Can't code? If you can read a sky, you can train the AI.
An AI is only as sharp as the people who teach it. StormSwipe turns storm knowledge into training data: we deal you real archived radar frames, and you make the call — clear, severe, or tornado — in one tap. Think Tinder, but you're swiping on supercells.
Every verified label sharpens the detector — especially on its hardest problem, the rotating storm that looks tornadic but isn't. That's a judgment call trained spotters make better than any algorithm. If you're a SKYWARN spotter, a chaser, a dispatcher, or just the person your family calls when the sirens go off: this is your seat on the project.
4 / 4 correct. The network wants your eyes.
Join the Spotter CorpsFull app in development · early access opening soon
Warnings measured in minutes. Decisions measured in seconds.
When a tornado forms, the national average warning lead time is roughly 13 minutes — and historically, a majority of tornado warnings verify as false alarms, eroding the public trust that makes people act when it counts.
The bottleneck isn't radar coverage. It's attention. During an outbreak, a handful of human forecasters must monitor dozens of rotating storms simultaneously, in real time, under extreme pressure. Signatures get missed. Triage happens by instinct.
And the emerging AI tools that could help are built cloud-first — useless to the rural county whose connectivity just went down with the same storm it's trying to survive.
Train in the cloud once. Run anywhere, forever.
TIMA's detector is a deliberately compact convolutional network — about 242,000 parameters, thousands of times smaller than a chatbot, purpose-built for one job. Heavy training happens on rented GPU clusters; the finished model then scores a full radar grid box in under 100 milliseconds on ordinary CPU hardware.
That single architectural decision changes everything: near-zero marginal inference cost, no vendor lock-in, and detection that keeps running when the internet doesn't.
CLOUD
EDGE
ANY BROWSER
Trained on the largest storms in American history.
TIMA learned what danger looks like from the ground truth: significant tornado reports in the NCEI Storm Events database, matched to the raw radar volumes that captured them. Negatives aren't easy clear-air fillers — they include a deliberately mined set of strongly rotating storms that didn't produce tornadoes, forcing the model to learn the difference that matters.
And the dataset grows itself: every live scan of an active NWS warning polygon is automatically archived with its score, then verified against post-event ground truth. Routine operation is data acquisition — a proprietary training asset that compounds every storm season.
Honest Science, Published Openly
When early training produced a suspiciously perfect 0.9947 AUC, we diagnosed it as data leakage, rebuilt the protocol around strict leak-safe splits, and published the honest figure — 0.944 ROC-AUC on held-out data — along with our open problems. Full methodology, limitations, and false-alarm analysis in the technical report.
Not claims. Receipts.
Since July 2026 a continuous tracking layer has watched every rotating storm across the MRMS domain — before and independent of official warnings. Each catch, each miss, and the running accuracy numbers are published from the unedited archives. Timestamps included; judge for yourself.
Interactive replay of the recorded track data for the Hafford/Lilac supercell, cross-verified against Canadian radar. Fragmentation and pending ground truth disclosed on the page.
Open Case FileRadar-indicated tornado warnings for Posey County — the same rotation physics TIMA scores, flagged three-quarters of an hour earlier.
Open Case FileWarning anticipation, false-alarm bands, and triage-tier outcomes, recomputed every morning from the raw track and warning archives — including the numbers that aren't flattering yet.
View Live ScorecardBuilt in the open.
TIMA Radar is released under AGPL-3.0. The architecture, training protocol, and evaluation methodology are published for the meteorological and open-source communities to inspect, reproduce, and improve. Life-safety AI should not be a black box.
It assists. It never replaces.
Built by a firefighter and trained storm spotter, TIMA follows one rule above all: it holds no warning authority, and a low score never guarantees safety. The National Weather Service is the sole official warning authority — always follow local emergency management.
Fund the minutes that matter.
We are engaging angel investors, grant committees, and strategic partners to accelerate the roadmap: dual-polarization debris fusion, environmental late fusion, and temporal nowcasting — converting detection into lead time.
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