BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Suman Ravuri (DeepMind) DTSTART:20211125T170000Z DTEND:20211125T180000Z DTSTAMP:20260423T003242Z UID:MPML/62 DESCRIPTION:Title: Sk ilful precipitation nowcasting using deep generative models of radar\n by Suman Ravuri (DeepMind) as part of Mathematics\, Physics and Machine Le arning (IST\, Lisbon)\n\n\nAbstract\nPrecipitation nowcasting\, the high-r esolution forecasting of precipitation up to two hours ahead\, supports th e real-world socioeconomic needs of many sectors reliant on weather-depend ent decision-making. State-of-the-art operational nowcasting methods typic ally advect precipitation fields with radar-based wind estimates\, and str uggle to capture important non-linear events such as convective initiation s. Recently introduced deep learning methods use radar to directly predict future rain rates\, free of physical constraints. While they accurately p redict low-intensity rainfall\, their operational utility is limited becau se their lack of constraints produces blurry nowcasts at longer lead times \, yielding poor performance on rarer medium-to-heavy rain events. Here we present a deep generative model for the probabilistic nowcasting of preci pitation from radar that addresses these challenges. Using statistical\, e conomic and cognitive measures\, we show that our method provides improved forecast quality\, forecast consistency and forecast value. Our model pro duces realistic and spatiotemporally consistent predictions over regions u p to 1\,536 km × 1\,280 km and with lead times from 5–90 min ahead. Using a systematic evaluation by more than 50 expert meteorologists \, we show that our generative model ranked first for its accuracy and use fulness in 89% of cases against two competitive methods. When verified qua ntitatively\, these nowcasts are skillful without resorting to blurring. W e show that generative nowcasting can provide probabilistic predictions th at improve forecast value and support operational utility\, and at resolut ions and lead times where alternative methods struggle.\n LOCATION:https://researchseminars.org/talk/MPML/62/ END:VEVENT END:VCALENDAR