The Impact of Assimilated Transformed Retrievals on the July 2020 Arctic Cyclone

The assimilation of transformed retrievals derived from hyperspectral IR satellite observations improves the distribution of temperature and moisture fields in the troposphere and lower stratosphere that are important for the numerical prediction of Tropopause Polar Vortices and Arctic cyclones. Case studies play an important role in refining the hyperspectral data stream and its use in model simulations and aid in understanding the interactions between these disturbances. A strong Arctic cyclone that entered the Beaufort Sea from Alaska in late-July 2020 exhibited a period of rapid cyclogenesis that generated maximum surface wind speeds of 20 m/s. The National Snow & Ice Data Center noted that the storm caused spreading of the ice pack, potentially leading to a faster decline in local sea ice extent. In this presentation, COAMPS simulations configured for Arctic prediction and the assimilation of temperature and moisture profiles from transformed retrievals are examined for the July 2020 Arctic cyclone. Model results are compared with reanalysis data and similarly-configured WRF-ARW simulations to investigate the impact of improved moisture field initial conditions on the evolution and intensity of the cyclone.