Abstract

Cycling data assimilation and forecast experiments in August 2016 together with a case study of an intense Arctic cyclone (AC16) are performed. Initial conditions from newly developed Multi-Resolution Incremental Four-Dimensional Variational (MRI-4DVAR) and Three-Dimensional Variational (3DVAR) d... Show moreCycling data assimilation and forecast experiments in August 2016 together with a case study of an intense Arctic cyclone (AC16) are performed. Initial conditions from newly developed Multi-Resolution Incremental Four-Dimensional Variational (MRI-4DVAR) and Three-Dimensional Variational (3DVAR) data assimilation along with forecasts from the polar version of the Weather Research and Forecasting (Polar WRF) model, mimicking operational configurations, are applied. The tasks are to evaluate MRI-4DVAR performance during a 20-day cycling run, to investigate the impacts of initial conditions on the forecast skill of AC16, and to identify the factors impacting AC16's predictability. The results from the 20-day cycling period demonstrate the robustness and reliability of MRI-4DVAR for data assimilation and subsequent forecast skill. Multiple processes, including mergers of Arctic cyclones, mergers of vortices, vertical coupling between low-level and upper-level circulations, baroclinic processes and jet stream forcing, contributed to the generation and development of AC16. Compared to the initial conditions from 4DVAR, 3DVAR produced amplified polar vortices, stronger baroclinic instability, intensified upper-level jet streams and a stronger low-level frontal zone, causing the overdevelopment of AC16 in 3DVAR-based forecasts. For MRI-4DVAR, the accurate prediction of AC16 5-7 days ahead is likely due primarily to the more accurate representation of upper-level atmospheric fields, that was facilitated by better satellite radiance assimilation with MRI-4DVAR that also produced a balanced initial model state. It is concluded that the high-resolution Polar WRF which is optimized for Arctic conditions combined with 4DVAR facilitated the improved prediction of AC16 compared to the Global Forecast System (GFS) operational deterministic global forecast. Show less