The Challenge and Promise of Hyperspectral Data Assimilation in the Case of the 2020 Arctic Cyclone

The water vapor distribution over the Arctic is poorly constrained in numerical models because in situ observations are scarce and current assimilation methods for satellite data render poor resolution in the initial condition. It is anticipated that improving the water-vapor distribution over the Arctic both in the horizontal and in the vertical will have important consequences for improving weather forecasts over the Arctic in general and for improving forecasts of Arctic Cyclones in particular. The impact water vapor gradients in the upper troposphere is anticipated to be an important driver for the formation of Tropospheric Polar Vortices (TPV’s), which in turn are known to trigger surface cyclogenesis. The presented work aims to repeat in the Artic region our experiment conducted over the central Pacific, in which Transformed Retrieval’s (TR’s), derived from hyperspectral data (e.g., Migliorini 2012, Antonelli et al. 2017), and microwave data were both assimilated by WRFDA. The greatest impact of the data assimilation in the model initial conditions was on the water-vapor distribution (Antonelli et al. 2020). This study will focus on a 3-day assimilation cycle during which an Arctic Storm formed at the end of July 2020. The 2020 Arctic Cyclone is an interesting case not only for the cyclogenesis and subsequent ice loss, but also for the challenges to the hyperspectral inversion algorithm caused by cloud cover and high latitude aerosols. The strategy for improving the cloud and aerosol masks used by the IR inversion system and the strategy to perform atmospheric profiling above clouds in overcast conditions will be discussed.