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In Madison, WI this week for the @ametsoc #AMSMadisonSummit! (The first photo is the view from my hotel room!) #SLS32 #wxtwitter

As it was the topic of a presentation at #sls32, here’s a wide angle pic of a large, ground-scraping wall cloud of a tornadic supercell thunderstorm near Glenn, TX on 5/25/25. The supercell produced hail up to 6” near this location around the time of this photo. #txwx #stormhour


CIWRO & @NWSSPC developed & implemented 2 types of guidance for forecasting tornado conditional intensity. Random forest slightly outperforms significant tornado parameter at higher intensity, but their differing tendencies complement each other. #SLS32 #WAF34 #AMSMadisonSummit

Matthew Jeglum, from a talk entitled, “Can a Tornado Warning Cause More Damage Than the Tornado Itself?”
One major point being: bad and/or unneeded warnings have not just a credibility cost, but economic too. #sls32

Derechos in summertime patterns are difficult to forecast. In a new long-term dataset, midlevel lapse rates, deep-layer wind shear, and upper-tropospheric mean wind speed are useful for distinguishing derechos from less extreme or widespread wind events. #SLS32 #AMSMadisonSummit

If you enjoyed yesterday’s QLCS session, we have round two this afternoon at #SLS32! Mesovortices, mergers, and rear inflow.

2/2 According to Cliff, this is an example of why: poor preparation and response, coupled with infrastructure deficiencies. #sls32

Cliff Mass (remotely): a frustrating dichotomy between improved forecasts of, yet worsening loss from, weather-related disasters. Why? … #sls32

Hosek: dryline-associated severe frequency (deviated from climatology, gray) generally greater in +ENSO than La Niña. #sls32


We’re underway in Madison at #SLS32! Starting off with an AI/ML session covering everything from thunderquakes to tornadic supercell predictability.

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