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:20241120T082410Z
LOCATION:HG F 30 Audi Max
DTSTART;TZID=Europe/Stockholm:20240604T100200
DTEND;TZID=Europe/Stockholm:20240604T100300
UID:submissions.pasc-conference.org_PASC24_sess158_pos124@linklings.com
SUMMARY:P51 - Tuning Atmospheric Turbulence Parameters with Machine Learni
 ng Surrogates
DESCRIPTION:Poster\n\nDana Grund, Sebastian Schemm, and Siddhartha Mishra 
 (ETH Zurich) and Oliver Fuhrer (MeteoSwiss)\n\nParameterizations of subgri
 d-scale (SGS) processes, like cloud microphysics, radiation, or turbulence
 , cause considerable uncertainty in numerical climate and weather models a
 t various  spatiotemporal scales. Tuning the involved model parameters is 
 challenging, given the immense computational cost of model evaluations, an
 d the reliance on empirical judgement. The transition of numerical weather
  prediction to convective scales (spatial resolutions of hundreds of meter
 s) is accompanied by new data assimilation methods including parameter est
 imation. However, their performance is limited by either simplified model 
 representations or repeated model evaluations. For more objective calibrat
 ion, using iterative Bayesian methods (MCMC algorithms), fast and accurate
  model surrogates are needed. The recent advances of data-driven full-mode
 l emulators, that avoid explicit SGS modeling, motivates the extension of 
 such models to capture the effects of SGS parameters. Here, we focus on tu
 rbulence parameterizations in large-eddy simulations (LES) with resolution
 s of tens of meters. In order to accurately represent turbulence, emulator
 s of LES simulations have to capture both the variability of the resolved 
 turbulent motion (probabilistic/ensemble forecast) and its mean state. To 
 this end, we compare extensions of deterministic forward emulators, such a
 s neural operators, for probabilistic forecasting of idealized atmospheric
  test cases, in order to assist model calibration.\n\nSession Chair: Iva K
 avcic (Met Office)
END:VEVENT
END:VCALENDAR