Скачать или смотреть Accumulated AROME-Arctic Parameterisation Tendency Stochastic Parameterisation Perturbation Patterns

Harald Sodemann, Geophysical Institute/University of Bergen

Stochastic parameterisations are an important way to represent uncertainty in the deterministic forecasting models underlying ensemble prediction systems. Current stochastic parameterisation approaches use random correlation patterns that are unrelated to the atmospheric flow to induce coherent perturbations to parameterisations. Here we replace these patterns by accumulated tendency fields from parameterized physical processes in the HARMONIE-AROME system. Our rationale is that by perturbing the parameterisations with a field that reflects where parameterisations are most active, rather than random, the model obtains a more targeted increase in the degrees-of-freedom to represent forecasting uncertainty.

Here we study a large marine cold-air outbreak over the Norwegian Sea. Strong heat fluxes persisted near the ice edge, and shallow convection dominated in the center of the model domain. Perturbation fields are diagnosed from individual tendency diagnostics implemented in AROME-Arctic within ALERTNESS. Total physical tendencies for the horizontal winds, for temperature and humidity are accumulated with a time filtering throughout the 66 h forecast period.

Accumulated tendencies show overlapping and differing centers of activity. Wind parameterisations are active near the ice edge, and with smaller scale variability over land areas. Temperature tendency patterns show activity more confined to the ice edge, and the coast of northern Scandinavia. Such spatially coherent patterns of parameterisation activity are meaningfully related to current weather. Sensitivity tests of cloud parameterisation parameters in a single-column model version MUSC and the full model version illustrate our progress towards the use of diagnostic perturbation patterns for stochastically perturbed perturbations in the HarmonEPS system.