellite launch and surface precipitation products. This rapid growth has yet to be matched by data validation efforts. Consequently, the need for a robust tool to detect anomalies in near-real-time data before it is disseminated to the public has become critical. In this paper, we present the development of an anomaly-detection system to identify artifacts in global satellite-based rainfall prod...
| Authors | Andres F. Monsalve, Hernan A. Moreno, Christian D. Kummerow |
|---|---|
| Primary category | physics.ao-ph |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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