Distributed Snow Prediction Platform
An operational ML platform fusing in-situ observations, remote sensing, and physically based models into near-real-time snow forecasts for federal water managers.
Research models are only useful to water managers if they run reliably every day. I own the end-to-end development of this operational machine learning platform for near-real-time snow prediction, built to support reservoir operations for the Bureau of Reclamation.
Overview
The platform fuses in-situ observations, remote sensing data (MODIS, Sentinel-1, Airborne Snow Observatory), atmospheric reanalysis, and physically based land-surface models into a single prediction pipeline — then serves the results through a web-based visualization layer that operators actually use.
Engineering Challenges
Every upstream data source has its own latency, failure mode, and format: satellite products arrive late or with gaps, in-situ station feeds occasionally drop out, and reanalysis products get revised after the fact. A model that's only correct when every input behaves is not operational — it's a demo. The core challenge was building a pipeline that degrades gracefully and keeps producing a usable forecast even when part of its input stack is late or missing.
Architecture
I built the full-stack workflow end to end: automated daily data ingestion and preprocessing, distributed model training, inference, and a monitoring layer that flags upstream data quality issues before they propagate into a bad forecast. I designed the software architecture to be modular, so the platform can expand to additional watersheds and forecasting applications without a rewrite — new watersheds are a configuration change, not a new codebase.
Lessons Learned
Owning production monitoring and troubleshooting for a live system — not just the modeling code — changes how you write the modeling code. Failure modes that never show up in an offline evaluation (a stalled cron job, a schema change in an upstream feed, a silent unit mismatch) are exactly the ones that erode an agency's trust in the system. Defensive data validation at every pipeline stage earns that trust back.
Future Directions
Expanding the platform's watershed coverage and adding automated retraining triggers based on drift detection, rather than a fixed retraining schedule.
Why it Matters
This is the difference between a model that produces a good paper and a system that produces a decision an agency can act on every morning.