A Sensor-Adaptive Incremental Learning Framework for Artifact Detection in Satellite Precipitation Data
Abstract
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 products. The developed framework leverages pre-trained computer vision models and incorporates scarce human-labeled data to detect specific anomalies. Our proposed anomaly detection strategy is tested on data from the Special Sensor Microwave Imager (SSMI) and the Special Sensor Microwave Imager/Sounder (SSMIS). Results demonstrate the efficacy of our approach at separating regular orbits from artifact-containing orbits for each satellite, with performance comparable to state-of-the-art in-place methods. Additionally, the framework offers explainability and the capacity for iterative refinement following false-positive or false-negative classifications.
Related
- GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products
- Low-rank tensor structure of precipitation and its application to satellite-reference merging
- Improving global precipitation forecasts with an AI weather model trained on satellite observations
- Composable multi-satellite precipitation estimation for evolving observing systems
- Added value of integrated satellite precipitation products for event-based flood modeling in semi-arid regions
BibTeX
@misc{monsalve2026sensoradaptive,
title = {{A Sensor-Adaptive Incremental Learning Framework for Artifact Detection in Satellite Precipitation Data}},
author = {Andres F. Monsalve and Hernan A. Moreno and Christian D. Kummerow},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01514},
eprint = {2609.01514},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.01514},
}
RIS
TY - UNPB
TI - A Sensor-Adaptive Incremental Learning Framework for Artifact Detection in Satellite Precipitation Data
AU - Andres F. Monsalve
AU - Hernan A. Moreno
AU - Christian D. Kummerow
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.01514
UR - https://arxiv.org/abs/2609.01514
AB - 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 products. The developed framework leverages pre-trained computer vision models and incorporates scarce human-labeled data to detect specific anomalies. Our proposed anomaly detection strategy is tested on data from the Special Sensor Microwave Imager (SSMI) and the Special Sensor Microwave Imager/Sounder (SSMIS). Results demonstrate the efficacy of our approach at separating regular orbits from artifact-containing orbits for each satellite, with performance comparable to state-of-the-art in-place methods. Additionally, the framework offers explainability and the capacity for iterative refinement following false-positive or false-negative classifications.
ER -