The PhD defence and trial lecture will be held in Auditorium 1 in The Geology Building. In some cases, it will be possible to attend the trial lecture and dissertation digitally, in that case a link to Zoom will be posted.
Trial lecture
Tuesday 27 October, 10:15-11:00, Aud 1, The Geology Building:
Physical land surface processes within km-resolution Earth system models
Conferral summary (in Norwegian)
God og pålitelig værvarsling er viktig, men mange faktorer spiller inn. Jordens overflate påvirker atmosfæren og værets utvikling gjennom utveksling av energi og masse. I sitt doktorgradsarbeid undersøker Åsmund Bakketun betydningen av nøyaktige beskrivelser av bakken og dens tilstand som er avgjørende for presise varsler med numeriske værvarslingsmodeller. Fokus er på bakkens rolle i værvarsler over nordlige strøk og Arktis, og han utvikler metoder for å forbedre initialiseringen og beskrivelsen av bakkevariable i både tradisjonelle og datadrevne værvarslingsmodeller.
Main research findings
Popular scientific article about Bakketun’s dissertation:
Improving weather prediction through advanced initialisation and representation of the land surface
Weather forecasts are important and valuable, helping us plan activities like work, transport, daily lives and also to prepare for extreme weather events. The accuracy of weather forecasts depends on several aspects, including the interaction between the land surface and the atmosphere. Variables like surface temperature, soil moisture and snow cover controls the surface energy balance and play a crucial role in shaping local weather. In the Nordic and Arctic regions, surface conditions are highly variable due to mixed surface properties and seasonal snow cover. The current operational weather prediction models use simplified description of surface conditions which leaves a potential for more accurate representation and followingly more accurate weather predictions.
This doctoral work by Åsmund Bakketun investigates the role of surface conditions in weather prediction models in Nordic and Arctic regions and explores methods for improving the representation of surface variables. This is done through Data assimilation methods for initialisation of soil and snow variables are implemented, leading to improved forecasts and state estimates within traditional weather prediction models. Additional surface details are incorporated into a data-driven weather model, enabling it to account for local effects. Together, these advancements significantly improve the accuracy of both traditional and potential future data-driven weather forecasting at high resolution in regions with challenging and diverse environments.
Photo and other information:
Press photo: Åsmund Bakketun, portrait; 700px. Photo: Private
