BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20241120T082409Z
LOCATION:HG F 1
DTSTART;TZID=Europe/Stockholm:20240603T143000
DTEND;TZID=Europe/Stockholm:20240603T150000
UID:submissions.pasc-conference.org_PASC24_sess155_msa280@linklings.com
SUMMARY:Towards Operational Data-Driven Forecasting at a National Weather 
 Service
DESCRIPTION:Minisymposium\n\nOliver Fuhrer (MeteoSwiss, ETH Zurich)\n\nDat
 a-driven probabilistic forecasts have become a tangible possibility within
  just a couple of years, thanks to breakthroughs mostly driven by the tech
  industry and building on existing open datasets from the weather and clim
 ate community. National weather services play a crucial role in providing 
 accurate and timely weather forecasts, essential for public safety and eco
 nomic planning. The integration of machine learning (ML) presents transfor
 mative opportunities and challenges in enhancing predictive capabilities a
 nd providing novel products. We will showcase a few applications of ML whi
 ch are already in operations, discuss some of the scientific and computing
  challenges which we have encountered, and present some early results from
  our efforts to build a regional data-driven forecasting model.\n\nDomain:
  Climate, Weather, and Earth Sciences, Computational Methods and Applied M
 athematics\n\nSession Chair: Karthik Kashinath (NVIDIA Inc., Lawrence Berk
 eley National Laboratory)
END:VEVENT
END:VCALENDAR