English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Computer power has grown to the point that very- fine-mesh mesoscale modelling is now possible. Going down through scales is clumsily supposed to reduce uncertainty and to improve the predictive ability of the models. This work provides a contribution to understand how the uncer- tainty in the numerical weather prediction (NWP) of severe weather events is affected by increasing the model grid res- olution and by choosing a parameterization which is able to represent turbulent processes at such finer scales. A deep moist convective scenario, a supercell, in a simpli- fied atmospheric setting is studied by mean of high resolution numerical simulations with COSMO-Model. Different tur- bulent closures are used and their impacts on the space-time properties of convective fields are discussed. The convective- resolving solutions adopting Large Eddy Simulation (LES) turbulent closure converge with respect to the overall flow field structure when grid spacing is properly reduced. By comparing the rainfall fields produced by the model on larger scales with those at the convergence scales it's possible to size up the uncertainty introduced by the modelling itself on the predicted ground effects in such simplified scenario.

Dealing with uncertainty: turbulent parameterizations and grid-spacing effects in numerical modelling of deep moist convective processes