Abstract
This study describes data-denial experiments conducted to examine the impact of assimilating subsets of data from the TORUS project on storm-scale ensemble forecasts of two supercells on 8 June 2019. Assimilated data from TORUS includes mobile mesonet, UAS, and radiosonde observations. The TORUS data are divided into three spatial subsets to evaluate the importance of observing different parts of the atmosphere on forecasts of this case: the SFC subset consisting of just the near-surface mobile mesonet observations, the PBL subset consisting of UAS observations and radiosonde profiles below 762 m, and the FREE subset consisting of radiosonde profiles above 762 m. Data denial experiments are then conducted by comparing analyses and free forecasts generated using a cycled EnKF data assimilation system assimilating conventional observations, radar observations, and all of the TORUS observations at once with experiments where one of the three subsets is removed in turn as well as a control experiment assimilating only conventional and radar observations. Our results show that assimilating all of the TORUS observations at once in the ALL experiment improves the storm-scale ensemble forecasts much more often than it degrades them, and that no one subset of the TORUS data was consistently most important for improving the analyses or forecasts.
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Matthew Wilson’s current affiliation: NSF NCAR, Boulder, Colorado
