Development of a next-generation snow and ice product (SNIP) for operational applications
Description
This project aims to develop interpretable AI/ML models to improve global snow depth retrieval from AMSR2 brightness temperature observations.
Detailed example
daily 10 km global snow depth estimates
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
This project establishes a benchmark for AI/ML applications in passive microwave remote sensing and demonstrates the potential for AI/ML to substantially advance snow depth estimation capabilities. The near-real-time global snow depth product supports critical operational applications like transportation safety, weather services, and seasonal water supply planning. It also opens the possibility for improved snow depth retrievals across the entire multi-decadal passive microwave satellite era – the longest continuous dataset available for global snow monitoring.
Controls / human review
ATO: Not reported; PIA: Not published