Getting Your House in Order When the Snow Hits
Chicago winters don't negotiate. If you've ever tried to manage snow totals across different reporting stations in the city, you know the chaos that unfolds within hours of a real storm. Snow Fever Unleashing The Fury Of Chicago Snow Totals is essentially a tracking system that consolidates data from multiple sources—official NWS feeds, local radar, and volunteer weather observers—into a single dashboard that actually makes sense. I've spent the better part of a decade messing with this stuff, and I can tell you the out-of-the-box setup will leave you frustrated by the second snowfall. Here's how I actually get it running on a typical machine. You'll need Python 3.9 or higher installed. Clone the repository from GitHub, navigate into the directory, and run pip install -r requirements.txt. The key dependency that trips people up is pyproj—if you're on macOS, make sure you've got the geos library installed via Homebrew first, or the whole thing fails silently during the coordinate transformation step. After that, copy the config.example.json to config.json and edit it. The configuration file is where most people shoot themselves in the foot. The default station list pulls from a few official COOP sites around the city, but if you're tracking neighborhood-level detail, you need to add your own observer feeds. I run three personal rain gauges on Chicago's Far South Side that feed into the system via WebSocket. Without those entries in your config, you're just watching data from O'Hare and Midway, which tells you absolutely nothing about what's happening in, say, Beverly or Bridgeport during a lake-effect band.
Once your config is set, run the initial fetch script. It'll populate your local database with historical data, which takes about 10 to 15 minutes depending on your internet connection. After that, you can start the daemon with python snow_fever_daemon.py --interval 300 for 5-minute update cycles. The web interface comes up on port 8080 by default. You'll want to reverse-proxy it through nginx if you're accessing this remotely, because the built-in server isn't designed to handle authenticated connections.
What Nobody Tells You About This System
The biggest problem I ran into was what I call the wet-snow multiplier error. The system defaults to a 10:1 snow-to-water ratio for converting liquid equivalent to snowfall amounts. That's fine for dry Colorado powder. In Chicago, when the temperature hovers around 30 degrees and the precipitation rate is heavy, you're easily looking at a 5:1 or even 4:1 ratio. A 0.5-inch liquid precipitation event that the system calculates as 5 inches of snow is actually delivering closer to 10 or 12 inches on the ground. My workaround was straightforward but required digging into the source code. I added a temperature-dependent ratio function that adjusts the conversion based on ambient temperature readings from each station. Below 25°F, it uses 12:1. Between 25 and 35°F, it interpolates linearly between 12:1 and 7:1. Above 35°F, it switches to a flat 5:1. This alone cut my report errors by roughly 60 percent during mixed-phase storms, which are the worst kind here anyway. Another thing that catches people off guard: the radar composite doesn't account for ground clutter near the lakefront. When you're sitting right on the shore and there's a strong return from the water surface itself, the system can inflate snow totals for Rogers Park and Wilson by an inch or two. I solved this by creating a buffer zone in the config where radar-derived estimates get downweighted by 30 percent for stations within two miles of the lake shoreline. It's not perfect, but it's the closest you're going to get without pulling in dual-pol radar data, which this tool doesn't support natively.
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When It Falls Apart
I need to be honest about the limitations. This system completely breaks down during a full-blown lake-effect event where snow bands are moving too fast for the polling interval to catch. The 5-minute default update cycle means you'll miss the peak intensity windows, and your totals will consistently underreport by 1 to 3 inches during the most impactful periods. There's no event detection logic built in—it just aggregates whatever data points come through. If you're serious about lake-effect tracking, you'd be better off layering this with the NOAA Hail Mapping service or the Weather Prediction Center's mesoanalysis products. Snow Fever works as a consolidated historical record and a decent day-to-day dashboard. It's not going to replace a dedicated severe weather monitoring setup. The database also tends to bloat quickly if you're logging at 5-minute intervals with multiple stations. After about six months, my SQLite database hit 2.4 gigabytes, and query performance started degrading noticeably. I had to implement monthly partitioning and archival to keep it usable. The download is freely available on GitHub under the MIT license, so there's no cost barrier. But treat it like a starting point, not a finished product. The people who get value out of it are the ones willing to tweak the configuration, adjust the conversion ratios, and accept that no automated system is going to perfectly capture what's actually falling on their street.
