Predictability of moist convection through ensemble-based convective-scale data assimilation

The development of atmospheric deep moist convection has been a challenging topic for numerical weather prediction, due to its chaotic nature of the development with multi-scale physical interactions. We recently found that greater than 20-km scale (as commonly known as meso-α (2000-200 km) and meso-β (200-20 km) scales) initial features helped to constrain the general location of convective activity with a few hours of lead time, but meso-γ (20-2 km) or even smaller scale features with less than 30-minute lead time were identified to be essential for capturing the spatiotemporal features of individual convection. To examine the potentials of ensemble-based data assimilation in capturing the individual convective development, as well as the subsequent development of severe weather events, we have conducted large ensemble convection-permitting data assimilation experiments with all-sky infrared satellite radiances from the latest-generation geostationary satellites. We found that the greater number of ensembles more effectively suppressed the spurious correlation for convective-scale data assimilation. However, the exact signals of convective development were not clearly captured in covariances even with thousands of ensemble members. These results suggest the potential limitation of the traditional “Eulerian” (i.e. physical grid-based) ensemble approach in convective-scale data assimilation.