Improved Forecasting of the Extreme Arctic Cyclone in August 2016 with WRF MRI‐4DVAR

Abstract

Cycling data assimilation and forecast experiments in August 2016 together with a case study of an intense Arctic Cyclone (AC16) are performed using the community Weather Research and Forecasting (WRF) model's Data Assimilation system (WRFDA). Three‐Dimensional Variational (3DVAR) and Multi‐Resolution Incremental Four‐Dimensional Variational (MRI‐4DVAR) data assimilation along with Polar WRF are applied 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. Six‐hourly continuous cycling experiments started from 1 August 2016 with 7‐day free forecasts initialized at each 0000 UTC. The results from departure statistics and forecast verification throughout the 20‐day period indicate the robustness and reliability of MRI‐4DVAR. For the AC16 case study, multiple processes, including merging of arctic cyclones, merging of vortices, vertical coupling between low‐level and upper‐level circulations, baroclinic processes and jet stream forcing, contributed to its generation and development. Compared to the initial conditions produced by 4DVAR, 3DVAR produced amplified vortices, stronger baroclinic instability, intensified upper‐level jet streams and a stronger low‐level frontal zone. These factors caused early strengthening of the dominant Arctic Cyclone and led to the early coupling between the low‐level Arctic cyclone and upper‐level vortices that resulted in the over development of AC16 in 3DVAR. For MRI‐4DVAR, the successful prediction of AC16 is likely due primarily to the more accurate simulation of upper‐level atmospheric fields, that was facilitated by better satellite radiance assimilation resulting from MRI‐4DVAR producing a balanced initial model state.This article is protected by copyright. All rights reserved.