FredRed

Looking at the track error... (would really appreciate someone validating these numbers...) happy to send the logic Enhanced Model Accuracy Comparison (atcf-model-error-viewer.replit.app) UKM (UK Met Office model): Consistently predicts the storm too far east in the first 60 hours Shifts to a southern bias around 60-66 hours Ends with a northwestern bias at 72-84 hours Average error: 44.97 miles (lowest among all models) EGRR: Similar to UKM, with an eastern bias early on Transitions to a northeastern then southeastern bias Ends with a southern then northwestern bias Average error: 46.12 miles OFCL (Official Forecast): Initially has a south-southwestern bias Transitions to a northwestern bias after 36 hours Shows increasing westward bias in later hours Average error: 47.00 miles UKMI and UKM2: Start with southern biases Shift to eastern or southwestern biases mid-forecast End with strong northwestern biases Average errors: 51.12 and 51.22 miles respectively EGR2: Similar pattern to UKMI and UKM2 Ends with a strong northwestern bias Average error: 54.22 miles DRCL: Consistent southern to western bias throughout Large western errors in later forecast hours Average error: 55.53 miles OFCI: Southern bias initially Transitions to a persistent west-northwestern bias Average error: 55.73 miles HFSA (likely HWRF): Consistent south-southeastern bias in early hours Gradually shifts to southwestern then western bias Errors increase with forecast time Average error: 58.01 miles EGRI: Initially southern bias Shifts between eastern and northwestern biases Ends with a strong northern component Average error: 58.20 miles HFAI: Persistent southern bias early on Transitions to a western bias in later hours Average error: 62.99 miles Overall trends: Most models show a tendency to predict the storm too far south and east in early hours. Many models transition to a western or northwestern bias in later forecast periods. The UKM and EGRR models have the lowest average errors. HFSA and HFAI show larger errors, especially in extended forecasts. There's significant variability in model performance, with average errors ranging from about 45 to 63 miles.

Here is one day make site for recon hd visuals... updates every minute! Used Claude Sonnet and built in one day... started this morning. Hover along the legend to filter winds at that speed... Hurricane Reconnaissance Viewer (hurricane-recon-viewer.replit.app)

I've developed several websites through conversations with Claude Sonnet, without writing a single line of code myself. It's fascinating to think that someone like me, with no coding experience, can create functional websites just by communicating with an AI in plain English. I'm curious to hear your thoughts on these sites - do you find them useful? Are there any additional features you think I should incorporate? We're truly living in a remarkable era where AI is making web development accessible to everyone. https://atcf-model-error-viewer.replit.app/ https://atcf-model-track-viewer.replit.app/

Storm Track: INVEST (AL902024) 9/4-9/6 (atcf-model-track-viewer.replit.app)

Has anyone else been playing around with Claude Sonnet 3.5 for crafting websites or apps, specifically for weather data? I've got to say, it's pretty mind-blowing. I managed to whip up a site without writing a single line of code myself – just some strategic copy-pasting from Claude. Took about 2 days to build from scratch. I've only tested it on Netlify so far, but I'm eager for your thoughts. What could I tweak to make it even better? The beauty is, I can just ask Claude in plain English to refine it! Check out my project here: https://atcfmodeltracks.netlify.app/ (hopefully it will work for you).I'm more than happy to share the code here. Keep in mind, I've never written a line of HTML/JS/CSS, so there's definitely room for improvement. After all, an AI built it! Can't imagine what IRL meteorologists will be building with this.