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SUMMARY:EESS talk on "Closing the survey-modelling loop: an analysis of co
 nsumer preferences for load-shifting via a model-driven adaptive survey de
 sign"
DTSTART:20240409T124500
DTEND:20240409T131500
DTSTAMP:20260509T111006Z
UID:2457d307a869621b6c2b6c75f1d5b7d58d6d5a0b7faebfbed90b543e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Lucile Richard\, LAPI\nAbstract:\nMost climate models show a p
 recipitation increase with warming that is smaller than the increase in mo
 isture\, which requires a weakening of the convective mass flux or a slowi
 ng of the overturning circulation. We analyze simulations of global-storm 
 resolving models (DYAMOND models) in which deep convection is explicitly r
 esolved to better understand the role of large- and small-scale drivers on
  precipitation. First\, δ-MAPS technique infers coherent regions of relat
 ive humidity in the high-resolution outputs\, which allows to both sample 
 the large datasets and control the large-scale environment. We train diffe
 rent Machine Learning approaches in these thermodynamically controlled reg
 ions to identify the nature and persistence of the dependencies\, before p
 roceeding to a process-oriented comparison in a causal framework. The anal
 ysis of causal effects reveals a strong link between small-scale dynamics 
 and quantiles of convective precipitation. We also show that the variabili
 ty of the convective area is driven by the large-scale relative humidity. 
 Finally\, the results are compared across the DYAMOND models to evaluate t
 he intermodel spread. By doing so\, we evaluate to what extent the spread 
 in precipitation in model ensemble may arise from the differences in repre
 sentation of the large-scale relative humidity or the vertical velocity in
  convective areas.\n\nShort Biography:\nI am last year Phd student in the 
 LAPI lab\, and part of the Marie Curie cohort iMiracli. I started studying
  physical oceanography in France\, and notably the dynamics of geophysical
  fluids. Then I arrive at EPFL to start my PhD thesis under the supervison
  of Pr. Athanasios Nenes to apply ML algorithms on climate model outputs\,
  and better understand some important climate processes. My focus is to be
 tter understand the contribution of thermodynamics and dynamics on the con
 vective rainfall in the tropics\, which is part of the globe where 50% of 
 the global population is expected to live by 2050.
LOCATION:GC B1 10 https://plan.epfl.ch/?room==GC%20B1%2010 https://epfl.zo
 om.us/j/69011077410
STATUS:CONFIRMED
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