Abstract: 

Atmospheric deep moist convection has emerged as one of the most challenging topics for numerical weather prediction, due to its chaotic nature of the development with multi-scale physical interactions. Not only do individual convective storms cause tremendous damage to society, such as through severe thunderstorms, but convective systems also play a critical role in developing organized severe weather events such as the rapid intensification of tropical cyclones. This study explores the potentials of the  convection permitting ensemble Kalman filter analysis and forecasts with the assimilation of all-sky satellite radiances from a water vapor sensitive band of the Advanced Baseline Imager on GOES-16 to reveal the dynamics and predictability of the development of convection, and the subsequent development of severe weather events. The case chosen is a linear weakly organized convective system over the Gulf of Mexico on 11 June 2017 observed to occur during the NASA Convective Processes Experiment (CPEX) field campaign and the hurricane Harvey (2017) that is characterized by the limited predictability of its rapid intensification process.
We found that meso-α (2000-200 km) and meso-β (200-20 km) scale initial features helped to constrain the general location of convective system with a few hours of lead time, contributing to enhancing convective activity, 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 individual convective storms. We further found that these meso-β and -γ scale variability of convective activity influenced whether early-stage vortex completes precession and initiates RI. These highlight the importance of high-resolution initialization of moisture fields for the prediction of convection and subsequent development of severe weather events. This study demonstrates the accuracy that is potentially indispensable for predicting the convection exactly when and where it occurs to provide insights for the design of future observations and data assimilation systems.