Prototype / Academic Project
A data engineering and analytics dashboard project that transforms large NOAA weather datasets into county-level severe-weather risk insights for Minnesota.
At a glance
Context
Prototype / Academic Project
Current state
Applied dashboard prototype
Role
Sole builder for data pipeline, analytics layers, dashboard, and case-study documentation
Screenshot placeholder
Actual screenshots are not included in this repository yet. This placeholder avoids inventing visuals while reserving space for dashboard, terminal, or demo evidence.
~35 GB
raw NOAA source archive
used in the working project
55,384
cleaned Minnesota storm-event records
9,008,748
Minnesota station-day weather observations
87
Minnesota counties mapped
Public weather data is large, messy, and hard to use directly for decision support. NOAA archives contain valuable severe-weather and daily-observation records, but the raw files need filtering, cleaning, unit conversion, county joins, and clear dashboard views before they become useful for Minnesota risk analysis.
I filtered, cleaned, transformed, and packaged NOAA Storm Events and GHCN-Daily data into Minnesota-focused analytics layers, then built dashboard views for statewide trends, county-level impacts, historical time progression, weather context, methods, and live-alert exploration.
Curated 55,384 cleaned Minnesota storm-event records and 9,008,748 station-day observations into reusable Parquet layers and a Python Shiny + Plotly dashboard spanning all 87 Minnesota counties.
These numbers describe project artifacts and sanity checks. They are not client ROI, deployment adoption, actuarial accuracy, or broad model-accuracy claims.
~35 GB
raw NOAA source archive
used in the working project
55,384
cleaned Minnesota storm-event records
9,008,748
Minnesota station-day weather observations
87
Minnesota counties mapped
1950-06-15 through 2025-12-28
storm-event range
1877-08-05 through 2026-03-22
weather-observation range
The pipeline is shown as explicit stages so the system boundary is inspectable.
Storm Events and GHCN-Daily source files collected as the raw archive.
Records scoped to Minnesota events, counties, and station observations.
Fields normalized, weather units converted, and records prepared for analysis.
Curated analytics layers stored for faster dashboard and analysis use.
County boundaries and population anchors support mapped and normalized comparisons.
Interactive views expose overview, county, temporal, methods, and alert surfaces.
Data sources
Where the pattern matters
WeatherForge is organized as a decision-support dashboard rather than a single chart export.
Available artifacts are labeled directly. Missing visuals stay as placeholders until real screenshots are added.
The project contains a dashboard structure with multiple decision-support views rather than a single static chart.
The pipeline runs from raw public archives through filtering, cleaning, Parquet outputs, county joins, and an interactive Shiny/Plotly surface.
The project demonstrates how public data can be productized into reporting surfaces without claiming forecasting or actuarial precision.
The case study uses verified project artifact counts and keeps limitations explicit.
What this does not claim
Reasonable next steps
The public code link is provided for review of the prototype and technical approach. This does not represent paid deployment, production adoption, or client ROI unless stated elsewhere on the page.
More portfolio context.
A small explainable Retrieval-Augmented Generation prototype that retrieves local evidence first, applies a relevance threshold, and refuses unsupported questions when the corpus does not justify an answer.
Workflow orchestration layer in active development for managing state, decision flow, and human review inside StormIQ.