Tools of the Alertness Project on the Polar Prediction website
/Marvin Kähnert, University of Bergen, Bjerknes Centre for Climate Research and PhD student in Alertness, is featured on the Polar Prediction webiste.
The YOPP-endorsed Norwegian Alertness project aims to improve AROME-Arctic – the recent entry-into-service weather forecast model at MET Norway. As an operational convection-permitting model system dedicated to the European Arctic it is one of the core models of the Year of Polar Prediction (YOPP). PhD student Marvin Kähnert employs a number of tools to enhance NWP capabilities in the Arctic as a collaborated effort within Alertness.
Many endeavours in the Arctic, from tourism to transportation to exploitation of natural resources require access to accurate weather forecasts. Yet, numerical weather prediction (NWP) models generally display comparatively low predictive skill at these high latitudes. Particularly, the sparse conventional observation network over the ocean and sea-ice as well as the pronounced impact of unresolved processes, such as surface fluxes, radiation or cloud microphysics on Arctic weather events pose a large challenge for numerical modelling.
The YOPP-endorsed Norwegian Alertness project, led by Jørn Kristiansen (The Norwegian Meteorological Institute) and Marius O. Jonassen (UNIS), aims to tackle these key specifically Arctic challenges, while exploiting the opportunities of the Year of Polar Prediction in terms of field campaigns, observations and modelling efforts. The methodological basis of the work within Alertness is formed by the operational forecast systems AROME-Arctic. One dedicated aspect of the Alertness project, supervised by Harald Sodemann (University of Bergen), is to enhance the capabilities and diagnostics of AROME-Arctic. Therefore, a team consisting of members from the University of Bergen, the Nansen Environmental and Remote Sensing Center (NERSC), and The Norwegian Meteorological Institute employs a variety of tools that allow for a deeper insight into the “inner workings” of the NWP models.
In this article, these tools and their utility are briefly introduced.